Metabonomics in Toxicity Assessment

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Author: Donald G. Robertson | John Lindon | Jeremy K. Nicholson | Elaine Holmes

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Metabonomics in Toxicity Assessment

Metabonomics in Toxicity Assessment edited by

Donald G. Robertson Pfizer Global Research and Development Ann Arbor, Michigan, U.S.A.

and John Lindon Imperial College London London, United Kingdom

Boca Raton London New York Singapore

A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc.

Published in 2005 by Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2005 by Taylor & Francis Group, LLC No claim to original U.S. Government works Printed in the United States of America on acid-free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number-10: 0-8247-2665-0 (Hardcover) International Standard Book Number-13: 978-0-8247-2665-2 (Hardcover) This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. No part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC) 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe.

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Preface

The role of the toxicologist in the pharmaceutical industry has changed significantly over the past 10 years. Historically, the toxicologist’s responsibilities were often thought by their colleagues in pharmaceutical companies as being akin to that of the grim reaper whose appearance at project team meetings, presaged bad tidings for the future of the drug under consideration. This news was seldom received before a significant investment in time and resources had already been placed in the development of the drug and cancellation of the project due to drug toxicity, meant a significant setback. However, the paradigm for development of new chemical entities has changed significantly in recent years. Toxicologists are now required to help in prelead prioritization to help manage the wealth of hits coming out of combinatorial synthesis and high throughput screening, with the goal of improving preclinical throughput by decreasing the number of failures due to untoward toxicity in safety studies. In silico and in vitro approaches certainly have a place in early toxicity assessment, but there frequently comes a point at which further distinction by these approaches is not possible or is iii

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self-defeating in terms of teasing out the real significance of any data. In vivo evaluation still remains the gold standard for safety assessment and will remain so for the foreseeable future, given the regulatory requirements for new drugs. Therefore techniques that enable rapid and=or more complete assessment of in vivo studies are of great interest to pharmaceutical toxicologists. A number of data–rich technologies have become available for toxicity studies. Among these, metabonomics represents a promising approach that enables relatively rapid throughput in vivo toxicity assessment, frequently providing basic biochemical information not typically available in standard clinical pathology assessment. Therefore, a byproduct (or arguably the primary product) of the technique is identification of individual biomarkers or combinations of biomarkers that can be associated with the toxicity, and which could act as surrogate endpoints. These biomarkers can be used for further prelead prioritization or may prove useful later in development for clinical assessment of toxicity. Clearly, metabonomic technology represents a promising means to achieve the goal of faster drug development with decreased preclinical and clinical failure rates due to toxicity. Metabonomics serves as one leg of the triad of ‘‘omic’’ technologies that includes transcriptomics (also known as toxicogenomics in toxicology circles) and proteomics. Although these other omic technologies are not the subject of this volume, it has become apparent to practitioners in the field, that a ‘‘systems’’ approach to toxicity evaluation that incorporates two or preferably all three omic approaches enables a synergy of data generation and more importantly data interpretation that is not possible with any one ‘‘omic’’ technology. While much has been written about transcriptomics and to a lesser extent proteomics, very little is available to the toxicologist considering use of metabonomic technology. This volume is meant to help fill that void. Metabonomics can enable rapid generation of a mountain of data. However, generating mountains is not the role of the pharmaceutical toxicologist, whose goal is to refine the mountain down to the valuable jewels of mechanistic-based

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safety screens and biomarkers. This is easier said than done, and this volume provides some essential wisdom for sifting through the tailings to find the jewels. Like other ‘‘omic’’ technologies, experimental designs, protocol conduct, and proper control are absolutely imperative to metabonomics. Some of the essential tools for the toxicologist evaluating metabonomic technology are covered in some detail in the volume including analytical, biological, and chemometric considerations. Additionally, varied applications are presented which provide a flavor for what metabonomics can do. The aim of this book is to present a summary of the use of metabonomics in the safety assessment of new chemical entities. Although a presentation of the instrumentation and methods that are required to perform biofluid NMR will be made for purposes of completeness, the main focus of the text will deal with the use of the technique by toxicologists to aid in safety assessment of novel prelead candidates with further application to biomarker identification. Given the limited exposure metabonomics has within the toxicology community, little background knowledge is assumed and each contributor has attempted to identify methods and materials used to generate data and to explain any assumptions made in their evaluations. This balanced critique of the present state of the art hopefully encompasses both the strengths and weaknesses of the technology. We believe that this book will serve as a basic reference tool, since each chapter provides a comprehensive bibliography. The worldwide pharmaceutical toxicologist and other preclinical and clinical scientists involved in safety assessment are the intended audience for this volume, although anyone involved in generating and=or interpreting safety data on chemical entities or anyone who has an interest in metabonomics and systems biology as an academic pursuit will find the reference thought provoking and useful. Donald G. Robertson John C. Lindon

Contents

Preface . . . . iii Contributors . . . . xi 1. An Overview of Metabonomics . . . . . . . . . . . . . . 1 John C. Lindon, Elaine Holmes and Jeremy K. Nicholson Introduction . . . . 1 The Metabolic Continuum . . . . 5 Biomarkers . . . . 12 Brief Overview of Metabonomics Techniques . . . . 13 Metabonomics Applications . . . . 18 Summary . . . . 21 2. Overview of Biomarkers . . . . . . . . . . . . . . . . . . . John Timbrell Introduction . . . . 27 Biomarkers of Exposure . . . . 31 Biomarkers of Response . . . . 44 Biomarkers of Susceptibility . . . . 56

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3. NMR Spectroscopy: Principles and Instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . Michael D. Reily and John C. Lindon Introduction . . . . 75 Principles of NMR Spectroscopy . . . . 77 Operational Methods . . . . 85 Realization of NMR Spectroscopy in a Metabonomics Laboratory . . . . 92 Conclusions . . . . 102

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4. NMR Spectroscopy of Biofluids . . . . . . . . . . . . . 105 John C. Lindon, Jeremy K. Nicholson and Elaine Holmes Introduction . . . . 105 Practicalities of 1D 1H NMR Spectroscopy of Biofluids . . . . 109 Techniques for Resonance Assignment in NMR Spectra of Biofluids . . . . 109 1 H NMR Spectroscopy of Cerebrospinal Fluid (CSF) . . . . 112 1 H NMR Spectroscopy of Blood Plasma and Whole Blood . . . . 115 1 H NMR Spectroscopy of Human and Animal Urine . . . . 129 1 H NMR Spectroscopy of Seminal Fluids . . . . 142 1 H NMR Spectroscopy of Bile . . . . 146 NMR Spectroscopy of Miscellaneous Body Fluids . . . . 148 NMR Studies of Dynamic Interactions . . . . 153 Concluding Remarks . . . . 158 5. 1H Magic Angle Spinning NMR Spectroscopy of Tissues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Julian L. Griffin, Jeremy K. Nicholson, Elaine Holmes and John C. Lindon Introduction . . . . 173 Magic-angle-spinning (MAS) NMR Spectroscopy: Principles and Practice . . . . 175 Applications of 1H MAS NMR Spectroscopy to Metabonomics . . . . 180

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Future Directions and Challenges for 1H MAS NMR Spectroscopy . . . . 188 6. The Application of Metabonomics as an Early In Vivo Toxicity Screen . . . . . . . . . . . . . . . . . . . 195 Gregory J. Stevens, Alan J. Deese and Donald G. Robertson Introduction . . . . 195 Experimental Considerations . . . . 197 Examples . . . . 211 Screening Models . . . . 218 Conclusion . . . . 220 7. Strategies and Techniques for the Identification of Endogenous and Xenobiotic Metabolites Detected in Metabonomic Studies . . . . . . . . . . . . . . . . . . . 225 John Shockcor and Ian D.Wilson Introduction . . . . 225 Xenobiotic and Endogenous Metabolite Identification Directly from Biofluids . . . . 226 Low Resolution, Off-Line, Techniques for the Isolation of Unknowns . . . . 233 Direct On-Line Methods of Identifying Unknowns . . . . 239 Miniaturization . . . . 252 Conclusions . . . . 257 8. Multi- and Megavariate Data Analysis: Finding and Using Regularities in Metabonomics Data . . . . 263 Lennart Eriksson, Erik Johansson, Henrik Antti and Elaine Holmes Introduction . . . . 263 Data-Analytical Methods . . . . 267 Results for Example Data Set I—A Metabonomic Investigation of Phospholipidosis . . . . 297 Results for Example Data Set II—Defining the Dynamic Sequence of Biochemical Events Following the Onset of Toxicity . . . . 308 Discussion . . . . 319 Concluding Remarks . . . . 330

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9. Use of Metabonomics to Study Target Organ Toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Craig E. Thomas, Elaine Holmes and Donald G. Robertson Introduction . . . . 337 Hepatic Toxicity . . . . 338 Renal Toxicity . . . . 359 Vascular Toxicity . . . . 374 10. Physiological Variation in Laboratory Animals and Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 M.E. Bollard, E.G. Stanley, Y. Wang, J.C. Lindon, J.K. Nicholson and E. Holmes Introduction . . . . 397 Physiological Variation in Laboratory Animals . . . . 401 Physiological Variation in Humans . . . . 432 11. Environmental Applications of Metabonomic Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453 Jacob G. Bundy Differences to Clinical Studies . . . . 454 Characterization of Baseline Data By NMR Spectroscopy . . . . 459 Toxicological and Related Studies . . . . 474 Conclusions and Future Implications . . . . 484 12. Current Challenges and Future Developments in Metabonomics Technology . . . . . . . . . . . . . . 499 Donald G. Robertson Perspective . . . . 499 The Power of the Metabonomic Approach in Toxicology . . . . 500 Metabonomics as an ‘‘Omic’’ Technology . . . . 504 Short Term Needs for Metabonomics as a Science . . . . 508 Cautionary Note . . . . 510 Conclusion . . . . 512 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517

Contributors

Henrik Antti Biological Chemistry, Biomedical Sciences Division, Faculty of Medicine, Imperial College of Science, Technology and Medicine, South Kensington, London, U.K. M.E. Bollard Biological Chemistry, Biomedical Sciences Division, Imperial College, University of London, South Kensington, London, U.K. Jacob G. Bundy Biochemistry Department, University of Cambridge, Cambridge, U.K. Alan J. Deese Analytical Research and Development, Pfizer Global Research and Development, La Jolla, CA, U.S.A. Lennart Eriksson

Umetrics AB, Umea˚, Sweden

Julian L. Griffin Department of Biochemistry, University of Cambridge, Cambridge, U.K. Elaine Holmes Biological Chemistry, Biomedical Sciences, Faculty of Medicine, Imperial College of Science, Technology and Medicine, London, U.K. xi

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Erik Johansson

Contributors


John C. Lindon Biological Chemistry, Biomedical Sciences Division, Faculty of Medicine, Imperial College London, South Kensington, London, U.K. Jeremy K. Nicholson Biological Chemistry, Biomedical Sciences, Faculty of Medicine, Imperial College of Science, Technology and Medicine, London, U.K. Michael D. Reily Pfizer Global Research and Development, Michigan Laboratories, Ann Arbor, MI, U.S.A. Donald G. Robertson Departments of Worldwide Safety Sciences, Pfizer Global Research and Development, Ann Arbor, MI, U.S.A. John Shockcor

Metabometrix, South Kensington, London, U.K.

E.G. Stanley Biological Chemistry, Biomedical Sciences Division, Imperial College, University of London, South Kensington, London, U.K. Gregory J. Stevens Drug Safety Evaluation Pfizer Global Research and Development, La Jolla, CA, U.S.A. Craig E. Thomas Investigative Toxicology, Lilly Research Laboratories, A Division of Eli Lilly and Company, Greenfield, IN, U.S.A. John Timbrell U.K.

Pharmacy Department, King’s College, London,

Y. Wang Biological Chemistry, Biomedical Sciences Division, Imperial College, University of London, South Kensington, London, U.K. Ian D. Wilson Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Mereside, Alderley Park, Macclesfield, Cheshire, U.K.

1 An Overview of Metabonomics JOHN C. LINDON, ELAINE HOLMES and JEREMY K. NICHOLSON Biological Chemistry Biomedical Sciences Division, Imperial College London, U.K.

1. INTRODUCTION The availability of the human genome sequence and the sequences of other species gave rise to hope that they would lead to a new set of molecular markers of disease. Although a vast amount of information on human make up has been unlocked, in terms of the development of validated biochemical markers of disease and identification of new drug targets, the published results have been so far disappointing. However, this process has provided the impetus for wider searches for biomarkers of diseases and drug safety and the understanding that single simple markers of complex processes are unlikely to be definitive. Thus in terms of the better discovery and development of new medicines, although the human and many other species 1

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genome sequences are now known, there has been surprisingly little impact on the numbers of new drug substances coming to the clinic, even though there is now a greater understanding of ‘‘druggable’’ targets (1). Over the last few years, billions of dollars have been pumped into a huge industry built up on measuring gene expression changes (transcriptomics), mostly involving the use of gene-chip technologies (2). This, in turn, has led to a corresponding expansion of proteomics that comprises largely mass spectrometry-based methods for characterizing the consequent changes in protein levels (3). It is now accepted that despite much hype, a huge spend and the provision of much new knowledge, genomics or proteomics is only just beginning to fulfill its promise. This could be because neither genomics nor, to a lesser extent proteomics provides evidence of real world end points for disease diagnosis, or evaluation of beneficial or adverse drug. This book brings together information on an emerging technology for completing an understanding of biological processes, namely metabonomics and the focus in this volume is the application of the technology to drug safety assessment. Metabonomics can be regarded as providing real biological endpoints and is defined as ‘‘the quantitative measurement of the time-related multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification’’ (4). Application of metabonomics involves the generation of metabolic databases for control animals and humans, diseased patients, animals used in drug safety testing, etc., allowing the simultaneous acquisition of multiple biochemical parameters on biological samples. Metabonomics is usually conducted on biofluids, many of which can usually be obtained non-invasively (e.g. urine) or relatively easily (e.g. blood), but other fluids such as cerebrospinal fluid, bile or seminal fluid can be used. It is also possible to use cell culture supernatants, tissue extracts, and similar preparations. The term metabonomics was derived from the Greek roots ‘‘meta’’ meaning change and ‘‘nomos’’ meaning rules or laws (as used in economics), to describe the generation of pattern recognition-based models that have the ability to classify changes in metabolism. There has been a parallel set of

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developments in a subject called metabolomics (5). This is similar to metabonomics but is regarded as a subset of the topics covered by metabonomics. Metabolomics has arisen from metabolic control theory (6) and was originally based on the metabolome, the metabolic analogy of the genome or proteome, which was defined as being the metabolic composition of a cell. In metabonomics, not only are static cellular and biofluid concentrations of endogenous metabolites evaluated, but also full time courses of fluctuations in metabolites, exogenous species, and molecules which arise from chemical rather than enzymatic processing (metabonates). In addition, as originally defined, metabonomics, as well as providing molecular concentrations, also covers the study of molecular dynamic information such as molecular reorientational correlation times and diffusion coefficients in intact tissues. Thus, metabonomics can be regarded as a full systems biology approach in that when studying a whole organism with separate organs and many cell types, effects which are displaced not only in time, but also in distance (e.g., the effects of one organ on another) can be integrated into a holistic view. Metabonomics is a successful approach because disease, drugs or toxins cause perturbations of the concentrations and fluxes of endogenous metabolites involved in key biochemical pathways. For example, the response of cells to toxic or other stressors generally results in an adjustment of their intra- and=or extracellular environment in order to maintain constancy of their internal environment (homeostasis). This metabolic adjustment is expressed as a fingerprint of biochemical perturbations which is characteristic of the nature or site of a toxic insult or disease process. Urine, in particular, often shows changes in metabolite profile in response to toxic or disease-induced stress because the attempt to maintain homeostasis in the face of a toxic challenge results in changes to the composition of biofluids, particularly excreted fluids like urine. Hence, even when cellular homeostasis is maintained, subtle responses to toxicity or disease are often expressed in altered biofluid composition (7). A variety of analytical methods could in principle be used to generate metabonomic data sets so long as the approach

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provides information on the molecules that give rise to the experimental data. Thus ultraviolet spectroscopy and other forms of electronic spectroscopy are less than ideal since they only provide information on molecular fragments, such as different types of aromatic molecules giving rise to the chromophores, and the spectral line widths are so broad that signals from all species overlap considerably. Infrared (IR) spectroscopy provides more molecular information in the form that any differences in spectra due to a perturbation can be interpreted crudely in terms of the functional groups of the substances involved. Again, resolution is limited, for example carbonyl stretch frequencies from all amides such as in different peptides appear overlapped and molecular identification is generally only possible by IR spectroscopy for pure compounds by direct comparison with a database of authentic spectra. However, the two most information-rich techniques that give atom-specific molecular structural information are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Currently, for MS-based metabonomics, it is generally necessary to carry out a separation step, usually using high performance liquid chromatography (HPLC) or chemical derivatization and gas chromatography (GC) before the MS stage (8). The use of Fourier transform MS with its exceptional resolution may remove the need for the separation step (9). Moreover, MS can be more sensitive than NMR spectroscopy and can give lower detection limits. However, there are problems of non-uniform detection caused by variable ionization efficiency. Nevertheless, a few metabonomics studies of mammalian systems using MS detection have now been reported and these will be mentioned in subsequent chapters. 1 H NMR spectroscopy (see Chapter 3) is especially suitable for metabonomics as it requires little or no sample preparation, is rapid and non-destructive, and uses small sample sizes (10,11). More recently, the technique of magicangle-spinning NMR spectroscopy (see Chapter 5) has opened up the possibility of metabonomics applied to tissue samples. The NMR-detected metabolic response of an organism to a particular disease, toxin or pharmaceutical compound can


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Figure 1 The relationship between the main ‘‘omics’’ technologies. Transcriptomics, the study of changes in gene expression, uses mainly gene chips in which RNA binding is monitored using fluorescent tags. Proteomics, the evaluation of protein expression, relies on a separation technique, usually 2D gel-electrophoresis followed by an analytical technique, usually MS. Metabonomics, the study of low molecular weight metabolites, has mainly used 1H NMR spectroscopy but other nuclides such as 13C can be used and increasingly LC–MS is finding a role. All approaches generate megavariate data which need interrogation by appropriate chemometric or bioinformatic software.

then be extracted from the complex data sets, which are also subject to biological variation, by application of appropriate multivariate statistical analyses. Figure 1 summarizes the three main ‘‘omics’’ subjects and the techniques used to generate the analytical data. The common philosophy that links all ‘‘omics’’ approaches lies in the need for in depth bioinformatics and chemometrics analyses and the generation of databases of results. 2. THE METABOLIC CONTINUUM As has been stated already, gene expression and proteomic data may only indicate the potential for pathophysiological changes because many pathway feedback mechanisms are

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simply not reflected in protein concentration or gene expression changes. This realization has led to increased efforts by pharmaceutical companies to try to model transcriptomic and proteomic data in relation to metabolic pathway activity, and to map such data onto well-known pathway databases such as those provided by KEGG (12). However the human ‘‘system’’ is very extensive and the functional integrity of man is also dependent on many external factors and even other genomes. The complexity of the situation is encapsulated in Fig. 2. Consideration of the interactions of the

Figure 2 The various levels of organization in molecular biology. As well as the genetic component, it is now clear that environmental effects such as diet and exposure to other substances in the real world have major consequences. Additionally in humans and other higher species, the interaction between the host genome and those of colonizing species such as gut microbial populations needs to be included.


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internal constituents and external factors in mammals indicates that simple pathway modeling will never capture the information richness needed to describe a human disease process or a drug interaction irrespective of the sophistication of the applied measurement technology. Disease or druginduced modulations in transcriptomic, proteomic or metabonomic data probably do not relate to standardized metabolic pathways that have no compartmental constraints, since mammalian metabolic control functions are dispersed across many cell types in topographically distinct locations that are in different physiological states at the same time. Also, there are many ‘‘extragenomic’’ sources of metabolites and other influences not described in the individuals’ genome that nonetheless have major effects on the integrated metabolism of the organism and on the disposition, fate and toxicity of drugs. The meaning of the term ‘‘metabolism’’ as applied to mammals has been reevaluated and a more complete classification of the range of metabolic processes found in higher animals and how they might interact probabilistically to determine the outcome of a disease process or a drug interaction has been proposed (13). Metabolic pathway diagrams have long been used as shorthand summaries of cellular biomolecular transformations, and are accepted as being representative of underlying cellular order and control. In single cell systems, metabolic control analysis (MCA) (6) has long been used to describe the fluxes of metabolites through individual pathways or pathway units and these methods can accurately describe kinetic properties of such systems. MCA cannot be applied so easily to mammals because metabolic control is dispersed in many cell types through space and time. Complex interactions occur between endogenous pathway control and the metabolism of foreign compounds many of which also induce their own metabolism. Mammals also have well-developed gut microfloral communities [the ‘‘microbiolome’’ (14)], with >500 individual species in man (15), which exert a strong controlling influence on the host immune system and may have significant effects on the host metabolism as gut microbes

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such as Bacteroides thetaiotaomicron even carry metabolic genes that may assist other species including man (16). The role of nutrition in the development of human disease at the molecular level is widely appreciated and cancer and cardiovascular diseases have both dietary and genetic components (17). It has also been shown that selective dietary supplementation can markedly affect the metabolism, and the toxicity of even commonly used drugs such as paracetamol (acetaminophen) (18). Dietary composition, in turn, influences gut microfloral selection and probiotic food products are now widely marketed ‘‘to improve gut health’’ (19). The general benefits and effects of probiotics are yet to be determined, but in a wider context could still influence metabolism in subtle and important ways that could be relevant to drug metabolism and toxicity. Undoubtedly all these nutritional and microbial factors also influence the efficacy, metabolism, and toxicity of drugs in a variety of ways in a diverse population. Only by accounting for all the major ‘‘metabolic axes’’, can it be possible to elucidate an individual’s overall metabolic status (part genome, part environment conferred) and relate this to the development of complex disease traits and the adverse idiosyncratic drug toxicity reactions that can be fatal to both the patient and the drug. It must be recognized that intracellular and extracellular metabolites can come from diverse sources that cannot be treated equally with respect to process control exerted by the mammalian genome. Hitherto, metabolites have been classified simply as being either ‘‘endogenous’’ or ‘‘xenobiotic’’, and have often been considered from different viewpoints with respect to pathway analysis. This whole concept has been under review (13) and endogenous and xenobiotic metabolites represent ends of a continuum of integrated metabolic and non-enzymatic transformations of many kinds with numerous intermediate categories that result from multisite processing. Many so-called endogenous and xenobiotic compounds may be interconverted by a number of processes mediated by extragenomic elements or by facile chemical reactions.


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The range of metabolic types is encompassed by a five level set of definitions. Endogenous species arise from processes under direct host cell genome=proteome control and=or synthesis. Sym-endogenous metabolites are essential to host biological function and are metabolized or utilized by the host, but biosynthesis is not in the host genome. Sym-xenobiotic substances are of extragenomic origin, are not necessarily essential to the host, but may influence, and be incorporated into, endogenous and xenobiotic metabolism. The process involves cometabolism by two or more organisms. Transxenobiotics are of extragenomic or chemical origin but are metabolically converted to endogenous species. Xenobiotics have no intrinsic biological function in mammals and are alien to the host genome but may have major effects on endogenous pathway control and can be extensively metabolized. Not all cellular transformations of small molecules require enzymes. There are many examples of these so-called metabonates, and these are well known in drug degradation studies, e.g., conversion of penicillins to penicilloic acids via b-lactam ring opening and these reactions may generate toxicologically or allergenically active species (20). Any compound of endogenous or exogenous origin that contains a carboxylate group can, in principle, undergo enzyme dependent ester glucuronidation (via UDP-glucuronosyl transferase) and these glucuronides readily undergo facile internal rearrangement reactions (21) producing positional isomers and anomers. The subsequent reactivity of the transacylated glucuronides toward macromolecules may be the basis of certain immunological and toxicological interactions (22). Reactive ester glucuronides can also be formed from endogenous metabolites with carboxylate groups including bile acids. Overall the widespread occurrence of these facile (but conditional) reactions can further detract from the determinacy of some metabolic processes in complex organisms, as such metabonates may also influence fluxes through enzymecontrolled pathways in unpredictable ways. To capture the full power of ‘‘omics’’ tools for drug discovery and development, it is necessary to measure and model the whole system which includes the environmental factors.

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Bioinformatic tools have a strong part to play in helping organize massive data sets, but in themselves cannot be expected to answer fundamental questions on ecological–biological– chemical interactions that influence the way a drug is metabolized or is toxic. There might be a number of useful approaches to global metabolic analysis. One is a mapping strategy in which an individual occupying a position in a metabolic (or other omic) hyperspace is visualized as a part of a large cohort. The individual’s map position is a result of the interactions of a series of multivariate ‘‘influence vectors’’ which exert a metabolic pressure on the individual. These pressures could be from intrinsic genetic sources or from exogenous factors. A very simple representation of this is shown in Fig. 3 using a principal components (PC) map representing the urinary composition of ca 5000 control Sprague Dawley (SD) rats. A series of hypothetical ‘‘influence vectors’’ are shown superimposed to indicate possible directions of metabolic pressure exerted by several interacting macroscopic factors. Of course, this is a huge simplification as the influence vectors may be highly non-linear and also urine is only one compartment that can be analyzed. The challenge is to find out the directions, magnitudes, and components of these ‘‘vectors’’ for each type of disease or physiological condition under study. This will lead to combinations of biomarkers from many pathways that are altered in each condition and probably originating in many different perturbed sites in the body. These are useful as they can act both as diagnostic parameters and also metrics of efficacy for treatments. Another approach could involve using the minimum imposed rules or structure to account for the metabolic observations, building a probabilistic system based on prior knowledge and outcome using the reverse procedure to the normal types of pathway analysis. In this type of model, one would take the observed data and work forward to find the best model that is consistent both with those data (given the analytical error) and prior knowledge. This would not necessarily result in an ordered sequence of metabolic conversions (pathways) but would relate metabolic parameters and disease or toxin-induced movements to each other in a probabilistic


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Figure 3 A principal component scores plot where each point represents an NMR spectrum of a control rat urine. Superimposed on the plot are a number of arrows representing vectors which can be regarded as indicating the various types of influence that can result in alterations to a metabolic profile.

way. The advantage of this is that it is not necessary to ascribe every parameter or component to a pathway in a particular cell type but to globally model the important changes for a particular disease or toxic process. This would allow the interventions of lifestyle change or drug treatment to be evaluated in terms of bulk metabolic movement of the integrated system into ‘‘beneficial’’ or ‘‘adverse’’ hyperspaces and thus enable the creation of new metrics of efficacy and=or toxicity based on probabilities. If idiosyncratic toxicity is really idiosyncratic, it will not be possible to predict. However, if it can be assumed that it is due to an unfortunate combination

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of measurable genetic and environmental factors, then by use of broader modeling concepts it may be possible to understand why idiosyncratic toxicity has occurred in particular cases and to find the relationships between gene and environment and metabolism that has led to this outcome.

3. BIOMARKERS The concept of a what constitutes a biomarker has been evolving to the stage where a comprehensive definition is needed and Chapter 2 provides a review. Biomarkers can be regarded of two main classes—those which provide a direct indication of the molecular events occurring such as the presence of high plasma glucose in diabetes as a direct result of insulin deficiency and those which are really surrogates of the pathology, e.g., elevated plasma low density lipoprotein level in atherosclerosis. Conventionally biomarkers have been regarded as single molecular species or enzyme activities (e.g., elevation of aspartate aminotransferase in liver conditions) which change outside normal limits in some pathological situation, but the newer systems biology approaches whereby many analyzes are measured simultaneously lead to a new type of biomarker fingerprint. These arise typically from transcriptomics studies using gene microarray technology, proteomics studies whereby many proteins are detected and identified using largely gel-electrophoresis MS approaches and finally metabonomics using NMR spectroscopy or MS where diverse small molecule metabolites are measured. Thus biomarkers can comprise anything from a single molecular species, either based on small molecules or macromolecules, through a complex fingerprint of molecular changes indicative of the pathology. Other types of biomarker can exist so long they fit with the general definition of a measurable change linked causatively with the pathology. As well as biomarkers being potentially useful for drug target identification, it is possible to generate biomarkers of adverse effects such as drug toxicity or disease progression, biomarkers of beneficial effects such as measures of therapeutic


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effectiveness or reversal of toxicity, and finally, especially in the environmental science area, biomarkers of exposure are important. It is the latest concept of a metabolic fingerprint as a new type of biomarker which is attracting much attention at present. One of the promises of this approach is the potential ability for such small molecule biomarkers to be less species dependent than gene or protein markers and hence the goal of using data obtained preclinically in the clinical situation is being addressed. A number of aspects relating to biomarkers have to be addressed and these include the analytical precision and reproducibility of the technology used to identify markers, and this is particularly true for gene expression arrays and proteomics platforms. In addition, the evaluation of statistical distributions for biomarker values for both normal populations and for pathological groups has to be achieved because of the need to understand biological variation. These areas will require further investigation before preclinical and clinical biomarkers can be used in submissions to regulatory authorities. It is clear that, given the decreasing efficiency and increasing expense of the drug discovery and development process and the associated need for therapies for chronic diseases especially in the neurological area where diagnosis is difficult and differential diagnosis crucial, there will be a growth in the use of surrogate measures and it is predictable that molecular biomarkers will play a prominent role.

4. BRIEF OVERVIEW OF METABONOMICS TECHNIQUES A variety of spectroscopic methods can be used to generate metabonomic data sets on complex biological samples so long as the data sets are rich in molecular information. A number of investigations, primarily in plant and microbiological contexts (23), have used MS, mainly because of its overall greater sensitivity compared with NMR spectroscopy. This

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has usually been coupled either to HPLC, or to GC after chemical derivatization. However, high resolution 1H NMR spectroscopy has proved to be one of the most powerful technologies for biofluids and essentially the only one capable of studying intact tissues, producing a comprehensive profile of metabolite signals without the need for preselection of measurement parameters or selection of separation or derivatization procedures (10). Furthermore, variable detection responses, such as differential volatilization or ionization effects as in MS, are not an issue for NMR spectroscopy. However, it is clear that the two approaches are complementary, giving information on different sets of biomarkers and integration of both technologies to provide more comprehensive classification and biomarker information is now occurring. As yet, there are few metabonomic studies on mammalian systems in the literature that have used MS as an experimental approach and even fewer that have identified novel biomarkers. Where such studies exist, they are mentioned, but at present since nearly all metabonomics studies of drug safety are based on 1H NMR spectroscopy, this book concentrates on this methodology. Typically, 1H NMR spectra of biofluids such as urine and plasma contain thousands of signals arising from hundreds of endogenous molecules representing many biochemical pathways. Conventional measurement of the major NMR signals can be used to detect biochemical changes, but the complexity of the spectra and the presence of natural biological variation across a set of samples often make it difficult to detect meaningful patterns of change by eye. Generally, it is necessary to use data reduction and pattern recognition (PR) techniques in order to access the latent biochemical information present in the spectra. Water is present at such high concentration in biofluids that its NMR peak is so huge that it can obscure other molecular information. It can also cause dynamic range problems in the NMR detector. For these reasons, 1H NMR spectra of urine are measured using a water peak suppression technique (see Chapter 3). For serum or plasma, a suite of 1H NMR spectra is usually measured, selecting either mainly small molecule resonances or macromolecule profiles.


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With developments in robotic sample preparation=transfer systems and in NMR flow probes, the capacity for NMR analysis has increased enormously and now up to 200–300 samples per day can be measured. Although, 1 H NMR spectra of urine and other biofluids are very complex, many resonances can be assigned directly based on their chemical shifts, signal multiplicities, and by adding authentic material. However, further information can be obtained by using spectral editing techniques, as described in Chapter 3. With the advent of NMR detectors cooled to near cryogenic temperatures (cryoprobes), a sensitivity gain of about 500% is achievable making it possible to measure smaller samples or use less time. In addition, natural abundance 13C NMR spectroscopy is now also feasible for metabonomics (24). Although identification of molecules is not necessary to achieve classification of samples, working out the identification of the molecules that differentiate spectra from different samples classes (biomarker combinations) can lead to insight into biochemical mechanisms of disease or drug effects. Usually, off-line chromatographic procedures such as solid phase extraction chromatography (SPEC) or HPLC can be used to simplify or clean up biofluid samples prior to NMR spectroscopy, as explained in more detail in Chapter 6. In selected cases, directly coupled HPLC–NMR and HPLC– NMR–MS methods can be of value in determining endogenous metabolite structures (25). If tissue samples are available, then complementary information to that in biofluids can be obtained. Although in vivo NMR spectroscopy has been used to investigate abnormal tissue biochemistry, spectral quality is always severely compromised by the low magnetic fields used, leading to poor sensitivity and peak dispersion. Heterogeneity in the sample results in magnetic susceptibility differences causing magnetic field inhomogeneity and this combined with constrained molecular motions of molecules in some tissue compartments leads to poor resolution and lower sensitivity. Therefore, NMR spectral analysis of tissues has largely relied upon tissue extraction methods. However, extraction processes can result in the loss of tissue components such as proteins and lipids.

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The development of high resolution 1H magic-angle spinning (MAS) NMR spectroscopy has had a substantial impact on the ability to analyze intact tissues (26), and this approach is the subject of Chapter 5. Rapid spinning of the sample (4–6 kHz typically) at an angle of 54.7 relative to the applied magnetic field serves to reduce line broadening effects due to magnetic field inhomogeneity caused by sample heterogeneity, dipolar couplings, and chemical shift anisotropy. Thus, it is possible to obtain very high quality NMR spectra of whole tissue samples with no sample pretreatment using only about 20 mg of material. Such experiments indicate that diseased or toxin-affected tissues have substantially different metabolic profiles to those taken from healthy organs (27,28). In addition, MAS-NMR spectroscopy can be used to access information regarding the compartmentization of metabolites within cellular environments (29). 1H MAS-NMR spectroscopic analysis of tissues has great potential within the pharmaceutical industry in toxicological screening of novel compounds. Using this technology, it is possible to ‘‘bridge the gap’’ between biofluid analysis and histopathology and to gain real insight into the mechanisms of toxicity at a molecular level. However, the problem of interpreting the data in metabonomics studies essentially reduces to how, in a large set of NMR spectra of a biofluid from a cohort of animals or humans demonstrating a variety of effects such as normal physiological variation, which might obscure a drug-induced effect, does one determine the significant changes? This is achieved through the use of PR methods. In chemistry, the term chemometrics is generally applied to describe the use of PR and related multivariate statistical approaches to chemical numerical data (30). Chapter 8 describes in detail the different types of chemometric approaches that can be used. The general aim of PR is to classify an object or to predict the origin of an object based on identification of inherent patterns in a set of experimental measurements or descriptors. Pattern recognition can be used for reducing the dimensionality of complex data sets, for example by two-dimensional (2D) or three-dimensional (3D) mapping procedures, thereby facilitating the visualization of inherent patterns in the data set.


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Alternatively, multiparametric data can be modeled using PR techniques so that the class of a separate sample can be predicted based on a series of mathematical models derived from the original data or ‘‘training set’’ (31). Pattern recognition methods can be divided into two categories, ‘‘unsupervised’’ and ‘‘supervised’’ methods. Unsupervised multivariate techniques are used to establish whether any intrinsic clustering exists within a data set and consist of methods that map samples according to their properties without a priori knowledge of sample class. Examples of unsupervised methods include principal components analysis (PCA) and clustering methods such as hierarchical cluster analysis. Supervised methods of analysis use the class information given for a training set of sample data to optimize the separation between two or more sample classes. These techniques include soft independent modeling of class analogy (SIMCA), K-nearest neighbor analysis, and neural networks. Supervised methods require a second independent data set to test or validate any class predictions made using the training set (31). For situations where large numbers of samples need to be processed, there is a need for automatic data reduction and PR analysis. One example of a robust automatic data reduction method, which has been widely used, is the division of the NMR spectrum into regions of equal chemical shift ranges followed by signal integration within those ranges (32). Automatic data reduction of 2D NMR spectra can be performed using a procedure similar to that for one-dimensional (1D) spectra, in which the spectrum is divided by a grid containing squares or rectangles of equal size, and the spectral integral in each volume element is calculated. This is not a universal solution and other approaches are possible and have been used, including shifting peak positions to take into account small pH-dependent variations in chemical shift, in which case the full NMR spectrum can be used for PR. However, it should be remembered that although the initial PR methods might have used segmented data, having identified regions of interest which are changed in some pathological situation, it is always possible to return to the real NMR spectra for peak assignment and metabolite identification.

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5. METABONOMICS APPLICATIONS Theoretically, healthy control animals or humans should occupy a defined region of multidimensional metabolic hyperspace and hence a similar position in a PR map based on 1H NMR biofluid data. However, within a control population, natural variation related to factors such as gender, diet, age, physiological rhythms, and genotype occurs. In order to improve the reliability of detecting specific pathological abnormalities in the biochemical profiles of biofluids, it is necessary to establish the extent of normal physiological variance within the control population, and to analyze factors contributing to alterations in normal physiological ‘‘mapping space’’. Given the higher variation in biochemical profiles of humans, the use of supervised PR methods and large data sets is usually necessary to delineate and understand the causes of the variation, and this is the subject of Chapter 10. One of the factors that contributes to the overall variation in control populations in laboratory studies is the availability of different strains of animals. Recent studies have highlighted the sensitivity of chemometric methods in differentiating between the 1H NMR urine profiles obtained from two standard strains of laboratory rats, SD and Han Wistar (HW) with 90% (33). However, although strain-related differences in the biochemical composition of urine could be detected using NMR–PR methodology, perturbations in the urinary profile caused by administration of toxins were found to be significantly larger than any strain-related urinary variation. 1 H NMR spectroscopy has been used to study the composition of biofluids before and after the administration of a wide range of toxins. Linked with PR methods the toxininduced deviation from normal metabolite profile can be measured efficiently. Chapter 9 highlights applications in this area. Predictive statistical models have been constructed to deal with toxicological profiling on three levels. The first and most basic level is distinguishing whether a sample is normal, i.e., belongs to a control population. The second level involves fitting abnormal samples to known classes of tissue


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or mechanism-specific toxicity with a view to predicting the toxicity of novel pharmacological compounds. The final stage is to identify the spectral regions that are responsible for the deviation from the normal profile and to determine the biomarkers of toxicity within those regions, which may help elucidate mechanisms of toxicity. Metabonomics has been used to evaluate many toxins, each toxin producing a distinctive series of metabolic perturbations that are characteristic of the type of tissue damage and=or the mechanism of toxicity. From fluids such as urine, plasma, and cerebrospinal fluid, the target organ of toxicity and, in some cases, the topographical region of injury within that organ have been identified. To date, toxicity of the liver and kidney has been most widely studied using metabonomic techniques but evaluation of testicular, cardiac, neuro- and mitochondrial toxicities has also proved successful. The administration of a toxin does not generally cause a single metabolic response but induces a series of metabolic events in time, which may or may not return to their previous homeostatic condition, depending on the severity of the lesion. Since the response to toxic insult is dynamic, biofluid profiles are in a constant state of flux and the time of metabolic responses is also characteristic for specific toxins. Where biofluids are sampled over a series of time intervals, a biochemical trajectory of response can be calculated either for individual animals or for groups. The extent and direction of deviation of the trajectory from the coordinate corresponding to the predose time period can yield information concerning the severity and type of biochemical lesion. Toxic effects in tissues themselves can also be studied using 1H MAS-NMR spectroscopy with examples of renal and liver toxins being investigated (34,35). The interest in the use of metabonomics to evaluate drug safety is highlighted by the Consortium on Metabonomic Toxicology (COMET) (36). This is sponsored by five pharmaceutical companies and is operated at Imperial College London. This project has derived and applied metabonomic data generated using 1H NMR spectroscopy of urine, blood serum, and tissues for preclinical toxicological screening of candidate

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drugs. It has generated databases of metabonomic results for a wide range of model compounds, toxins, and drugs linked to computer-based expert systems for toxicity prediction. Efforts have concentrated on liver and kidney toxicity in the rat and mouse. During the initial phase of the project, a successful detailed comparison was made of the ability of the companies to provide consistent urine and serum samples using a common study of the toxicity of hydrazine in the male rat. A detailed statistical model has also been constructed based on the NMR spectra of urine from control rats and this enables identification of outlier samples and the metabolic reasons for the deviation. Chemometric models have also been constructed for the urine and serum data of dosed animals and excellent consistency among all companies was demonstrated. Finally, a computer-based expert system for toxicity prediction has been generated and delivered to the sponsoring companies. Metabonomics has also been applied in fields outside human and other mammalian systems. For example, studies in the environmental pollution field have highlighted the potential benefits of this approach in studies of caterpillar hemolymph and earthworm biochemical changes as a result of soil pollution. In addition, a study of heavy metal toxicity in wild rodents living on polluted sites has been concluded successfully. Chapter 11 highlights some of these studies. The use of chemometrics in the interpretation of NMR spectra in the clinical area has an established history. NMR spectroscopy is a powerful tool in the investigation of many diseases such as inherited metabolic disorders, organ failure, and cancers. For example, PCA has been used to differentiate between tissue extract spectra obtained from normal tissues (extracts) and to classify further tumors into type including differentiation among pituitary tumor, fibrosarcoma, hepatoma, and Walker sarcoma (37). NMR–PR has been used to establish normal physiological variance in a population of human urine samples (38), to classify several inborn errors of metabolism from PCA of urine spectra (38), and monitor the growth of tumors from analysis of NMR spectra of serum samples (39). Recently the use of metabonomics to identify patients suffering from coronary artery occlusion


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based on 1H NMR spectra of blood serum has been highlighted (40). In addition to monitoring the onset or progression of a disease, NMR–PR can be used to assess the therapeutic efficacy of treatment.

6. SUMMARY Metabonomics is a powerful approach for generating a substantial body of information on an intact biological sample. Because NMR spectra and mass spectra contain information on a wide range of biochemical pathways, they are both useful pathological fingerprinting tools. This metabolic fingerprint is perturbed in a characteristic fashion in disease or toxic processes and this shift in position can be readily visualized and modeled using a range of chemometric techniques. Understanding the biochemical reason for such a shift in metabolic space leads to the identification of biomarkers of disease or toxicity. Taken together, these methods constitute a metabonomic approach to studying the quantitative metabolic consequences of pathophysiological insult. NMR-based metabonomics is a relatively new tool in the armory of ‘‘omics’’ techniques, but shows considerable promise in its efficiency of acute lesion detection and, perhaps more importantly, in its ability to give mechanistic insight into toxic and disease processes. However, all of the ‘‘omics’’ approaches, genomics, proteomics, and metabonomics offer complementary information on physiological function and pathological dysfunction in their respective systems of analysis. Furthermore, these approaches also share the use of the same basic bioinformatic and chemometric tools that are needed for enhanced information recovery. It is, therefore, possible to integrate the databases and search for relationships among genomic, proteomic, and metabolic perturbations using appropriate statistical methods, leading to ‘‘bionomics’’. A particular strength of spectroscopic-based metabonomic methods is that they are rapid and not labor intensive. Furthermore, the recurrent expenditure is very low when flow-injection high-throughput studies are considered. In

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biological terms, the most important advantage of metabonomics lies in its ability (especially with urinalysis) to follow individual animals or subjects non-invasively through a complete toxin-related metabolic trajectory giving a holistic picture of integrated biological function over time. This is particularly important where multiorgan effects are a possibility. In such cases, genomic or proteomic methods are weaker because of the analytical necessity of choosing very limited time points for a study and selected tissue or cell samples. MAS-NMR spectroscopy of tissues also represents a significant advance in clinical chemistry and experimental toxicology, but is not yet suited to high-throughput screening because of the technology limitations. Nevertheless, MASNMR spectroscopy is complementary to biofluid NMR spectroscopic studies as it allows biochemical evaluation of the target organs for disease and toxicity and the identification of novel tissue-specific biomarkers of damage. It also enables an important new bridge to be constructed between tissue biochemistry studies and conventional histopathology in a way that has not been possible previously. As spectroscopic technologies and chemometric methodologies continue to advance, they are likely to make increasingly important contributions to experimental toxicology studies and disease diagnosis in the future. REFERENCES 1. Stumm G, Russ A, Nehls M. Deductive genomics—a functional approach to identify innovative drug targets in the postgenomic era. Amer Pharmacogenom 2002; 2:263–271. 2. Kahl G. The Dictionary of Gene Technology. Weinheim: Wiley-VCH, 2001. 3. Aebersold R. Constellations in a cellular universe. Nature 2003; 422:115–116. 4. Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR data. Xenobiotica 1999; 29:1181–1189.


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Brindle JT, Antti H, Holmes E, Tranter G, Nicholson JK, Bethel HWL, Clarke PM, Scofield E, McKilligan DE, Mosedale D, Grainger D. Rapid and non-invasive diagnosis of the presence of coronary heart disease using 1H NMR-based metabonomics. Nat Med 2002; 8:1439–1444.

2 Overview of Biomarkers JOHN TIMBRELL Pharmacy Department King’s College, London, U.K.

1. INTRODUCTION When studying the toxicity of drugs and other chemicals in humans and other animals, it is necessary to use biological markers or biomarkers. There are three reasons for this; firstly, the study of toxicity requires a knowledge of the dose or level of the substance to which the animals or patient is exposed. Secondly, the study of toxicity also requires a means of detecting and quantifying the pathological effect. Finally, the study of the toxicity may require an understanding of the factors which affect the occurrence of the pathological response. Thus, we need to be able to measure exposure to the drug or other chemical and a knowledge of the external 27

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concentration or administered dose is often not enough. We need to be able to quantify the toxic response caused by that drug or chemical. Finally, we need if possible to be able to predict the response or effect in sensitive individuals. Biomarkers are tools that enable us to do these three things. The use of biomarkers in toxicology is, therefore, becoming of increasing importance, especially in relation to risk assessment. There are three types of biomarker in relation to exposure to chemicals as originally defined by the U.S. National Academy of Sciences Committee on Biological Markers Biomarkers of exposure; Biomarkers of response; Biomarkers of susceptibility. Each of these aspects of the toxicity of a drug or chemical requires a different type of biomarker which include a large variety of biological end points (Table 1). These are all inter-related and are part of the general scheme shown in Fig. 1. The categories may overlap sometimes and some biomarkers may fall into more than one category. Thus, biomarkers of exposure are required to determine what level of chemical is present in the body of the patient or animal. There are three different types of such biomarker, depending on the stage at which they are measured. Some biomarkers of exposure are closely associated with the mechanism. Table 1

Examples of Types of Biomarker

Type of Biomarker Biomarker Exposure Exposure Response Response Susceptibility

Specific Example

Metabolite S-phenyl mercapturic acid DNA O6 Methyldeoxy adduct guanine Plasma ALT enzyme Protein Metallothionein phenotype Acetylator phenotype

Toxicant Benzene

Medium Urine

N-methyl-NLymphocytes nitroso urea Paracetamol Plasma serum Cadmium Liver tissue Hydrazine Urine drugs

Overview of Biomarkers

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Figure 1 Inter-relationships between the different types of biomarkers. From Ref. 2.

Provided exposure has occurred, biomarkers of response are measured in order to both detect and quantify any toxic and pathological effects the chemical may have caused.There are many different types of biomarkers of response and some are directly related to mechanism of toxicity. Biomarkers that indicate which factors may be important in determining which species or individual within the species is affected by the chemical, such as genetic factors, are known as biomarkers of susceptibility. These are often intimately connected with the mechanism of the toxicity. Therefore, for some, but not all of these biomarkers, a knowledge of the mechanism underlying the toxicity may be important. Also the use of biomarkers must be part of a holistic approach to the study of and evaluation of the toxicity of the chemical in which a number of markers are used together. Furthermore, new biomarkers need to be validated in relation to specificity and critically compared with other markers.

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Table 2 Types of Biomarker Divided into Invasive and Noninvasive and of Differing Complexity and Specificity Noninvasive Body weight Urine volume Urinary=serum enzymes Urinary=serum metabolites WBC DNA adducts DNA fragments Serum=urinary proteins

Invasive Organ weight Histopathology Tissue enzyme levels=activity Gene changes Antibodies Genotyping

Some biomarkers may be measured both in vivo and in vitro, some may be specific to a particular organism, whereas others apply to most species. Biomarkers may be simple or very sophisticated and invasive or noninvasive (Table 2). Ideally, for use in vivo they should be noninvasive, specific, diagnostic, early warning, sensitive, easily measured, and related to the mechanism of toxicity. It also should be borne in mind that single biomarkers are rarely enough, as chemicals often damage more than one organ and biomarkers are rarely totally specific. Therefore, several biomarkers will usually be needed and interpreted together in order to give a complete picture. The term biomarker is very broad and covers many measurements and parameters some of which have been in use for many years in medicine and toxicology. Some, however, are very new and utilize the latest technologies such as genomics, proteomics and, as described in this text, NMR spectroscopy. This latter technology may be used for the detection or evaluation of all three types of biomarker. Consequently, in this chapter biomarkers will be restricted to those relevant to exposure to drugs and chemicals and having application to mammals as used in drug safety evaluation. Although biomarkers of disease may be dual purpose neither these nor biomarkers of efficacy will be specifically discussed.


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Before discussion of the individual types of biomarker, it is necessary to put them in context relative to the toxicity of drugs and chemicals. Thus, the overall process of exposure to a chemical through to development of a disease resulting from that exposure can be represented as shown in Fig. 1. This is not a series of isolated steps but a continuum and the various types of biomarkers and their inter-relationships are shown in this diagram also. Thus, the process starts with an exposure phase in which a proportion of the chemical is absorbed into the blood stream, possibly followed by metabolism and metabolic activation. The metabolite will then interact in some way with a target molecule, possibly a specific receptor or simply available enzymes or other proteins, DNA, lipids, or carbohydrates. One or more of these interactions may lead to a biochemical response which could presage a pathological process which eventually leads to a gross pathological lesion or a physiological change. Thus, biomarkers are involved at each step. Biomarkers of exposure are required to determine the extent of exposure and the nature and quantity of the metabolites produced. Biomarkers of effective dose will reveal any interactions with macromolecules which may be relevant targets. Biomarkers of response indicate that biochemical changes have taken place and possibly that a pathological lesion has been produced. Biomarkers of susceptibility will be markers such as genetic parameters relating to the particular exposure, such as variability in metabolism or a particular type of response. Thus, the details of the overall process, what was once simply described as a ‘‘black box’’, are now known to include the stages of toxicokinetics, toxicodynamics, and pathogenesis (Fig. 1). The development of biomarkers relating to these has allowed risk assessment and prediction to progress.

2. BIOMARKERS OF EXPOSURE Biomarkers of exposure can be conveniently divided into: Biomarkers of internal dose, for example, the compound or perhaps a metabolite of a drug in a body fluid;

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Figure 2 Effect of biological barriers on exposure of target to a toxic chemical.

Biomarkers of effective dose, for example, a conjugate= adduct formed at the target site. The first type gives an indication of the occurrence and extent of exposure of the whole organism to the drug or other chemical. The exposure of the organism may be very different from that expected from the dose because of the intervening processes of absorption, distribution metabolism, and excretion. The absorbed dose will often be less than the exposure dose. Thus, although an animal may be exposed to the chemical, these intervening processes may reduce the amount reaching the target to zero effectively (Fig. 2). The second type of marker includes the process of metabolic activation if relevant, and so is a composite or aggregate biomarker. It reflects the true exposure of the target site but will usually be a fraction of the absorbed dose and reflects the toxicokinetics and physicochemical characteristics of the chemical. Both types of marker take into account the processes of absorption, distribution, excretion, and metabolism. Therefore, individual variation in the animal is included in the measurement and can give rise to biomarkers of susceptibility.


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Examples of each of these types of biomarkers will be discussed particularly in relation to the mechanism of toxicity. 2.1. Biomarkers of Internal Dose These markers indicate that exposure to a particular compound has taken place by measuring the compound or its metabolite(s) in body fluids. Although human exposure to a particular chemical may be estimated from biomonitoring studies using workplace monitoring, for example, or preferably personal monitors, there is individual variability in absorption, and the distribution and excretion of a chemical may influence exposure of the target site. Therefore, it is preferable to measure the actual amount of compound or better its metabolite in a tissue or fluid from an individual in order to estimate the actual exposure rather than the expected exposure. This is particularly important for environmental and industrial chemicals where the dose is only often approximately known. Where there are several possible routes of absorption, actual measurement of the internal dose is essential. Even with medicines where a defined dose is administered, determination of the actual internal dose is still essential in animal studies and in initial studies in human volunteers. Sophisticated techniques are now available for measuring chemicals and their metabolites at very low levels in order to assess exposure. Particular attention has been paid to metabolites derived from glutathione conjugation as potential markers of exposure (3). This is because glutathione (GSH) detoxifies reactive chemicals to which biological systems are exposed. The result of this conjugation is the excretion of a variety of sulfur containing metabolites. These may give some indication that metabolic activation has taken place (4). Measurement of specific metabolites such as particular mercapturic acids, the final product of GSH conjugation, is a better biomarker of internal dose but requires prior information about the structure of the compound and often sophisticated analytical techniques.

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Figure 3 Metabolism of benzene to S-phenylmercapturic acid, measured in urine as a biomarker of exposure.

For example, for determination of benzene exposure in industry or from gasoline, there are a number of candidate metabolites. However, although benzene is metabolized by hydroxylation to several metabolites, none of these could be used as a specific biomarker of exposure (internal dose). The metabolite used for confirming exposure and measuring this exposure, which is specific, is the phenyl N-acetylcysteine conjugate which results from glutathione conjugation (Fig. 3). This is a minor metabolite which can be quantitated by GC=MS or now immunoassay and HPLC (5,6). This particular metabolite is probably not be involved with the toxicity of benzene, therefore, would not be classified as a biomarker of effective dose. However, some glutathione conjugates may be used as biomarkers of effective dose because they are closely related to the mechanism of toxicity. For example, the glutathione derived metabolites of the drug paracetamol indicate the level of metabolic activation in humans and it


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has been shown that this varies in individuals by as much as tenfold between the limits of the frequency distribution (7). Another is the chemical and experimental anticancer drug N-methylformamide. The N-acetylcysteine conjugate derived from this compound was first detected by proton NMR of urine from dosed animals and is believed to directly result from conjugation of glutathione with the reactive metabolite responsible for the hepatotoxicity (8). It should be mentioned at this point that the term biomarkers of exposure (internal dose) can also be applied to drugs in relation to metabolites which are pharmacologically active. Thus, therapeutic drug monitoring requires a biomarker which can quantitate the true dose received by the patient. In some cases, the biomarker may be the parent drug but could be a metabolite if this is responsible for the pharmacological effect of the drug. 2.2. Biomarkers of Effective Dose Biomarkers of effective dose indicate that exposure has resulted in a biologically active compound reaching a toxicologically significant target. This is often a reactive metabolite rather than the compound to which the organism was exposed (Fig. 1). This is crucial to the toxicity, and a knowledge of this will dramatically improve risk assessment and the interpretation of toxicological data. Because of the many possible interindividual differences in the rate and route of metabolism of compounds, the effective dose at the target site is a preferred measurement over the internal dose. This is often determined by measuring specific adducts in tissues or body fluids.Chemicals that are reactive or are metabolized to reactive intermediates which react with DNA are of particular interest and concern in relation to genotoxicity and therefore possible carcinogenicity. Thus, protein and DNA adducts in blood are used as biomarkers of exposure to reactive alkylating agents such as methylating and hydroxyethylating agents in tobacco smoke and also from many other sources (8a). DNA adducts, such as 7-methylguanine and N-7-(2-hydroxyethyl) guanine, have been detected in

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lymphocytes (9) and a adducts such as N-(2-hydroxyethyl)valine have also been detected in hemoglobin from smokers (10). Hemoglobin adducts are surrogate markers which have a longer half-life than DNA adducts in lymphocytes. Thus, hemoglobin adducts reflect longer term exposure. DNA adducts have also been detected in the white blood cells and urine of patients treated with anticancer drugs such as N-methyl-N-nitrosourea (11). It is not the remit of this chapter to discuss the details of biomarker methodology. However, it can be mentioned that the measurement of macromolecular adducts either DNA or protein can be approached in several ways. For example, the whole adduct macromolecule can be measured or it can be degraded into nucleotides or individual amino acids. A recent study on polycyclic aromatic hydrocarbon adducts to DNA in white blood cells from smokers has shown that the dosimetry from these adducts will significantly predict risk of cancer (12). Detection and quantitation of specific DNA adducts or the fragments from them can be used very effectively to determine true exposure to environmental chemicals and therefore improve risk assessment. For example, in countries such as China many people may have dietary exposure to aflatoxin B1, a potent carcinogen produced by the mold Aspergillus flavus, which grows on nuts and grain. Reactive metabolites of aflatoxin interact with DNA bases and one of the products of this interaction, 2,3-dihydro-2-(N-7-guanyl)-3-hydroxyaflatoxin B1, can be detected in the urine of people exposed to aflatoxin in the diet. Measurement of DNA adducts in blood or such fragments in urine has allowed an association between the incidence of liver cancer and intake of the toxin to be made and has been invaluable in epidemiological studies and risk assessment (12a). However, it should be noted that the particular metabolite or adduct measured is very important. For example, with aflatoxin exposure in both experimental animals and humans environmentally exposed, the amount of total metabolites excreted in urine is not related to risk of aflatoxin-inducd liver disease. However, this minor urinary metabolite (2,3-dihydro-2-(N-7-guanyl)-3-hydroxyaflatoxin B1)


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does reflect relevant exposure and is a good short-term noninvasive biomarker for aflatoxin exposure and risk of genetic damage. This biomarker reflects effective dose but only relatively recent exposure. However aflatoxion-albumin adducts in blood reflect exposure over 2–3 months (12b). It must also be recognized that both carcinogenic and noncarcinogenic chemicals can interact with a variety of target molecules and in a variety of ways. Therefore, measuring an adduct needs to be based on a knowledge of the mechanism of toxicity in order to be able to detect and quantitate the biomarker relevant to the subsequent pathological processes. This, therefore, must be borne in mind especially when measuring adducts by 32P postlabeling or other nonspecific means (see below). Even measuring specific biomarkers in tissues may be problematic if a surrogate tissue or target molecule is used. Although in experimental animals all tissues can be taken at postmortem, and subsequently analyzed, in humans it is often not possible to obtain the target tissue except from a biopsy which is inherently hazardous or when a postmortem is carried out. Therefore, surrogate tissues or molecules have to be used such as white blood cells, hemoglobin, or serum albumen. However, the different types of white cells have different lifetimes and also different amounts of adduct may be formed in the different cell types. Thus, such surrogate biomarkers must always be validated in relation to the true target exposure if possible or alternatively in relation to the pathological change of interest. One human tissue which may be used effectively and is available is placenta, and a study has shown using 32P postlabeling that DNA adducts occur and that these were higher in urban as opposed to rural areas (13). Other factors such as DNA repair (see below) may also lead to variation in the amount of adduct in any particular tissue, and when this is not the true target tissue may result in an underestimate or indeed overestimate of the exposure. A recently devised method for detecting DNA adducts, accelerator mass spectrometry, utilizes radiolabeled compound. The technique is exquisitely sensitive, being able to detect 1 adduct per 1014 bases, which is probably equivalent

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to less than 1 adduct per cell. Immunochemical techniques for the detection of adducts may also be very specific and sensitive. Although the amount of DNA available for detecting adducts may be limited, with adducts to protein, such as hemoglobin, much greater amounts are readily available. Another advantage of hemoglobin adducts is that they can give an indication of chronic exposure, as the turnover time of hemoglobin is up to 4 months in humans. Thus, such adducts have been explored as biomarkers of exposure to herbicides such as propanil in experimental animals (14) and also in human industrial workers exposed to hexahydrophthalic anhydride (15). However, it should be reiterated that adducts with blood proteins such as hemoglobin or DNA adducts in white blood cells are strictly speaking ‘‘surrogate’’ markers because the actual target molecule or tissue is different. Thus, they may not be true markers of effective dose, especially protein adducts when used as surrogates for DNA adducts of carcinogens. One potential problem of measuring hemoglobin and white blood cell DNA adducts is that these surrogate markers require the reactive metabolite to leave its site of formation in a metabolically active tissue and travel through the red or white blood cell membrane. For some reactive metabolites, this will not occur. Adducts with serum albumen avoid the problem of crossing cell membranes and are therefore an alternative but the half-life of hemoglobin is much shorter (20 days in man). For DNA up to 18 sites for the formation of adducts exist although some such as the N7 of guanine and N3 of adenine and O6 of adenine are more commonly found. The thiol, amino, carboxyl and side chain hydroxyl groups of proteins tend to be those commonly targeted. Many DNA adducts have now been described and some used as biomarkers of exposure for carcinogens. For example, DNA adducts of polycyclic aromatic hydrocarbons such as those found in cigarette smoke were detected in both lung tissue and white blood cells from lung cancer patients and compared with those in control patients. Higher levels of adducts were found in the lung cancer patients (16) (Fig. 4). The reactivity of the nucleophilic


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Figure 4 Correlation between DNA adducts in lymphocytes and those in lung tissue from patients with lung cancer. Data from Ref. 16.

site on the macromolecule and the electrophilic metabolite of the particular chemical will vary and these will affect the extent and rate of adduction. As well as environmental and industrial chemicals such as polycyclic aromatic hydrocarbons, nitrosamines, and aflatoxins, drugs such as the anticancer drugs have also been shown to form adducts with DNA and protein. Most DNA, hemoglobin, and albumen adducts are ‘‘selective’’ biomarkers of effective dose because the identity of the adduct is known.

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2.2.1. Aselective Biomarkers—32P-postlabeling There are also ‘‘aselective’’ biomarkers which indicate that a reaction has taken place but give no information about the structure of the adduct. These include the 32P-postlabeling assay which is a widely used, aselective biomarker for DNA adducts (17,18). It is an extremely sensitive technique for detecting DNA adducts (it will detect 1 adduct per 109–1010 bases) but gives no structural information. This assay has been used in both mammalian studies (including human) and in other animals such as fish (18a). The technique involves preparation of DNA from a tissue such as white blood cells for example (Fig. 5) Other aselective biomarkers are those for oxidative DNA damage and lipid peroxidation such as urinary 8-hydroxy-20 deoxyguanosine which has been proposed as a biomarker of oxidative damage to DNA (19) and various aldehydes (20), respectively (Fig. 6). Recently urinary 5-hydroxymethyluracil has been proposed as a general biomarker of oxidative stress in humans (21). Although malondialdehyde is a widely used biomarker of lipid peroxidation, other aldehydes such as pentanal and hexanal can also be used. These, however, should perhaps be considered as biomarkers intermediate between exposure and initial response. The advantages of aselective markers include: (i) a specific assay for a known adduct need not have to be devised; (ii) they can be used as markers in human populations where exposure is lower than experimental studies; (iii) they can be used to detect exposure to a variety of potential toxicants, several of which might produce DNA adducts or oxidative damage. Despite the fact that many nongenotoxic toxic chemicals also produce reactive metabolites, few studies have been carried out to establish biomarkers of effective dose of such compounds, apart from experimental studies utilizing the covalent binding of radiolabeled metabolites to proteins. This reflects the fact that the target molecules, if they exist, are generally unknown although surrogates such as hemoglobin or serum albumen can be used. Recent studies with paracetamol, however, identified that some


Figure 5 The adducts.

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P-postlabeling technique for detecting DNA

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Figure 6 Products of oxidative processes which may be used as biomarkers. (From Ref. 20.)

cellular protein targets are now known and fragments of protein adducts have been detected in serum during paracetamol-induced liver damage (Fig. 7). There is a good correlation between the degree of liver damage and the level of this adduct in the serum (21a). Again this may be considered an intermediate biomarker between exposure and effect. However, there are relatively few studies on protein adducts of chemicals and their metabolites in relation to nongenotoxic endpoints such as might be relevant to drug toxicity. The use of biomarkers of effective dose such as adducts to protein or DNA is particularly important in toxicology and risk assessment because it can reduce the uncertainty associated with environmental and industrial exposures and


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Figure 7 Mechanism underlying paracetamol-induced liver damage and potential biomarkers of exposure in urine and plasma.

where administered doses of drugs are variably and poorly absorbed. Thus, for exposure to a carcinogen in relation to low dose risk assessment (Fig. 2), a knowledge of exactly how much actually gets to the target molecule reduces the error in estimating the risk from exposure. This is well illustrated by studies with aflatoxin and tobacco smoke constituents in humans.

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Other techniques can also be used such as collection of expired air or other tissues. For the detection of volatile chemicals to which individuals are exposed, collection and analysis of the exhaled breath may be carried out using specialized techniques (22). A novel source of tissue for evaluating exposure is human hair. Thus, dietary derived heterocyclic aromatic amines and drugs have been detected in human hair (23,24).

3. BIOMARKERS OF RESPONSE Biomarkers of response are parameters measured in order to both detect and quantify any toxic and pathological effects a chemical may have caused. There are many different types of biomarkers of response and some are directly related to the underlying mechanism of toxicity. Biomarkers of effect=response range from the simple such as monitoring body weight and population changes, to the sophisticated such as determination of specific isoenzymes by immunochemical techniques. They can be broadly divided into invasive and noninvasive and those that indicate pathological damage and those that detect biochemical changes or responses. Some biomarkers of response may measure or detect the progress of pathological damage caused by a drug as well as its initial occurrence. There are a very large number of potential markers for determining the biological effect of chemicals and it is not within the scope of this chapter to attempt to review them all; some of the types are illustrated in Table 3. Invasive markers in tissues cover an array of pathological techniques including gross pathology and histopathology using either light or electron microscopy through to measurement of biochemical changes. These are usually carried out at postmortem. Biomarkers of pathological change in response to exposure to chemicals applicable to continuous exposure such as markers sampled in blood may be more useful. This type of biomarker includes a wide range of enzymes. The sophistication and usefulness of this technique may be


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Table 3 Types of Biomarker of Response Biomarker Body weight Organ weight Serum enzymes Enzyme activity

Urinary metabolites Temperature

Type Integrative, noninvasive Integrative, invasive Specific, noninvasive Invasive=noninvasive depending on tissue=method Noninvasive Noninvasive

Effect=Response Dysfunction Organ damage Organ=tissue damage Adaptive response, mechanism of toxicity Biochemical perturbation= tissue damage Biochemical perturbation

further enhanced by the separation of the activity into isoenzymes (e.g., lactate deydrogenase (LDH) or creatine kinase (CK) which may indicate more precisely which organ, tissue, or organelle is damaged. The disadvantage of serum enzymes is that the changes are usually transient, depending on the stability of the enzyme, rate of leakage, and excretion. Such biomarkers usually only indicate that significant pathological damage has occurred but are useful during subchronic and chronic toxicity tests. These markers are often used to detect damage to major organs such as liver, heart, and kidney, for example. Bai et al. (25) found that the levels of certain bile acids in serum, notably cholic, glycocholic, and taurocholic acids, were more sensitive markers of liver dysfunction than serum enzymes. Exposure of humans to low levels of organic solvents has been shown to lead to significant increases in serum bile acids (26). Blood cells of various types can yield different types of information which can be used as biomarkers of response to chemicals. Thus, both the number cells and type of damage to blood cells can be evaluated. For example, the presence of sister chromatid exchanges in white blood cells indicates potential damage to the chromosomes and has been detected in workers exposed to ethylene oxide. Similarly, a reduction in numbers of particular lymphocytes may be caused by a

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chemical and is indicative of immunosuppression which may be caused by compounds such as dioxin (TCDD). The presence of particular antibodies (such as antinuclear antibodies) may represent a biological response to exposure to a drug or other chemical (e.g., the drug hydralazine). Specific antibodies produced by the animal against a particular chemical may be detected and measured and used as biomarkers of response to that particular chemical. For example, in some individuals dosing with the drug penicillin can lead to specific antibodies being raised and circulating in the blood. For example, the anesthetic halothane, which may cause a serious immunotoxic effect, produces metabolites which bind to proteins and antibodies are generated against the conjugates which are detectable in blood. A recent study evaluating such a specific biomarker, IgG specific for hexahydrophthalic anhydride in exposed workers, however, found no significant correlation with exposure (15). Such antibodies have not been widely used as biomarkers but could hold great potential in terms of sensitivity and specificity possibly indicating potential immunotoxicity. For detecting chemical-induced damage to most organs or tissues, the available biomarkers usually require either tissue samples or blood. Although blood sampling is technically invasive, it is not normally a problem in either humans or experimental animals. However, the sampling of tissues requires either a biopsy or tissue to be taken at postmortem which may not be possible or appropriate. Apart from metabolic dysfunction which can be detected with a variety of biochemical markers, detection of tissue damage is generally limited by the availability of specific biomarkers. For liver damage, a number of enzymes=isozymes are available. By using ratios of isozymes, it is possible to detect damage in some other organs but for some organs few if any specific biomarkers are known. Recently, a plasma protein specific for lung damage has been described, the socalled CCl6 protein (26a) used in humans (26b). Furthermore, it has been found relatively recently that exhaled substances may indicate damage to the lungs and also other organs such as the liver (see below).


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For the detection of damage to the kidney, there are a variety of markers in serum but also noninvasive urinary biomarkers. These include specific enzymes released from damaged tissue as well as metabolites, proteins, amino acids, and glucose, all of which can be used as biomarkers of kidney dysfunction. However, there are urinary biomarkers for pathological responses and damage to tissues other than the kidney which are noninvasive. For example, urinary metabolite profiles can indicate liver dysfunction and the sulfur amino acid taurine has been shown to be elevated in the urine of animals in which there is liver dysfunction including steatosis, caused by a variety of chemicals (27). Recent research has indicated that changes in certain endogenous urinary metabolites (28) and serum (29) are associated with abnormal phospholipid accumulation which have potential as useful biomarkers for this pathological effect commonly associated with drug exposure in experimental animals. 3.1. Enzyme Activity Measurement of enzyme activity can be an important and sensitive biomarker of response which in some cases may be measured in the blood. However, in other cases the activity of the enzyme can only be measured in tissues which requires a biopsy or postmortem. Alternative ways to determine enzyme activity are to measure metabolites of endogenous substrates or to give the patient or experimental animal a specific substrate and then determine amounts of known metabolites. The measurement of enzyme activity can reveal either increases (induction) or decreases (inhibition) due to exposure to chemicals. Inhibition and induction of enzyme activity may be sustained and so can be an important and useful biochemical marker of effect. However, changes in enzyme activity per se may not necessarily be indicators of a toxic response; this depends on the relationship to the toxicity. For example, there are several markers for the biochemical and toxic effects of lead on the red blood cell resulting from the inhibition of several of the enzymes of hemoglobin synthesis (Fig. 8). The pathological effect detectable

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Figure 8 The effect of lead on hem synthesis in the red cell and the application to biomarkers. Ref. 29a

by microscopy, for example, is a reduced red blood cell count which indicates that an exposed individual is suffering from anemia, a pathological effect. However, more subtle biochemical measurements may be made such as measurement of serum aminolaevulinic acid dehydrase activity which is reduced by lead exposure. However, this is an overly sensitive marker and many normal healthy individuals in an urban population will have decreased enzyme activity. A less sensitive and more useful marker is zinc erythrocyte protoporphyrin. Lead blocks the final stage of haem synthesis by inhibition of ferrochelatase which prevents iron incorporation into the protoporphyrin. Consequently excess protoporphyrin becomes available and zinc is incorporated instead. This zinc erythrocyte protoporphyrin can be detected and quantitated using fluorescence spectroscopy. A less sensitive but


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noninvasive marker of lead toxicity is the level of urinary aminolevulinic acid. The level of this precursor rises when aminolevulinic acid dehydrase is inhibited by lead (Fig. 8). There are also effective biomarkers of exposure for lead, both acute (blood lead levels) and chronic (x-ray analysis of bone). Therefore, coupled with the biomarkers of effect described, lead toxicity can be effectively detected, controlled, and treated. Induction of cytochrome P450 isozymes, an adaptive response, may be used as a biomarker of the effect of exposure of many species to a variety of chemicals such as organochlorine compounds and polycyclic hydrocarbons. Detection and measurement of this response can be carried out in a variety of ways: enzyme levels can be determined in tissue homogenates or microsomal fractions. Alternatively, the activity of cytochrome P450 may be determined in vivo by studying the metabolism of selected xenobiotics in exposed organisms. Also there are urinary markers for cytochrome P450 induction, such as increased D-glucaric acid excretion (30) and the excretion of 6-b-hydroxycortisol for which there is a readily available method (31). These are especially useful for the determination of enzyme activity in humans (32) but can also be used in other species (33). Recently, analysis of breath constituents has been shown to reflect cytochrome P450 activity. Thus, in rats exposed to a cytochrome P450 inhibitor large rises in volatile organic compounds occurred which diminished when enzyme activity returned (34). Such biochemical effects as enzyme induction may be adaptive or protective responses to exposure to toxic chemicals. Another important example is the induction of metallothionein in response to exposure to metals such as cadmium (35). However, a number of other insults will cause this response such as oxidative stress. Similarly, another potential biomarker for toxic effects is the induction of heat shock or stress proteins (36). An increase in the synthesis of these proteins results from alterations in gene expression in response to a variety of environmental stressors such as temperature, salinity changes in aquatic organisms, teratogens, oxidative stress, chemical exposure, and anoxia. The response is relatively rapid (occuring in

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hours) and leads to the accumulation of proteins such as hsp 90 and hsp 70, hsp 60 (chaperonin) and ubiquitin. Several methods can be used to measure these proteins such as (i) metabolic labeling followed by autoradiography; (ii) cDNA probes to measure the mRNA coding for the protein; and (iii) immunocytochemistry. Hsp 72 is one of the better markers, as normally very little is present in biological systems and so any increase is obvious and easily measured. It is found in mammals and other animals, in plants and microorganisms, making it a widely applicable biomarker. However, studies have revealed that although levels of hsp 72 are increased by cadmium another toxicant, hydrazine, did not raise levels (37). For any given toxicant there may be several biomarkers of effect which can be measured in different body fluids and tissues and which may have different levels of sensitivity and specificity. The questions of sensitivity and specificity are important because if a biomarker is too sensitive or is nonspecific it may detect effects which are not toxicologically relevant. There are unfortunately relatively few biomarkers measureable in urine which indicate significant biochemical or pathological changes, but here NMR has made and will continue to make an enormous contribution by facilitating the detection and measurement of novel biomarkers as discussed in this volume. For example, NMR analysis of urine (38) lead us to investigate changes in the urinary levels of the amino acid taurine as a biomarker for various types of liver dysfunction (27). NMR also revealed a potential urinary biomarker for testicular damage, urinary creatine (39). Reliable biomarkers of testicular damage are few and generally require blood or tissue samples. A rise in urinary creatine in rats has been shown to reflect testicular damage of different types caused by a variety of toxicants (40), and the effect has also recently been shown in mice (41). Levels of mutation and mutation frequency are a biomarker of response which may be directly related to DNA damaging chemicals such as aflatoxin. For example, in individuals exposed to dietary aflatoxin in China, the HPRT


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mutation frequency was measured in T cells as a biomarker of response and correlated with aflatoxin–albumen adducts from serum (42). This study showed that for those with high exposure to aflatoxin, the odds ratio for high HPRT mutation frequency was 19.3. This suggests that the DNA damage in lymphocytes caused by aflatoxin leads to increased mutations. This is an example of two types of biomarkers being used together to improve diagnosis and risk assessment . 3.2. Breath Analysis A relatively new technique which is applicable to the field of biomarkers is the analysis of breath for volatile components which may be used as biomarkers of response reflecting underlying pathological change caused by disease or chemical exposure. The concept of breath as a diagnostic aid is not new, however, and distinctive breath odors have been recoznized and used for diagnosis by physicians for centuries. This technique has become more viable due to recent technological advances for the collection, concentration, and sensitive analysis of the components of breath which are generally present at low concentrations. The advantages of the technique are that it is noninvasive and breath can be repeatedly sampled. The details of the techniques, which will be dependent on the substances being measured, can be found elsewhere (43,44). Breath analysis has been used therefore to detect lung disease (44), liver and other diseases (43), and for the detection of the effects of chemicals on metabolic processes (34,45). Thus, for lung disease a range of substances can be detected in breath, especially NO, which occur in response to inflammation or oxidative stress. Thus, exhaled NO is raised in atopic asthma but reduced in cystic fibrosis. CO is also increased in asthma. As well as analyzing the volatile=gaseous gaseous components, it is also possible to analyze breath condensate. For example, in inflammatory lung diseases there are increased amounts of isoprostane, hydrogen peroxide, nitrite, and 3-nitrotyrosine in the breath condensate. Furthermore, it is possible to monitor the efficacy of treatment with drugs

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administered to patients on these parameters. With NO the effects, for example, of corticosteroid treatment can be detected relatively rapidly—i.e., within a few hours. Occupational asthma induced by dusts can also be detected and monitored using these techniques. Similarly, liver diseases give particular but different profiles of exhaled markers. The most abundant hydrocarbon in human breath is isoprene which is a possible marker for cholesterol metabolism. Thus, when the drug lovastatin, which blocks HMGCoA reductase, is given to patients, isoprene exhalation is decreased (45). It has long been known from animal experiments that ethane and ethylene exhalation increase as a result of lipid peroxidation caused by substances such as carbon tetrachloride and this correlates with the production of malondialdehyde which is often used as a biomarker for lipid peroxidation. Ethane and 1-pentane are increased when there is reactive oxygen-mediated damage to tissues. Liver disease has been especially studied in relation to exhaled biomarkers and sulfur containing compounds feature especially prominently. Thus, exhaled carbonyl sulfide levels are increased in various types of liver disease and possibly lung necrosis. Other sulfur compounds can be detected in breath in liver cirrhosis, such as methyl and ethyl mercaptan, dimethyl sulfide and dimethyl disulfide (43). Renal disease can also give rise to changes in exhaled breath, for example, dimethylamine and trimethylamine levels are increased. 3.3. Genomics The recent development of -omics, viz. genomics, proteomics, and metabonomics has already had an impact and undoubtedly will have an increasing impact on the development of new biomarkers of response=disease and of susceptibility. More importantly, the integration of these three technologies and the application of bioinformatics will potentially revolutionize the detection of the effects of drugs and disease processes at earlier stages. The three approaches reflect a


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biochemical continuum, as the information encoded in the genome is transcribed and then translated into proteins which function in various ways leading to metabolic changes. Although any of the techniques can be used in isolation, integrating them is more powerful and gives a mechanistic basis to the biomarker which makes it potentially more robust, reliable, and more specific. Genomic tools include transcript profiling, which measures the steady state levels of mRNA, can give rise to potential biomarkers such as the human metallothionein gene which is influenced in humans by exposure to cadmium (46). However there may be many hundreds of gene changes occurring. The use of software packages will allow analysis of the data to discern clustering of genes with similar expression patterns, for example. It is not the purpose of this overview to give the details of these technologies which can be found elsewhere (47). If a database of gene profiles can be assembled for particular toxic endpoints, this may be useful for predicting toxicity and for identifying potentially new biomarkers. Some profiles are available commercially. For instance, a gene profile for pancreatic cancer has been described (48). However, gene changes must really be confirmed using other methods such as RT PCR. Validation in relation to a particular toxic endpoint is essential. Thus, an increase in the expression of a gene and an increase in mRNA does not necessarily mean an increase in the corresponding protein or the activity of an enzyme. Therefore, a change in gene transcription may not have relevance as a toxic endpoint. Furthermore, changes in protein levels can occur as a result of post-translational modifications without there being any change in gene expression and transcription. Thus, there is a need to show that gene changes in a gene, in mRNA protein level, or enzyme or other activity are consistently associated with a toxic endpoint either with a specific toxicant or a group of toxicants. A combination of genomic techniques may be used, such as RT PCR and SSH. For example, one study examined the gene changes in hepatocytes from dogs exposed to a novel

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drug which caused lipid accumulation. It was found using the techniques of RT PCR and SSH that genes associated with hepatic steatosis were changed. Thus, the gene for the protein APOAII was downregulated, whereas the genes for the enzyme CYP2E1 and protein APOB100 were upregulated. It was found that in general PCR data correlated with that from SSH except in the case of the gene for the enzyme stearoyl CoA desaturase where the two techniques gave contradictory effects (47). Data such as this indicate that potential biomarkers have been identified. Other factors that need to be taken into account are the time course of changes in relation to pathological changes, the time when the gene changes are measured and the relationship between changes in genes and the dose of the chemical. It should be said, however, that genomics has yet to make its mark in relation to biomarkers useful in the drug safety evaluation process (47).

3.4. Proteomics Proteomics involves the separation, identification, and quantitation of proteins typically using 2D gel electrophoresis to separate on the basis of charge and then molecular weight (49). An important recent improvement which has made the technique more amenable is the enrichment of samples and the removal of abundant proteins such as albumen. This allows the separation and visualization of many otherwise hidden proteins. The pattern can be digitally imaged and analyzed and the spots of interest can be excised and analyzed for structure using mass spectrometry. Other techniques have become available such as the use of tandem mass mass spectrometry for successive fragmentation and anlysis. Systems exist for the separation of proteins by the use of ‘‘proteinchips’’ with attached antibodies or anionic or cationic surfaces which bind particular types of proteins and then the chips can be analyzed by SELDI TOF mass spectrometry. The use of stable isotope coded affinity tags (ICAT) for isolating peptides


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is a useful technique. Capillary electrophoresis coupled to mass spectrometry is another very promising technique for the separation and analysis of proteins. However, improvements to the technique will still be necessary before widespread use of the technique, but improvements can be done (50). After the large or small proteins are analyzed by mass spectrometry, this can be coupled to peptide sequence searching. Bioinformatic techniques can then help to identify the protein. Indeed for genomics, proteomics, and metabonomics, bioinformatics is an essential additional tool for the complete analysis of the data generated. Using these techniques, changes in the protein complement of a cell or organism in response to exposure to a chemical can be evaluated. A particular protein can then be used as a biomarker. The changes can be most easily detected in vivo as changes in serum or urinary proteins. This is a distinct advantage over genomics where cells or tissue are required to generate samples of DNA. Furthermore, transcriptomics will only indicate the potential for increase in a protein, by virtue of increased mRNA, for example, but this may not be translated into a functional protein. Post-translational modification and assembly of protein complexes are also important factors. The disadvantage of proteomicsis is that when 2D electrophoresis is used, the throughput is slow and requires considerable automation. It is important to relate the function of the protein to the mechanism of toxicity if possible. Although it is early, the data published to date indicate that proteomics can identify potentially important biomarkers. For example, study of the drug lovastatin, a lipid lowering agent which inhibits HMGCoA reductase, revealed that treatment of rats with the drug changed 36 different liver proteins. As well as those expected from the action of the drug, other proteins were changed which indicated potential toxicity such as changes in stress proteins, calcium homeostasis, and cytoskeletal structure. Such subtle effects would not be likely to be detected by conventional methods used in safety evaluation.

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If the proteins changed by exposure to a chemical can be easily detected and measured in serum or urine, then these changes may become sensitive biomarkers. With all of these techniques, however, changes observed may not be necessarily related to the toxicity mechanistically. Also some changes may not be useful biomarkers because of lack of sensitivity or specificity.

4. BIOMARKERS OF SUSCEPTIBILITY Different species of animals and individual animals within a species and individual humans often differ in their handling of drugs and other chemicals and in their responses to the exposure to these chemicals. This variation often has a genetic basis and gives rise to a third type of biomarker which may indicate susceptibility to damage and dysfunction caused by the chemical. These are unlike the two previous types of biomarkers as they do not form part of the continuous process from exposure to pathological change shown in Fig. 1. Rather, biomarkers of susceptibility are measurable factors inherent in the organism irrespective of the chemical exposure, but which influence the mechanism of toxicity and so the processes generating biomarkers of exposure (both internal dose and effective dose) and response. For example, biomarkers of exposure (internal dose) can be part of the toxicokinetic phase as metabolites of the particular chemical of interest. The production of these metabolites and their detoxication may be influenced by genetic factors peculiar to the individual such as variations in the enzyme(s) catalyzing a metabolic route. Measurement of the variability of these enzymes either by measuring the genotype or the phenotype can be used therefore as biomarkers of susceptibility. However, not all enzymes involved in the metabolism of chemicals show significant genetic or other variation. Alternatively, the response to a chemical may be modulated in the toxicodynamic phase by a process such as DNA repair. Variations in this process can be measured and used as biomarkers of susceptibility. If receptors are involved


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with the pathological response, or oncogenes, tumor suppressor genes, or immune system components, any of these may be biomarkers of susceptibility if there is significant variability between individuals. In some cases, as discussed below there may be several factors having an impact on the overall response, each of which is a different biomarker of susceptibility. The variation in disposition and response is of crucial importance in risk assessment and, therefore, there is now increasing interest in biomarkers which can detect increased susceptibility to drugs and other chemicals. Of the four phases of disposition of a xenobiotic, the most important source of variability is metabolism and this has been the area of most research. As biotransformation and metabolic activation are so often intimately involved in the toxicity of chemicals, individual genetic variation in the enzymes controlling these processes is often the basis of variations in susceptibility to toxicity (51). These differences may be reflected in differences in biomarkers such as DNA or protein adducts in individuals who have the same exposure. There are a number of enzymes which show genetic polymorphisms and which have been associated with diseases such as cancer (52). Differences in susceptibility due to variation in other processes such as DNA repair could lead to increased mutation rates and hence greater incidence of tumors following exposure to carcinogens. For determination of genetic polymorphisms, several techniques are available. Thus, either the genotype or phenotype can be measured. Genotyping will give information about the genes and alleles for specific enzymes. However, although there may be variations at this level, these may not be manifested in the response of the whole organism. Phenotyping which looks at the difference between individuals in terms of the response of the whole organism, takes into account both genetic variation and other factors such as influencing and other processes and environmental influences. Genotyping can be carried out by analysis of restriction fragment length polymorphisms (RFLPs) by Southern blot

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analysis or by amplification and analysis of cDNA or mRNA sequences using the polymerase chain reaction (PCR). Phenotype can be determined by quantitating the relevant metabolites of a probe substrate such as caffeine, in human subjects or animals. Alternatively, determination of the enzyme activity could be carried out in vitro in tissue or cells. Measurement of the level of the specific enzyme protein or the amount of specific mRNA can also reveal the phenotype. Data from the two techniques are often, but not always, in agreement. 4.1. Enzymes of Biotransformation as Biomarkers of Susceptibility Because metabolism is such a crucial part of the disposition and toxicity of drugs and other chemicals, variations in the enzymes which catalyze the process are important biomarkers of susceptibility. A number of different enzymes and enzymes systems have been studied in this regard. Cytochrome P450, the CYP enzyme system, is the most important Phase 1 metabolizing system, particularly localized in the liver. There are known to be polymorphisms of a number of the isozymes in humans, namely CYPs 1A1, 1A2, 2C19, 2D6, and 2E1 (53–55). The first polymorphism in this system to be described was that affecting CYP2D6 which metabolizes debrisoquine and sparteine. The ability to hydroxylate debrisoquine shows a clear bimodal distribution in a human population (Fig. 9). Consequently, there are two phenotypes for this enzyme, labeled extensive metabolizers and poor metabolizers. They can be typed on the basis of a ratio of metabolites of debrisoquine in urine. Dextromethorphan, however, may be the preferred probe substrate because of its safety margin (56). The poor metabolizer phenotype has been associated with increased susceptibility to toxicity of a number of drugs such as penicillamine and perhexiline. In the former case there was found to be an increased incidence of skin rashes, in the latter case an increased incidence of liver damage. Conversely, the extensive metabolizer phenotype has been associated


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Figure 9 Frequency distribution for the debrisoquine hydroxylator status or the variation in activity of the CYP2D6 isozyme in a human population. Data from Ref. 57a

with increased cancers in smokers. Polymorphisms of CYP1A1 (MspI and Val=Val) are associated with increased rates of lung cancer especially in the Japanese population. This may be due to increased activity of the enzyme which metabolizes polycyclic hydrocarbons or possibly increased inducibility. Using adducts to DNA from white blood cells as a surrogate marker of exposure, a relationship between CYP1A1 activity and adduct levels was found (57). Caffeine is in many respects an ideal probe substrate for determining the in vivo activity and phenotype for

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cytochrome P450 isozymes and indeed other enzymes such as xanthine oxidase and N-acetyltransferase (NAT2, see below). Thus, N-3 demethylation to paraxanthine, the major pathway of metabolism for caffeine, is catalyzed by CYP1A2 and the level of this metabolite and plasma caffeine clearance may reflect CYP1A2 activity in vivo. However, it must be recognized that other factors may influence the urinary metabolic ratios and other isozymes may also be involved (e.g., caffeine is also a nonspecific substrate for CYP1A1) (58,59). As well as enzymes catalyzing Phase I reactions, those catalyzing Phase II metabolic pathways may also be used as biomarkers of susceptibility. One well-studied example is the enzyme N-acetyltransferase (NAT) and both enzymes (NAT1 and NAT2) show genetic variation and each allele shows several genotypes (60). However, for aromatic amines and hydrazine derivatives, NAT2 is much more active (10). The acetylator phenotype is a biomarker of susceptibility for a number of toxic responses including drug-induced liver damage, drug-induced lupus erythematosus, and aromatic amine-induced cancer (see Table 4). However, resolution of the particular genotype will be important, for example, the slowest NAT2 acetylator phenotype, genotype NAT2( )5, is the most susceptible to aromatic amine-induced bladder cancer (60). Thus, in humans (and some other species) the ability to acetylate amines, hydrazines, and sulfonamides varies and shows a bimodal frequency distribution. This gives rise to

Table 4

Acetylator Phenotype and Susceptibility to Toxic Effects

Drug=Toxicant Isoniazid Procainamide Hydralazine Sulfasalazine Aromatic amines

Adverse Effect Liver damage and peripheral neuropathy Lupus erythematosus Lupus erythematosus Hemolytic anemia Bladder cancer

Susceptible Group Slow acetylators Slow Slow Slow Slow

acetylators acetylators acetylators acetylators


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two distinct groups, known as slow and fast acetylators, the proportions of which vary with ethnic origin. Slow acetylators are homozygous for the dominant allele, fast acetylators may be either homozygous or heterozygous. Slow acetylators have mutations which result in the production of less functional N-acetyltransferase enzyme. The acetylator status or phenotype can be readily measured by giving animals or human subjects drugs such as sulfamethazine and analyzing urine for free and acetylated drug. A more acceptable alternative for human subjects is to use caffeine metabolism, as one of the metabolites is further metabolized by acetylation (NAT2) to yield 5-acetylamino-6-formylamino-3-methyluracil (AFMU). A number of adverse drug reactions are associated with the slow acetylator phenotype and bladder cancer is associated with occupational exposure to aromatic amines. However, other types of cancer (colorectal cancer) have been linked to the fast acetylator phenotype (52). Another Phase II enzyme system which shows genetic polymorphisms which affect susceptibility to chemical exposure is the glutathione transferase system (GST). This is perhaps the most important detoxication system as it catalyzes the removal of toxic metabolites by conjugation with glutathione, a protective agent. As with cytochrome P450, there are several isozymes and polymorphisms with each isozyme. Thus, genes for GST-M1, M3, P1, and T1 have been shown in humans with polymorphisms for each. As with NAT 2, the distribution of the polymorphism varies with ethnic origin, and with GST-M1, for example, some individuals have a decreased capability for conjugating with glutathione due to inheriting a gene deletion which is homozygous. This null genotype has been correlated with increased susceptibility to cancer of the colon, bladder, and lung, enhanced sister chromatid exchange and increased levels of DNA adducts (61). Individuals with both the GST-M1 deficiency (GST M1 null, =) and CYP1A1 Val=Val genotypes may be more at risk from cancer than those with only one of the null genotypes.

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Because of the rapid advance of molecular biology, it is now possible to determine the genotype instead of the phenotype in individuals for these biomarkers of susceptibility. However, a particular genotype may not necessarily result in a significant effect on susceptibility or metabolism. 4.2. Other Biomarkers of Susceptibility As well as enzymes involved in the metabolic activation and detoxication of drugs, other factors that influence the processes of exposure and response may be used as biomarkers of susceptibility. For example, repair of damage to DNA may involve several enzymes which show genetic variability and this could be a major factor in determining the occurrence of damage and cancer after exposure to a DNA damaging agent (62). For example, specific repair enzymes such as O6-alkyldeoxyguanine-DNA alkyltransferase and uracil DNA glycosylase show very large levels of variation (200–300 fold) in the human population. Humans with lung cancer have been found to have decreased levels of the alkyltransferase activity. The capability for DNA repair can be measured in white blood cells and used as a biomarker of susceptibility (63). Human subjects with reduced levels of DNA repair capacity were found to have a fivefold increased risk of skin cancer. As multiple pathways may be required for DNA repair, several defects could increase susceptibility; simultaneous evaluation may be needed therefore (62). Levels of receptor proteins, oncogenes and gene products, and tumor suppressor genes also show variability which may influence the outcome of the response to exposure and hence can be used as biomarkers of susceptibility. Components of the immune system, both humoral and cellular, also show variability and some may be used as biomarkers of susceptibility. Receptor proteins may be possible biomarkers of susceptibility such as the aryl hydrocarbon receptor (AhR) which binds various hydrocarbons. This results in various responses including induction of cytochrome P4501A1. It has been found that human individuals show variation with some (10%)


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having a high affinity form of the receptor (64). This leads to greater expression of cytochrome P4501A1 and increased susceptibility to cancer and other toxic effects. Tumor suppressor genes, such as p53, are important factors that can affect susceptibility to cancer and hence are potential biomarkers of susceptibility. If one of the alleles is inactive in humans, as in the heterozygous genotype, this is associated with an increased rate of cancer. This increased susceptibility is also found in mice heterozygous for the p53 when they are exposed to chemical carcinogens. Polymorphisms in the ras oncogene family are also linked to high cancer rates. It must be noted, however, that although some mutations may be biomarkers of susceptibility, they may also be consequences of the exposure to a chemical and, therefore, a marker of response rather than susceptibility. Levels of antioxidants in tissues may also be factors which can increase susceptibility. For example, studies in rats have suggested that urinary taurine levels may be a potential marker of susceptibility. As urinary taurine correlates with and therefore reflects the liver taurine concentration, low urinary taurine levels in an animal can be an indication of low levels of liver taurine .There was a significant correlation between the levels of taurine in the urine of rats and their susceptibility to a variety of hepatotoxic agents (64a). This implies that taurine present in the liver is protective and when levels are low, the liver is more susceptible to damage. This has important implications as humans are relatively poor synthesizers of taurine and rely partly on diet as a source. The interaction of several genetic and possibly environmental factors will often be important in the development of a particular toxic response, some of which will be biomarkers of susceptibility. An example of this is the development of drug-induced lupus syndrome in patients taking the drug hydralazine, which depends on several factors. Apart from dose and duration of dosing, the acetylator phenotype, the HLA (tissue) type, and gender are all biomarkers of susceptibility. Thus, this syndrome only occurs in those patients with

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the slow acetylator phenotype and although the drug is acetylated, it is not clear which metabolite is responsible for the toxic effect. The syndrome is four times more common in females than males but again the reason is not known. Finally, the occurrence of the HLA type DR4 is a factor and an indicator of susceptibility as it is more prevalent in those with the disease, occurring in 33% of controls and in 73% of patients who present with the syndrome. All of these factors are biomarkers of susceptibility and could be used to reduce the occurrence of the syndrome. 4.2.1. Validation and Interpretation of Biomarkers The validation of biomarkers before serious use is essential and critical to their effective use in relation to safety evaluation and risk assessment. Some aspects of validation will vary depending on the type of biomarker although some features will apply to all three types. Thus, some considerations such as the sensitivity, specificity, variability, availability, and robustness of the analytical technique(s) apply to all three types of markers. Also another important feature of all three types of biomarker is whether it requires an invasive procedure: it is clearly preferable if it does not. Sampling urine or blood is generally acceptable but tissue is more difficult as this requires a biopsy in humans and either this or a postmortem in animals. For determination of exposure, the biomarker should be specific for the compound of interest, which may be a metabolite of the compound to which the individual is exposed or an adduct with a macromolecule such as DNA or hemoglobin. Thus, the same metabolite may be formed from a number of similar chemicals and the same adducts may also result from active metabolites from different chemicals. The factors which can affect the level of the biomarker must also be considered and if possible evaluated, such as the toxicokinetics and the relationship with dose or ambient concentration. For surrogate biomarkers such as hemoglobin adducts, the relationship to and relevance to the real target must be established. In some cases it may be possible to correlate the biomarker of


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exposure to a toxic effect, as for example with the DNA adducts of aflatoxin, but not all measurable metabolites or adducts relate to the toxic effects of aflatoxin (see above). Another important consideration is the temporal relationship between the biomarker and the exposure. After acute exposure most biomarkers will only be useful for a few hours or at the most days after the exposure for most readily metabolized and excreted chemicals. The exception to this are DNA adducts which may persist in white blood cells for longer or hemoglobin adducts in red blood cells. With continuous exposure, however, many biomarkers may reflect the current exposure if the steady state situation is achieved. Therefore, it may be necessary to use several biomarkers for retrospective exposure. For example, urinary metabolites of a chemical will be excreted and measurable early after exposure, typically a few hours, whereas DNA and albumen adducts may be detectable for a number of days in the blood. Breakdown products of these may be detectable in urine possibly a month after exposure and adducts with hemoglobin can survive for 120 days. With biomarkers of response, as well as the general considerations already mentioned, validation in relation to the exposure and a particular pathological effect(s) is essential. Thus, biochemical changes in particular which may occur before any observable pathological changes must be validated in relation the toxic and or pathological effect and must be shown to be dose related. Although the ultimate aim is to find biomarkers of response which are very sensitive and ‘‘early warning,’’ occurring prior to pathological or other adverse changes, the problem is to interpret changes which may simply be reversible, adaptive, biochemical perturbations of no consequence. This dilemma is revealed in the following statement ‘‘it is becoming more difficult to distinguish between measured alterations that are ‘‘adaptive and reversible’’ and those that are ‘‘pathological and irreversible’’ ’’ (65). As with biomarkers of exposure, the relationship to time is important. Does the alteration in the biomarker of response occur before or at the same time as the pathological change. Is it transient or sustained. These factors will determine whether a biochemical change is a useful biomarker of response.

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Similarly, biomarkers of susceptibility must show a significant correlation with a pathological or toxic outcome such as cancer. This may require considerable statistical power for validation. This will have significant implications in terms of the number of individuals evaluated and the number and specificity of the polymorphisms or variants. The multifactorial nature of toxic responses, therefore, necessitates the use of early biomarkers of effect as well as biomarkers of exposure and susceptibility. Although as indicated in this brief review there are a large number of biomarkers available to indicate exposure, response, and susceptibility, the difficulty lies in the interpretation in relation to risk. The detection of potentially toxic compounds in biological samples is now possible at the level of 1 DNA adduct per cell. Biological responses may be measured in terms of increases or decreases in enzyme activity or levels of proteins. Genetic factors may be defined precisely. Yet our ability to translate this knowledge into reasonable risk factors is relatively poor. Just because we can detect the presence of a chemical or measure a biochemical effect does not mean that this represents a hazard and therefore that the individual is at risk. Some biomarkers may be irrelevant to toxicity, whereas others may be too sensitive. Therefore, different types of biomarker need to be used in combination where possible and appropriate with a holistic approach being adopted. REFERENCES 1. National Research Council, US National Academy of Sciences Committee on Biological Markers, 1989. 2. Waterfield CJ, Timbrell JA. Biomarkers. In: Ballantyne B, Marrs T, Syversen S, eds. Textbook of Applied and General Toxicology. 2 Macmillan Ltd, 1999. 3. Van Welie RTH, van Dijck RGJM, Vermeulen NPE, van Sittert NJ. Mercapturic acids, protein adducts, and DNA adducts as biomarkers of electrophilic chemicals. Crit Rev Toxicol 1992; 22(5=6):271–306.


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Hu JJ, Mohrenweiser HW, Bell DA, Leadon SA, Miller MS. Symposium overview: genetic polymorphisms in DNA repair and cancer risk. Toxicol Appl Pharmacol 2002; 185:64–73.

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64a. Waterfield CJ, Turton JA, Scales DC, Timbrell JA. Correlation between urinary and liver taurine levels and between predose urinary taurine and liver damage. Toxicology 1993; 77:1–5. 65.

Kelce WR, Ewing LL. Male reproductive toxicology. Biomed Environ Sci 1991; 4:35–47.

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Biomarkers in Human Cancer. Part I, Predisposition and use in risk assessment. Environmental Health Perspectives. Vol. 98, 1992. Part II, Exposure Monitoring and Molecular Dosimetry. Environmental Health Perspectives. Vol. 99, 1993.

3 NMR Spectroscopy: Principles and Instrumentation MICHAEL D. REILY

JOHN C. LINDON

Pfizer Global Research and Development, Michigan Laboratories, Ann Arbor, MI, U.S.A.

Biological Chemistry, Biomedical Sciences Division, Faculty of Medicine, Imperial College London, South Kensington, London, U.K.

1. INTRODUCTION The objective of this chapter is to familiarize the reader with nuclear magnetic resonance (NMR) spectroscopy, its basic principles, its utility as an analytical tool for investigating biofluids, and to describe the instrumentation and related hardware necessary to operate a functional NMR-based metabonomics laboratory. Nuclear magnetic resonance spectroscopy is a powerful approach because it combines the provision of detailed molecular information with the 75

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possibility of understanding whole molecule dynamic properties such as diffusion, plus the ability to carry out quantitation. Although powerful in its own right, NMR spectroscopy can be regarded as complementary to other analytical chemical techniques. For example, it can provide information on substances with no UV chromophores such as carbohydrates. It is a universal detector in that if the molecule under study contains NMR-active nuclei these should be detectable, unlike in mass spectrometry where analyte observation can be influenced by selective ionization. Most NMR spectroscopic experiments are carried out in solution for the purpose of identifying the structures of small chemical molecules, including natural products, but there is a wealth of high resolution applications in other areas, such as determining the threedimensional (3D) structures of proteins as well as analyzing complex biological mixtures such as biofluids for metabonomics applications. In addition, there is much effort devoted to solid state NMR spectroscopy where special techniques have to be used to overcome very broad NMR peaks and hence to recover useful chemical information. Finally, NMR spectra can be obtained from living humans and animals and in vivo NMR or magnetic resonance spectroscopy (MRS), as it is known, has found use in disease diagnosis. The same technology and principles lie behind magnetic resonance imaging (MRI), now widely available in hospitals for clinical diagnosis. Since it was first commercialized in the 1950s, NMR spectroscopy has been an invaluable analytical tool for structure elucidation of solubilized analytes. In its humble beginnings, NMR spectrometers were only sufficient for analyzing relatively simple organic molecules that could be dissolved at high concentrations (0.1 M). The limitations of early systems were primarily due to low sensitivity and poor resolving power. As more powerful systems became available in the 1970s, researchers began looking at more complicated analytes and mixtures. Soon, it was realized that NMR could also be also be a valuable bioanalytical tool and manufacturers of NMR equipment responded. Gradual advances in NMR spectroscopy beginning in the 1980s through to the present time have led to its routine use in analyzing increasingly

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complex samples, including medium-sized biomolecules (up to 40 KDa) and biological matrices comprising hundreds of spectroscopically distinct molecules with thousands of resonances. Indeed, as a direct consequence of these advances, entirely new applications of NMR have emerged, including biomolecular NMR and metabolic profiling, which can be considered sub-fields in their own right. The latter, the theme of this book, relies on the ability of NMR to provide detailed analysis on the biomolecular composition (primarily of molecules less than 500 molecular weight) very quickly with relatively little sample preparation. There are several excellent textbooks on NMR spectroscopy that describe the theoretical basis of the subject, provide information on operational methods and give good descriptions of applications (1–3). For these reasons, only an overview of the technique is given here.

2. PRINCIPLES OF NMR SPECTROSCOPY 2.1. Basic Theory The phenomenon of NMR arises because the positively charged nuclei of certain atoms possess a quantized property called spin. This spin is associated with a nuclear magnetic moment, also quantized, such that in a magnetic field, it is possible for the nuclear magnetic moment to take up various orientations with respect to the field. Each orientation is associated with a discrete energy state and in the presence of the magnetic field these states have different energies. The quantized state is characterized by specific values that can have an integer or half-integer value including zero. For nuclei with spin ¼ 0, there is no magnetic moment and this is the case where both the atom number and the atomic weight are even, such as 12C and 16O. For the simplest magnetic nuclei, with spin ¼ 12, there are just two levels. As a consequence of the differing energies of the states, the populations of spins in the states are not equal and there will be an excess of nuclear spins in the lower level. It is possible to induce transitions of nuclear spins between these levels by applying an

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oscillating frequency field and for commercially available NMR magnets, these transitions are in the radio-frequency region of the electromagnetic spectrum. There is a linear relationship between the magnitude of the nuclear magnetic moment and the observation frequency of the NMR phenomenon for a given magnetic field strength. There is also a linear relationship for a given nucleus between observation frequency and magnetic field strength. As will be seen below, not all nuclei of a given atomic isotope in a molecule will have the same resonance frequency and hence the NMR phenomenon gives rise to a range of resonance frequencies corresponding to peaks in an NMR spectrum. Most NMR spectra are based on just a few nuclear types. There are several reasons for this. One is that nuclei with spin > 12 have a property called a nuclear quadrupole moment which, in general, results in short lifetimes in the excited spin states and a rapid return to the low energy state, resulting in very broad NMR lines. Second, many nuclei exist at low natural abundances and so are difficult to detect without isotopic enrichment. Third, the strength of the NMR response is related to the size of the nuclear magnetic moment and many nuclei have rather small values of this and so have low detectability. Finally, some nuclei, once excited to the upper level, are slow to relax back to the ground state and this must occur before another scan can be added. This then incurs a time penalty for acquiring summed scans necessary to improve detection limits. Sometimes, these difficulties of low sensitivity, low natural abundance, and long relaxation times come together. The ubiquitous 1H nucleus, or proton, has one of the highest relative sensitivities, surpassed only by its radioactive isotope tritium, 3H. The 13C isotope is useful for characterizing the carbon skeleton of organic molecules and with a natural abundance of about 1.1%, the chance of finding two 13 C nuclei in a given molecule is thus only about 0.01% and this simplifies the spectra considerably. Many spectroscopic tricks have been developed to allow routine observation of 13 C NMR spectra of organic molecules. The 19F nucleus is almost as sensitive as the 1H nucleus (83%) in NMR terms


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and 19F NMR spectroscopy has been used extensively in studies of the metabolism of fluorine-containing drugs. More limited use is made of other nuclei in pharmaceutical and biochemical research and nuclei such as 15N have been used extensively for protein structure determination following isotope enrichment. The use of 31P NMR spectroscopy has been widespread in biochemistry and medicine as a means of investigating the various phosphorylated molecules important in biology, including many studies in vivo. In addition to being an unparalleled structural tool, NMR can also provide dynamical information on molecular diffusion, orientational correlation times, and intermolecular interactions in solution. The molecular self-diffusion coefficient is a valuable measure of molecular mobility and in free solution is directly related to molecular size. Using NMR techniques, it is possible to separate individual components in a sample such as urine based on their molecular self-diffusion coefficients (4,5). Also as molecular correlation times increase, they lead to broadening of the resonance lines, an effect that is roughly proportional to the molecular weight. Thus, although albumin is highly abundant in plasma, its line width makes it a component that is difficult to characterize in a normal NMR spectrum. Conversely, very rapid internal motion in the lipid groups of very large lipoprotein particles produce lines narrow enough to interpret. When small molecules bind to large ones, the NMR properties depend on the association and dissociation rates and the molecular size of the macromolecule. Such protein–ligand interactions are especially prominent in protein-rich biofluids, such as plasma and in intact tissues and are manifested in broadening of NMR peaks for some small molecules that would ordinarily have very sharp lines. High-resolution NMR studies of small tissue samples in very high magnetic field have been reported using the technique of magic angle spinning (MAS) as detailed in Chapter 5. This technique, also developed originally for chemical analysis, overcomes some of the unfavorable aspects of intact tissues for NMR analysis and holds great promise, but currently is rather low throughput. In the case of intact

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tissues, differential relaxation effects of a small molecule can be used to distinguish compartmentalization. In NMR spectroscopy, only a very small excess of the spins are in the low energy state. The net result of this is that NMR is a rather insensitive technique relative to many other analytical methods. Typically, even todays spectrometers require a minimum of several nanomoles of material for analysis in reasonable times. This can be compared with femtomoles or less for mass spectrometry under favorable conditions. Thus, poor sensitivity has been the bane of bioanalytical uses of NMR and increasing NMR sensitivity has been the focus of most of the technical developments that have occurred over the past four decades. However, in contrast to the low intrinsic sensitivity in the applications of NMR to biofluids, the non-selectivity of NMR makes it a very powerful tool for surveying the molecular content of a sample without prejudging which analytes to search for. What was mentioned as an advantage above can also be a nuisance. Scarce analytes often need to be measured and although above the limit of detection, these lower level species may be fully or partially obscured by analytes at much higher concentrations. Similarly, exogenous components that contain interfering nuclei (water, buffers, xenobiotics, etc.) can obstruct regions of the NMR spectrum. 2.2. Parameters from an NMR Spectrum Not all nuclei of a given isotope resonate at exactly the same frequency. This is because a given nucleus is surrounded by electrons, which are also magnetic and in the presence of the magnetic field, these provide a fluctuating magnetic field opposing the main field of the NMR magnet. As a consequence, the nuclei are shielded from the main magnetic field and require a higher field to bring them to resonance and thus they can be considered to have higher resonance frequencies. The degree of shielding depends on the electron distribution around the nucleus and hence on the chemical environment. Thus, the exact nature of the chemical bonds and non-bonded interactions that are experienced by the nucleus influence its


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resonant frequency. This is termed the chemical shift and is predominantly determined by close-range effects within the molecule itself. Thus, while solvent and salt can affect the chemical shift to a limited extent, the NMR chemical shift is largely insensitive to the analytical matrix. Chemical shifts are measured relative to that of a reference substance placed into the sample. For 1H and 13C shifts in organic solvents, this is tetramethylsilane (TMS). The chemical shift is then defined as d(H) ¼ (difference in the resonance frequency in Hz between the analyte and TMS) 106=(operating frequency of the spectrometer). Chemical shifts are thus quoted in ppm and are independent of the operating frequency of the spectrometer, allowing comparisons irrespective of magnetic field strength. For aqueous samples, an alternative reference compound is used and trimethylsilyl [2,2,3,3-2H4] propionic acid sodium salt (TSP) is the most commonly used example. The chemical shifts for TMS and TSP are set arbitrarily to 0. If the intrinsic resonant frequency of a proton in a magnetic field of 14 Tesla is 600 MHz, 1 ppm is 600 Hz. If nitrotyrosine is taken as an example, a proton contained in the aromatic ring will have a different chemical shift, or characteristic frequency, than one in the aliphatic portion, as seen in Fig. 1. This shows separate peaks for each chemically distinct hydrogen. It should be noted that the two hydrogens of the CH2 group have different chemical shifts because they are chemically distinct due to the proximity of the asymmetric carbon atom. Indeed, the resolving power of modern spectrometers allows distinguishing even between different aromatic and aliphatic protons. This results in a unique fingerprint for virtually every molecule, including geometric isomers of the same molecule. The resonance lines of individual nuclei can show further splitting due to indirect spin–spin coupling. This, given the symbol, J, is measured in Hz and is independent of the observation frequency. Such spin coupling arises from a magnetic interaction between NMR-active nuclei and is transmitted via the intervening electrons, hence the term ‘‘indirect.’’ Coupling is only observed within a molecule. Thus, for two spin-12 nuclei such as protons, the resonance line for each proton will

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Figure 1 500 MHz NMR spectrum of 10 mM 3-nitrotyrosine dissolved in 66 mM sodium phosphate buffer, pH 7.4. The buffer contains 0.33 mM trimethylsilyl [2,2,3,3-2H4] propionic acid sodium salt (TSP) as an internal chemical shift standard. Labels assign specific protons in the molecule to specific NMR signals in the spectrum. In aqueous solvents, the amino and carboxylate protons are generally exchange broadened and not observed.

be split into a doublet, the two lines corresponding to the two possible orientations of the adjacent proton relative to the magnetic field. For extended coupling chains, each component of a doublet can be split further into doublets of doublets and so on. An example of this is shown in Fig. 2 for 3-nitrotyrosine where the J-coupling between the CH and CH2 groups results in such splittings. If a given proton is adjacent to two equivalent other protons, as in a CH2 group, then, of the four possible orientations of the two protons, two of them are identical (up=down is the same as down=up) and a 1:2:1 triplet results. For such ‘‘first-order’’ systems, the multiplicity can be deduced on the basis of Pascal’s triangle according to the number of equivalent coupled nuclei. In situations where the chemical shift difference between the protons is large compared to the J-coupling, then this simple rule applies.


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Figure 2 Expansion of the 500 MHz 1H NMR spectrum of 3nitrotyrosine showing J-coupling splitting patterns in the aliphatic region. The superscript denotes the number of bonds between the coupled nuclei. It should be noted that the different magnitudes for the triple bond couplings are a direct result of differential orbital overlap (and hence geometry) between the methine proton (a) and the two magnetically non-equivalent prochiral methylene protons (b, b0 ).

For situations where the chemical shift between coupled partners is not large compared to the magnitude of the coupling constant (d=J < 10) or in symmetrical molecules, more complex rules have to be applied and sometimes the only solution to interpreting a spectrum is via a computer simulation. For 1 H–1H interactions, the coupling does not normally extend beyond three bonds, with four-bond couplings being quite small, if resolvable. Three bond 1H–1H couplings provide valuable information on the dihedral angles between C–H vectors through an empirical equation known as the Karplus equation. Hence the J-coupling is a valuable parameter for distinguishing between isomers and for measuring molecular conformations. Compilations of coupling constants have been made and empirical models for calculating them in various conformations have been proposed (6). If the NMR data are acquired under conditions where each scan is acquired on a spin system at equilibrium, then the areas under the NMR peaks are directly proportional to the number of nuclei contributing to that peak and to the

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concentration of the molecule in the sample. If an internal standard of known concentration is added to the sample, then absolute concentrations can be determined. Because the NMR phenomenon is a quantum mechanical one, to a first approximation the signal intensity is linear, directly proportional to concentration over many orders of magnitude and independent of molecular characteristics. Thus, there is no requirement for extinction coefficients as in ultraviolet spectroscopy, and unlike mass spectrometry, NMR is unaffected by the ability of the analyte to ionize or to other matrixrelated suppression issues. Since NMR is non-selective, one can quantitatively assess the concentration of many components in a biofluid spectrum without a prior knowledge of what one might want to measure. There are two times which define how fast a nuclear spin interacts with the rest of the sample as a whole (known as the lattice) and how nuclear spins interact with each other in a pairwise fashion. These are designated T1 and T2. T1 is known as the spin-lattice or longitudinal relaxation time, and is the characteristic time for the process of nuclear spins reaching equilibrium populations in the spin states. For small molecules in non-viscous solutions, 1H T1 values are usually in the range of 1–10 sec. The other relaxation time is known as T2, the spin–spin or transverse relaxation time, and is related to the rate of spin-dephasing caused by spin–spin flips. For small molecules in free solution T1 ¼ T2. However, macromolecules and exchanging species have short T2 times, typically in the range 10–100 msec, even though T1 may be much longer. The difference in values of T2 between small molecules and macromolecules can be used to edit NMR spectra. As mentioned earlier, the molecular self-diffusion coefficient is a whole molecule property which does not normally appear in NMR spectra. However, it is a valuable measure of molecular mobility and in free solution is directly related to molecular size. It is possible to measure diffusion coefficients using a specially designed NMR experiment, which includes the application of magnetic field gradients (4,5).


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There is another interaction which is important in NMR spectroscopy called the dipolar coupling. This is a direct magnetic interaction between nuclei through space, not through bonds as for J-coupling. This dipolar coupling can be several orders of magnitude larger than J-couplings. However it is averaged to 0 in isotropic liquids, but in solids is largely responsible for the observed very broad resonance bands. In semisolids such as tissues, the dipolar couplings between nuclei are partially averaged out by the considerable molecular freedom and the residual couplings and hence the line broadening can be removed by the technique of MAS as described in more detail in Chapter 5. However, for molecules tumbling in solution, the fluctuating dipolar interaction is an important relaxation mechanism and because of the distance dependence involved in its definition, it can be used to provide molecular structural information (7).

3. OPERATIONAL METHODS A major gain in efficiency is obtained by simultaneously detecting all signals. This is achieved by the application of a short intense pulse of r.f. radiation to excite the nuclei followed by the detection of the induced magnetization in an r.f. detector coil as the nuclei relax. The decaying, time-dependent signal, known as a free induction decay (FID) is then converted to the usual NMR spectrum by the process known as Fourier transformation (FT). The efficient calculation of the digital Fourier transform requires the number of data values to be a power of 2, typically perhaps 16 K points for modest spectral widths, up to 128 K or even 256 K points for wide spectral widths on high field spectrometers (1 K is 1024 or 210 points). Acquisition of a 1H FID requires typically a few seconds and opens up the possibility of adding together multiple FID scans to improve the spectrum signal-to-noise ratio (S=N) since for perfectly registered spectra, the signals will coadd but the noise will only increase in proportion to the square root of the number of scans (8). The S=N gain therefore is proportional to the square root of the number of scans. This for the first time

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made routine the efficient and feasible acquisition of NMR spectra of less sensitive or less abundant nuclei such as 13C. In many molecular systems such as proteins and other macromolecules, or in multicomponent mixtures such as biofluids, the spectral complexity and signal overlap in 1D NMR spectra can be too great for simple assignment of NMR resonances. Under these circumstances, it would be desirable to improve the dispersion of the signals. One approach is to simplify the complex 1D NMR spectrum to leave only resonances, which are amenable to interpretation by the use of special pulse sequences. These can edit the original spectrum into subsets of data, or excite or detect only specific types of resonances, for example, only those which are spin–spin coupled to a given resonance. An alternative approach to achieve this dispersion is through the use of two-dimensional (2D) or higher dimensional (n-D) methods. The pulse sequences which form the basis of such multidimensional NMR approaches not only enable increased spectral dispersion, but can also have the added advantage of giving information on relationships between the various resonances in the spectrum, for example showing which ones are coupled to each other by spin–spin coupling (9). The general approach adopted in 2D NMR is to apply a series of r.f. pulses and delay periods to the sample such that there are two independent variable time intervals in the pulse sequence. One of these is the acquisition time, denoted by t2, and the other is some incremental delay denoted t1. If an NMR FID is acquired for a period t2 for each of a set of t1 values, the digital NMR signal intensity (S) will be a function of both t1 and t2 giving a matrix of data S(tl,t2). If FT is carried out with respect to both t1 and t2 a matrix of NMR intensity as a function of two frequencies will result. This is now a 2D NMR spectrum as it represents signal intensity as a function of two frequency axes. The delay t1 and the actual nature of the pulse sequence will define exactly what the two frequency axes will represent. The 2D NMR spectra are usually plotted as contour maps as though the 2D spectral peaks are a series of mountains viewed from above relative to the orthogonal o1 and o2 axes.


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There are basically two types of 2D NMR experiments. These are termed (a) resolved and (b) correlated, according to the type of dispersion in the second frequency dimension o1. Resolved experiments are a way of improving spectral dispersion by rotating the appearance of one NMR parameter at right-angles to another so that the o1 and o2 axes correspond to different parameters. The most common version of this is to cause the rotation of all spin–spin coupled multiplets by 90 , thus producing only chemical shifts along the o2 axis and at each chemical shift the spin–spin coupled multiplet is spread along the o1 axis. Generally, no information is available from a resolved experiment on the spin–spin coupling connectivity between resonances. The other class of proton 2D NMR experiment, the correlated type, provides information on connectivity between resonances such as those which have a common spin–spin coupling or which arise from nuclei which are close together in space. In these cases, for identical nuclei, the o1 and o2 axes are identical. There are further classes of experiment that result in pseudo-2D NMR spectra. These do not have a second frequency axis resulting from FT of a variable time, but the second axis is some other parameter. One example is provided by continuous-flow directly coupled HPLC–NMR spectra where the second axis in the pseudo-2D plot is the chromatographic retention time (10) (see Chapter 7). Another example is diffusion-ordered NMR spectroscopy (DOSY) where the second axis plots the molecular diffusion coefficient associated with each NMR peak, this parameter being derived from the dependence of peak intensities on the square of an applied magnetic field gradient (4). The principal pulse sequence in the resolved experiment category for high-resolution solution state NMR is the J-resolved experiment (JRES), which produces a 2D NMR spectrum with both chemical shifts and spin–spin couplings on the o2 axis at their normal frequency positions, but only the couplings appear on the o1 axis. In fact, the pulse sequence results in multiplets which appear at an angle of 45 relative to the axes and a representation is usually shown in which these multiplets have been further tilted by 45 to

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make them orthogonal to the o2 axis. Under these circumstances, the projection of the spectrum on to the o2 axis produces a 1H NMR spectrum consisting of singlets at each 1H chemical shift, i.e., a fully proton-decoupled proton NMR spectrum. The JRES experiment can be a very useful aid to the assignment of resonances in the complex spectra which arise from mixtures of small molecules such as biofluids where many of the individual components give first-order spectra. Also, because the JRES experiment is based on the spin echo sequence, any nuclei with short T2 values will relax within the t1 evolution period and not contribute to the final spectrum. This has proved useful for the analysis of blood plasma by proton NMR as the broad, short T2 resonances from proteins and lipoproteins are greatly suppressed. There is a corresponding heteronuclear experiment, usually for 13C NMR, where the 13C–1H coupling patterns for each resonance are rotated into the o1 dimension. The proton homonuclear experiment, termed COSY (COrrelation SpectroscopY), is used to show which resonances in a proton spectrum have mutual spin–spin couplings. The resulting spectrum has the conventional 1D NMR spectrum along the diagonal and off-diagonal cross-peaks at chemical shifts corresponding to pairs of coupled nuclei. This is exemplified by the spectrum of 3-nitrotyrosine given in Fig. 3. A modification to the COSY sequence called COSYLR is possible which is better suited to elucidating connectivities through small- or long-range spin couplings. A double-quantum filtered COSY (DQF-COSY) approach causes the suppression of diagonal peaks arising from singlets (i.e., resonances without any spin couplings). This simplifies complex spectra and improves resolution. Refinements of the technique can be used which eliminate all resonances from one- and two-spin systems, i.e., singlets and doublets (triple-quantum filtered COSY), and so on for higher spin systems. The DQF-COSY experiment is not as sensitive as the normal COSY approach. A very powerful experiment, called TOCSY, provides information on unbroken chains of coupled protons in one 2D NMR spectrum. The experiment is sometimes also called the homonuclear Hartmann–Hahn experiment (HOHAHA).


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Figure 3 Contour plot of the 1H–1H 2-dimensional 500 MHz COSY-NMR spectrum of 3-nitrotyrosine. Peaks labeled on the diagonal correspond to the 1D 1H NMR spectrum and off-diagonal peaks correlate nuclei that are spin-coupled to each other.

Again, the results are not very dependent on the magnitude of the spin–spin couplings involved in the spectrum. The method relies on the application of a pulse to produce what is called a spin-lock field, which causes the nuclear magnetizations to precess about this r.f. field, i.e., the spins are said to be locked to this field. During this period, transfer of magnetization occurs between coupled spins. The longer the spin-lock period is applied, the further the magnetization will be transferred down a chain of coupled nuclei. The most widely used spinlock sequence is based on a train of pulses called MLEV-17.

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There is a very useful 2D NMR approach, which provides connectivities based on the nuclear Overhauser effect (NOE). The NOE is an alteration in signal intensity based on a direct through-space mechanism which is distance dependent, and thus the experiment can be used to probe internuclear distances (7). The 2D NMR NOE experiment is called NOESY and the spectrum consists of a diagonal, plus cross-peaks at the chemical shifts which demonstrate an NOE. One of the problems with this experiment is that both chemical exchange and NOEs cause transfer of magnetization, and so any peaks involved in chemical exchange which is slow on the NMR timescale (i.e., those components which give rise to separate peaks) will also show cross-peaks in the 2D NOESY spectrum. On the other hand, this can be useful for studying complex exchanging systems. The 2D NOESY experiment is the major tool in the determination of the detailed 3D structure of proteins. The magnitude and sign of a proton–proton NOE is dependent on both the molecular tumbling rate and the NMR observation frequency. When using high-field NMR spectrometers, for molecules in the molecular mass range around 1000 Da, NOEs can be close to 0, even if the nuclei are close together in space. One way of overcoming this limitation is to carry out a so-called ROESY experiment. The pulse sequence is identical to the TOCSY sequence given earlier for connectivity via spin–spin coupling. The distinction of NOE cross-peaks from other effects in ROESY spectra is not always easy. Cross-peaks are observed at the chemical shifts of protons, which experience a direct through-space dipolar interaction which is usually strongly distance dependent, thus indicating which hydrogens are close in space. Correlation between 1H chemical shifts and heteronucleus chemical shifts such as 13C or 15N can be achieved through a number of 2D NMR experiments in which the heteronucleus is detected directly. However, it is also possible to obtain the same type of correlation but with the advantage of the detection (i.e., the t2 dimension) at the much more sensitive 1H nucleus. This type of approach is called inverse detection. One problem is that 1H NMR signals from protons


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attached to the 99% of naturally abundant 12C nuclei have to be suppressed, leaving only the 1H signals from protons attached to the 1.1% of carbon nuclei which are 13C at natural abundance. This can be readily achieved, and if the pulse sequence also includes broadband heteronucleus decoupling (e.g., covering the 13C chemical shift range) then a 2D NMR presentation is possible in which each 1H–13C correlation peak appears as a singlet, the connectivity being based on 1 H–13C spin coupling. This presentation, unlike the homonuclear correlation experiments, has no diagonal peak and the axes correspond to the appropriate chemical shift ranges of the 1H and 13C nuclei. One main experimental approach is termed heteronuclear multiple quantum coherence, or HMQC, and the result is achieved by making use of the fact that coupled 1H and 13 C nuclei can experience magnetization effects involving both spins, i.e., multiple quantum effects, which evolve during the evolution period t1. The 1H–13C HMQC spectrum of 3-nitrotyrosine is shown in Fig. 4. Another related pulse sequence is termed heteronuclear single quantum coherence (HSQC) and this gives analogous information, but with better sensitivity. Analogous pulse sequences exist based on longrange 1H–13C couplings, which can then give connectivity information for quaternary carbons. A cross-peak is observed where the chemical shifts of directly bonded C–H nuclei intersect. It should be noted that where a CH2 group has two protons with different chemical shifts, then two peaks appear on the 1H axis at the same position on the 13C axis. Many methods have been introduced for removing spectral artifacts. These often use pulsed magnetic field gradients along the main magnetic field axis. At least two such gradients are inserted into a pulse sequence, the first to cause dephasing of transverse magnetization and the second, some time later, to refocus only the components of magnetization that are desired. This can all be done in one scan, so removing the need for multiple scans for each t1 increment. This results in much more efficient data acquisition. Field gradients are now used routinely for selecting exactly which part of the magnetization is detected.

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Figure 4 Contour plot of the 1H–13C 2D HMQC-NMR spectrum of 3-nitrotyrosine. Each peak identifies a single C–H pair at the intersection of the 13C (vertical axis) and 1H (horizontal axis) chemical shifts for all CH and CH2 and groups in the molecule.

4. REALIZATION OF NMR SPECTROSCOPY IN A METABONOMICS LABORATORY All NMR spectrometers are comprised of several basic components that are illustrated in Fig. 5. The components that make up the core of an NMR spectrometer include the magnet, r.f. console, and probes. Additional ancillary equipment


Figure 5

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A block diagram of an NMR spectrometer.

that makes application of metabonomics simpler includes automation for sample preparation, and methods for automatically introducing samples into the spectrometer. The latter comprise tube-changing robots, fluid handling devices, and various chromatographic approaches. Each of these, along with typical specifications, is discussed further below. At the heart of any NMR system is the magnet and these are based on solenoids of super-conducting wire cooled to 4.2 K or lower, using liquid helium. The higher the magnetic field, the greater the dispersion and sensitivity, but it is also important to consider the homogeneity and stability of the magnet. It is also the single most costly part of a spectrometer, often representing at about half of the purchase price and this cost goes up non-linearly with field strength. Commercial instruments are available which allow 1H observation at 400, 500, 600, 700, 750, and 800, and 900 MHz. For instruments which operate for 1H NMR at 700 MHz or greater, the liquid helium bath is kept at about 2 K by a pumped refrigeration system so that the higher currents need for the higher fields can be achieved. For reasons of sensitivity and spectral

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dispersion, it is important to utilize the highest field strength that the budget will allow. Nearly all systems that can be purchased today, up to 800 MHz, are available with actively shielded magnets, which are usually specified at an extra cost. However, this extra expense is more than offset by dramatically smaller space requirements. This also greatly reduces the distance from which necessary accessories (e.g., liquid handling robotics, cryogenic probe accessories, and chromatography systems) can be located. The footprint of an NMR magnet is usually given by the area defined by the radial 5 G line, which for an actively shielded 600 MHz spectrometer can be a factor or 10 or more smaller than with a conventional magnet. Typically, the magnetic field drift should be less than 10 Hz=hr and relatively unaffected by barometric changes. Ultra high magnetic field homogeneity (in the parts per billion) is required to do high resolution NMR. The overall homogeneity of an NMR magnet is achieved by a combination of the stable field of the superconducting solenoid, the superconducting shims (usually an orthogonal set of three electromagnetic windings that can add to or subtract from the solenoids intrinsic field) and the room temperature shims (anywhere from 28 to 40 non-superconducting coils). The superconducting shims are used to rough in the homogeneity and are not readily adjustable after the system is installed. The room temperature shims are then routinely adjusted to fine-tune homogeneity for each sample. Homogeneity is typically assessed as the line shape on a standard sample such as chloroform or water with certain minimum widths at half-height and near the baseline. One if the hallmarks of a good magnet are excellent ‘‘cryo line shape,’’ that is, the line width of an H2O proton response with no current in the room temperature shims. In part, this parameter is also dependent on the probe design and sample configuration. An important aspect of conducting NMR spectroscopy on biological fluids and tissues is suppression of large interfering resonances, in particular from water, buffers, and cosolvents (in the case of extracts). It is also important to be able to apply accurately shaped (non-rectangular) r.f. pulses and=or


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magnetic field gradients across samples to enable diffusion measurements, multidimensional NMR experiments, and the latest solvent suppression approaches. It is also useful to be able to observe non-hydrogen nuclei. To achieve these needs, the spectrometer should be equipped with a minimum of two r.f. excitation channels (one capable of observing hydrogen only and one tunable for other nuclei), both of which should have the capability of producing shaped pulses. The spectrometer should also have a gradient amplifier capable of producing a gradient across the sample of 30 G=cm in the direction of the applied static magnetic field (the z axis) with a rapid gradient recovery time. The NMR probe consists of one or more saddle-type r.f. coils that enclose a cylindrical space (the coil volume) filled by sample contained in a cylindrical tube. Solution state NMR spectra are usually measured in glass tubes of standard external diameters, 5 mm being the most common, but a range of narrow tubes are available for limited sample studies (4, 3 mm, and even smaller specially shaped cavities such as capillaries or spherical bulbs, etc.). Very small sample sizes can be accommodated in specialized microprobes with sample volumes in the microliter range. As described below, it is now possible to measure NMR spectra using special probes in a flow-injection mode avoiding the use of sample tubes completely. Surrounding the sample and r.f. coils are one or more gradient coils. This whole assembly is located in the center of the static magnetic field and the room temperature shims. The r.f. coils deliver excitation energy and detect the subsequent response for amplification and digitization. For a given field strength, the probe design can have a large impact on sensitivity, homogeneity, stability, and sample throughput. In any kind of NMR probe, there are two sample volumes to consider. First is the total volume of sample required (the ‘‘sample volume’’) and second is the ‘‘active volume,’’ or the volume of sample that is exposed to the r.f. coils as depicted in Fig. 6. For probes with the commonly used saddle coil, the ratio of active volume=sample volume is 0.5. This is necessary due to the need for homogenous magnetic susceptibility above and below the tops of the saddle coils. Typical

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Figure 6 Illustration of a typical NMR probe showing the saddle coil arrangement.

sample volumes for metabonomics applications range from 120 to 500 mL, a range that is normally adequate for commonly available biofluids such as urine or plasma from anything larger than a mouse. There are also numerous examples of small volume probes (1–30 mL) that could have potential uses in certain applications on rare or hardto-obtain biofluids such as CSF or synovial fluids from small laboratory animals. Increasing the active volume will provide substantial sensitivity gains but can be mediated by several factors. First, it is crucial that the receiver coil be as close to the sample as possible to maximize the filling factor, approximated as the (active volume)=(coil volume). For many biological fluid applications, the actual amount of sample is


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limited, and so any gains from a larger coil volume are lost if dilution is required to achieve a necessary sample volume. Also, the ability to achieve a highly homogeneous magnetic field across the entire sample becomes a challenge for large volumes. If cost and commercial availability were not an issue, ideally one would have a probe whose coil volume matched the anticipated sample volume within the constraints outlined above. For example, a probe with a large active volume (250 mL) might be chosen for dog, primate, or human urine analysis, whereas one with an active volume of 60–120 mL would be best for small rodent urine or blood analysis. If mouse CSF was the primary analytical matrix, a microcoil probe with 5 mL active volume might be the most appropriate. Probes with cryogenically cooled r.f. coils represent a milestone achievement in NMR systems development, with up to a factor of four sensitivity improvements over probes with room temperature coils. The sensitivity increase is diminished by salty samples such as urine, but initial results suggest that cryogenically cooled probes will perform at least as well on such samples but markedly better on samples with low salt and particularly with samples analyzed with hyphenated systems such as HPLC–NMR. Overall magnetic field homogeneity should ultimately be specified and measured for each probe that is to be used. This parameter is typically assessed as the line shape on a standard sample such as chloroform with certain minimum line widths at half-height and at 0.55% of the peak height. For biological fluids, good water suppression often requires even tighter tolerances than most manufacturers publish, since contributions from the water (present at 110 M proton concentration) at 0.001% of its full height can be comparable in intensity to analyte peaks of interest. Hence, it is advisable to specify water suppression performance on each probe. An excellent and accepted standard is 2 mM sucrose in 90% H2O=10% D2O. The signal-to-noise ratio of the anomeric proton signal is indicative of sensitivity and homogeneity, and overall water suppression capabilities of the system are revealed by observation of the residual water. The importance

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of good water suppression is illustrated in Fig. 7. Finally, the probe should be coupled to a sample temperature control unit capable of maintaining a uniform temperature across the sample of 0.1 C or better. The performance of an NMR spectrometer depends a stable environment. The room should be isolated from unnecessary traffic, vibrations, and r.f. noise and be outfitted to maintain a consistent temperature 1 C. It should also be free of large amount of ferrous metals, particularly those that have any chance of moving during the course of an experiment. Periodic cryogen filling (weekly for liquid nitrogen and bimonthly to quarterly for liquid helium) is facilitated by plumbed-in liquid nitrogen and gaseous helium with nearby access doors large enough to allow ready access to portable liquid helium tanks. A raised aluminum floor surrounding the NMR magnets facilitates cryogen fills and loading samples into the system sample changers. Alternatively, one must have a high ceiling and non-ferrous ladders to access the top of the magnet. One of the advantages that NMR analysis provides is the ability to measure samples with little or no sample preparation. However, due to the large number of samples that are typically analyzed in a metabonomics study, automation of both sample preparation and introduction into the NMR spectrometer is an important aspect of setting up a laboratory. Sample preparation requirements are typically simple for metabonomics studies, with normally only addition of a buffer required. However, a single metabonomics study can contain several hundred samples so automated sample preparation can maximize precision and minimize the tedium of mass sample production. The basic work flow for sample preparation involves transfer of tens to hundreds of microliters of biofluid from the collection vessel into a final analysis container containing dilution buffer. Most flow injection devices that are interfaced with NMR spectrometers are based on a commercially available liquid handling devices and an ideal sample container for use with this is a polyethylene 96 well deep well plate. Samples can be made up in 96-well plates using a specialized robotic system; the plate is then transferred to a


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Figure 7 Demonstration of the importance of good water peak suppression for 1H NMR spectra of aqueous solutions. All spectra were recorded at 500 MHz on a sample of dilute rat urine in 20 mM phosphate buffer, pH 7.4. Top trace: Simple single-pulse 1 H NMR spectrum with the vertical scale expanded 4000-fold higher than the height of the water peak. Middle trace: Same experiment as shown in the top trace, but with the addition of a selective 100 mW irradiation of the water resonance for 1 sec prior to data acquisition. This serves to equalize the spin population of the water protons so that they do not contribute to the NMR spectrum and is referred to as presaturation. Bottom trace: Onedimensional version of the NOESY experiment with 100 msec mixing time and presaturation of the water peak. The 100 msec delay and phase cycling utilized in the pulse sequence cancel out broad components from the intense water signal arising from inhomogeneous parts of the sample and results in better overall water suppression than presaturation alone.

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second robotic system in which the contents of a well can be extracted and flowed into the NMR probe where the sample is stopped and any NMR experiments carried out. After measurement, the sample is then sent back to the same well, to a well in a different plate, or to waste as desired. As such, there are many off-the-shelf devices available that can manage sample preparation for flow injection NMR. If the final destination is a conventional NMR tube, then customization of third party equipment or a solution from one of the NMR manufacturers will generally be required. Under many circumstances, flow NMR can provide higher throughput than conventional tube automatic samplers. Firstly, unlike tubes that vary slightly in concentricity, wall thickness, and straightness, the sample geometry is identical from sample to sample because of the fixed flow cell design. This minimizes the time required to perform homogeneity optimization after delivery of each samples. Also pumping the sample into the probe can be quicker than the robot arms that pick up and deliver individual NMR tubes. The system should be coupled to a liquid handling device capable of holding deep well 96-well polyethylene plates or other sample containers of choice. Sample handling equipment and the lines attaching it to the flow probe should be capable of delivering and withdrawing 60–500 mL of aqueous samples to and from the probe at a minimum rate of 1–2 ml=min without cavitation. The flow probe design requires that the samples be free of solids to avoid clogging. Even though samples may be clear when first prepared, precipitation of various amounts of calcium phosphates and other particulates after dilution with buffer is generally observed. As a result, care should be taken to leave a few millimeters clearance between the bottom of the sample tube or well and the tip of the probe to avoid aspirating any settled particulate. A needle with a side orifice is also advisable for avoiding this. Obtaining the liquid handler from the NMR spectrometer supplier is advisable, since this provides direct software control during automated data acquisition. Third party accessories that enhance the liquid handling hardware include an electronic valve that allows selection of various


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solvents for probe cleaning, storage, and inter-sample rinsing and electronic cooling racks that can maintain the sample containers at a temperature of 5–10 C while they are in the queue for analysis. Since it was first commercialized, NMR spectroscopy has been carried out on samples contained in glass tubes, the most common of which is 5 mm in diameter and requires approximately 0.5 ml of solution. Nuclear magnetic resonance vendors have met the demand for higher throughput applications by developing robotic delivery systems for conventional NMR tubes with a capacity of up to 120 samples. While the development of higher capacity flow based systems that can handle many hundreds of samples in the late 1990s has found a new niche market, there are still advantages associated with using tubes even in metabonomics studies. The benefits of using tubes are twofold: recovery and containment. It is certainly possible to reclaim samples from a flow system, but it is very difficult to recover 100% of the sample or to avoid dilution with buffer used to wash the flow cell. When using samples such as human blood materials that may pose a health threat, or when analyzing very precious samples that need to be used for subsequent analysis, tubes provide a convenient way to ensure that the sample is sealed from outside exposure. At present, commercially available systems that can either make samples just-in-time for analysis or chilled automatic tube samplers are just coming onto the market and should provide a resurgence of tube-based approaches for metabonomics which has been dominated by flow NMR. NMR-based metabonomics studies can be broken down into two broad components that are partially but not entirely separable: (1) pattern recognition of processed NMR spectra for sample classification, and (2) biomarker identification. For the former, the NMR spectrum serves as the raw input for multivariate analysis and little additional work need be done with the sample. For biomarker analysis, follow-up studies are sometimes required to identify unknown endogenous components that change in response to external stimulation. For example, one peak of unknown origin may appear in a spectrum of urine from an animal treated with a toxicant

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and other peaks from that molecule may be obscured from identification. The challenge is then to isolate the component from which that one peak arises and identify the component as a potential biomarker. This is the subject of Chapter 7. One powerful method for such a follow-up analysis on individual samples is HPLC–NMR (10). With the peak locations of unknown components from the initial spectral changes in a given sample, it is a straightforward matter of using HPLC to fractionate the sample while continuously monitoring the NMR spectrum of the eluent. Once the peaks of interest elute, the spectrum obtained is free from interfering peaks and is more analytically useful for identifying the unknown. The analytes need not have UV chromophores to be detected (because of the non-selective nature of the NMR spectrometer) nor does separation need to be complete, since the inherent frequency resolution of the NMR spectrum makes it possible to analyze more than one component in a single spectrum. The biofluid complexity problem is thus reduced chromatographically. Of course, the eluent can be collected in fractions on the outlet side of the NMR flow probe for further work up and analysis. By incorporating an inline mass spectrometer analyzing a small fraction of the HPLC eluent, valuable supplementary information on the eluted analytes can also be obtained. The addition of HPLC and MS capabilities to an NMR system used for metabonomics has clear advantages. Currently, HPLC systems and mass spectrometers with integrated software to control both the NMR spectrometer all of the other components are available from major NMR manufacturers.

5. CONCLUSIONS All of the tremendous advantages outlined above for NMR spectroscopy do not come without a price. A modern 600 MHz NMR spectrometer with all of the accessories necessary to carry out metabonomics studies and to identify metabolites will, as of this writing, cost in excess of $1.5 M. Upgrading this to an 800 MHz system the price goes up to $3.7 M. If one


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wants the largest and most sensitive commercially available magnet (900 MHz) the price tag goes up to about $5.3 M. These estimates do not include space considerations that also go up disproportionately with field strength. In summary, the real utility of NMR spectroscopy in metabonomics-type analyses derives from the fact that it is non-selective and provides both a qualitative profiling tool and a quantitative analytical tool simultaneously on a single sample. This gives NMR an advantage over many other analytical techniques that can be applied to metabonomics and related technologies. For example, other analytical techniques, such as direct infusion mass spectrometry (MS), and infrared (IR) and Raman spectroscopies are very good at providing reproducible biofluid profiles, but are either very selective (e.g., not everything ionizes in a mass spectrometer) or hopelessly unable to directly provide the identity of individual components that contribute to the pattern. Often these also require elaborate sample preparation procedures. Technologies that are powerful at measuring individual components often involve coupling to chromatographic methods and are therefore not as amenable to higher throughputs (e.g., LC– MS, GC–MS, etc.). These adjunctive methods are important for follow-up identifications of unknown metabolites. As mentioned earlier, NMR spectrometers are expensive and are likely to represent the bulk of equipment investment in a metabonomics facility. Metabonomics technology is advancing rapidly and NMR equipment continues to evolve that is especially suited to biofluid analysis, although it is quite possible to perform metabonomics studies with NMR equipment that is not specifically designed for it. Collaborating with an existing NMR group is an excellent way for a laboratory to initiate proof-of-concept studies before making a large investment in capital and expertise. Virtually any NMR spectrometer with an operating frequency of 400 MHz or greater with probes that provide good water suppression can be used to obtain useful data. Designing a dedicated NMR laboratory requires careful consideration of many factors, including cost and space, and the exact equipment needed will depend on what types of analyses are desired.

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REFERENCES 1. Ernst RR, Bodenhausen G, Wokaun A. Principles of Nuclear Magnetic Resonance in One and Two Dimensions. Oxford: Clarendon Press, 1987. 2. Sanders JKM, Hunter BK. Modern NMR Spectroscopy: A Guide for Chemists. 2nd ed. Oxford: Oxford University Press, 1993. 3. Claridge TD. High-resolution NMR Techniques in Chemistry. Oxford: Elsevier Science & Technology Books, 1999. 4. Johnson CS Jr. Diffusion ordered NMR spectroscopy: principles and applications. Prog Nucl Magn Reson Spectrosc 1999; 34:203–256. 5. Lindon JC, Liu M, Nicholson JK. Diffusion coefficient measurement by high resolution NMR spectroscopy: biochemical and pharmaceutical applications. Rev Anal Chem 1999; 18:23–66. 6. Pretsch E, Siebl J, Simon W, Clerc T. Tables of Spectral Data for Structure Determination of Organic Compounds. Berlin: Springer-Verlag, 1989. 7. Neuhaus D, Williamson MP. The Nuclear Overhauser Effect in Structural and Conformational Analysis. New York: VCH Publishers Inc., 1989. 8. Lindon JC, Ferrige AG. Digitisation and data processing in Fourier transform NMR. Prog Nucl Magn Reson Spectrosc 1980; 14:1–27. 9. Martin GE, Zektzer AS. Two-dimensional NMR Methods for Establishing Connectivity. A Chemist’s Guide to Experiment Selection. Performance and Interpretation. New York: VCH Publishers Inc., 1988. 10.

Lindon JC, Nicholson JK, Wilson ID. Direct coupling of chromatographic separations to NMR spectroscopy. Prog Nucl Magn Reson Spectrosc 1996; 29:1–49.

4 NMR Spectroscopy of Biofluids JOHN C. LINDON, JEREMY K. NICHOLSON, and ELAINE HOLMES Biological Chemistry, Biomedical Sciences Division, Imperial College of Science, Technology and Medicine, University of London, South Kensington, London, U.K.

1. INTRODUCTION Analysis of biofluids can provide a window into the biochemical status of a living organism. The composition of a given biofluid is changed according to the level of function of the cells that are intimately concerned with its manufacture and secretion. For this reason, as described elsewhere in this volume, the composition of a particular fluid carries biochemical information on many of the modes and severity of organ dysfunction whether due to beneficial or adverse drug effects or disease processes. Dietary and diurnal variations may also influence biofluid compositions. One of the most successful 105

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approaches to biofluid analysis has been the application of NMR spectroscopy (1,2) with the technique described in detail in Chapter 3. In addition to analytical applications, it is possible to obtain a detailed understanding of the interactions of the various components in the whole biological matrix, such as enzymatic biotransformations, metal complexation reactions, binding of small molecules to macromolecules, and cellular and micellar compartmentation which occur in different biofluids to varying degrees. The information content of biofluid spectra is potentially very high and the complete assignment of the 1H NMR spectrum of most biofluids is not possible (even by using 900 MHz NMR spectroscopy) due to the complexity of the matrix. However, the assignment problems vary considerably between biofluid types. For instance, blood plasma and seminal fluids have highly regulated metabolite compositions and the majority of the NMR signals have been assigned for normal human individuals. Urine composition is much more variable because its composition is normally adjusted by the body in order to maintain homeostasis and hence complete analysis is much more difficult. There is also an enormous variation in the concentration range of NMR-detectable metabolites in urine samples. Those metabolites present in concentrations close to the limits of detection for one-dimensional (1D) NMR spectroscopy at around 100 nM for many metabolites, have a 109 concentration range compared to water on a proton basis. Even if the water resonance is suppressed, as is usual, by a factor of 104, there is still a concentration range of around 105 to be accommodated and this can lead to problems in spectral detection and assignment. With every new increase in available spectrometer frequency, the number of resonances that can be resolved in a biofluid increases and although this has the effect of solving some assignment problems, it also poses new ones. Furthermore, there are still important problems of spectral interpretation that arise due to compartmentation and binding of small molecules in the organized macromolecular domains that exist in some biofluids such as blood plasma and bile. Although lower field strength measurements can be useful for the detection of

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the most abundant metabolites, and in certain circumstances, give quantitatively accurate results, comparable higher field 1 H NMR measurements are generally more accurate. Even the dispersion gain on going from 600 to 800 MHz is significant in 1H NMR spectroscopy of biofluids and allows more signals to be assigned, considerably easing the analysis of complex biofluid spectra. In addition to the usual limitations caused by instrument noise in the spectra, for complex mixtures such as biofluids, there is an additional type of noise, known as chemical noise. Unlike instrument noise, chemical noise is related to the sample itself and is the result of the extensive overlap of signals from compounds that are low in abundance in the matrix and, individually, close to the detection limits of the spectrometer. Nonetheless, they give rise to detectable 1H NMR responses due to their frequency superimposition. It is generally true that for 1H NMR work on biofluids, it is the chemical noise rather than instrument noise that usually limits the amount of recoverable spectral information. Normally only increasing the NMR frequency can allow recovery of the information that was in the chemical noise at lower frequencies. Furthermore, the problem of chemical noise interference varies in severity according to the biofluid type and chemical shift ranges that are under consideration for each fluid. Two types of information, which are potentially available in an NMR spectrum of a biological fluid, have been termed as latent and patent (3). Patent biochemical information has been defined as that which can be measured quantitatively in a single pulse experiment. Latent information in an NMR spectrum measured at a particular field is not available in the single pulse spectrum and the biochemical data contained therein can only be obtained by selection of appropriate multipulse sequences to achieve either spectral editing or frequency dispersion in a second or higher dimension. Latent information can also be transformed into patent data by increasing the measurement field strength. Biochemical information can be latent in two ways, namely, through multiple peak overlap and by undergoing some type of dynamic

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molecular interaction resulting in the lack of resolved signals for a compound present at levels in the NMR detection range for the spectrometer involved. On increasing the frequency at which an NMR experiment is performed, there is a consequent increase in sensitivity and so signals from molecules in solution that were too dilute to be measured may become measurable at a higher frequency and become latent or patent information. Thus, there is an effective increase in the amount of information of all levels on increasing field strength, thereby increasing the amount of useful biochemical data in the NMR spectrum. The complexity of the NMR spectra of biofluids can be judged from the 1H NMR spectrum of control human urine measured at an observation frequency of 900 MHz as shown in Fig. 1.

Figure 1

900 MHz 1H NMR spectrum of control human urine.


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It is clear that even at the present level of technology in NMR, it is not yet possible to detect many important biochemical substances, e.g., hormones, in body fluids because of problems with sensitivity, dispersion, and dynamic range and this area of research will continue to be technology-limited. Alternative analytical approaches such as LC–MS then become necessary and applications of this technique for metabonomic studies are now appearing in the literature. 2. PRACTICALITIES OF 1D 1H NMR SPECTROSCOPY OF BIOFLUIDS One major advantage of using NMR spectroscopy to study complex biomixtures is that measurements are often made with minimal sample preparation, usually with only the addition of 5–10% D2O, and a detailed analytical profile can be obtained on the whole biological sample (1). The main NMR spectroscopic techniques used for biological fluid studies are covered in Chapter 3 of this volume. However, much effort has been expended in discovering efficient new NMR pulse sequence techniques for spectral simplification and water suppression especially for biofluids and more recently, the commercial availability of microprobes (4) and cryoprobes (5) has led to improved sensitivity or shorter data collection times. 3. TECHNIQUES FOR RESONANCE ASSIGNMENT IN NMR SPECTRA OF BIOFLUIDS Usually in order to assign 1H NMR spectra of biofluids, comparison is made with spectra of authentic materials and by standard addition to the biofluid sample. This has served to assign the major peaks in biofluids. Chapter 3 of this volume covers the theoretical and practical aspects of NMR spectroscopy and NMR instrumentation and describes the major types of NMR spectroscopic experiments, which can be used to provide information on biofluids.

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Thus complex 1H NMR spectra can be simplified by attenuating resonances due to macromolecules and other species with short spin–spin relaxation times by employing a spin-echo editing approach, nowadays usually comprising a Carr– Purcell–Meiboom–Gill (CPMG) pulse sequence. Alternatively, the NMR resonances of the small metabolites which diffuse rapidly can be attenuated by using a diffusion-edited spectrum where the intensities of the resonances are related to the diffusion times of the molecules giving rise to them. Thus molecules which diffuse rapidly have resonances attenuated when a longitudinal-eddy-current-delay (LED) pulse sequence is used with a relatively long diffusion period built into it. Additional confirmation of assignments can be achieved with the application of two-dimensional (2D) NMR methods, particularly COSY and the total correlation spectrum (TOCSY) and, increasingly, inverse-detected heteronuclear correlation methods such as HMQC and HSQC. Additionally, the application of the 2D J-resolved (JRES) pulse sequence is important for spreading out the coupling patterns of the multitude of small molecules in a biofluid. Even 2D correlation NMR spectra of complex biofluids show much overlap of cross-peaks and further editing is often desirable. Thus simplification of NMR spectra of biofluids can also be achieved using (i) spin-echo methods particularly for fluids containing high levels of macromolecules, (ii) relaxation editing in general based on T1 and=or T2, (iii) diffusion editing, and (iv) multiple quantum filtering. To this end, a method based on the separation of 1H NMR resonances into subspectra according to whether the protons arise from CH, CH2 or CH3 groups via use of maximum quantum coherence spectroscopy (MAXY) (6) has been demonstrated. This has been extended to produce 2D NMR spectra such as MAXY–TOCSY, MAXY–NOESY, and MAXY–JRES (7). The use of 800 MHz NMR spectroscopy combined with 1H–13C HSQC studies including band-selective pulses has aided assignment of lipid signals (8,9). The development of metabonomics has required the automatic reduction of large numbers of complex NMR spectra into easily manipulated descriptors and one way to


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do this has been using spectral segmentation (10). A method for determining the actual temperature inside a biofluid sample inside an NMR magnet has been presented (11). Molecular diffusion coefficients are parameters that are not related directly to NMR spectral intensities under normal conditions. However, molecular diffusion can cause NMR signal intensity changes when pulsed field gradients are applied during the FT NMR experiment. A number of pulse sequence developments, particularly the LED sequence, have meant that measurement of diffusion coefficients is relatively routine (12). The editing of 1H NMR spectra of biofluids based on diffusion alone or with a combination of spin relaxation and diffusion has been demonstrated (13). This has been termed the Diffusion and Relaxation Editing (DIRE) pulse sequence. This approach is complementary to the editing of 1 H NMR spectra based on differences in T1 and T2. New methods for editing TOCSY NMR spectra of biofluids have been proposed based on differences in molecular diffusion coefficients and this has been termed diffusion edited TOCSY (DETOCSY). This approach complements the editing of TOCSY NMR spectra based on coherence selection and promises to provide an efficient alternative strategy for assignment of resonances in complex mixtures such as biofluids and cell extracts (13,14). When a very low gradient strength is used, the spectrum is similar to that in the absence of gradients. Increasing the gradient strength causes the resonances from the small molecules to be reduced substantially due to their relatively fast diffusion compared to those of the larger molecules that give rise to the broad peaks in the spectrum. Finally identity of metabolites which give rise to NMR peaks in biofluid spectra can be obtained using conventional means by separation and off-line spectroscopic methods. Alternatively as explained in Chapter 7, the employment of directly coupled HPLC–NMR–MS can prove to be an efficient approach. Detailed 1H NMR spectroscopic data for a wide range of metabolites and biomolecules found in biofluids have been given in several literature compilations of data (15,16).

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H NMR SPECTROSCOPY OF CEREBROSPINAL FLUID (CSF)

4.1. Properties and Biochemical Composition of CSF The CSF surrounds the brain and spinal cord, where it acts as a barrier against mechanical shock, as a lubricant between the brain surface and the meninges, and helps support the weight of the brain. By means of the CSF, substances are removed from the brain and spinal tissue and returned to the blood stream. Drugs may also be distributed within the brain with the aid of the CSF circulation. CSF is normally a crystal clear, low viscosity liquid of pH 7.3–7.4. The cerebrospinal fluid has a much lower protein content than plasma, although this may be elevated significantly in various disease states. Altered composition and physical characteristics of CSF can reflect damage to the central nervous system or meninges, and CSF is often sampled from patients with suspected cerebral disease. In general, the biochemical composition of CSF reflects the composition of the ultrafiltered blood plasma, but it also contains a number of metabolites that are secreted by the CNS tissue. It may also be depleted of certain constituents (e.g., glucose) relative to plasma, because of their utilization by the cerebral cells. It is, therefore, a good practice to compare concentrations of metabolites in CSF with those in the plasma, because alterations in the latter may be reflected in the CSF even if cerebral metabolism is normal. CSF is normally collected for diagnostic purposes by lumbar puncture from the subarachnoid space. Given the invasive nature of this intervention, it is only performed with good reason and it is extremely rare to obtain CSF from a truly healthy donor. The cerebrospinal fluid may also be collected at post mortem, but the composition may be very different to that found ante mortem. After collection of CSF, samples can be measured directly by NMR or freeze-dried and reconstituted in D2O. In the latter case, the preconcentration step allows many more minor metabolites to be detected. Unlike plasma, most CSF samples can be concentrated by up to 10 times after freeze-drying because of the low protein


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content, and this allows collection of good quality NMR data in reasonable times. Fluoride can be added to the CSF soon after collection where glucose measurements are required as this strongly inhibits glycolysis. 4.2. Assignment of 1H NMR Spectra of CSF Due to the low protein content of normal human CSF, it is possible to use standard 1H NMR experiments to obtain biochemical information without recourse to the spin-echo techniques often required for studies on blood and plasma. However, where serious cerebral damage has occurred, or in the presence of an acute infection, spectra may become dominated by protein resonances. Problems may also arise from protein-binding and metal binding of some metabolites, with a consequential broadening of their proton resonances. There are a number of 1H NMR studies that assign peaks in CSF (17,18), with Petroff et al. (17) showing that high quality 2D 1H NMR spectra can be obtained from human CSF, and that changes in NMR patterns can be related to disease states in the donor. In addition, many resonances have been assigned through the use of 2D JRES spectra and COSY-45 spectra (19). Examination of a number of ex vivo control samples showed high consistency in the aliphatic region of the 1D 1H NMR spectra of CSF and many assignments could be made by inspection but glutamate and glutamine had distinctly broadened resonances. Wevers et al. (20) have proposed a standardized method for acquiring NMR spectra of CSF and identified 50 compounds in CSF. 4.3. NMR Spectroscopy of CSF in Disease Studies Although this volume is primarily concerned with application of metabonomics to toxicity, and toxicological studies have not used this fluid in the main, CSF remains a potentially useful fluid for such work. However, NMR spectroscopy of CSF has found use in human disease studies with a number of metabolites detected and quantified (21,22). For patients with

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lumbar disk herniations, no significant differences from controls were found. However, differences were observed between controls and one patient with a medulloblastoma that showed a decreased glucose level plus new signals, which were assigned to valine and alanine. Population overlap prohibited diagnosis based on any one ratio, and so discriminant analysis using 16 metabolite concentration ratios was performed to investigate the diagnostic potential of NMR. The results demonstrated good predictive capability, except for tumor diagnosis. A number of other studies have been carried out including studies from patients suffering from drug overdose (17), bacterial meningitis (23), Huntington’s disease (24), and diabetes (24). Changes in the 1H NMR spectra of CSF from patients included the detection of methylmalonate in a patient with vitamin B12 deficiency (25). One study of CSF from rats in a model of stroke has been reported using 500 MHz 1H NMR spectroscopy including 2D COSY methods and a number of spectral changes could be observed as a result of the experimentally induced lesion (26). NMR spectroscopy of CSF has been used in a number of studies of multiple sclerosis (MS). One study showed increased lactate and fructose levels in MS patients but no correlations between NMR spectra and the differentiation of relapsing, remitting, and primary, progressive MS (27). Another study examined cases of MS plus others with a variety of neurological disorders and showed that the observed level of lactate correlated with the number of CSF mononuclear cells in patients with clinical activity. Also a decrease in formate was detected during active and inactive clinical phases of MS (28). A third study examined CSF from patients with actively progressing MS and found that acetate levels were higher in patients whilst formate levels were lower in patients compared to controls. There were no significant differences between spectra from early and longstanding MS patients. An unidentified peak, probably from an N-methyl compound, was seen in spectra from patients with actively progressing disease. However, this was not found in spectra of CSF from patients with AIDS dementia complex


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or Parkinson’s disease but it did appear in one out of three Creutzfeld–Jakob disease patients and one out of seven patients with Guillan–Barre syndrome (29). One study (30) has reported 500 and 600 MHz 1H NMR data on the post mortem CSF from Alzheimer’s disease (AD) patients and controls. The main differences between the spectra of the two groups were found to be in the region d2.4–2.9, where the resonances of aspartate, N-acetylaspartate, citrate, glutamate, and methionine occur. Principal components analysis showed that separation of the two groups was possible and that citrate was the principal marker with citrate levels in the AD patients much reduced when compared with the controls. Patient age and the time interval between death and autopsy were examined to see whether these factors might account for the differences between the AD and control groups. Allowing for these factors, the inter-group differences were reduced but still significant (p < 0.05). It was hypothesized that the reduction in CSF citrate found in the AD patients may be due to the reductions in pyruvate dehydrogenase (PDH) reported in the parietal cortex and temporal cortex of AD patients.

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H NMR SPECTROSCOPY OF BLOOD PLASMA AND WHOLE BLOOD

5.1. Properties of Blood and Blood Plasma Vertebrate blood consists of cellular elements suspended in a complex fluid matrix of proteins, principally of albumin, immunoglobulins, glycoproteins and lipoproteins, together with a large number of inorganic and low molecular weight organic solutes. The functions of blood include the transport of oxygen, carbon dioxide, products of metabolism, and hormones. It is through the medium of the circulating blood that the constancy of the internal environment is maintained; disease processes or abnormalities anywhere in the body are reflected to various extents in altered blood composition. The dominant cell type is the erythrocyte that normally makes up about 45–50% of the blood volume. Other cell types

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normally make up only about 1% of the packed cell volume. The remaining volume of the blood is the plasma. Plasma is the term given to the fluid separated from whole, untreated blood by centrifugation, whereas serum is separated from whole blood after the addition of an anticoagulant (normally trisodium EDTA or lithium heparin). Serum is depleted in fibrinogen (the major clotting protein precursor) and is consequently less viscous than plasma. The physico-chemical complexity of plasma is expressed in its 1H NMR spectra by the range of linewidths of the signals. This means that a number of different NMR experiments and=or physico-chemical interventions must be applied to extract useful biochemical information. Numerous high resolution 1H NMR studies have been performed on the biochemistry of blood and its various cellular components and plasma. The physical properties of whole blood pose serious limitations on direct NMR investigations, but packed erythrocytes yield more useful information on cell biochemistry. The best resolved spectra are given by plasma and serum and such 1H NMR measurements can provide a plethora of useful biochemical information on both low molecular weight metabolites and macromolecular structure and organization (3). The difficulties of obtaining quantitative determinations of substances in blood plasma using 1H NMR spectroscopy have been noted (31). The relative benefits of using formate over the more widely used TSP as an internal standard has been evaluated (32). 5.2. Comparative Biochemistry of Blood Plasma using 1H NMR Spectroscopy Standard 1H NMR spectra of human blood plasma are very complex and resonances of metabolites, proteins, lipids and lipoproteins are heavily overlapped even at 800 MHz 1H observation frequency. Most blood plasma samples are quite viscous and this gives rise to relatively short T1 relaxation times for small molecules compared to simple aqueous solutions allowing relatively short pulse repetition cycles without signal saturation. The complex spectral profile given in the


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750 MHz 1H NMR spectrum of blood plasma (Fig. 2(a)) can be simplified by use of spin-echo experiments with an appropriate T2 relaxation delay to allow signals from broad macromolecular components and compounds bound to proteins to be attenuated. The effect of applying the CPMG spin-echo pulse sequence to blood plasma results in a substantial reduction in the contributions from the albumin, lipoprotein and lipid (Fig. 2(b)). A number of metabolites have been detected in normal blood plasma using 400 MHz spectrometers, but assignments were, in general, based on the observation of only one or two resonances for each metabolite. In addition, peaks from mobile N-acetyl neuraminic acid and related sialic acid fragments of certain macromolecules such as a1-acid glycoprotein have been assigned and used diagnostically (33). The signals from some lipid and lipoprotein components, e.g., very low density lipoprotein (VLDL), low density lipoprotein (LDL), high density lipoprotein (HDL) and chylomicrons, have also been partially characterized. In the single pulse NMR spectrum of blood plasma in the chemical shift region d0.7–1.5, there are also many overlapping signals from small organic species such as lactate, 3D-hydroxybutyrate, alanine and branched chain amino acids together with those from terminal CH3 and long chain (CH2)n groups of fatty acids and triglycerides integral to the various lipoprotein particles, especially VLDL, LDL, and HDL (34). Two approaches can be adopted for dealing with the overlap problem. Firstly, a simple sample preparation such as centrifugal ultrafiltration can be used to remove the macromolecules. This results in a spectrum of all the non proteinbound metabolites contributing to the spectrum. Alternatively, to avoid sample manipulation, a spectral editing technique can be applied. Although the Hahn spin-echo (HSE) method was used originally, nowadays the Carr–Purcell–Meiboom–Gill (CPMG) spin-echo method is the principal method. Both are highly effective means of editing plasma 1H NMR spectra according to solute T2 relaxation times. Many signals from low molecular weight species are readily using the spin-echo approach that cannot normally be resolved. The HSE spectrum gives phase-modulation of signals that is dependent on the

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Figure 2 750 MHz 1H NMR spectra of control human blood plasma. (a) Conventional spectrum acquired with the NOESYPRESAT pulse sequence; (b) CPMG spin-echo spectrum; (c) diffusionedited spectrum.


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spin–spin coupling multiplicity so that singlets are always phased upright as are triplets when the spin-echo delay is selected to be 1=2JHH. With a delay of about 68 msec, doublets and quartets with coupling constants of approximately 7.4 Hz appear phase-inverted. The CPMG spin-echo pulse sequence does not J-modulate signal phases and losses of signal intensity during the T2 relaxation delays by diffusion through field gradients are minimized by virtue of the short delays employed. For normal plasma at pH 7, the largest peak in the spectral region to high frequency of water is that of the a-anomeric H1 resonance of glucose at d5.223 (which provides a useful internal chemical shift reference). Alternatively, if it is only the macromolecules which are of interest, then as shown in Fig 2(c), spectral editing according to molecular diffusion coefficients is possible, attenuating peaks from fastdiffusing small molecule metabolites (14). The use of 2D NMR methods is important for spectral assignment. The application of the JRES experiment results in a dramatic simplification of the blood plasma spectrum, and hence enables the complex overlapped resonances in the chemical shift range from d3–4 to be more completely resolved (35). Furthermore, the protein resonances are attenuated as effectively as was seen in the application of the simple spin-echo experiment. The skyline projection through the JRES map results in a greatly simplified spectral profile of the effectively 1H-decoupled 1H NMR spectrum of the motionally unconstrained metabolites in plasma. The skyline projection might, therefore, offer an attractive method for quantitating minor metabolites in plasma where attenuation due to T2 relaxation can be accounted for or calibrated. It should be noted that signals from any small molecules that are extensively protein-bound are also severely attenuated due to constrained molecular tumbling and a shortening of the T2 relaxation time. 1 H–1H COSY spectra provide an additional assignment aid enabling the confirmation of the presence of a number of amino acid resonances and those from the acids 3D-hydroxybutyrate, citrate, taurine and lactate as well as polyols such as myo-inositol and all resonances of a- and b-glucose.

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In addition, the resolving power of the COSY experiment is demonstrated by the connectivities observed for many of the lipidic resonances, which are not observable in the 2D-JRES experiment because of their short T2 relaxation times. The total correlation spectrum (TOCSY) of human blood plasma allows the assignment of extra metabolites because of the narrower lineshape of the TOCSY spectrum and because the coupling connectivity along complete chains of protons provide a more certain indication of the molecular identity. Figure 3 shows a 1H–1H TOCSY NMR spectrum of human blood plasma which has also been edited on the basis of T2 relaxation times (36). The 1H–13C 2D HMQC and HSQC experiments applied to blood plasma are also useful for metabolite identification giving information on 13C chemical shifts using 1H detection.

Figure 3 A 600 MHz T2-edited 1H–1H TOCSY NMR spectrum of human blood plasma.


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5.3. Lipoprotein Analysis in Plasma from NMR Spectra Much study has been devoted to the problem of lipoprotein analysis in blood plasma using 1H NMR spectroscopy and this has been comprehensively reviewed recently by Ala-Korpela (37). Lipoproteins are complex particles that transport molecules normally insoluble in water. They are spherical with a core region of triglyceride and cholesterol ester lipids surrounded by phospholipids in which are embedded various proteins known as apolipoproteins. In addition, free cholesterol is found in both the core and surface regions. The lipoproteins are in a dynamic equilibrium with metabolic changes going on in vivo. Lipoproteins are usually classified into five main groups, chylomicrons, VLDL, LDL, intermediate density lipoprotein (IDL), and HDL based on physical separation using centrifugation. Based on the measurement of 1H NMR spectra of the individual fractions and using lineshape fitting programs, it has been possible to identify the chemical shifts of the CH2 and CH3 groups of the fatty acyl side chains. Quantification can be carried out using timedomain NMR data. Alternatively, a frequency domain approach has used the program FITPLA which uses resonance positions and linewidths from model solutions to fit the overall lineshape of the CH2 and CH3 signal region (37). The usefulness of 1H NMR spectra for lipoprotein analysis (38) and 31P NMR spectroscopy for phospholipid analysis in blood plasma has been explored (39). The fatty acyl peaks have also been deconvolved based on diffusion-edited NMR spectra, providing confirmation of the assignment of the bands to particular lipoprotein fractions (40) and a neural network software approach has been used to provide rapid lipoprotein analyses (41). 5.4. Molecular Dynamics and Interactions from 1 H NMR Spectra of Blood Plasma Bell et al. (42) have shown that a significant proportion of plasma lactate is present in an ‘‘NMR-invisible’’ pool due to binding to transferrin, a-1-antitrypsin and possibly other plasma proteins and it has been suggested that this may have

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an important role in lactate transfer in plasma. The spin-echo NMR assay of serum lactate is reported to underestimate by about 30% when compared with conventional biochemical procedures. Lactate can be liberated from its protein binding site by the addition of 0.5 M ammonium chloride or the anionic detergent SDS and then becomes NMR detectable in spin-echo spectra together with non-protein-bound lactate; a similar effect was reported for 3D-hydroxybutyrate and acetoacetate. In conventional and spin-echo spectra of normal human and animal plasma, there are only a few low intensity resonances in the chemical shift range to high frequency of d5.3 when measured in the pH range 3–8.5. However, on acidification of the plasma to pH < 2.5, resonances from histidine and phenylalanine become easily detectable. In the plasma from patients with Wilson’s disease (liver degeneration secondary to an inborn error of caeruloplasin=copper metabolism), weak signals from histidine and tyrosine are seen in spin-echo spectra at pH 7.6, but increase in strength on acidification and signals from phenylalanine appear at pH 1.8 or below. Experiments with model solutions suggested that serum albumin has a high capacity for binding aromatic amino acids and histidine at neutral pH and this is responsible for their NMRinvisibility in normal human blood plasma. Serum albumin also binds a large number of other species of both endogenous and xeniobiotic origin (3). Blood plasma also has intrinsic enzymatic activities although many of these are not stable (particularly if the sample is not frozen immediately on collection). It has been noted that under certain pathological conditions, such as those following liver or kidney damage, enzymes that are present at elevated levels in the plasma because of leakage from the damaged tissue, can cause NMR-detectable alterations to spin-echo spectra of plasma. The levels of these enzymes, e.g., alanine aminotransferase (ALT), are often used as primary evidence for organ damage. In order to observe the effect of elevated ALT it is neccessary to freeze-dry the plasma sample soon after collection and then to reconstitute the sample in D2O. The metabolites involved are at equilibrium in the plasma when collected. However, reconstitution in D2O is then associated with the


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establishment of an isotopic equilibrium that results in the progressive incorporation of deuterons at the a-CH position of alanine (43). Consequently, the alanine methyl protons no longer experience the coupling to the CH and the signal changes from a doublet to a singlet with a small deuterium isotope shift. This is clearly observed in Hahn spin-echo spectra because the phase of the signal is shifted by 180 and this can also be monitored by adding ALT to normal blood plasma redissolved in D2O. More complex signal modulations also occur on enzymatic incorporation of deuterons into glutamine. 5.5. NMR Spectra of Blood Plasma in Pathological States In 1986, a paper was published which reported that 1H NMR spectroscopy of human blood plasma could be used to discriminate between patients with malignant tumors and other groups, namely normals, patients with non-tumor disease and a group of patients with certain benign tumors (44). This paper stimulated many other research groups but they were unable to repeat the original observations, either wholly or in part. The test as originally published involved the measurement of the averaged width at half signal height of the two composite signals at d1.2 and d0.8 in the single pulse 1 H NMR spectrum of human blood plasma. On comparing the averaged signal width, W, for normal subjects with those for patients with a variety of diseases and with those for pregnant women, it was reported that several statistically significant differences existed among the groups. It was proposed that the lowering of W observed in the malignant group was due to an increase in the T2 of the lipoprotein signals at d0.8 and d1.2, due in turn to ‘‘lack of supramolecular ordering of lipoprotein lipids’’ in the plasma of the patients with malignant tumors. In addition to the presence of cancer, a number of other factors have been found to cause changes in the linewidth index, W. These include diet, age and sex, pregnancy, and trauma as well as hyperlipidemia (45–48). In fact, the observed changes in W are caused by alterations in the plasma lipoprotein composition, especially the VLDL=HDL ratio.

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Much useful information on the variation of blood plasma lipoprotein content as a function of malignancy was obtained during studies on the cancer test. After the realization that this test was unlikely to be of any clinical utility, attention has again focused on the changes that occur in the profiles of plasma metabolites. A series of experiments have focused on changes in the ratios of signals from lipoproteins and glycoproteins in the 1H NMR spectra of the plasma from patients with a variety of different cancers. A ‘‘star plot’’ pattern recognition (PR) method was used to distinguish three types of metabolic alterations induced by the cancer metabolism: (i) an ‘‘inflammatory’’ pattern, (ii) a ‘‘lipid modified’’ pattern, and (iii) a ‘‘sarcoma’’ pattern (49). Other studies of 1H NMR spectra of blood plasma in cancer have been published (50–52). Mountford et al. (53) reported the isolation by ultracentrifugation of two lipoprotein bands, lying between the HDL and LDL bands of the plasma of a patient with a borderline ovarian tumor. These bands were later termed ‘‘malignancy associated lipoprotein’’ (MAL).1H NMR spectroscopy studies on these bands showed that they contained a methyl signal at d1.3 which correlated in 2D 1H–1H COSY NMR spectra with a methine signal at d4.2. The methyl signal had an abnormally long T2 value and was consistent with that of a fucose moiety, a carbohydrate that is often found in the antigenic compounds on the surface of cancer cells, but this identification was not proved. It was suggested that this MAL could provide a non-invasive and specific method of assaying for cancer. In a larger study, less than 50% of the cancer patients had the MAL band, but so did the same number of normal controls. No correlation was found between the linewidth index W and the appearance of the MAL. However, problems were reported with the isolation of the MAL, and the fractions of interest are dialyzed for up to two hours to remove KBr and any free lactate prior to analysis. The signal of interest was lost if the dialysis was too extensive. It was suggested that the MAL particle may be disassociating during the dialysis, but other explanations are possible (53). Lipoprotein-bound lactate, which would slowly dissociate during dialysis, would be one possibility. Unless the nature and


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origin of the MAL band are conclusively defined, its significance for cancer detection and diagnosis remain unclear. Reports of the measurement of the linewidth index W for the assessment of heart graft rejection after transplantation have been published using a total of 410 measurements on a total of 46 patients (54). However, the overlap between the values observed for each group meant that the W value alone could not be used to classify the patients into the four rejection grades. It has also been reported that the areas of two glycoprotein signals in the spin-echo 1H NMR spectra of blood plasma from heart transplant patients correlated with a standard echocardiography parameter used to monitor rejection (55). The sum of the areas of the N-acetyl signals of N-acetyl glucosamine and N-acetyl neuraminic acid moieties of plasma glycoproteins (NAG þ NANA) was measured and then this area was divided by that of the methyl signal of alanine. When this area ratio was plotted against isovolumetric left ventricular relaxation times (measured by Doppler echocardiography), a good correlation was found in five patients and an acceptable correlation in three, but only a poor correlation in five further patients. It was noted that infections and inflammatory states unrelated to rejection interfered with the correlation. Nishina et al. (56) have reported W and lactate concentration measurements on the blood sera from 20 Nigerians seropositive to Plasmodium, 13 seronegative Nigerians and six healthy Japanese controls. Significantly lower W values and higher lactate concentrations were reported for the sera of the malaria positive group than for the other two groups. In human beings, diabetes is a relatively common condition which can have serious, complex and far-reaching effects if not treated. It is characterized by polyuria, weight loss in spite of increased appetite, high plasma and urinary levels of glucose, metabolic acidosis, ketosis and coma, but in many cases, diabetes can be controlled by the administration of insulin. Based on NMR spectra, there are marked elevations in the plasma levels of the ketone bodies and glucose, postinsulin withdrawal (57). The levels of these metabolites, as well as lactate, valine and alanine, were also measured by standard

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clinical chemistry methods, in order to test the accuracy of the NMR method. In general, the NMR results were in good agreement with the conventional assay results. The CH3 and CH2 resonances of the lipoproteins VLDL and chylomicrons decreased significantly in intensity relative to the CH3 signal of HDL and LDL, indicating the rapid metabolism of the mobile pool of triglycerides in VLDL and chylomicrons. By fluorimetric measurements, the concentration of the so-called ‘‘free fatty acids’’ rose from 0.33 to 1.92 mM in the period from 0 to 12 hr postwithdrawal. However, these free fatty acids are immobile (due to binding to albumin) and NMR invisible. The 1H NMR spectra of the blood plasma from patients with chronic renal failure during dialysis, patients in the early stages of renal failure and normal subjects have been analyzed. For patients on acetate dialysis, the method clearly showed how the acetate was accumulated and metabolized during the course of the dialysis, as well as allowing changes in the relative concentrations of endogenous plasma components to be monitored. 1H, 13C and 14N NMR spectra of the plasma and urine from chronic renal failure patients showed that the plasma levels of TMAO correlated with those of urea and creatinine, suggesting that the presence of TMAO is closely related to the degree of renal failure. Differences in the interaction of lactate with the plasma proteins were also observed in the uremic patients. One study of renal failure patients also showed increased levels of lactate in the plasma, ascribed to metabolic disturbances (mainly acidoses) associated with decreased renal function. Elevated creatinine levels were found in the plasma of renal failure patients. The patients undergoing hemodialysis were differentiated by the presence of elevated dimethylamine, whilst glycine was predominantly raised in the plasma of the peritoneal dialysis patients (58–61). 5.6.

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H NMR Spectroscopy of Whole Blood and Red Blood Cells

Conventional 1H NMR measurements on whole blood give very little biochemical information due to the presence of a


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broad envelope of resonances from hemoglobin and plasma proteins but spin-echo spectra can give rise to moderately well resolved signals from plasma metabolites and those present inside erythrocytes, notably glutathione. However, whole blood spectra are not easily reproducible because of erythrocyte sedimentation which progressively (within a few minutes) degrades the sample field homogeneity during the course of collecting a series of FIDs. Furthermore, there are substantial intracellular=extracellular field gradients which give a major contribution to the T2 relaxation processes for the nuclei of molecules diffusing through those gradients. Spin-echo NMR measurements on packed erythrocyte samples do give rise to well-resolved signals from intracellular metabolites and a variety of transport and cellular biochemical functions can be followed by this method. The first paper which showed that NMR spectra of endogenous small molecules could be obtained from erythrocytes appeared in 1977 (62) and this also showed how using a spin-echo approach the large broad hemoglobin resonances could be eliminated. In cells which were washed with H2O and D2O, time courses for the conversion of glucose to lactate were determined. In another study, the cells were washed to remove all glucose and lactate and the intracellular glutathione (GSH) was oxidized to GSSG and the conversion monitored by 1H NMR of the GSH resonances. After conversion to GSSG, glucose was added to the suspension and the reformation of GSH was determined as a function of time. Isotope exchange methods have been developed and used to study the kinetic effects of intra-erythrocyte enzymes by adding a labeled compound to a suspension of red blood cells and then monitoring the subsequent distribution of label with time (63). Reactions of GSH have been also studied. In erythrocytes, GSH and its constituent amino acids (cysteine, glycine, and glutamate) are in dynamic exchange and this has been followed using 2H-labeled glycine (64). One major parameter which can be obtained from NMR is the intracellular pH, and the pH inside erythrocytes can be measured using 1H NMR spectroscopy, using a suitable

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H NMR pH indicator which has an NMR chemical shift which varies with pH in the range desired. Candidates include the C2-H protons from histidines in hemoglobin (65) but also an exogenous compound could be added as an indicator. The transport of substances between the inside and outside of the red cell can be monitored using NMR if the resonances from the two environments have different chemical shifts or intensities. In spin-echo NMR spectra of erythrocytes, the intensity of resonances from metabolites inside the cells is less than outside because of magnetic susceptibility differences inside and outside the cells. Also, resonances outside the cell can be selectively broadened by the addition of paramagnetic species that do not cross the red cell membrane. Those used include the ferric complex of desferrioxamine, dysprosium-DTPA and the copper–cyclohexanediaminetetraacetic acid complex (66). To measure the rate of influx of a compound into red cells, the compound is added with the paramagnetic agent to a red cell suspension and the intensity of the resonance from the intracellular component is monitored as a function of time. This approach has been used to study the transport of a number of small molecules including glycerol, alanine, lactate, choline, and glycylglycine (67). Kuchel et al. (68) have used NMR spectroscopy of erythrocytes to study the effect of lithium treatment in manic depressive patients showing an increase in choline. In heavy metal poisoning, a considerable proportion of the metal is found in the blood and the binding of heavy metals inside erythrocytes has received much attention. For example, methylmercury(II) was shown to cross the red cell membrane rapidly and be complexed principally to the thiol groups of GSH and hemoglobin. The antiarthritic gold drug aurothiomalate has also been studied in intact red cells using NMR spectroscopy. Rabenstein (69) has reviewed much of this work in the area of red blood cell NMR spectroscopy. Diffusion coefficient measurement has also been used to study the binding between diphosphoglycerate and hemoglobin inside intact red blood cells (70).


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The Kuchel group has applied a new form of analysis called ‘‘diffusion–diffraction’’ to suspensions of red blood cells. This approach is based on the pulsed-field gradient diffusion NMR experiment and demonstrated that the cells are aligned by the static magnetic field of the NMR spectrometer. In addition, conversion of the intracellular hemoglobin caused a predictable change in the diffraction pattern and it was also shown that water transport inhibition affected the results. Finally the cell diameter and inter-cell spacing could be measured from the diffraction plots and these have been compared with electron micrographs (71,72). The technique of magic angle spinning (MAS) has also been applied to 1H NMR spectra from red blood cell suspensions and under the MAS conditions two water peaks can be observed, corresponding to intra- and extracellular water (73). Under relatively rapid spinning conditions, the cells can centrifuge to the perimeter of the sample space and the extracellular water arises from both bulk water and interstitial water (74).

6.

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H NMR SPECTROSCOPY OF HUMAN AND ANIMAL URINE

6.1. Sample Details for NMR Spectroscopy The composition and physical chemistry of urine is complex and highly variable both between species and within species according to lifestyle. A wide range of organic acids and bases, simple sugars and polysaccharides, heterocycles, polyols, low molecular weight proteins and polypeptides are present together with inorganic species such as Naþ, Kþ, Ca2þ, Mg2þ, HCO32, SO42 and phosphates. It is possible to detect large numbers of the organic species with modern NMR spectrometers. Many of these moieties also interact extensively, forming complexes that may undergo chemical exchange reactions on a variety of different time-scales, some of which are amenable to NMR study. The ionic strength of urine varies considerably and may be high enough to adversely affect the tuning and matching of the RF circuits of a spectrometer

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probe, particularly at high field strengths. Because of its high ionic strength, it is sometimes counterproductive to concentrate urine samples by freeze-drying and subsequent reconstitution in smaller volumes of solvent, unless this is performed conservatively. Urinary osmolalities vary from about 150 to 1300 mOsmoles in normal human urine, but animal urines can have much higher osmolarities (up to 2000 mOsmoles in rodents and >3000 mOsmoles in desert species). The viscosity of human urine samples is normally low, but may be higher in laboratory rodents that are physiologically proteinuric; this will generally shorten relaxation times in comparison with pure aqueous solutions of metabolites. The presence of high concentrations of protein in the urine, e.g., due to renal glomerular or tubular damage, can result in the broadening of resonances from low molecular weight compounds which may bind to these urinary proteins. Urinary pHs may vary from pH 5 to pH 8, according to the physiological condition in the individual, but usually lie between 6.5 and 7.5. Urine samples should be frozen as soon as possible after collection if NMR measurements cannot be made immediately. When experiments involve collections from laboratory animals housed in metabolic cages, urine samples should be collected into receptacles that are either cooled with dry ice or have a small amount of sodium azide present as a bacteriocide. However, both these procedures may inhibit or destroy urinary enzymes that may frequently be assayed by conventional biochemical methods for assessment of kidney tubular integrity in toxicological experiments. Correction of urinary pH to a standard value of 7.4 can be attempted for normalization of chemical shifts. However, pH correction can be time consuming and it can be shown that urinary pHs are unstable (even with added strong buffer) because of the progressive and highly variable precipitation of calcium phosphates which may be present in urine close to their solubility limits. One possible solution to this problem is the addition of 100–200 mM phosphate buffer in the D2O added for the lock signal followed by centrifugation to remove precipitated salts. This has the effect, in most cases, of normalizing the pH to a relatively narrow range near neutrality


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which is stable for many hours during which NMR measurements can be made. Relatively few metabolites show major chemical shift variations over this pH range (with the exception of histidine and citrate). The vast majority of urinary metabolites have 1H T1 relaxation times of 1–4 sec, the relaxation process being slightly more efficient than in pure aqueous solutions due to the presence of small amounts of paramagnetic metal ions in the urine. Given this range of T1s, the general rule of applying a 5 T1 relaxation delay between successive 90 transients to obtain >99% relaxation and hence quantitative accuracy cannot be applied routinely. Applying this delay to the longest T1 signal in the sample unnecessarily compromises the best signal-to-noise ratios for most metabolites (unless the measurement of a selected group of metabolites is particularly important in which the delays are optimized for those). Therefore, by using smaller tip angle pulses and leaving a total pulse recycle period of 5 sec between successive pulses, spectra can usually be obtained for most metabolites in 5–10 min at high field with a generally low level of quantitative signal distortion. For a particular quantitative problem on a defined set of metabolites, the instrumental conditions and relaxation delays would then be optimized for those measurements. But it is the overall speed (and lack of necessity of rigorous optimization) with which biofluids can be screened and fingerprinted that makes 1H NMR particularly useful in studies on comparative biochemistry and physiology. It has become a standard practice to replace simple solvent resonance presaturation with pulse methods which either induce such saturation or which leave the solvent water resonance unexcited. One popular method involves using simply the first increment of a two-dimensional NOESY sequence and, as a consequence, this requires the use of only 90 pulses. A vast number of metabolites may appear in urine samples and problems due to signal overlap can occur in single pulse experiments. The magnitude of the signal assignment problem in urine is also apparent at ultra high field. There are probably >5000 resolved lines in ultra high field 1H NMR spectra of normal human urine (Fig. 1), but there is still

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an extensive peak overlap in certain chemical shift ranges. Thus chemical noise is still a significant feature of NMR spectra of urine even at 800 MHz and many signals remain to be assigned. The dispersion gain at 800 MHz even over 600 MHz spectra is particularly apparent in two (or three) dimensional experiments that can be used to aid signal assignment and simplify overlapped spectra. 6.2. Chemical Exchange and Solvent Effects on NMR Spectra of Urine In urine samples, T2 relaxation times for some metabolites are dominated by chemical exchange contributions and can be variable according to the pH and endogenous metal ion concentrations. For example, the T2 of citrate CH2 protons is dependent on pH, and the relative concentrations of the acid itself and the Ca2þ and Mg2þ ions which are present in concentrations ranging from 1 to 10 mM. The T2 relaxation time of water in urine is highly dependent both on pH and the presence of the more abundant endogenous species which have exchangeable protons. In normal human subjects, the most important of these are urea, uric acid (and allantoin in most non-primates), phosphate and ammonium ions. Urea is the most abundant proton-exchanging solute and is often present at concentrations of up to 0.7 M in human urine, and possibly twice this in rodents. The urinary water NMR linewidth is typically about 4 Hz at 400 MHz, broadening to >10 Hz at 600 MHz. Supplementation of the natural amounts of these compounds in urine and adjustment of pH can give water linewidths of >30 Hz and allow efficient water suppression via the selective augmented T2 relaxation (WATR method) (75). At low pH (i.e., RA patients. Since it is known that the consequence of hyaluronate depolymerization may be articular cartilage damage, it was concluded that 13C NMR spectroscopy might be a valuable method for studying these clinically relevant biophysical changes in SF. 9.4. Miscellaneous Fluids The first study by NMR spectroscopy on aqueous humor was on nine samples taken during surgery for other conditions and NMR spectra were measured at 400 MHz (119). A number of metabolites were detected, including acetate, acetoacetate, alanine, ascorbate, citrate, creatine, formate, glucose, glutamine or glutamate, b-hydroxybutyrate, lactate, threonine and valine. Following this, there have been a number of other studies. These include 1H NMR spectra from aqueous humor of rabbits and 31P NMR spectra of aqueous and vitreous humor from pigs (120,121). Limited studies using NMR spectroscopy of saliva have been reported. Initially Harada et al. (122) used 1H NMR spectroscopy of human saliva in a forensic study. No age- or sex-related differences were observed for saliva from healthy subjects but marked differences were observed in cases of sialodentitis (123). The studies have been broadened by the application of 1H and 13C NMR spectroscopy, which was used to distinguish endogenous substances from those related to oral health care and pharmaceuticals (124). Finally, the biochemical effects of an oral mouthwash preparation and a tooth-whitening substance have been studied using 1H NMR spectroscopy (125).

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The analysis of pancreatic juice and small bowel secretions using 1H NMR spectroscopy has also been reported (126). Foxall et al. (127) have reported a 1H NMR study of the fluid from the cysts of six patients with autosomal dominant polycystic kidney disease (ADPKD). Autosomal dominant polycystic kidney disease in adults is characterized by the slow progressive growth of cysts in the kidney which are lined by a single layer of renal tubular epithelium. When these cysts reach a large size they can significantly distort the kidney, and disrupt both the blood supply and renal function. Autosomal dominant polycystic kidney disease is one of the commonest causes for renal transplantation in adults. Little was known about the exact biochemical composition of cyst fluids prior to this study, or about the relationship between cyst fluid composition and the pathogenesis of the disease. The 1H NMR spectra of the cyst fluids were assigned by standard methods developed earlier for other biofluids and by the use of 600 MHz 1H spin-echo and 2D J-resolved experiments. The spectra revealed a number of unusual features and showed the cyst fluids to be distinct from both blood plasma and urine. Isoleucine, lysine, threonine, and valine were present at millimolar concentrations. High concentrations of acetate, lactate, succinate, creatinine, and dimethylamine were also found in the cyst fluids, and in ratios different from those of blood plasma or urine. Glucose concentrations varied from 3.4 to 9.6 mM, and the majority of the fluids contained signals from the N-acetyl groups of mobile glycoprotein sugar sidechains. Unusually, the fluids from all the six patients contained high levels of ethanol, which was not related to consumption of alcoholic beverages or drug preparations. In general, there was little variation in the composition of the cyst fluids as revealed by 1H NMR, although the protein signal intensity did vary somewhat. It was hypothesized that this constancy of composition reflected the chronic nature of the accumulation of the cyst fluid and a long turnover time of the cyst components, which thus has the effect of averaging the compositions. The unique biochemical composition of the cyst fluids was ascribed to abnormal transport processes


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occurring across the cyst epithelial wall, reflecting polarity reversal of the cystic epithelium. It should be remembered that it is also possible to study artificial fluids that have been administered. These include renal dialysis fluids, rectal dialysates, and bronchial alveolar lung fluid (BALF). An example of a 1H NMR spectrum of BALF is given in Fig. 6, showing the quality of the data that can be obtained. Finally, it is possible to examine biofluids which have been derived by extraction of tissue or cell samples. Figure 7 shows an example of a 1H–31P HMQC-TOCSY two-dimensional NMR spectrum of a lipid extract of mouse liver tissue. This shows which 1H NMR peaks are spin-coupled to 31P, the 31P chemical shifts being easily assigned to various phospholipids based on the literature. The experiment also allows assignment of unbroken chains of 1H–1H coupling to the hydrogen that is coupled to the phosphorus, thus giving useful information on the chemical nature of the phospholipid head groups (128).

10. NMR STUDIES OF DYNAMIC INTERACTIONS Although NMR spectroscopy of biofluids is now a wellestablished technique for probing a wide range of biochemical problems, there are still many poorly understood physicochemical phenomena occurring in biofluids, particularly the subtle interactions occurring between small molecules and macromolecules or between organized multiphasic compartments. The understanding of these dynamic processes is of considerable importance if the full diagnostic potential of biofluid NMR spectroscopy is to be realized. Many biological fluids contain significant amounts of active enzymes. This may be because they fulfill a biological function in the fluid, e.g., the esterase and peptidases present in prostatic fluid. Additionally, they may have leaked into the fluid due to disease or toxin-induced organ damage such as raised plasma alanine aminotransferase levels in liver and kidney disease, or raised urinary N-acetylglucosaminidase

Figure 6 600 MHz 1H–31P HMQC–TOCSY NMR spectrum of a chloroform–methanol extract of mouse liver tissue.

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Figure 7 600 MHz 1H NMR spectrum of bronchial alveolar lavage fluid (BALF). (Top) one-dimensional NMR spectrum; (center) twodimensional COSY spectrum; (bottom) two-dimensional J-resolved spectrum.

in kidney disease. When provided with the appropriate substrates, these enzymes will manufacture new products which can be NMR-detectable. Collection of sequential NMR data may then allow the time course of this enzymatic conversion to be followed. This may yield kinetic data on the activity of the enzyme in a ‘‘real’’ biological medium and may also provide indirect NMR evidence of organ damage.

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Many biofluids are not chemically stable and for this reason care should be taken in their collection and storage. For example, cell lysis in erythrocytes can easily occur. In addition, if the biofluid has been reconstituted into D2O after freeze-drying or if a substantial amount of D2O has been added to provide an NMR field lock, then it is possible that certain 1H NMR resonances will be lost. These include not only NH and OH protons as expected but also CH groups where the C–H bond is labile such as H2 of imidazole moieties (as in histidine or histidinyl-containing proteins such as hemoglobin) or the CH2 group of acetoacetate which participates in keto-enol tautomerism. It should be noted that freeze-drying of biofluid samples also causes the loss of volatile components such as acetone. Biofluids are very prone to microbiological contamination, especially fluids, such as urine, which are difficult to collect under sterile conditions. Samples should be stored deep frozen to minimize the effects of such contamination but evidence of bacterial growth will be seen in a time-dependent pattern of metabolites if NMR spectra are measured over a period of time or if the sample is kept at room temperature for extended periods. It has been noticed that bacteria can incorporate a 2H atom from D2O into metabolites and the presence of isotopically labeled acetate (CDH2COOH and CD2HCOOH observable in the 1H NMR spectrum) for example is a good indication of bacterial contamination (129). Prevention of microbial growth can be achieved by the addition of sodium azide at the point of sample collection and during preparation of samples for NMR spectroscopy. Some biofluids such as blood plasma contain high levels of proteins and many endogenous metabolites bind to such macromolecules. There are many examples of this such as aromatic amino acids binding to serum albumin in blood plasma and this has a direct consequence of making the detection and quantitation of such species less easy (130). In NMR terms, the molecule can appear to be in fast, intermediate or slow exchange with the macromolecule. Thus interpretation of metabolite levels by NMR spectroscopy must always be undertaken after consideration of whether the result is


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perturbed by macromolecule binding with resulting relaxation and line width changes. Many widely used therapeutic compounds also bind to biofluid macromolecules and this process can be studied using NMR spectroscopy based on changes in chemical shifts, line widths, relaxation times and diffusion coefficients. Some biofluids, particularly blood, contain cells with intact cell membranes. Other fluids such as bile or blood plasma have high levels of lipids organized into supramolecular particles such as micelles in bile and lipoproteins in blood plasma. Small molecules can therefore be inside or outside of the lipid membrane or even inside the lipid membrane itself. In the extreme case of micellar aggregations, e.g., in bile, this can be regarded as compartmentation as the small molecule can be outside the micelles, in the micelle wall or within the micelle itself. Many small molecules are more freely soluble in biological fluids than they are in water alone (e.g., cholesterol and its esters in blood plasma). In all cases, compartmentation of small molecules results in changes in the rotational correlation times and hence relaxation properties of their nuclei with respect to free solution conditions, and in some cases chemical shift and coupling constant changes as well. Observation of such species may be further complicated by chemical exchange. Visualization of the signals from compartmentalized molecules usually requires some physical perturbation of the sample to make the NMR lines sharp enough to be detected, e.g., methanol extraction to observe cholesterol in blood or seminal plasma. All biological fluids contain a variety of potential metalchelating agents, sometimes at very high concentrations. The most ubiquitous metal chelators in biofluids are free amino acids, especially, glutamine, glutamate, cysteine, histidine and aspartate, and organic acids such as citrate and succinate. Ca2þ, Mg2þ and Zn2þ are the main endogenous metal ions involved in complexation reactions with organic biofluid components and many of these reactions can be studied using NMR spectroscopy. Chelation reactions and the physico-chemical effects of other metals such as Fe3þ=Fe2þ metallodrugs and toxic metals such as Cd2þ in red blood

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cells and whole blood can also be studied under certain circumstances. Paramagnetic ions such as Gd2þ and Mn2þ can also be used to effect chemical editing of NMR spectra (and spin-echo based solvent suppression) by selectively binding to endogenous metal chelating agents such as citrate and hence broadening their signals (131). The metal-chelating agent ethylenediaminetetraacetic acid (EDTA) is very effective for many di- and trivalent metal ions and can be added to biological fluids to remove metal from the endogenous chelating species with consequent changes in the NMR signal pattern and the appearance of signals from metal EDTA complexes (130). EDTA addition also results in a general sharpening of NMR signals from biofluids because it also complexes trace levels of paramagnetic ions. Biofluids contain many endogenous species that can participate in chemical exchange processes covering a variety of exchange time-scales. These processes may be connected with macromolecular binding or with metal complexation reactions or, more simply, involve exchange of protons with each other and=or solvent water. Some molecules such as citrate may be involved with all the three types of chemical exchange phenomena, and hence signal positions vary considerably according to solution conditions. Depending on the exchange rate, spectral lines may be broadened or shifted from their free solution condition. Citrate signals are generally broadened in the presence of the metal ions present in biofluids (usually Ca2þ, Mg2þ and Zn2þ), and this broadening is reversed by the addition of EDTA which outcompetes citrate for binding of divalent metal ions.

11. CONCLUDING REMARKS Nuclear magnetic resonance spectroscopy, particularly using the 1H nucleus, of biofluids has been able to characterize the normal metabolic profile in animal and human species, to evaluate the type and degree of natural physiological metabolic variation and to demonstrate altered metabolic profiles due to human disease processes and xenobiotic adverse effects


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5 1

H Magic Angle Spinning NMR Spectroscopy of Tissues JULIAN L. GRIFFIN

Department of Biochemistry, University of Cambridge, Cambridge, U.K.

JEREMY K NICHOLSON, ELAINE HOLMES, and JOHN C. LINDON Biological Chemistry, Biomedical Sciences, Faculty of Medicine, Imperial College of Science, Technology and Medicine, London, U.K.

1. INTRODUCTION If the ultimate aim of metabonomics is to detect every small molecule metabolite and xenobiotic in a biofluid, tissue, or organism then it would be supposed that the most sensitive analytical techniques should be used. One of the most sensitive atom-specific analytical approaches remains mass spectrometry (MS) but this is destructive. However, one advantage that is intrinsic to NMR spectroscopy is that the 173

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technique is nondestructive and in many cases noninvasive. Indeed, this has led to many medical applications of the NMR effect to detect molecules in vivo, particularly in terms of imaging (magnetic resonance imaging, MRI). However, the initial successes of in vivo magnetic resonance spectroscopy (MRS) have since been impeded by the relatively small number of metabolites that can be observed routinely. While MRS has found extensive applications in following cerebral disorders, the biochemical changes recorded have been confined to a small number of metabolites that are readily observable using this technique as depicted in Fig. 1. For example, in cerebral tissue the major observable metabolites are choline, N-acetyl aspartate (NAA), creatine, and lactate. However, the exact role of NAA, often the largest resonance detected in vivo and the level of which has since been correlated with the progression of diseases such as Parkinson’s disease, Huntingdon’s disease, Duchenne muscular dystrophy, and stroke (1–3), is still strongly debated.

Figure 1 A 1H MRS in vivo spectrum from the human brain. Such spectra are dominated by the large singlets arising from N-acetyl aspartate (NAA), creatine, and choline, and thus, most of MRS studies have to date focused on biochemical changes in these metabolites. Spectrum supplied By Dr. C. Rae, University of Sydney.

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NMR spectroscopy in vivo is impaired by a number of physical processes which serve to broaden spectral resonances. Typically, for in vivo studies complex editing sequences and spatial localization approaches have to be used. In addition, relaxation times are often short giving rise to broader lines, and finally, in heterogeneous samples such as tissue biopsies, anisotropic NMR parameters are not averaged completely to zero, also causing line broadening. A number of second rank tensor interactions which might include dipolar couplings, chemical shift anisotropy, bulk magnetic susceptibility differences, both across the whole sample and microscopically, all give rise to broadened lines in spectra. (For an excellent discussion see Ref. 4.) To overcome these problems, it is possible to spin the sample at the so-called magic angle (the theory is shown later in Sec. 2.1) An alternative approach to gain information on low-concentration metabolites in tissues is to make tissue extracts. However, the concentration of a metabolite in an extraction medium depends both on the metabolite’s relative solubility as well as its tissue concentration. If aqueous and lipophillic metabolites are investigated simultaneously, this requires multiphase extractions which can be time consuming, and metabolites may even be trapped in the remaining tissue pellet. An example is shown in Fig. 2. For example, synaptic glutamate in cerebral tissue is contained within a lipid vesicle, and thus may be resistant to aqueous extraction processes.

2. MAGIC-ANGLE-SPINNING (MAS) NMR SPECTROSCOPY: PRINCIPLES AND PRACTICE 2.1. Theory High resolution magic angle spinning (MAS) 1H NMR spectroscopy circumvents both sets of problems associated with in vivo spectroscopy and tissue extracts. The tissue is examined directly avoiding tissue extraction. The process averages all second rank tensor interactions to zero, hence removing

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Figure 2 Six hundred megahertz high resolution 1H NMR spectroscopy of bank vole renal tissue. Renal tissue was examined using either a perchloric acid extraction procedure (A), 1H MAS NMR spectroscopy (B) or using a chloroform=methanol lipid extraction procedure (C). The 1H MAS NMR spectrum demonstrated features present in spectra A and C showing its ability to detect both lipophilic and hydrophilic metabolities. In D, a 1H MAS NMR spectrum of the extraction pellet following aqueous and lipid extraction procedures is shown. As well as broad lipid resonances, a number of sharp resonances are also clearly visible, demonstrating that a number of low molecular weight metabolites were not extracted.

line broadening mechanisms. For 1H NMR spectroscopy, chemical shift anisotropies are small, quadrupolar couplings are not present and the J-coupling anisotropy is negligible. However, both dipolar coupling and diamagnetic susceptibility anisotropy are both significant. Hence as an example,


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the dipolar Hamiltonian (in Hz) for two spin one-half nuclei in a rigid solid is 2 HD =h ¼ Sðh=8p2 Þgi gj r3 ij ð3 cos yij 1ÞðIi Ij 3Izi Izj Þ

The value of the dipolar coupling depends on the angle (y) which the internuclear vector makes with the field direction and the internuclear distance. For two protons close in space in a rigid solid, the value can be of the order of 20 kHz. The isotropic average of this is zero and is the reason why dipolar couplings do not appear in spectra measured on free solutions. If there is some molecular motion, the angular term is partially averaged and for tissues this can be considerable leaving line widths of the order of 1 kHz. If the sample is spun at some angle (b) to the field then this also causes an averaging process according to HD =h ¼1=2ð3 cos2 b 1ÞSðh=8p2 Þgi gj r3 ij ð3 cos2 yij 1ÞðIi Ij 3Izi Izj Þ pffiffiffi Hence if b is set to cos1(1= 3) and the spinning rate is large compared to the partially averaged dipolar coupling, then the angular term also goes to zero irrespective of the average value due to theta. As an example of the effectiveness of MAS for tissue NMR spectra, Fig. 3 shows a typical result for a liver sample. 2.2. Sample Preparation One of the practical advantages of 1H MAS NMR spectroscopy is that there is relatively little sample preparation necessary. Despite this, the technique can simultaneously detect changes in both aqueous and lipophilic environments (6), making the technique ideal for use as a diagnostic tool. Samples are loaded into ZrO2 rotors as depicted in Fig. 4. The tissue may be soaked in a small quantity of D2O prior to loading if spectra are acquired ‘‘locked.’’ When used in conjunction with 4 mm diameter rotors and Teflon spacers, tissue size may be limited to 5 mg wet weight, with the spacer also improving sample packing and homogeneity by forcing out trapped air ((7) Fig. 4). To ensure that spinning

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Figure 3 A comparison of 1H NMR spectra from an intact piece of liver tissue either (a) static or (b) spun at 6000 Hz to demonstrate the spectral improvements produced by magic angle spinning (Ref. 5).

Figure 4 A schematic diagram showing sample position in a HRMAS NMR spectroscopy zirconium oxide rotor with (right) and without (left) teflon spacer (Kel-F). Figure provided by Dr. N. Waters (Ref. 8).


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side bands associated with the spectra are outside, a 10 p.p.m. region was used to observe metabolites in the tissue. Samples are spun at a rotation rate to ensure that spinning sidebands of the peaks lie outside the typical 10 p.p.m. spectral window, i.e., at a 600 MHz observation frequency, a 6000 Hz rotation rate would be used. With increasing spin rate, the samples may suffer degradation, especially for softer tissues or cells, as centripetal force increases with the square of spin speed. This has led to a number of research groups developing pulse sequences, such as TOSS and PASS, for use with 1H MAS NMR spectroscopy to minimize tissue degradation Wind, Hu and Rommerein (2001). However, even at speeds of 5000–6000 Hz Griffin et al. (9,10) found no evidence of increased metabolic changes in cultured neuronal cells at these speeds, and only minor damage to cell membranes according to the Trypan Blue exclusion assay. Furthermore, to minimize enzymatic degradation, samples can be chilled to within a few degrees of freezing during the acquisition of spectra. However, MAS NMR spectroscopy does not detect all the metabolites found within a tissue. Fats within restricted environments such as the cell membrane are subjected to dipolar couplings greater than those removed by the typically modest spin speeds used in tissue-based 1H NMR NMR spectroscopy (11), and thus, the lipids observed using 1H MAS NMR spectroscopy are relatively mobile (rotationally, if not translationally). These lipids still consist of a large number of different chemical moieties, as 1H NMR spectroscopy detects the different chemical groups present within the lipid compounds rather than returning a single resonance for each lipid as a whole. Thus, broad resonances associated with lipids obscure large proportions of spectra from lipid rich tissues, such as adipose tissue, skeletal muscle, and cardiac tissue (12–14). For metabolites that are coresonant with intense lipid signals, spectral editing is needed either using the molecular environment as a contrast agent (14) or using spin coupling along the molecular backbone in two dimensional pulse sequences such as during COSY and TOCSY pulse sequences (15). Using the cellular environment as a contrast mechanism, small and large molecules can be readily separated.

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Metabolites within restricted environments have short longitudinal (T2) relaxation times, resulting in broad resonances in the spectra. However, if a delay is placed in the pulse sequence so that the signals from these restricted metabolites have decayed to an insignificant level, the signals from the unrestricted metabolites can be more readily detected. One pulse sequence that utilizes this procedure is the Carr– Purcell–Meiboom and Gill (CPMG) sequence, where magnetization is trapped in the transverse plane by a train of 180 pulses, while the signal from the broad, short T2 resonances decays at a relatively faster rate than those from more mobile metabolites. Alternatively, a pulse sequence can be used which edits spectra based on differences in molecular diffusion coefficients. This involves the application of magnetic field gradients which code the spatial position of the molecules. At some later time, a decoding gradient is then applied and if no molecular translation motion has occurred, the full NMR signal intensity is retained. However, if there is diffusion, then the signals will be reduced in intensity such that they can be effectively completely removed for fast diffusing molecules (16). For field gradients rectangular in time, the attenuation of a resonance intensity is given by Ag ¼ A0 exp½g2 d2 g2 DðD d=3 t=2Þ where A is the signal intensity at gradient strength g, d is the time the gradient is applied for, g is the gyromagnetic ratio of the nucleus, and D is the total diffusion time. Thus, data sets can be produced that are dependent on the cellular environment of the metabolites. In this respect, NMR spectroscopy has a huge intrinsic advantage over mass spectrometry for deriving metabolic profiles. 3. APPLICATIONS OF 1H MAS NMR SPECTROSCOPY TO METABONOMICS 3.1. Toxicology in Animal Systems While the use of metabonomic-based techniques to biofluids offers a minimally invasive procedure for investigating drug


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toxicity, it is often necessary to confirm specific organ toxicity that has been suggested by biofluid NMR spectroscopy. 1 H MAS NMR spectroscopy provides a rapid mechanism for investigating the biochemical changes that occur in intact tissue. It also has a large practical advantage over other ‘‘omic’’ technologies such as transcriptomics and proteomics in that metabolism is readily transferable from one species to the next.1H MAS NMR spectroscopy provides a quick and convenient technique to investigate potential toxicity in tissues from any species. However, diet affects tissue composition, particularly for renal and liver tissue. While this has been previously examined in many laboratory species, little is known about wild small mammals, with these animals being most at risk of exposure to agrochemicals and pesticides. Furthermore, the high lipid content of renal and liver tissue of many wild rodents suggests that these animals may be particularly vulnerable to lipophilic xenobiotics when compared with the laboratory rat (17). Indeed, comparing the herbivorous bank vole (Clethrionomys glareolus), the granivorous wood mouse (Apodemus sylvaticus), and the insectivorous white-toothed shrew (Crocidura suaveolens) with a widely used strain of laboratory rat (Sprague Dawley), 1H MAS NMR spectra from all the wild species were found to contain large lipid resonances, particularly wood mice as shown in Fig. 5 (18). Extending the hypothesis that wild mammals may be particularly prone to toxic insults when compared with laboratory species, metabonomic-based 1H MAS NMR spectroscopy was used to examine cadmium and arsenic toxicity in the bank vole (18,19). Following acute exposure of bank voles to cadmium chloride, biochemical changes in lipid and glutamate metabolism that preceded classical nephrotoxicity were detected. Furthermore, these changes occurred after chronic dosing, at a low level of exposure and at a renal cadmium concentration (8.4 mg=g dry wt) that was nearly two orders of magnitude below the WHO critical organ concentration (200 mg=g wet wt) (20). These early stage effects of cadmium on the biochemistry of renal tissue may reflect adaptation mechanisms to the toxic insult or the preliminary

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Figure 5 1H MAS NMR spectra from the outer renal cortex of tissue taken from (A) rat, (B) bank vole, (C) shrew, and (D) wood mouse kidneys. Increased lipid triglyceride content (corresponding to the broad resonances in the spectra such as at 1.3 and 1.0 p.p.m. chemical shift) was found in the wild mammals compared with the laboratory rat. As well as lipids, smaller metabolites such as amino acids and sugars could be detected in all animals.

stages of the toxicological cascade, and demonstrated the potential sensitivity of the technique. Intriguingly, bank voles also appear susceptible to arsenic toxicity, with diffusionweighted 1H MAS NMR spectroscopy being used to follow


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the effects of arsenic-induced hemorrhage via changes in the diffusion properties of water (18). To follow such investigations using transcriptomics and proteomics would be vastly complicated by the lack of a sequenced genome for the animal and species-related differences in protein structures. Despite a recurring problem of coresonant lipid peaks obscuring low molecular weight metabolites in many tissues, including the liver, kidneys, and cardiac tissue, the technique has proved sensitive not only to biochemical changes across tissue types but also to drug toxicity, and is particularly useful at confirming organ specific toxicity following the identification of biomarkers in biofluids. Garrod et al. (21) explored the techniques potential of linking histopathology and urinary biomarkers by investigating 2-bromoethanamine toxicity, a known renal papillary toxin, in two regions of the kidney and the liver. The drug induces mitochondrial dysfunction and inhibits fatty acyl-CoA dehydrogenases, with 1H MAS NMR spectroscopy detecting a transient rise in glutaric acid in all three tissue types. While both the renal cortex and papilla demonstrated evidence of changed osmolarity, with decreases in known osmolytes detected in both tissues, the osmolytes were different for the two tissue types; decreases in glycerophosphocholine, betaine, and myo-inositol in the renal papillar and TMAO in the renal cortex. Furthermore, toxicity was also detected in the liver where BEA caused increases in lipid triglycerides, lysine, and leucine. Waters et al. (6,7) took this analysis one stage further when investigating alpha-napthylisothiocyanate toxicity in rats livers, by comparing urinary and blood plasma biomarkers of toxicity directly with metabolite changes in liver tissue. 1H MAS NMR spectra of intact liver clearly showed increases in hepatic liver triglycerides, accompanied by decreases in glucose and glycogen. This perturbation in lipid and carbohydrate handling was correlated with increased plasma ketone bodies, and a decrease in TCA cycle intermediates in urine. Such a holistic approach clearly demonstrates the correlation between the biofluid markers detected and hepatic tissue toxicity, and hence could be used to confirm specific organ toxicity during the drug development process.

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Furthermore, 1H MAS NMR spectroscopy can demonstrate when a biofluid biomarker does not originate in a given organ. Nicholson et al. (22) demonstrated that acute exposure of male rats to cadmium chloride resulted in creatinuria following organ-specific toxicity in the testes. Thus, it seemed reasonable that similar creatinuria detected in a chronic exposure study of male rats to cadmium chloride may also result from testicular damage (23). However, on closer inspection using 1H MAS NMR spectroscopy, no biochemical changes were detected in testicular tissue, and in particular there was no decrease in tissue creatine content or a change in redox potential in the tissue, known to precede cadmiuminduced testicular toxicity. Instead, the most likely explanation for the creatinuria was breakdown of muscle tissue in order to supply glutamine to renal tissue and prevent renal tubular acidosis in the tissue. The small sample size of tissue required for 1H MAS NMR spectroscopy is also a highly attractive feature of the technique. With high-quality spectra still being obtainable from as little as 5 mg of tissue, it is possible to sample organs in a region-specific manner. This has allowed the monitoring of cerebral toxins such as kainic acid and 3-nitropropionic acid across different regions of the rat brain, with toxicity and histology being directly related by the co-preparation of 1 H MAS NMR spectroscopy and histology samples. 3.2. Toxicology Using Cell Culture Systems One of the first applications of 1H MAS NMR spectroscopy was the investigation of cultured adipocytes and how such cell culture systems could be used to follow cell proliferation in adipocarcinomas (24). However, cells obtained from cultures have no connective structures to protect them from the mechanical degradation induced by spinning and there was a possibility that improved spectral resolution arose from cell lysis rather than the physical averaging of dipole–dipole interactions and chemical shift anisotropy. To examine this, Weybright et al. (24) stained cells with Trypan Blue to investigate whether cell membrane damage was significantly


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greater after spinning at speeds up to 3000 Hz. Reassuringly there was only a modest increase in compromised cells, even for particularly large adipocytes, which might be expected to be particularly vulnerable to such damage. To demonstrate that this was not confined to one cell type and was true for higher speeds up to 6000 Hz, Griffin et al. (9) demonstrated similar cell membrane stability and metabolite content in neuronal cells, suggesting this technique would be valuable for following cell culture investigations into drug toxicity. This speculation has since been confirmed by two studies investigating cell culture-based methodologies. Ishikawa cells are a human cell line derived from endometrial adenocarcinoma, and being hormone responsive are potentially very useful for investigating drugs which modulate the estrogen receptor and hence may be agents that will cause epigenetic cancer (25). 1H MAS NMR spectroscopy of intact cells produced spectra with line widths typically less than 5 Hz, and low molecular weight metabolites could further be investigated by applying a spin-echo to edit out the more intense lipid signals (Fig. 6). Examining the action of tamoxifen, a prediction to latent structures through partial least squares model (PLS), a regression extension of principal component analysis was built to model metabolic changes caused by tamoxifen against dose (10). Amongst the metabolites that contributed to this model were ethanolamine, myo-inositol, uridine, and adenosine, suggesting both alterations in cell membrane turnover and RNA transcription. Furthermore, the metabolic effects of other estrogen modulators could be monitored using this PLS model, effectively scoring these drugs in terms of tamoxifen doses (26). Bollard et al. (27) have also examined d-galactosamine in liver spheroids, a cell culture preparation of hepatocytes that maintain cell–cell interactions. Using a combined approach of PCA and Orthogonal Signal Correction (OSC) to examine changes in NMR spectral profiles, treated spheroids had increased lipid triglycerides and cholesterol content, paralleling the changes known to occur in hepatic tissue. As previously mentioned these lipids are most likely to arise from cytosolic lipid pools, rather than cell membranes, with 1H

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Figure 6 Aliphatic region of two 1H MAS NMR spectra acquired using the CPMG pulse sequence with 10 ms (B) and 320 ms (A) total spin echo time. The expanded region shows the effects of T2 attenuation on the resonances of choline containing metabolites between 3.2 and 3.3 ppm. Key: 1. CH3CH2 lipid groups, 2. leucine, valine, isoleucine, 3. lactate, 4. CH2CH2 lipid groups, 5. alanine, 6. COCH2CH2 lipid groups, 7. C¼CHCH2CH2 lipid groups, 8. acetyl groups, 9. glutamate, 10. creatine, 11. choline, 12. phosphocholine, 13. glycerophosphocholine=phosphatidylcholine, 14. phosphoethanolamine, 15. taurine, 16. myo-inositol.

MAS NMR spectroscopy providing a unique mechanism for investigating these metabolites. While chloroform=methanol extraction followed by either NMR spectroscopy, HPLC, or mass spectrometry would be alternatives, the extraction procedure would also extract membrane bound lipids, diluting any changes detected in the cytosol. Potentially, such techniques could be used to monitor drug interactions and subsequent toxicity in any cell culture system. However, the technique is also sensitive to certain chemical and physical changes that are not often considered, for example, during molecular biology manipulations. Choline


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containing metabolites have been associated with a number of disorders, including malignant cell growth, Duchenne muscular dystrophy and multiple sclerosis (2,28,29). During transient transfection of an unknown cloned gene thought to be involved in lipid metabolism using electroportation into hepatocytes, large changes in the relative proportion of choline to phosphocholine and phosphatidylcholine were detected as shown in Fig. 6. Under such circumstances it would be tempting to suggest that the gene’s function was related to choline metabolism. However, on closer inspection, such changes were found to accompany transient transfection with the LacZ reporter gene and even naked plasmids, demonstrating that the effect arose from the action of electroportation rather than anything related to the gene function (30). 3.3. Correlation of Metabonomics Data With other ‘‘-omic’’ Technologies One potential application of 1H MAS NMR spectroscopy is applying the technique to provide a metabolic phenotype to correlate with other so-called ‘‘omic’’ technologies, such as transcriptional analysis and proteomics during drug toxicity. By allowing the dual observation of aqueous and lipid soluble metabolites, transcriptional changes can be correlated directly with all the metabolites present within a tissue rather than limiting the analysis to a particular subset. Griffin and co-workers (unpublished work) have examined orotic acid-induced fatty liver disease in rats using metabonomics, transcriptomics, and proteomics. The metabolic changes detected in liver tissue consisted of both increases in lipid triglycerides and cholesterol esters, as well as choline containing metabolites and their degradation products as seen in Fig. 7. Only by observing metabolites directly in the tissue could this range of compounds be followed. Furthermore, by providing a metabolic phenotype, or metabotype (31), different time points and strains of rats could be compared directly in the subsequent analyses. Effectively, transcriptional changes could be modeled in terms of a natural ‘‘metabolic time’’ rather than the artificial sampling time used in the study.

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Figure 7 1H MAS NMR spectroscopy of liver tissue from rats exposed to orotic acid over a 3 day time period. Clear increases in -CH2CH2- and -CH3 lipid moieties could be detected across the time period. These changes were also accompanied by alteration in the relative proportions of choline containing metabolites and a decrease in both glucose and glycogen content within the tissues.

4. FUTURE DIRECTIONS AND CHALLENGES FOR 1H MAS NMR SPECTROSCOPY Although spinning speeds used at present do not appear to produce significant damage in tissue samples, as superconducting magnets move to increased field strength and spectrometers attain higher observation frequencies samples will potentially be spun at higher speeds to remove spinning side bands from the region of interest. At some point, tissue degradation and the heating effect caused by spinning the sample will become significant. Thus, there is a need to develop pulse sequences that can operate at slower speeds but still provide high resolution MAS 1H NMR spectra. Wind et al. (32) have


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recently described a pulse sequence capable of operating with a sample spinning speed of only 1–4 Hz. While their system was somewhat artificial, examining finely chopped up liver pieces, it does suggest that slower spinning speeds are possible while still maintaining high resolution. Meanwhile, the development of in vivo MRS continues, and with improved

Figure 8 A 1H MAS NMR spectrum of an intact glioma removed from a rat brain obtained at 600 MHz compared with two in vivo pulse MRS sequences (LASER and STEAM) examining the same glioma in vivo at 400 MHz. Spectra were provided by Dr. Griffin, University of Cambridge and Prof. Kauppinen, University of Manchester.

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coil design and better localization pulse sequences spectra are obtainable that are comparable in resonance line width to those obtainable using 1H MAS NMR spectroscopy, particularly for fatty tissue as demonstrated in Fig. 8. Thus, there will be wider scope for metabonomic studies in vivo. Even prior to these advances, the scope of being able to detect metabolites in tissues on a comparable scale to those used by histologists makes 1H MAS NMR spectroscopy a desirable technique for toxicologists. If the technique can be fully automated a routine step in histopathology will be sending sections to be analyzed by both histology and 1H MAS NMR spectroscopy, providing another tier in the systems approach to drug toxicity. REFERENCES 1. Matthews PM, Francis G, Antel J, Arnold DL. Proton magnetic resonance spectroscopy for metabolic characterization of plaques in multiple sclerosis. Neurology 1991; 41(8):1251–1256. 2. Cadoux-Hudson TAD, Blackledge MJ, Rajagopalan B, Taylor DJ, Radda GK. Human primary brain tumour metabolism in vivo. Br J Cancer 1989; 60:430–436. 3. Rae C, Scott RB, Thompson CH, Dixon RM, Dumughn I, Kemp GJ, Male A, Pike M, Styles P, Radda GK. Brain biochemistry in Duchenne muscular dystrophy: a IH magnetic resonance and neuropsychological study. J Neurol Sci 1998; 160(2):148–157. 4. Andrew ER. The narrowing of NMR spectra of solids by highspeed specimen rotation and the resolution of chemical shift and spin multiplet structure for solids. Prog Nucl Magn Reson Spectrosc 1972; 8:1. 5. Bollard ME, Garrod S, Holmes E, Lindon JC, Humpfer E, Spraul M, Nicholson, JK. High resolution 1H and 1H-13C magic angle spinning NMR spectroscopy of rat liver. Magn Res Med 2000; 44:201–207. 6. Waters NJ, Holmes E, Waterfield CJ, Farrant RD, Nicholson JK. NMR and pattern recognition studies on liver extracts


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6 The Application of Metabonomics as an Early In Vivo Toxicity Screen GREGORY J. STEVENS

ALAN J. DEESE

Drug Safety Evaluation Pfizer Global Research and Development, La Jolla, CA, U.S.A.

Analytical Research and Development, Pfizer Global Research and Development, La Jolla, CA, U.S.A.

DONALD G. ROBERTSON Drug Safety Evaluation Pfizer Global Research and Development, Ann Arbor, MI, U.S.A.

1. INTRODUCTION 1.2. Why Use Metabonomics as a Screening Tool? High-throughput screening efforts in early drug discovery, combined with combinatorial and computational chemistry produce ever-increasing numbers of potential leads for 195

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further testing. On the other end of the spectrum in drug development, increased regulatory hurdles and complex clinical programs have significantly raised the cost of drug development. Drugs withdrawn from the market due to safety issues have also highlighted the need for better safety testing prior to marketing new drugs. This presents a unique challenge to the pharmaceutical and biotechnology industries to reduce attrition by bringing forward safe and efficacious drugs with greater survival rates. A significant bottleneck in the drug discovery and development process is the nonclinical efficacy and safety evaluation, in particular the conduct of in vivo studies. While genomic and proteomic technology will play a part in alleviating this bottleneck in the future, they are not well positioned to assess in vivo toxicity of new chemical entities. Which tissue do you profile? When? What dose? Not to mention the low-throughput nature of these technologies. Metabonomic technology can help bridge this gap by providing a more rapid throughput method to identify target organ effects, as well as dose and time relationships early within a drug discovery program. The subject of recent reviews (1–3), metabonomic technology has evolved into a novel tool that can rapidly evaluate the metabolic consequences of disease and drug-induced toxicity through the use of 1H-NMR spectroscopy of biofluids coupled with pattern recognition (4). This new tool allows for the non-invasive evaluation of toxicity by monitoring changes in endogenous biochemicals from a single animal, and if needed these findings can be confirmed with traditional toxicological endpoints such as clinical and histopathology. Changes in the levels of endogenous biochemicals are caused by toxicant-induced perturbation in homeostasis, disrupting the normal composition of key cellular metabolic pathways in targeted tissues. These disruptions result in changes in the composition and quantity of biochemicals in various biofluids such as urine and blood. The biochemical composition of the biofluid reflects, in part, specific target organ dysfunction and host response. While individual biochemicals present in biofluid may offer clues to cellular pathways perturbed by a particular toxicant, a simple visual comparison of changes

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associated with treatment is better suited for screening. In addition, high resolution NMR could provide specific biomarkers of toxicity, however, these concepts are outside the scope of this chapter. This chapter will focus on the logistics of metabonomics in rodent models and provide examples of metabonomic profiling in rats and mice. The objective of this chapter is to provide toxicologists, who may be unfamiliar with the technology, with information required to assess the practical applicability of the technology as a screening tool in support of drug discovery. 2. EXPERIMENTAL CONSIDERATIONS 2.1. Urinary NMR: Why? and How? 2.1.1. Urine is the Biofluid of Choice One of the many advantages of the metabonomic technology is that almost any biological fluid can be measured. Whole blood, plasma, serum, saliva, spinal fluid, seminal fluid, aqueous humor, etc., can all be used for metabonomic studies (1,5). With the use of microflow probes, even small amounts (2 mL) of cerebral spinal fluid can be monitored by 1H-NMR (6). Profiling of these fluids can provide powerful mechanistic information; however, to be applied in a screening paradigm the biofluids used need to be simple to obtain, require few processing steps and of sufficient quantity for analysis. Therefore, blood and urine are the logical samples for screening applications. Urine has significant advantages over blood for screening applications. Refrigerated serum or plasma can be viscous due to high protein content, which can be problematic with auto-injectors. Excess protein can also stick to instrumentation leading to problems when hundreds of samples are analyzed. Another concern is that withdrawing blood is an invasive procedure that can induce some stress in animals (7). This stress can add additional variability due to sample collection. Blood volume in rodents (particularly mice) is limited; therefore, repeated analysis is not always possible and

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adds systemic variability due to blood loss. Perhaps the biggest problem with blood as a sample, is timing the sample for collection. The volume limitations mentioned above as well as practical and ethical considerations limit the number of samples that can be obtained in any 24-hr period. While selecting time-points relative to dose is standard practice, there is no guarantee that any individual animal will respond to meet a preestablished timetable of sampling. Unlike blood collection, collecting urine from animals housed in metabolism cages is non-invasive, and comprehensive, avoiding many of these complications. Thus, the biofluid of choice for the application of metabonomics as an in vivo screen in rodents is urine. The biochemical analytes present in urine provide a unique ‘‘fingerprint’’ of the host physiology. Within a given urine sample, multiple endogenous analytes can easily be detected by 1H-NMR in both rats and mice (Fig. 1). These

Figure 1 Representative 500 MHz 1H NMR spectrum of urine from an untreated male rat and mouse. Common urinary analytes are highlighted.


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analytes are consistent in control animals and do not vary substantially in healthy animals over a short period of time (Fig. 2). Repeated sampling of urine from a single animal during the course of a toxicological or pathological event provides a rich source of information. These data could be used not only to follow toxicological consequences of a given treatment but also provide the opportunity to monitor reversibility. 2.1.2. Urine Collection One of the most important components in applying metabonomics as a screening tool is the experimental design and proper collection of urine. Bacterial contamination can significantly alter the 1H-NMR profile. Therefore, urine should be collected under refrigerated (0–4 C) conditions to diminish bacterial growth. A small amount of 1% sodium azide is added to the collection tube to control bacterial contamination. The

Figure 2 1H NMR urinary profile of a single male B6C3F1 mouse over a 5-day period.

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preferred method of collection is the use of refrigerated metabolism racks (VWR, West Chester, PA). Each rack allows for the simultaneous housing of 12 individual metabolism cages, equipped for automatic timed collection of urine in conical tubes situated in a refrigerated unit. Nalgene cages come with inserts that allow for simple conversion for housing mice and rats. One downside to this rack system is the high cost; over $72,000 per rack (with cages) and an estimated 6-month delay in delivery of the units upon ordering. While metabolism cage collection is preferred for the reasons stated above, any urine collection procedure that is capable of collecting clean (non-bacterially contaminated) urine in sufficient volume (e.g., >100 mL) can be utilized. Addition of sodium azide or other ‘‘NMR friendly’’ (i.e., without 1H-NMR peaks) bacterial-static compound should be considered since samples will frequently thaw and come to room temperature at some time during processing or analysis. A final concentration of approximately 0.1% sodium azide is adequate for this purpose. 2.1.3. Sample Handling One of the greatest hurdles in applying metabonomics as an early screen is the large numbers of samples that require analysis. Therefore, sample preparation requirements should be kept to a minimum. Several factors related to sample handling could impact the outcome of NMR results. The design of flow probes require that samples be free of solids to avoid clogging. Therefore, samples should be clarified via gravity precipitation or centrifugation prior to placing into 96 well plates. Osmolarity of the sample can alter the efficiency of energy transfer from the probe and sample pH can influence the chemical shifts of molecules with ionizable groups such as amines and carboxylic acids. Both of these effects can be diminished by diluting urine using a strong buffer (0.2 M sodium phosphate, pH 7.4) in a 1:2 (buffer:urine) ratio. For rats and mice, the urine can be diluted using a 1:2 ratio of urine to buffer without significant perturbations of the spectrum. The dilution of the urine does not impact the


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interpretation of NMR data, since the endogenous analytes are present at reasonably high concentrations. Also, the data are normalized to account for variability in metabolite concentration from sample to sample. As an internal chemical shift standard, sodium 2,20 ,3,30 deutero-3-trimethylsilylpropionate (TSP) provides a single resonance, which defines 0 ppm in an NMR spectrum. A lock solvent such as D2O should also be added. Both steps are efficiently done by adding 1.0 mM solution of TSP dissolved in D2O to the buffered sample to a final D2O concentration of 5–10%. Samples should be stored at 20 C after dilution and protected from light. Upon addition of buffer and TSP, a small amount of precipitation, possibly calcium salts, usually occurs. Therefore, to avoid problems with sample loading care should be taken in setting up the needle depth to avoid aspirating solid material. If a liquid handling system is used, samples waiting in the queue should be maintained at 10 C. The minimum sample volume after addition of buffer and TSP is approximately 340 mL. For flow probes with 60 mL flow cells and 500 mL for probes with 120 mL flow cells. 2.1.4. Typical NMR Analysis Other chapters in this volume provide a greater level of detail around analyzing samples by 1H-NMR. This brief description outlines the processes used in the data provided in this chapter and options for conducting these studies in a screening mode. As described above, urine is processed in 96 well plates, assisted with liquid handling systems equipped with autoinjectors. Each sample is loaded into an NMR flow cell and allowed to come to probe temperature. For spectrum acquisition a minimum of 64 pulses are recommended. The 1H-NMR can be derived from a variety of instruments, however, for this chapter they were generated from either a 500 or 600 MHz instrument equipped with a 60 mL flow probe using a NOESY one-dimensional (1D) preset sequence (13). Although two-dimensional (2D) 1H-NMR (8,9) and solid tissue NMR via magic angle spinning can provide additional insight into interpretation of biochemical changes (10–12), these

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more labor-intensive methodologies are not suitable for initial screening but extremely powerful as follow-up to observed findings. Free induction decays (FIDs) are multiplied by an exponential decay function (LB ¼ 1.0 Hz), zero filled from 64 K to 96 K data points, and then converted to frequency domain spectra using fast Fourier transformation. The amount of data present within a single 1D 1H-NMR spectrum is quite extensive and magnified in the context of a metabonomic study; repeated sampling over time from multiple animals creates a dilemma in rapid data interpretation. One area that slows the data analysis is the base line correction and phasing that is often done manually for each spectrum. Automated phasing and base-line distortion correction algorithms will help speed up this portion of spectral data analysis (13). Newer software and methodologies are currently being developed to address this issue. In order to deal with the complex data sets derived from these studies, the spectra are data reduced using AMIX software (Analysis of MIXtures, Bruker GmbH, Karlsruhe, Germany). This reduction method allows for multivariate statistical analysis of the data, reducing the data to comprehensive plots and the generation of models for classification (4). For a given 1H-NMR spectrum within the chemical shift range of 0.2–10.0, spectral integrals are measured over 0.04 ppm contiguous regions. Areas devoid of endogenous peaks at either end of the spectrum and the region containing urea and water resonances (6.0–4.50 ppm) are excluded from data reduction. All data are normalized in AMIX by dividing each integrated segment by the total area of the spectrum (minus the excluded region). The resulting integrals are exported as ASCII files into Microsoft Excel (Microsoft Corporation) prior to performing principal component analysis. 2.1.5. PCA Analysis A single spectrum may contain over 250 spectral integral regions following data reduction. One approach in dealing with these complex data sets is through the use of pattern recognition methodologies. One widely used pattern


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recognition method for metabonomic analysis is the use of multivariate analysis and principal component analysis (PCA) (4,14). Pirouette (V2.7, InfoMetrix, Inc., Woodenville, WA, USA) and SIMCA-P (UMETRICS, Inc., Kinnelon, NJ) are two software packages commonly used for analysis of metabonomic data (14). These statistical software packages allow for graphical representation of independent indices (e.g., principal components, PCs) derived from the reduced data set. The integral spectral regions represent linear combinations of variables or PCs, which do not correlate to each other, but do reflect the variance in the original data set. The first few PCs describe the greatest variation in the data. When plotted against each other to display patterns or groupings, the difference in NMR spectral patterns can easily be visualized. A good example of this is presented in Fig. 3. Mice

Figure 3 Effect of animal feed on non-treated mouse urinary profile. Male B6C3F1 mice were maintained on standard rodent chow (Purina #5002; circles) or folate deficient chow (triangles) for two weeks prior to urine collection. A 24hr urine collection was performed and resulting 1H NMR was reduced and PCA plot constructed.

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fed two different types of food significantly changed the urinary output of endogenous biochemicals. These changes are difficult to discern from an NMR plot but can easily be distinguished using PCA plots. A detailed discussion of statistical methods appropriate for use in metabonomic studies can be found in Chapter 8. 2.1.6. Sample Variability Variability is inherent to any experimental test system and this variability is often magnified when experimental data are derived from highly sensitive techniques. This is particularly true in the context of metabonomic profiling using high-resolution 1H-NMR and pattern recognition. Holmes et al. (15) demonstrated that different strains of rats are easily distinguishable by PCA. Others have shown differences in diurnal rhythm and estrus cycle (16). Gender differences, animal health, and stress may all affect urinary analyte profiles. Age has also been shown to have a significant impact on the biochemical pattern in rats (17). In mice, differences in types of chow demonstrate a dramatic difference in the PCA plots of non-treated animals solely based on the type of food the animals are provided (Fig. 3). One additional variable that can confound the interpretation of the NMR spectrum during the conduct of toxicology studies is the type of vehicle used in an in vivo study (18). Oral administration of 0.5% carboxymethylcellulose=0.2% tween, 0.5% 0.5% hydroxypropyl methylcellulose (methocel), and 0.1 M sodium phosphate buffered water have demonstrated to be suitable for metabonomic studies. PEG 200=300=400, microemulsions containing propylene glycol and labrafil=corn oil, induce significant changes in the urinary spectrum and could be problematic if used for metabonomic studies. Thus, careful consideration of study design should be considered prior to the conduct of metabonomic studies. A major advantage in the use of urine as a sample is that continuous sampling from pretest through termination can be performed. This allows for the observation of urinary changes including onset of effects, peak changes, and regression at the


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individual animal level. What may at first appear to be internal variability in response may actually be a temporal difference at the individual animal level. Despite our intentions, animals tend not to respond to toxic insult at the same rate. Unfortunately, when we employ standard toxicity endpoints, we have to guess at when the peak toxicity will occur and sample accordingly, with the assumption that all animals will respond in more or less the same time course. In reality, peak effects for one animal may not be the same for another animal leading to temporal heterogeneity in the data. To compensate for this variability, we typically use larger ‘‘N’’s so that the mean response will be more likely to reflect the severity of toxicity at any given time. This effect is demonstrated in Fig. 4, which shows metabonomic data, extracted from a study looking at several nephrotoxins. The data presented

Figure 4 PCA plot from rats treated with 150 mg=kg PAP. Twenty-four hour urine samples were collected from four rats (a–d) over a period of 4 days after treatment. The number in parentheses indicated total urinary protein levels (mg=24 hr).

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were collected from a group of four rats (a–d) administered 150 mg=kg of the tubular nephrotoxin, paraaminophenol (PAP) with 24 hr urine samples collected pretest and daily through Day 4 (96 h post dose). The numbers in parentheses next to the animal identification are the total urinary protein measurements (in mg=24 hr) from those same samples. An overt effect is obvious from all treated animals with a marked northwest trajectory change on Day 1 with subsequent regression of the samples toward pretest metabolic space. Concurrently, total urinary protein increased as much as 50-fold in some treated animals. It is readily apparent that the metabonomic data correlate with peak changes in total urinary protein based on an individual animal basis, but the changes were not absolutely proportional to urinary total protein with proteins ranging from 14.4 to 150.6 mg=24 hr in close proximity in metabolic space. Two observations can be made from these data. First, metabonomic data are more than just a surrogate measure of urinary total protein (if it were not why do it?). This is consistent with the concept that metabonomics is a systems evaluation of the whole animal not just a tubular or glomerular functional marker. Second, one animal (animal b) had a distinctly different temporal response with a peak metabonomic effect on Day 1 consistent with the other three animals, but a much more rapid return to control. These data are consistent with the urine protein data, with peak protein elevation, evident on Day 1 that was only 67% of the next closest Day 1 sample with the PC pattern back to pretest space by Day 3. Evaluation of single time point samples from Day 3 may have lead to the incorrect conclusion that the compound affected only three of four animals. Additionally, a single time point evaluation on Day 1 could have been interpreted correctly as a lesser response by animal B or it may have been considered typical functional response variation to an identically severe lesion. Only when the entire temporal data set is observed can the conclusion be made as to a difference in severity. Since continuously monitored urinary protein was the ‘‘standard’’ tox endpoint in this example, we could have arrived at this conclusion without metabonomic data. However, standard urinary measurements are typically


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only useful as markers of nephrotoxicity. The systems biological approach of metabonomics allows for potential monitoring of many target organs. The temporal advantage of metabonomics technology was also demonstrated in rats treated with the hepatotoxin carbon tetrachloride (17). 2.2. Rodent Models Theoretically, any species can be used as a predictive model of toxicity, however, in the context of screening for drug discovery, rodents are the species most often employed. 2.2.1. Rat The majority of metabonomic literature data exists solely for rats. Their size and 24 hr urinary output makes the rat, a species of choice for metabonomic studies. Both Wistar and Sprague–Dawley rats weighing from 200–250 g eliminate over 13 mL of urine over a 24 hr period (Table 1). As discussed related to variability, metabonomics is a very sensitive technique for measuring subtle changes in urinary analytes. Therefore, the same gender, strain, age should be used within a given experiment, with the animals housed under controlled conditions with access to the same water and chow. These important considerations are no different than those Table 1 Average 24-hr Urinary Output from Different of Male Rat and Mouse Strains Species

Strain

N

Weight range (g)

Collection period (hr)

Volume (mL) mean SD

Rat Rat Rat Mouse Mouse Mouse Mouse Mouse Mouse

Sprague–Dawley Sprague–Dawley Wistar B6C3F1 C3H CD-1 C57BL=6 B6D2F1 A=J

30 30 36 30 4 4 4 4 4

200–250 200–250 200–250 19–26 19–26 19–26 19–26 19–26 19–26

8 24 24 24 24 24 24 24 24

4.0 1.2 13.5 2.5 15.5 5.7 0.81 0.45 0.74 0.28 0.87 0.19 0.77 0.18 0.76 0.20 0.36 0.11

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considered in designing in vivo toxicity studies using conventional toxicological methods such as clinical chemistry or histopathologic assessment. However, predictive metabonomics technology will rely heavily on a database of spectral data that will probably be generated in a single strain, single gender under specific control conditions. To date, most metabonomic data have been generated in either the Sprague–Dawley or Wistar rat. Male rats and mice are predominately used to avoid urinary changes associated with the estrous cycle (16). COMET consortium has elected to use the Sprague–Dawley (Crl:CD(SD)IGS BR) rat as the animal modes for developing a database of liver and kidney toxicants (19). Strain differences and variations in response to treatment can limit the broad utility of such a database; however, such a database may not be needed in the context of applying metabonomics as an early screen. Where a simple assessment of changes with respect to an appropriate control may be all that is required. 2.2.2. Mouse The mouse is the ideal screening species given their small size. Early in the drug discovery process, the amount of test material is often less than a gram and considered very precious indeed due to the large number of tests needed to advance a compound through the discovery process. Mice will typically use one-tenth the amount of material required for a rat study and the potential exists to conduct early in vivo toxicology studies with as little as 50–500 mg of test material (dependent on the screening study design). In addition to their small size, numerous pharmacological and disease models exist in mice including a host of genetically modified mice. The unique nature of biochemical profiling using metabonomics affords this technique as a useful phenotyping tool. The urinary NMR profile has been used to investigate genetic strain differences between C57BL10J and Alpk:ApfCD mice (20). The major differences observed between these two phenotypically normal mice were observed in the tricarboxylic acid cycle intermediates and methylamine


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metabolism intermediates (20). Metabonomics has also been used in a mouse model of neuronal ceroid lipofuscinosis (21). The biochemical changes that occur with vitamin E treatment were used to characterize the pathological response in the disease model (21). Other potential metabonomic studies in mice could include monitoring responses to pharmacological treatment of disease models early in the discovery process; potentially providing a non-invasive surrogate of efficacy. One example of such a study was shown with an antidiabetic agent, BRL 49653, in a diabetic mouse model (22). This study showed improved urinary glucose excretion upon treatment with BRL 49653, demonstrating the utility of metabonomics as a non-invasive method for measuring efficacy in a mouse model. Unfortunately, there are few reports of metabonomic studies in mice treated with known toxicological agents. One recent example shows the utility of 1H MAS NMR of hepatic tissues derived from mice treated with acetaminophen (23), however, urinary NMR profiling was not performed. One possible reason for few urinary metabonomic investigations in mice may be due in part to the limited amount of urine produced by mice in a 24-hr period. On average, the 24-hr urinary output of a single mouse is less than 1.0 mL (Table 1) with some variability in output among several mouse strains. Out of six different mouse strains examined, A=J mice appeared to have the lowest urinary output, while the other strains tended to produce sufficient quantity of urine for analysis. The availability of flow probes requiring less than 100 mL and the fact that mouse urine tends to be more concentrated than rats overcome the limitation of detecting analytes in smaller injection volumes. Mouse urine is collected in a similar fashion to the rat, using the same refrigerated rack system but with inserts designed for housing mice (which are commercially available). To avoid extensive dilution of urine, the amount of sodium azide used is 100 mL instead of the 1 mL that is typical for the rat. Care should be given to the cages with daily washing of the collection pan during the course of a study. Small amounts of animal feed and feces can block or absorb urine

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prior to reaching the collection tube. Powdered chow seems to diminish the amount of food particulates. Mice also tend to play with their watering mechanisms so a trap under the water outlet helps to divert the water from the collection tube. Although a small amount of water does not impact the NMR spectrum due to the water suppression procedure, excess water will result in dilution of the sample and possibly limit analyte detection. One additional complication in using the mouse as a test system is the variety of mouse strains commonly used in support of drug discovery. Similar to the strain differences noted in the rat, mouse strains also exhibit differences in urinary NMR profile (Fig. 5). The PCA plot shows that A=J mice are quite different than the other strains tested. Given that

Figure 5 Differences in urinary metabonomic profile of various mouse strains presented as a principal component map. A total of 4–6 untreated mice from each strain were placed in metabolism cages for urine collection over a 24-hr period. Data derived from urinary 1H NMR were used to construct the principal component map.


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C57BL=6, C3H and the various B6 strains have common lineages, it is not surprising to find that A=J mice differ from these other strains. These data suggest that metabonomics can be used to study phenotypic differences among various animal models. In theory, any mouse strain could be used in the course of a metabonomic study as long as an appropriate control group is included. 3. EXAMPLES 3.1. Examples of Metabonomics for Rat-Induced Toxicities A number of reports demonstrate the utility of metabonomics to identify biochemical changes in rats after treatment with known toxicants. In addition to examples provided elsewhere in this volume, two examples are provided below. 3.1.1. Liver Application of metabonomic technology to assess liver injury in the rat has been extensive (12,17,24–28). Known biliary toxicants have been shown to increase urinary excretion of bile acids in rats and exhibit unique patterns of toxicity as evident in the NMR profile (17,24). Common finding in rats treated with liver toxicants are trajectories within PCA maps. Within the PCA plot, the greatest distance from control space often corresponds to the time of greatest cellular injury as determined by clinical or histopathology (24,25). 3.1.2. Kidney—BEA As described above for PAP, metabonomics is quite sensitive in identifying kidney toxicity in rats. However, the type of toxicant and lesions will alter the urinary profile. A good example of these differences is data derived from rats treated with 2- bromoethylamine (BEA). 2- Bromoethylamine produces distinctive renal papillary pathology in rats (26). In a metabonomic study, eight rats were administered a single dose of 150 mg=kg BEA with 24 hr urine samples collected pretest, and daily thereafter. Four animals were euthanized

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96 hrs after dosing (Day 4) and the remainder euthanized 240 hr after dosing (Day 10) for histopathologic assessment. Urine total protein was measured concurrently. The data from the study are summarized in Fig. 6. The pathology was progressive with moderate papillary effects noted 96 hr after dosing with marked effects evident 240 hr postdose. Interestingly, the metabonomic data suggested the most pronounced

Figure 6 Metabonomic and histopathology data obtained from eight rats treated with 150 mg=kg BEA. Four rats were euthanized 96 hr after dose with the remainder euthanized 10 days postdose. Twenty-four hour urine samples were collected pretest and daily through Day 4 with an additional sample collected on Day 10. Total urine protein is also presented. Ninety-six hours after dosing the papillary region of the kidney had minimal tubular dilatation, with extensive papillary necrosis evident by Day 10. The metabonomic data revealed a marked effect on Day 1 followed by regression towards control such that samples were nearly back to pretest metabolic space by Day 10. This trajectory correlated nicely with the urinary protein data but appeared discordant with the histopathology. See text for further explanation.


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affect on Day 1 with subsequent regression towards control, such that the Day 10 samples were nearly completely reversed. This would appear to indicate a significant discrepancy between the metabonomic data and the histopathology. However, total urinary protein, as an indicator of renal functional patency, was similarly affected, with approximately a 63-fold increase in mean urinary protein evident 24 hr after dose, but protein levels were essentially normal on Day 10. These data suggest that while the morphology of the kidney was tremendously altered, it was functioning fairly normally. Two observations can be made from these data. First, metabonomics cannot be seen as a simple surrogate for histopathology. Second, while the 240 hr sample NMR patterns were nearly back to pretest space, they were still distinct from pretest. It is always dangerous to relate trajectory distance to systemic severity when using principal component analysis in this fashion. The data simply indicate that the spectra on Day 10 had many more features in common with pretest than with the Day 1 samples, but those subtle differences that still were evident on Day 10 may have tremendous biologic importance and hence toxicological significance. A PCA plot does not speak to the relative importance of the spectral differences. To define the importance of the spectral differences requires biological and toxicological interpretation of the entire data set (clinical pathology, histopathology, and renal function) to establish importance of any experimental differences. 3.2 Example of Metabonomics for MouseInduced Toxicities 3.2.1 Liver—CCl4 Carbon tetrachloride is a classical hepatotoxin that induces centrilobular hepatotoxicity in a variety of species including mice (27). In effort to assess the utility of metabonomics in mice as a sensitive tool to measure hepatotoxicity, mice were treated with CCl4. A total of eight B6C3F1 male mice per group were treated orally with either vehicle control (corn oil) or 2400 mg=kg CCl4. Four animals from each group were euthanized 48 hr after dosing (Day 3) and the remainder

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euthanized 168 hr after treatment (Day 8). Terminal blood collections were performed for analysis of clinical chemistry changes and target tissues (liver and kidney) were collected for histological analysis. At the 48-hr time point, a marked increase (170-fold) in serum ALT was present which corresponded to diffuse centrilobular hepatocellular necrosis (Table 2). Mild to moderate increased incidence of hepatocellular mitosis and moderate hepatocellular vacuolation was present in hepatocytes interspersed between the necrotic zones. Reversibility of these changes was evident in animals euthanized 168 hr after treatment. ALT values were at control values and pathological changes were limited to diffuse necrosis with minor evidence of centrilobular mononuclear infiltrates. Complicating interpretations of a true hepatic effect, kidney lesions were also apparent. Lesions included mild to moderate cortical tubular epithelial necrosis, mild increase in presence of mitotic figures, and eosinophilia or vacuolation of tubular epithelial cells. By Day 8, these changes were still present however, a regenerative response was evident by a marked, multifocal tubular basophilia and nuclear hyperplasia. The metabonomic data were consistent with the histopathologic changes. Twenty-four hours after dosing, a

Table 2 Clinical and Histopathology Findings from Mice Treated with BEA and CCl4 Treatment

Dose (mg=kg)

Necropsy findings

ClinPath effects

BEA

100

#BW (5%), #KW, #LW

#AST, #ALT, #AlkP, #Phos

CCl4

3000

#BW (7%) "LW

"ALT, "AST, "AlkP, "TB

Histopath Renal tubular cell death, lymphoid depletion (bone marrow, spleen) intestinal crypt cell death Multifocal degeneration and necrosis of cortical tubular epithelium; centrilobular hepatocellular necrosis

Abbreviations: Body weight (BW), kidney weight (KW), liver weight (LW), spleen weight (SW), alanine aminotransferase (ALT), aspartic aminotransferase (AST), alkaline phosphatase (AlkP), total bilirubin (TB), phosphorus (Phos).


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considerable difference in urinary NMR profile was evident. A circular trajectory was apparent in the PCA plot (Fig. 7), suggesting a unique glimpse into the changes associated with the initial insult followed by a repair process. The PCA plot also shows that by Day 8 most animals are still not completely returned to control values, suggesting that recovery was not entirely complete a week after treatment. This correlated to the pathological changes remaining in the animals. Although the clinical chemistry changes return to control values, the histological examinations show hepatic and renal lesions remaining by Day 8 possibly resulting in the observed changes in urinary metabolites (Fig. 8). Many of the analytes responsible for these changes appear to be related to the Krebs cycle. Although outside the scope of a screening paradigm, looking within the data at individual analytes, may provide unique

Figure 7 PCA plot derived from control and CCl4 treated mice. Male B6C3F1 mice were treated with a single oral dose of CCl4 and urine collected over a period of 8 days. Each point within the plot represents an individual animal with the number representing the study day. Each group is represented as PD (predose), C, (control) and H, (high).

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Figure 8 Representative 1H-NMR profile of a mouse treated with a single acute dose of CCl4 (3000 mg=kg). Data represent a typical pretest, Day 3 and Day 8 spectrum with several of the analytes accounting for differences over time outlined in the spectrum.

urinary biomarkers of target organ effects. Other chapters in this volume and several reports highlight these possibilities (28). The metabonomic effects of CCl4 in the mouse are comparable to those observed in rats. Rats treated with 0.5 mL=kg CCl4 (17) showed a maximal response 24 hr after treatment and a similar circular trajectory during the course of recovery. Therefore, the rat and mouse may be used in screening for assessing hepatic injury. 3.2.2 Kidney—BEA In effort to evaluate the urinary changes induced following renal damage, mice were treated with BEA. Similar to rats, BEA is also a renal toxicant in mice; however, BEA has been shown to target both tubular and papillary epithelium (29). Male mice (eight=group) were treated with a single IP dose


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of 100 mg=kg BEA or vehicle (saline). Four per group were euthanized 48 hr after dosing (Day 3) and the remaining animals euthanized 192 hr after treatment (Day 8). There were no changes in clinical chemistry parameters indicative of kidney toxicity despite the presence of lesions on Day 3 and 8. Histological changes in the kidneys included the presence of proteinaceous material in the tubules of the mid-cortex and medulla, tubular dilation, and mild tubular epithelial necrosis. Lesions identified on Day 3 were also present on Day 8 but were less severe. Metabonomic changes were consistent with the pathological observations in the kidney. Figure 9 shows changes in the PCA occurring as early as Day 2 with progression through Day 8. Unlike the CCl4 PCA, there was no apparent trajectory back to controls by Day 8. This could be due in part to the continued presence of the same histological changes as those observed on Day 3. Of particular importance in this study was the finding that metabonomics could idendtify a change in BEA treated

Figure 9 PCA plot derived from mice treated with a single IP dose of 100 mg=kg BEA. Each point represents an individual animal with the number representing the day postday, H ¼ high dose, and C ¼ control.

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animals in the absence of significant changes in clinical chemistry parameters. Both BUN and creatinine serum levels were unchanged despite the pathological changes observed within the kidneys. These pathology changes were apparent in the urinary NMR spectra as shown in the PCA plot (Fig. 8). These data highlight the sensitivity of metabonomics as a diagnostic tool for assessing kidney toxicity. The effect of BEA in the mouse differed from those shown above in the rat. In the rat, BEA produced profound papillary necrosis, while in the mouse the lesion was mixed between tubular and papillary effects. In addition, the urinary NMR profile for rats appeared to reverse back to control values despite the appearance of continued pathological changes within the papillary region of the kidney (Fig. 6). While in mice the trajectory did not appear to exhibit a reversal type of response.

4. SCREENING MODELS Although the data from the few examples presented here are too limited to draw definitive conclusions regarding the predictive nature of the technology, the utility of the metabonomics approach should be readily apparent. The technology can not only demonstrate onset of a toxic event, but also severity and reversal of toxicity can be monitored from a peripheral sample even at the individual animal level. The technique appears to, at least in some cases, exhibit greater sensitivity than traditional clinical chemistry indices. These data, in conjunction with the temporal sequence of events, can provide mechanistic insights into the etiology of observed lesions. Taken together, these data demonstrate the enormous potential this technology has in a screening environment. Metabonomics overcomes many of the limitations in conducting in vivo safety studies within a drug discovery-screening paradigm. Due to limited blood volume, repeated sampling for clinical pathology prevents the early acquisition of a time– response relationship. Many of the traditional clinical pathology biomarkers are transient and short lived in the serum;


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therefore, significant changes can easily be missed in the context of a screening study where animal numbers may be limiting. The non-invasive nature of urine collection provides an easy way to capture a time profile following treatment with a potential drug candidate. Metabonomics may also provide greater sensitivity to changes induced by xenobiotics than those observed with traditional clinical pathology. Moreover, a major limitation to early in vivo testing is the resource and time commitment it takes to conduct histological examination of tissues. Although metabonomics will not replace pathological examination of potential target tissues, urinary profiling will provide an early read of possible changes that could be used to accelerate early decision making. Outlined in this chapter, were examples from toxicants that produce effects following an acute dose. However, many compounds may require repeat dosing to observe an adverse effect. Metabonomics can easily accommodate these repeat dose studies. Nothing differs in regards to urine collection or analysis. In some cases, drug metabolites within the urine can confound the NMR profile. This can be corrected by subtracting those peaks from the spectrum prior to data reduction. Further, collecting urine several days after post dose can often shed light on treatment related effects after several days of treatment, and after which drug related metabolites in the urine have typically cleared. So how would metabonomics be used within a screening paradigm supporting final compound selection? In general, metabonomics could be deployed in two different scenarios. The first approach is to apply metabonomics within programs with known target organ effects (e.g., back-up programs). This is the ideal situation in which to deploy metabonomics for the first time; allowing time to build confidence within management and discovery teams by demonstrating how metabonomics can be used to improve safety. Having a compound with a known toxic effect, such as in a back-up program, and the ability to monitor these effects using simple PCA plots could function as a nice positive control. Once confidence in the utility of metabonomics as an early screen is established, a second scenario could include the use of

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metabonomics as an early in vivo screen with 5–10 representative compounds from one or two different chemical series. These early assays would help to nominate the final lead compounds that exhibit minimal changes within the PCA plot. Another important question to ask is how well will metabonomics detect other target organ effects such as bone marrow, heart, or intestines? Recently, metabonomics was shown to identify rats that exhibit vasculitis (30) and reversal with anti-inflammatories (31). This complicated lesion in animals makes it difficult to identify biomarkers and screen for compounds that induce these effects. However, metabonomics may be the sensitive diagnostic to identify animals with vasculitis without actually euthanizing animals. Other potential targets organs (testes, ovary, pancreas, spleen, and adrenals) that also suffer from poor diagnostics could benefit from this new technology.

5. CONCLUSION Metabonomic technology has great potential to offer researchers a sensitive tool to non-invasively identify toxicological events that could be applied as an early screen for drug discovery. Currently the realization of this potential is underway and the literature on novel applications will undoubtedly expand exponentially in the next few years. Biological fluids represent a vast reservoir of information that can be sampled and assessed using metabonomics technology. Clearly one of our biggest hurdles in the near future will be designing approaches to collect, collate, and comprehend this wealth of information. The task for this technology will be similar to those toxicogenomic and proteomic initiatives. Metabonomics will not replace these sister technologies, but should serve as an extension of them, aiding in placing data gleaned from these approaches in proper context. One can easily envision a combined genomic=proteomic=metabonomic tactical approach to addressing etiology and pathology from the gene through the protein to the phenotype. A key to the success of these endeavors will be a bioinformatics tool that allows visualization and querying


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of data sets regardless of the source. When combined with metabolic pathway maps annotated with gene, protein, and metabolite identification, the tremendous synergy of the technologies will be realized to its fullest potential. Animal Use Disclaimer All animal experimentation reported in this chapter were approved and conducted in compliance with the Animal Welfare Act Regulations (9 CFR Parts 1, 2 and 3), the Guide for the Care and Use of Laboratory Animals (ILAR, 1996), as well as all internal corporate policies and guidelines. REFERENCES 1.


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Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999; 29(11):1181–1189.

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Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 2002; 1(2):153–161.

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Holmes E, Nicholls AW, Lindon JC, Connor SC, Connelly JC, Haselden JN, Damment SJ, Spraul M, Neidig P, Nicholson JK. Chemometric models for toxicity classification based on NMR spectra of biofluids. Chem Res Toxicol 2000; 13(6):471–478.

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Lindon JC, Nicholson JK, Everett JR. NMR spectroscopy of biofluids. Annu Reports NMR Spectrosc 1999; 38:1–88.

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Griffin JL, Nicholls AW, Keun HC, Mortishire-Smith RJ, Nicholson JK, Kuehn T. Metabolic profiling of rodent biological fluids via 1H NMR spectroscopy using a 1 mm microlitre probe. Analyst 2002; 127(5):582–584.

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McGuill MW, Rowan AN. Biological effects of blood loss: implications for sampling volumes and techniques. ILAR News 1989; 4:5–20.

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8. Willker W, Flogel U, Leibfritz D. A 1H=13C inverse 2D method for the analysis of the polyamines putrescine, spermidine and spermine in cell extracts and biofluids. NMR Biomed 1998; 11(2):47–54. 9. Foxall PJ, Parkinson JA, Sadler IH, Lindon JC, Nicholson JK. Analysis of biological fluids using 600 MHz proton NMR spectroscopy: application of homonuclear two-dimensional J-resolved spectroscopy to urine and blood plasma for spectral simplification and assignment. J Pharm Biomed Anal 1993; 11(1):21–31. 10.

Garrod S, Humpfer E, Spraul M, Connor SC, Polley S, Connelly J, Lindon JC, Nicholson JK, Holmes E. Highresolution magic angle spinning 1H NMR spectroscopic studies on intact rat renal cortex and medulla. Magn Reson Med 1999; 41(6):1108–1118.

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Tate AR, Foxall PJ, Holmes E, Moka D, Spraul M, Nicholson JK, Lindon JC. Distinction between normal and renal cell carcinoma kidney cortical biopsy samples using pattern recognition of (1)H magic angle spinning (MAS) NMR spectra. NMR Biomed 2000; 13(2):64–71.

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Bollard ME, Xu J, Purcell W, Griffin JL, Quirk C, Holmes E, Nicholson JK. Metabolic profiling of the effects of d-galactosamine in liver spheroids using (1)H NMR and MAS-NMR spectroscopy. Chem Res Toxicol 2002; 15(11):1351–1359.

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Holmes E, Foxall PJ, Nicholson JK, Neild GH, Brown SM, Beddell CR, Sweatman BC, Rahr E, Lindon JC, Spraul M. Automatic data reduction and pattern recognition methods for analysis of 1H nuclear magnetic resonance spectra of human urine from normal and pathological states. Anal Biochem 1994; 220(2):284–296.

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Holmes E, Antti H. Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra. Analyst 2002; 127(12):1549–1557.

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Holmes E, Nicholson JK, Tranter G. Metabonomic characterization of genetic variations in toxicological and metabolic responses using probabilistic neural networks. Chem Res Toxicol 2001; 14(12):182–191.

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Bollard ME, Holmes E, Lindon JC, Mitchell SC, Branstetter D, Zhang W, Nicholson JK. Investigations into biochemical


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changes due to diurnal variation and estrus cycle in female rats using high-resolution (1)H NMR spectroscopy of urine and pattern recognition. Anal Biochem 2001; 295(2):194–202. 17.

Robertson DG, Reily MD, Sigler RE, Wells DF, Paterson DA, Braden TK. Metabonomics: evaluation of nuclear magnetic resonance (NMR) and pattern recognition technology for rapid in vivo screening of liver and kidney toxicants. Toxicol Sci 2000; 57(2):326–337.

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Beckwith-Hall BM, Holmes E, Lindon JC, Gounarides J, Vickers A, Shapiro M, Nicholson JK. NMR-based metabonomic studies on the biochemical effects of commonly used drug carrier vehicles in the rat. Chem Res Toxicol 2002; 15(9):1136–1141.

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Lindon JC, Nicholson JK, Holmes E, Antti H, Bollard ME, Keun H, Beckonert O, Ebbels TM, Reily MD, Robertson D, Stevens GJ, Luke P, Breau AP, Cantor GH, Bible RH, Niederhauser U, Senn H, Schlotterbeck G, Sidelmann UG, Laursen SM, Tymiak A, Car BD, Lehman-McKeeman L, Colet JM, Loukaci A, Thomas C. Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol Appl Pharmacol 2003; 187(3):137–146.

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Connor SC, Hughes MG, Moore G, Lister CA, Smith SA. Antidiabetic efficacy of BRL 49653, a potent orally active insulin sensitizing agent, assessed in the C57BL=KsJ db=db diabetic mouse by non-invasive 1H NMR studies of urine. J Pharm Pharmacol 1997; 49(3):336–344.

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Coen M, Lenz EM, Nicholson JK, Wilson ID, Pognan F, Lindon JC. An integrated metabonomic investigation of

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acetaminophen toxicity in the mouse using NMR spectroscopy. Chem Res Toxicol 2003; 16(3):295–303. 24.

Beckwith-Hall BM, Nicholson JK, Nicholls AW, Foxall PJ, Lindon JC, Connor SC, Abdi M, Connelly J, Holmes E. Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chem Res Toxicol 1998; 11(4):260–272.

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Nicholls AW, Holmes E, Lindon JC, Shockcor JP, Farrant RD, Haselden JN, et al. Metabonomic investigations into hydrazine toxicity in the rat. Chem Res Toxicol 2001; 14(8): 975–987.

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Holmes E, Bonner FW, Sweatman BC, Lindon JC, Beddell CR, Rahr E, Nicholson JK. Nuclear magnetic resonance spectroscopy and pattern recognition analysis of the biochemical processes associated with the progression of and recovery from nephrotoxic lesions in the rat induced by mercury(II) chloride and 2-bromoethanamine. Mol Pharmacol 1992; 42(5):922–930.

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Mansour M. Protective effects of thymoquinone and desferrioxamine against hepatotoxicity of carbon tetrachloride in mice. Life Sci 2000; 66:2583–2591.

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Clayton TA, Lindon JC, Everett JR, Charuel C, Hanton G, Le Net JL, Provost JP, Nicholson JK. An hypothesis for a mechanism underlying hepatotoxin-induced hypercreatinuria. Arch Toxicol 2003; 77(4):208–217.

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Gregg NJ, Bach PH. 2-Bromoethanamine nephrotoxicity in the nude mouse: an atypical targetting for the renal cortex. Int J Exp Pathol 1990; 71:659–670.

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Robertson DG, Reily MD, Albassam M, Dethloff LA. Metabonomic assessment of vasculitis in rats. Cardiovasc Toxicol 2001; 1(1):7–19.

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Slim RM, Robertson DG, Albassam M, Reily MD, Robosky L, Dethloff LA. Effect of dexamethasone on the metabonomics profile associated with phosphodiesterase inhibitor-induced vascular lesions in rats. Toxicol Appl Pharmacol 2002; 183(2):108–109.

7 Strategies and Techniques for the Identification of Endogenous and Xenobiotic Metabolites Detected in Metabonomic Studies JOHN SHOCKCOR

IAN D. WILSON

Metabometrix, South Kensington, London, U.K.

Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Mereside, Alderley Park, Macclesfield, Cheshire, U.K.

1. INTRODUCTION The metabolic profiles of biological fluids from normal individuals contain a plethora of endogenous low mass metabolites, the composition of which depends upon the sample type (plasma urine, bile, etc.) and factors such as the species, strain, age, gender, diet, and gut microfloral composition of the organism from which the sample derives and, indeed, even 225

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the time of day at which the sample was taken. To this can be added the changes brought about in these endogenous profiles due to disease or toxicity, and the presence of the drugs and xenobiotics (and their metabolites) used to treat the condition, or in the case of toxicity, that caused it. To understand and interpret the changes in profiles observed during metabonomic studies, it is vitally important to identify and characterize these metabolites, both known and unknown, when they are observed. It is the authors’ intent to illustrate how both endogenous molecules and the metabolites of xenobiotics can be identified using modern spectroscopic techniques either alone or combined with either off-line methods or fully on-line hyphenated techniques. Whilst there is some overlap between the techniques used for exogenous and endogenous compounds, the strategies adopted for them do show some differences and these will be highlighted by the use of suitable illustrative examples. In general, we make the assumption that metabonomic analysis of biofluids will begin with 1H NMR of the unprocessed biofluid, complemented as required with HPLC–MS and work with extracts. 2. XENOBIOTIC AND ENDOGENOUS METABOLITE IDENTIFICATION DIRECTLY FROM BIOFLUIDS 2.1. NMR-Based Techniques 2.1.1. Endogenous Compounds Clearly, the simplest and least time-consuming strategy for the identification of endogenous metabolites of interest in metabonomic work is to use the already very high information content available in the 1H NMR spectrum of the neat biofluid itself. This is often possible because biofluids are composed primarily of known biochemicals many of which have characteristic 1H NMR spectra. Having reduced the number of possible candidates, confirmation of these assignments can be made by using the spectral data of compounds typically found in biofluids. Spiking, and overspiking, of the biofluid sample with a standard to confirm an assignment is also a useful

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technique and this is particularly the case where the resonances for a compound are affected by small variations in the pH of the sample. In such circumstances, overspiking the sample provides a rapid and simple approach to identification. As an example, Fig. 1A–C shows an expansion of spectra for (A) control rat urine, (B) a dosed animals urine, apparently with elevated concentrations of phenylacetylglycine (PAG), and (C) control urine spiked with PAG, confirming the assignment. However, because of the large numbers of compounds giving rise to resonances in biofluids, it is often not possible

Figure 1 (A) a partial 600 MHz 1H NMR spectrum of control rat urine, (B) urine from a dosed animal with resonances corresponding to the presence of PAG and (C) control urine spiked with PAG.

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to assign many low concentration peaks directly from the standard one-dimensional (1D) 1H NMR spectrum. In these circumstances, a two-dimensional (2D) spectrum can be employed to spread out the spectral information over both dimensions. TOCSY (TOtal Correlated SpectroscopY) is the principal experiment for this purpose. Protons within a spin system, especially when there are overlapping multiplets or there is extensive second-order coupling, can be observed as off-diagonal elements in the TOCSY spectrum. TOCSY provides long-range as well as short-range correlations and is especially useful when coupling constants are small. However, not all correlations necessarily appear. Often multiple experiments, with variation to the mixing time parameter, are required to observe all correlations. Fig. 2 shows a TOCSY on a urine sample with the spin systems annotated and assigned in order to illustrate the utility of the experiment. The pattern of the off-diagonal elements of many endogenous compounds in biofluids is often unique enough to use them to identify a metabolite. Where such strategies fail then recourse must be made to the techniques outlined below. 2.1.2. Xenobiotic Metabolites If dosed in sufficient quantity, xenobiotics and their metabolites can often be observed directly in the sample. Their identification as xenobiotic-related compounds can be important for a number of reasons not least of which is to eliminate them from consideration in the metabonomic study itself. However, sometimes such metabolites can provide important clues as to, e.g., the nature of the toxic insult if they should be discovered to be mercapturates, etc. resulting from the production of a chemically reactive species. An example of the ready detection of drug metabolites by 1H NMR is shown in Fig. 3A and B by the partial spectra of the aromatic portion of a control rat urine and sample obtained following paracetamol (acetaminophen) administration with various compound-related signals, including unchanged parent, indicated. The assignment of these metabolites is straightforward because there is a great


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Figure 2 A 600 MHz1H NMR TOCSY spectrum for nicotinamide N-oxide with the spin systems annotated and assigned.

deal of background literature. Had this been a compound whose metabolism was unknown, it would still have been possible to deduce the presence of a number of metabolites, including the probable glucuronide from the resonances visible in the spectrum. If a compound contains a fluorine (or fluorines, e.g. CF3) then quantitative metabolite profiles can also be obtained using 19F NMR spectroscopy (e.g., see Ref. 1 and references cited therein), and this can provide a useful additional source of information on the number and nature of the xenobiotic metabolites in a sample.

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Figure 3 A partial 500 MHz 1H NMR spectrum (aromatic region) of (A) control rat urine and (B) a sample showing the resonances for the major metabolites of paracetamol following administration of 300 mg of the drug. Key: a ¼ glucuronide, b ¼ sulfate, and c ¼ paracetamol.

For a novel compound, should the direct NMR approach prove inadequate for the identification of the metabolites in the samples then, further characterization would be required as described in the next section. 2.2. Liquid Chromatography–Mass Spectrometry-Based Methods 2.2.1. Endogenous Compounds Whilst much of the metabonomic literature is based on NMRcentered strategies, the use of HPLC–MS for metabolite


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profiling is currently in a state of rapid development (2–5). Metabolite profiling by HPLC–MS usually involves a ‘‘generic’’ gradient separation on a reversed-phase column with profiling accomplished using both positive and negative electrospray ionization (ESI). A typical example, obtained for the HPLC–MS of control mouse urine, is shown in Fig. 4 showing the different results obtained for negative and positive ESI. The strategies for metabolite identification in HPLC–MS are essentially the same as those described above for NMR-based investigations. Thus, the retention time and mass spectral data can be compared with those of known biochemicals, and overspiking can then be used to confirm identity. Where the compound detected is an unknown then examination of its mass spectrum and atomic composition (if accurate mass data have been acquired) may provide the basis for a provisional identification that can subsequently be confirmed if the appropriate standard can be obtained. Where such approaches fail then the isolation of the

Figure 4 A typical example of the total ion current chromatograms obtained for mouse urine for a sample obtained using gradient HPLC–MS with positive electrospray and negative ESI.

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compound for further spectroscopic characterization by NMR spectroscopy must be performed (see below). 2.2.2. Xenobiotic Metabolites The detection of xenobiotic metabolites in biofluid samples by HPLC–MS is now a well-established technique (e.g., see Refs. 6, 7). The easiest procedure is to examine the total ion current obtained for a postdose sample with one from a predose or control animal. Examination of the mass spectra of the peaks that appear as a result of xenobiotic administration will often reveal a compound-related metabolite and, with luck, enough information will be derived from this to provide a reasonably good idea of the structure. Certainly, phase I reactions such as hydroxylations, hydrolysis of esters, oxidations and reductions should be readily apparent. In addition, phase II conjugations to amino, acetic, glucuronic, and sulfuric acids should also be readily detected by the appropriate MS experiments. Classically, metabolism studies are performed using radiolabeled compounds (most commonly 14C or 3H) and an in-line radioactivity monitor can be used to direct MS investigations to particular peaks. If a suitable radiolabeled form of the compound under study is not available, the detection of metabolites can be assisted by looking for characteristic fragmentation patterns associated with the parent compound. If bromine or chlorine are present as substituents, the resulting isotope patterns can provide diagnostic signals in the mass spectrum [e.g., see Fig. 5A for a mass spectrum of the major hydroxysulfate metabolite of 4-bromoaniline (8)]. Alternatively, the deliberate use of a mixture of isotopically-labeled compounds (i.e., 12C=13C or 14N=15N, etc.) can be used to generate a suitable isotopic fingerprint. Almost inevitably, however, it will not be possible to fully define the metabolic fate of the compound under study by HPLC–MS as there are often difficulties in, e.g., distinguishing between positional isomers by MS alone (e.g., the position of hydroxylation on an aromatic ring, etc.). At this point, isolation for NMR spectroscopic characterization, or simply HPLC–NMR, is often required for unambiguous assignment of structure.


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Figure 5 (A) The mass spectrum and (B) the stopped-flow 1H NMR spectrum of 2-amino-5-bromophenyl sulfate obtained during the reversed-phase HPLC–NMR=MS analysis of a sample of rat urine obtained following administration of 4-bromoaniline.

3. LOW RESOLUTION, OFF-LINE, TECHNIQUES FOR THE ISOLATION OF UNKNOWNS 3.1. Solid Phase Extraction=Chromatography (SPEC)-NMR One of the simplest methods for the extraction and concentration of analytes from biofluids is the technique of solid phase extraction (SPE). In this technique, the sample is applied to a

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suitably activated SPE cartridge which contains a quantity, usually several hundred milligrams, of a chromatographic stationary phase such as a C18-bonded silica gel that acts as a sorbent. Cartridges packed with other polymeric materials such as anion or cation exchangers, or with different carbon chain lengths or loadings (C2, C8, etc.) have also been utilized. Examples of the use of SPEC are numerous and typical examples can be found in Refs. 9–11. The particular advantage of the SPEC approach is that it is simple, readily implemented and requires no special equipment. The technique of SPEC has been used for both endogenous and xenobiotic metabolites, and also as a preconcentration step prior to other techniques such as HPLC–NMR, etc. In SPEC, the sorbent extracts the metabolites from the sample matrix, from which they can then be recovered by elution with a suitably eluotropic solvent such as, e.g., methanol. In this way, several milliliters of sample (depending upon the weight of sorbent used) may be extracted, desalted, and concentrated into a few hundred microliters very rapidly. The urine and bile samples are usually loaded onto a cartridge preconditioned with an organic solvent such as methanol followed by a buffer (and eluted under gravity or a low applied pressure depending upon sample viscosity). After the samples have been passed through the cartridge, a simple wash with deionized water (at an appropriate pH) is performed to remove any inorganics and salts. If all that is being performed is the concentration of the sample, elution can be performed with a strongly eluotropic solvent such as methanol. However, when the SPE is combined with a stepwise gradient elution protocol (e.g., using sequential washes of a few milliliters of 20%, 40%, 60%, 80%, and 100% organic solvents), to give essentially a low resolution chromatographic separation, it is often possible to obtain a fraction enriched in the target analyte and, on occasion, it has proved to be possible to actually isolate them in a spectroscopically pure form. A particular advantage of the SPEC approach is that, even if it does not provide the required clean-up of the target analyte(s), the stepwise gradient elution steps employed do enable the investigator to get a feel for the relative chromatographic


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properties of the compounds under study. For example, unretained compounds, or those eluting in the earlier, highly aqueous fractions from the SPE column will be ‘‘polar’’ and require chromatographic eluents for HPLC of low eluotropic strength. Conversely, metabolites eluting in organic-rich fractions will require strongly eluotropic solvents. If desired, elution from the SPE cartridges can also be performed with deuterated solvents in order to provide a concentrated sample in a form suitable for NMR spectroscopy. Once collected the fractions are then analyzed directly by NMR and MS (if appropriate) or by HPLC–MS in order to locate the fraction(s) containing the metabolite of interest. If sufficient quantities of the metabolite are present and purity is adequate, identification can be made rapidly. If, on the other hand, there is not enough material, the process can be repeated several times and fractions combined or the SPE can be scaled up to increase the yield. 3.2. Examples of SPEC for Unknown Endogenous Metabolites An illustration of the use of this simple SPEC-based approach to isolation and identification is provided by the case of some unusual resonances detected in urine 3 weeks after the commencement of the administration of acetaminophen to rats at 1% of the diet (12). The 1D proton NMR spectra of the urine of these animals showed four large multiplets at 2.05, 2.41, 2.51 and 4.18 ppm (1:2:1:1 ratio, respectively). Based on 2D NMR, the connectivities between these separate signals were demonstrated showing that all four belonged to the same molecule. Based on the chemical shifts and relative intensities of the signals, it was possible to suggest that one belonged to a methylene (CH2, triplet, 2.41 ppm) group adjacent to a carbonyl function. This was coupled to two other, strongly coupled highly non-equivalent methylene protons (a second-order spin system at 2.05 and 2.51 ppm). These protons were also coupled to a single methine proton (4.18 ppm), with a chemical shift similar to that of an alpha-CH proton of an amino acid, that formed the X of an ABX spin system.

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However, detailed as this information was, it was insufficient to provide an identity. Solid phase extraction chromatography was therefore undertaken on a mixed mode cartridge with cationic, anionic, and reversed-phase properties, using 1 H NMR to monitor the fractions. The partially purified fraction containing these unknown resonances was then subjected to fast atom bombardment (FAB) MS. This gave the essential further information that the unknown had a molecular mass of 129 Da. Based on the ‘‘nitrogen rule,’’ this meant that it must contain, in addition to two methylene and a methine groups at least one nitrogen. Based on this information, only a limited number of structures were considered to be possible, and the unknown was rapidly identified by reference to standards to be 5-oxoproline (5OXP). This molecule is an intermediate in the gamma-glutamyl cycle that is involved in the biosynthesis of glutathione which is presumably disrupted following the chronic administration of acetaminophen in this type of experiment leading to a build up and then excretion of 5OXP. Coadministration of methionine (1%) with acetaminophen in the diet completely prevented the appearance of 5OXP, presumably by providing a source of sulfur-containing amino acids for glutathione biosynthesis. The identification of 5OXP was, in the first instance, an analytical challenge requiring both NMR and MS data in order to achieve a successful outcome. However, once characterized its identification when encountered in subsequent, human-derived, samples (where it was present as an inborn error of metabolism) proved to be trivial by comparison (13). Another example of the use of SPEC for the identification of a major unknown resonance detected in the urine of Han–Wistar and Zucker rats is the identification of 3-(3-hydroxyphenyl)propionic acid (3-HPPA) (14,15). This compound is derived from dietary chlorogenic acid via the gut microflora. Normally, the metabolism of chlorogenic acid results in the production of benzoic acid which is subsequently conjugated with endogenous glycine and excreted as hippuric acid. However, changes in the gut microfloral composition can result in a change in the fate of chlorogenic


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acid and 3-HPPA (amongst other things) is excreted in the urine instead. The 3-HPPA was partially characterized from an examination of the urinary spectrum which showed four aromatic multiplets between 7.3 and 6.7 ppm and two triplets at 2.84 and 2.48 ppm integrating to two methylene and four aromatic protons, respectively. The coupling interactions of the aromatic protons were consistent with 1,3-disubstitution whilst the chemical shifts suggested that a phenolic OH might be present. In the case of the methylene groups, the chemical shifts were consistent with the presence of a carboxylic acid. Solid phase extraction chromatography on C18-bonded silica gel was used to obtain a concentrated and essentially pure fraction for 13C NMR which indicated that the unknown contained nine carbon atoms. The likely identity of this compound as 3-HPPA was then confirmed by comparison with an authentic standard.

3.3. An Example of SPEC for Xenobiotic Metabolites Applications of SPEC approaches to xenobiotic metabolites are now very numerous and in principle the general procedures are identical to those used for endogenous metabolites. The greatest practical difference when attempting to identify xenobiotic metabolites is that the investigator at least starts from the position that the structure of the starting material is known. In addition, many potential metabolites can be predicted from the structure of the parent (although this does not mean that they will be produced!), and these can be actively sought in the SPEC extracts using both NMR an MS. A fairly typical example of the use of SPEC combined with 1H NMR in this area is the isolation and identification of the glucuronide conjugates of the non-steroidal anti-inflammatory drug (NSAID) naproxen and its O-desmethyl metabolite (9). Here, the urine sample was acidified to ensure that the glucuronides would be stabilized against alkaline hydrolysis and retained on the C18-bonded SPE phase. The bulk of the endogenous contaminants were eluted in the wash and 20% methanolic

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eluents with the O-desmethyl naproxen glucuronide and naproxen glucuronide recovered in the 40% and 60% methanolic fractions, respectively. 3.4. Solvent Extraction Whilst not as widely applied for isolation in metabonomic studies as SPE, simple liquid–liquid extractions (LLE) do have the potential to provide a means of isolating compounds from biological samples. An example of the use of LLE is provided by studies on the compound dimethylformamide (16). Here, the metabolite was clearly visible in the 1H NMR spectra of rat urine and a good extraction and clean-up were achieved by extracting into ethyl acetate under acidic conditions (pH 2). Identification of the metabolite as the N-acetylcysteinyl conjugate was then performed using a combination of the use of chromatography (TLC), NMR and MS, and comparison with a synthesized standard. In general, it has to be said that, in the authors’ opinion, SPE-based approaches are probably more versatile that LLE, though no doubt the latter may offer advantages in certain circumstances. 3.5. Characterization and Identification of Compounds in SPEC Fractions If a suitably purified fraction is obtained from these low resolution methods then further NMR experiments can be performed in order to characterize the unknown. In addition to these NMR methods, both MS and LC=MS should be employed to provide critical information on the mass of the unknown. The introduction of low cost, high-resolution, instruments like the Quadrapole-Time-Of-Flight (Q-TOF) mass spectrometer has made acquisition of elemental composition possible, which further enhances the assignment process. The unique accurate mass and elemental composition of known endogenous metabolites can provide rapid identification of these compounds. In the case of unknown and novel metabolites MS2 or MSn experiments can provide information on fragmentation that may allow their assignment.


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4. DIRECT ON-LINE METHODS OF IDENTIFYING UNKNOWNS Whilst the SPEC and solvent extraction procedures can often provide a useful route toward identification of the unknowns, there is no doubt that they represent low resolution techniques and are really only well suited to the characterization of major sample components. In cases where the unknown is a more minor component, or the extraction techniques described above provide too little purification to obtain an unambiguous assignment then either a full-blown preparative chromatographic isolation with subsequent NMR and MS may need to be performed or, alternatively, fully on-line methodologies (HPLC–NMR, HPLC–MS) will need to be employed. Even where SPEC fails to provide a pure enough sample for identification purposes, it often provides a useful method for providing a partially purified concentrate on which these fully on-line techniques can be performed. 4.1. HPLC–NMR The on-line hyphenation of NMR spectrometers to HPLC has resulted from a series of technical advances over many years. In particular, robust NMR flow-probes and efficient methods for solvent suppression were required to turn HPLC–NMR from a novelty into a routine analytical tool (e.g., see Ref. 17 and references therein). To be able to perform effective HPLC–NMR requires the detection of the signals of low concentrations of the compounds of interest in the presence of very much larger resonances resulting from the HPLC solvents. This is somewhat problematic for NMR spectroscopy as the analog-to-digital signal converter has a finite dynamic range. The solution has been to develop methods that either suppress the unwanted signals for the solvent or simply do not detect them. These methods are very effective and can deal with the problems resulting from the use of the common HPLC solvents, such as methanol=water and acetonitrile= water (as well as even more complex solvent combinations) and gradient separations. These methods are sufficiently

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effective that in many cases the need for deuterated solvents, with the increased expense associated with their use, has been greatly reduced. However, for practical reasons, D2O is still preferred over H2O simply because this results in a simplification of the solvent suppression. As D2O is relatively inexpensive, compared to the operating costs of the NMR spectrometer, its use represents a good compromise between economy and the effective use of the instrument. Similarly, acetonitrile-d3 and methanol-d4 are also being used increasingly in the pharmaceutical industry because the cost of these solvents is far outweighed by the substantial gain in quality of the results. Another problem currently associated with HPLC–NMR is the stray magnetic field from the spectrometer which places a limit on how close the HPLC system can be positioned without adversely affecting performance. The need to keep the HPLC equipment at a distance thus necessitates the use of long column-to-NMR flow-probe transfer lines which can result in peak broadening. The latter is compounded by the relatively high volume of the NMR flow cells. However, in practice, with sufficient care and attention to minimizing the lengths and diameters of the tubing used to connect the end of the columns to the flow-probes, very satisfactory results can be obtained. A detailed analysis of the flow and NMR requirements for optimum operation of HPLC–NMR has been described [see Ref. 17] and a number of reviews have described applications in drug metabolism studies, e.g., Refs. 18,19. Compared to mass spectrometry, NMR spectroscopy is often relatively insensitive and, where low concentration analytes of the type encountered in metabonomic or xenobiotic metabolism studies are encountered, the analytical separation and spectroscopic strategy have to be designed accordingly. There are a number of recognized modes of HPLC–NMR that can be used depending upon the nature of the sample. In cases where the biofluid sample (or SPEC extract) is reasonably concentrated spectroscopy can be performed on-flow with spectra acquired continuously through the run. However, in general, analytes are present at concentrations that require


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the use of a stopped-flow technique of one sort or another. In the simplest of these, the flow from the column is stopped when the peak corresponding to the analyte (observed using, e.g., UV, MS or some other conventional HPLC detector) reaches the NMR flow-probe. It is then held there until sufficient FIDs have been acquired to obtain a satisfactory spectrum (minutes to hours depending upon the concentration of the analyte). After the spectrum has been acquired, chromatography can be restarted and continued until the next peak of interest is eluted. Whilst it might be thought that such practices would lead to excessive chromatographic band broadening and a consequent loss of resolution, in practice, it has been found that stopped-flow HPLC–NMR can be performed on many peaks in a separation without degrading the separation. As the analyte is stationary in the flow-probe for as long as the investigator wishes more complex (and time consuming), NMR experiments can be performed such as 2D NMR (e.g., TOCSY, COSY, etc.). Where the peak is composed of more than one partially resolved component, the technique of ‘‘time slicing’’ can be used wherein the flow is restored for a few seconds to move the peak a little further through the flow-probe and then acquiring a further spectrum. By taking a number of spectra across an eluting chromatographic peak in this way, it may be possible to obtain spectra of the individual components. An example of the usefulness of time slicing was seen with, e.g., the partially resolve diaseteroisomers of RS-flurbiprofen and RS-hydroxyflurbiprofen glucuronides. Here, the leading and tailing edges of the metabolite peaks were essentially composed of one individual diastereoisomer whilst the middle of the peak was a mixture (20). An extreme, but very powerful, approach employing time slicing is where the entire separation is examined using in this way (usually using an overnight run) and an example of this is discussed below. A very similar alternative to this ‘‘continuous time slicing’’ is the use of on-flow HPLC–NMR at very low flow rates. Both of these techniques are valuable when information is required on all of the peaks in a separation, a situation that could arise when the retention time of the compounds of interest is not known.

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An alternative to stopped-flow HPLC–NMR is the collection of the peaks of interest as they elute from the column in sample loops (‘‘peak parking’’ or ‘‘peak picking’’). At the end of the run, the selected chromatographic peaks can then be transferred from the sample collection loops into the NMR flow-probe for subsequent spectroscopy. The only limitation to the number of peaks that can be collected is the number of available loops for storage, and current designs allow for up to 36 individual peaks to be collected. It is also possible to collect peaks via an on-line extraction process. In principle, several runs can be performed, collecting the required peak from each run, to provide a concentrate. The peak can then be eluted into the NMR flow-probe using a fully deuterated organic solvent to give the best possible NMR data (21). In principle, it is possible to effect NMR detection for any of the magnetically active nuclei. In the case of endogenous metabolites, the most important nuclei to consider are 1H and 13C (possibly 31P) whilst for xenobiotics 19F is also often encountered. However, because of the low concentration present in samples only 1H and 19F, the most sensitive nuclei, have been used to any great extent. In the absence of a specific 13C-isotopically labeled compound, 13C NMR detection in HPLC–NMR can be facilitated through indirect detection of 13 C resonances via the much more sensitive 1H NMR signals of attached protons using 2D methods such as 1H–13C heteronuclear single quantum coherence (HSQC). In xenobiotic metabolism studies, the ability to use 19F NMR spectroscopy for the detection of fluorine-containing molecules is a great advantage in that the background is negligible (unlike that for 1H NMR spectroscopy). A typical example of the use of HPLC–NMR for the identification of diet-derived compounds is provided by metabonomic studies on differences in the metabolism of chlorogenic acid in two populations of rats by examining the compounds excreted in urine (22). In this study, a freeze–dried urine sample was separated on a C18-bonded reversed-phase HPLC column using a gradient separation based on D2O-acetonitrile (with the D2O acidified with deuterated formic acid). The separation was effected using a simple linear gradient from


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0% to 50% ACN over 25 min and then to 100% ACN over the period 25–35 min. Spectra were acquired in the stopped-flow mode with peaks selected for analysis on the basis of the UV chromatogram. This investigation provided spectral information on three chromatographic peaks, eluting at 16.3, 19.9 and 20.9 min, respectively, that were significant in separating the two groups of animals when pattern recognition was used to analyze urine spectra. From the spectra obtained, the peaks were identified as hippuric acid (16.3 min), 3-HPPA (19.9 min) and 3-hydroxycinnamic acid (20.9 min). In addition, a further metabolite was detected (possibly a conjugate of some sort) that remained unidentified. The chromatogram obtained for this study and the 1H NMR spectrum of 3-hydroxycinnamic acid are shown in Fig. 6. Applications of HPLC–NMR for the detection and identification of xenobiotic metabolites are numerous, and an

Figure 6 A UV-detected reversed-phase HPLC chromatogram obtained for the urine of a male rat. Inset, the stopped-flow 1H NMR spectrum of 3-hydroxycinnamic acid (contaminated by a small amount of 3-HPPA from the preceding peak).

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illustrative example of the sort of result that can be obtained is shown for the major hydroxymetabolite of 4-bromoaniline obtained following reversed-phase HPLC of rat urine (8) in Fig. 5B (the mass spectrum obtained at the same time is shown in Fig. 5A). Another good example of the power of this technique is provided by the separation and identification of the positional isomers of the ester glucuronide metabolite of 6,11-dihydro-11-oxodibenz[b,e]oxepin-2-acetic acid (23). In this application, an aliquot of human urine containing the metabolites was concentrated by freeze drying and the components were separated using reversed-phase HPLC. Spectra were obtained using the stop-flow technique and the elution order of the isomers was determined to be 1-O-acyl, 4-O-acyl, 3-O-acyl, and 2-O-acyl, with the alpha anomer eluting before the beta anomer in all cases. This is an interesting example as, because all of the isomers have exactly the same mass, HPLC–MS could not have been used to provide these data. Another typical example of the use of HPLC–NMR for the characterization of drug metabolites is the analysis of human urine following oral dosing with antipyrine. Freeze drying enabled a 2.5-fold concentration of the samples which were separated by reversed-phase gradient HPLC. UV-detected peaks were subjected to stopped-flow NMR spectroscopy leading to the firm identification of the ether glucuronide of 4-hydroxyantipyrine, norantipyrine glucuronide, and 4-hydroxyantipyrine. A fourth drug-related component was also observed that was tentatively identified as 3-hydroxymethyantipyrine glucuronide (24). As well as biofluid samples HPLC–NMR, like HPLC–MS, can also be used to analyze samples obtained from various types of in vitro techniques such, e.g., tissue slices, cell suspensions, and subcellular fractions. For example, directly coupled, stop-flow, 750 MHz HPLC–1H NMR spectroscopy was used for the detection and identification of minor metabolites produced by rat microsomes from 3-nitro-2-(2-fluorophenoxy)pyridine and 3-amino-2-(2-fluorophenoxy)pyridine (25). The metabolism of the mono-amine oxidase-A inhibitor 1-ethyl-phenoxathiin-10,10-dioxide has been studied in human liver microsomes using 600 MHz 1H HPLC–NMR in


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stop-flow mode (26). The peaks of interest were detected by monitoring the eluent by UV and a 1H NMR spectrum obtained was obtained for six compound-related peaks. Similar work has been undertaken on the multidrug resistance inhibitor LY335979 and 7-ethoxycoumarin using human liver microsomes (27). 4.2. HPLC–NMR–MS It is often necessary to have both NMR and mass spectral data to determine the structure of these compounds. To achieve this correlation of MS and NMR without having to isolate the metabolite is a challenge for which the application of LC–NMR–MS provides an elegant solution. A typical system is illustrated in Fig. 7. In the same way that care has to be taken with the location of the HPLC system in relation to the NMR spectrometer, it is also necessary to exercise care in positioning the mass spectrometer. However, the increasing availability of actively shielded magnets, with their greatly reduced magnetic footprint, is reducing this problem. In HPLC–NMR–MS, the NMR and mass spectrometers can be arranged either in parallel or in series. As the NMR spectrometer is generally the least sensitive instrument in this combination, high concentration samples are analyzed wherever possible to ensure the best chance of success and to reduce analysis time. As a result, it is usual to employ 4.6 mm i.d. HPLC columns which have a good sample capacity (several milligrams of sample can often be loaded if the chromatography is robust) with flow rates of the order of 0.5– 1.0 mL=min. These flow rates are also compatible with MS. Another reason for the parallel configuration is that MS is destructive. Placing the NMR and MS in parallel, e.g., see Ref. 28, and thus splitting the flow such that a minor fraction goes to the MS, enables the bulk of the peak of interest to be collected for further testing if required. If the flow is split prior to the NMR spectrometer (typically 20:1), with the length of the capillary to the MS adjusted such that the analyte peak is detected by the MS as it fills the NMR flow

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Figure 7 A schematic diagram showing a typical layout for an HPLC–NMR–MS system. Also included are UV and radioactivity detectors for monitoring the eluent.

cell, the MS can be used to select peaks for subsequent NMR experiments. Operating the spectrometers in series (with MS after NMR) has been demonstrated (29), but can cause the NMR flow cell and its connections to be operated at higher pressures than they were designed for, with the consequent possibility that leaks are more likely. Series operation also fails to take advantage of the mass spectrometrs ability to flag up peaks of interest quickly. Correct solvent selection for HPLC–NMR–MS is a key issue for succesful results and has to be a compromise between the ideal requirements of each instrument. In the case of HPLC–NMR, the use of inorganic buffers such as


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sodium phosphate, to modify the pH of the eluent is often the optimum solution as it introduces no new signals into the resulting NMR spectrum. The problem is that the inorganic buffers are quite unsuitable for this role in HPLC–MS and an alternative acidic modifier, that is also suitable for NMR spectroscopy, is required. For NMR, an ideal alternative is trifluoroacetic acid (TFA), which has no protons to cause interferences in the NMR spectrum. However, whilst preliminary experiments [using acetaminophen metabolites (28) or propranolol (30) as models] showed that 0.1% TFA could be used in HPLC–NMR–MS, this proved to be true only for a limited range of analytes present at high concentration (>1 mg on column) in positive ion mode. With acidic analytes, such as the NSAID ibuprofen and its metabolites, ion suppression was complete when TFA was used, even when concentrated samples were studied, and no MS data could be obtained (30). The best compromise seems to be formic acid as the single proton of formic acid, which has a sharp, readily suppressible NMR singlet near d8.5, gives minimal interference in the resulting NMR spectra whilst MS data can also be acquired for acidic analytes. The first application of HPLC–NMR–MS to drug metabolism, the detection and identification of the sulfate and glucuronide conjugates of acetaminophen (as well as the detection of the parent compound itself), also provides an example of how endogenous metabolites can also be characterized (28). Thus in addition to the drug metabolites, hippuric acid was readily identified as well as a rather more unexpected endogenous compound eluting shortly after hippurate, namely phenylacetylglutamine. This compound has been found as a component of human plasma in uremic patients. Here, the urine extract injected onto the column had been made sufficiently concentrated by freeze drying as to make it possible to obtain all the required spectra on-flow, utilizing a linear reversed-phase gradient separation from 0% to 50% acetonitrile over 30 min. The spectra shown in Fig. 5A and B for the hydroxysulfate of 4-bromoaniline were also obtained using an integrated HPLC–NMR–MS system (8). Another typical example of the use of HPLC–NMR–MS for xenobiotic metabolite identification is provided by studies

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on the b-blocker practolol (31). In this instance, the compound was radiolabeled with 14C enabling specific detection by onflow scintillation counting of the chromatographic eluent. Stopped-flow 1H NMR was then performed on the three major radiolabeled peaks detected using the radio-flow cell. The radiochromatogram for this experiment is shown in Fig. 8A, together with HPLC–MS total ion current chromatogram (Fig. 8B). These compounds were identified as the parent compound, the ring-hydroxylated metabolite and its corresponding phenolic glucuronide (Fig. 9A–C). An interesting

Figure 8 (A) The [14C]-detected HPLC-radiochromatogram obtained for a urine sample following administration of radiolabeled practolol to a male rat and (B) the total ion current obtained simultaneously. The MS data identified the peaks as (A) the glucuronide of hydroxypractolol (B), hydroxypractolol, and practolol itself.


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feature of this study was the use of a 13C-label in the N-acetyl group of the practolol. As a result of spin–spin coupling between the 13C and the 1H on the CH3 of the acetyl methyl group, this label provided a characteristic doublet in the 1H NMR spectrum of the drug and its metabolites. The presence of this label allowed an assessment of the degree of de- and reacetylation (so called futile acetylation) that had occurred prior to excretion in the urine showing that some 7–10% of the ‘‘unchanged’’ parent compound had undergone this sort of metabolism. As indicated above, in the schematic showing the typical HPLC–NMR–MS system shown in Fig. 7, the system is configured in such a way as to allow the MS to act as an intelligent detector for the NMR so that it is possible to select a peak for NMR analysis based on its mass. In the case of an unknown endogenous metabolite one can simply allow the LC–NMR–MS control software to detect the mass of interest and stop the flow from the chromatograph when that peak is in the NMR probe. Normal NMR analysis can now be carried out on the sample yielding the spectroscopic data needed to complete the assignment. However, the situation can arise when the unknown signal is seen in the NMR spectrum but nothing is known about its mass. It is therefore necessary to resort to on-flow NMR methods. In on-flow LC–NMR–MS a series of 1D spectra are acquired for 16–32 transients into 2–8 K data points. Total acquisition time for each transient is typically around 1 sec. The data are multiplied by a line-broadening function of 1–3 Hz to improve the signal-to-noise ratio and zero-filled by a factor of 2 before Fourier transformation in the F2 domain only. This results in a contour plot of intensity with 1H or 19 F NMR chemical shift on the horizontal axis and chromatographic retention time on the vertical axis (Fig. 10). If on-flow detection is required during a solvent gradient elution, the NMR resonance positions of the solvent peaks will shift as the solvent proportions change (see solvent resonance in Fig. 12). For effective solvent suppression, it is therefore necessary to determine these solvent resonance frequencies as the chromatographic run proceeds. This is accomplished

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Figure 10 On-flow 19F LC–NMR–MS data displayed as a plot pseudo-2D plot with 19F chemical shift on the horizontal axis and retention time on the vertical axis. Metabolites of interest: the 8-O-glucuronide conjugate (MW 507 Da), a novel hydroxylated cyclopropyl ring, 8-O-sulfate cysteinylglycine di-conjugate (MW 605 Da), and the N-glucuronide (MW 491 Da).

by measuring a single exploratory scan as soon as a chromatographic peak is detected in real time during the chromatographic run and then applying solvent suppression irradiation at these frequencies as the peak elutes. While the data are being collected by the NMR spectrometer, the mass spectrometer is collecting the data on the same chromatographic run so that it is possible to correlate the mass from a specific retention time to the NMR spectrum at that same retention time. The major problem with this approach is the J Figure 9 1H NMR spectra of (A) Practolol, (B), hydroxypractolol, and (C) hydroxypractolol glucuronide obtained by stopped-flow NMR on the [14C]-detected peaks of the radiolabeled practololrelated compounds detected in Fig. 8A.

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Typical time-slice LC–NMR data on a rat bile fraction.

NMR spectrometers low sensitivity compared to the MS. This problem can be overcome by stopping the flow at intervals over a chromatographic peak and collecting NMR data. This technique is referred to as ‘‘time slicing’’. The time-slicing method may also be useful if there is poor chromatographic separation, if the compounds under study have weak or no UV chromophores or if the exact chromatographic retention time is unknown. By time slicing through an entire chromatographic run, one produces the equivalent of a continuous-flow experiment with higher signal-to-noise (Fig. 11) thus overcoming the sensitivity problem to some degree, however, the need to stop the flow for fairly long periods of time causes problems for the mass spectrometer which are often not easily resolved. 5. MINIATURIZATION It is already the case that miniaturized HPLC–MS systems are available where the separation is performed on narrow, micro or even nanobore columns. These systems provide the potential


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for very high resolution, if required, coupled to very modest sample requirements. Other high resolution miniaturized separations based on capillary electrophoresis–MS are already available but as yet have not been applied to metabonomic samples. In the case of NMR, miniaturization is happening with respect to both the separation and the spectrometers detection system [see Ref. 17]. Capillary LC–NMR systems (CapLC–NMR) are based on an NMR probe which has a very small (5 ml) flow cell coupled to a capillary-HPLC. The reduction in RF coil size results in a direct sensitivity enhancement which can be as high as a factor of four over a conventional LC–NMR system when the same mass is in the flow cell. The use of a capillary HPLC, with its low flow rates, decreases the amount of expensive deuterated solvents consumed during the separation and allows the use of deuterated solvents in both the aqueous and organic phases. It is, however, often impossible to achieve the level of concentration in a CapLC–NMR system that one might have in traditional LC–NMR and thus the full enhancement in sensitivity is not always achieved. Additionally, because of the capillary tubing needed to plumb the systems, they are often tedious to use. The primary application of these systems is thus analysis of mass limited samples. It has also been shown that CapLC–NMR excels in obtaining onflow LC–NMR. An example of such data is shown in Fig. 12, the pseudo-2D spectrum obtained following LC–NMR of an SPE concentrated human urine after dosing with phenacetin. Here, most of the major metabolites of phenacetin are detected easily with excellent chromatographic resolution. The introduction of cryogenic NMR flow-probes is a recent development and has significantly advanced the sensitivity of NMR. In these NMR probes, the electronic components are cryogenically cooled to 20 K, while the sample remains at ambient temperature, resulting in a dramatic reduction in the electronic noise. As a result, the signalto-noise ratio for cryoflow-probes is increased on average fourfold over that of conventional probes. This increase in signal-to-noise ratio provides a fourfold increase in sensitivity and yields a fourfold lower detection limit for a given amount

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Figure 12 Typical on-flow 1H LC–NMR data with chemical shift on the horizontal axis and retention time on the vertical axis. These data were obtained using a capillary LC flow-probe and show the metabolites of phenacetin in human urine at low microgram levels.

of sample thus reducing experiment time is reduced by a factor of 16 over that of a conventional probe. These enhancements are ideal for the detection of xenobiotic and endogenous metabolites in biofluids, where the analyte in a sample is often mass limited, NMR experiments of low intrinsic sensitivity are required, experimental time is necessarily short such as in a high-throughput analytical regime or analytes are chemically unstable. An application of cryoflowprobes to the analysis of xenobiotic metabolites in human urines has been described by Spraul et al. (32). In addition, a number of studies have demonstrated the use of microcoils for HPLC–NMR applications such as capillary HPLC with microcoil NMR for the detection of terpenoids. This system had an observation volume of 1.1 mL and enabled the detection of 37 ng of a-pinene (33). The detection of low nanogram amounts of compounds has been shown in 3–4 min under stop-flow conditions.


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In addition to HPLC-based separations capillary electrophoresis (CE), and the related hybrid technique capillary electrochromatography (CEC), coupled to NMR provides a potentially powerful approach, with impressive separation efficiencies. Both CE–NMR and CEC–NMR, at an observation frequency of 600 MHz, have been applied to the identification of acetaminophen metabolites in a SPEC extract of human urine (34,35). This experiment produced results of the type shown in Fig. 13, which shows the on-flow CEC–NMR

Figure 13 The pseudo-2D on-flow results obtained for the CEC– NMR of an SPEC extract obtained from the urine of a human volunteer following the oral administration of a normal therapeutic dose of 600 mg of paracetamol (acetaminophen). Key: A ¼ paracetamol glucuronide, B ¼ paracetamol sulfate, and C ¼ hippuric acid (for spectra see figure 14).

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spectrum obtained for the extract. These data were acquired with eight scans per row (i.e., about 10 sec acquisition time) and the contours seen correspond to the glucuronide and sulfate conjugates of the drug together with signals for the endogenous hippuric acid. Spectra extracted from individual rows for these three substances are shown in Fig. 14. The detection limit for this type of on-flow experiment was in the order of 300 ng of paracetamol glucuronide on column. A major problem with these capillary systems is the limited amount of material that can be applied to the column.

Figure 14 Typical on-flow NMR spectra obtained for the metabolites of paracetamol (acetaminophen) following separation by CEC as shown in Fig. 13. Key: A ¼ paracetamol glucuronide, B ¼ paracetamol sulfate, and C ¼ hippuric acid.


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The use of capillary isotachophoresis (cITP) prior to NMR detection enables sample focusing method, and thus the loading of larger amounts of sample. The technique depends on the use of leading and terminating electrolytes. The leading electrolyte has a high electrophoretic mobility, the sample follows and the terminating electrolyte, which has a low electrophoretic mobility, brings up the rear. The application of an electric field causes the components to separate into discrete bands, with the sample components focused as a function of the ion concentration of the leading electrolyte. Such techniques can result in a 100-fold increase in NMR signal-to-noise ratio when comparing non-focused samples (36).

6. CONCLUSIONS Analytical tools of great power are now available to enable the rapid and efficient identification of both endogenous compounds and drug metabolites detected as part of metabonomic studies. However, as the authors have learned, over many years, no amount of technology can compensate for a poorly designed experiment. Therefore, we recommend a stepwise strategy, whereby the available analytical data are carefully scrutinized to obtain the maximum amount of information on the unknown(s) detected in the sample. This, combined with good experimental design, before proceeding to the next stage, will yield the best results. The greatest confidence in the conclusions derived from studies on the identification of unknowns will be obtained when more than one spectroscopic technique, (e.g., both NMR and MS) support the proposed structure, or comparison with an authentic standard has been made.

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Wilson ID, Nicholson JK, Lindon JC. The role of nuclear magnetic resonance spectroscopy in drug metabolism. In: Woolf TF, ed. Handbook of Drug Metabolism. New York: Marcel Dekker, 1999:523–550.

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2. Plumb R, Granger J, Stumpf C, Wilson ID, Evans JA, Lenz EM. Metabonomic analysis of mouse urine by liquid chromatography—time of flight mass spectrometry (LC-TOFMS): detection of strain, diurnal variation and gender differences. Analyst 2003; 128:819–823. 3. Plumb RS, Stumpf CL, Gorenstein MV, Castro-Perez JM, Dear GJ, Anthony M, Sweatman BC, Connor SC, Haselden JN. Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development. Rapid Commun Mass Spectrom 2002; 16:1991–1996. 4. Lafaye A, Junot C, Gall BR, Fritsch P, Tabet JC, Ezan E. Metabolite profiling in rat urine by liquid chromatography=electrospray ion trap mass spectrometry. Application to the study of heavy metal toxicity. Rapid Commun Mass Spectrom 2003; 17:2541–2549. 5. Idborg-Bjorkman H, Edlund P-O, Kvalheim OM, SchupeKoistinen I, Jacobsson SP. Screening for biomarkers in rat urine using LC=electrospray ionisation-MS and two-way data analysis. Anal Chem 2003; 75:4784–4792. 6. Oliveira EJ, Watson DG. Liquid chromatography–mass spectrometry in the study of the metabolism of drugs and other xenobiotics. Biomed Chromatogr 2000; 14:351–372. 7. Clarke NJ, Ringden D, Kormacher WA, Cox KA. Systematic LC=MS metabolite identification in drug discovery. Anal Chem 2001; 73:430A–439A. 8. Scarfe GB, Nicholson JK, Lindon JC, Wilson ID, Taylor S, Clayton E, Wright B. Identification of the urinary metabolites of 4-bromoaniline and 4-bromo-[carbonyl-13C]acetanilide in rat. Xenobiotica 2002; 32:325–337. 9. Wilson ID, Ismail IM. A rapid method for the isolation and identification of drug metabolites from human urine using solid phase extraction and proton NMR spectroscopy. J Pharm Biomed Anal 1986; 4:663–665. 10.

Wilson ID, Nicholson JK. Solid-phase extraction chromatography and NMR spectroscopy for the identification and isolation of drug metabolites in urine. Anal Chem 1987; 59:2830–2832.


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Wilson ID, Nicholson JK. Solid-phase extraction chromatography and NMR spectroscopy (SPEC–NMR) for the rapid identification of drug metabolites in urine. J Pharm Biomed Anal 1998; 6:151–165.

12.

Ghauri FYK, Mclean AEM, Beales D, Wilson ID, Nicholson JK. Induction of 5-oxoprolinura in the rat following chronic feeding with N-acetyl 4-aminophenol (paracetamol). Biochem Pharmacol 1993; 46:953.

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Ghauri FYK, Parkes HG, Nicholson JK, Wilson ID. Asymptomatic 5-oxoprolinuria detected by proton magnetic resonance spectroscopy. Clin Chem 1993; 30:1341.

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Phipps AN, Wright B, Stewart J, Wilson ID. Use of proton NMR for determining changes is metabolite excretion profiles induced by dietary changes in the rat. Pharm Sci 1997; 3: 143–146.

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Phipps AN, Stewart J, Wright B, Wilson ID. Effect of diet on the urinary excretion of hippuric acid and other dietaryderived aromatics in rat. A complex interaction between diet, gut microflora and substrate specificity. Xenobiotica 1998; 28:527–537.

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Tulip K, Timbrell JA, Nicholson JK, Wilson ID, Troke J. A proton magnetic resonance study of the metabolism of N-methylformamide in the rat. Drug Metab Dispos 1986; 4:663–665.

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Albert K. ed. On-line LC-NMR and Related Techniques. Chichester: Wiley, 2003.

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Lindon JC, Bailey NJC, Nicholson JK, Wilson ID. Biomedical applications of directly coupled-nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry. In: Wilson ID, ed. Handbook of Analytical Separations, Amsterdam: Elsevier. 2003:293–329.

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Shockor J. Application of on-line LC–NMR and related techniques to drug metabolism studies. In: Albert K, ed. On-line LC–NMR and Related Techniques. Chichester: Wiley, 2003: 89–108.

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Spraul M, Hofmann M, Wilson ID, Lenz E, Nicholson JK, Lindon JC. Coupling of HPLC with 19F- and 1H-NMR spectro-

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scopy to investigate the human urinary excretion of flurbiprofen metabolites. J Pharm Biomed Anal 1993; 1:1009–1015. 21.

Griffiths L, Horton R. Optimisation of LC-NMR III—increased signal-to-noise ratio through column trapping. Mag Res Chem 1998; 36:104–109.

22.

Gavaghan CL, Nicholson JK, Connor SC, Wilson ID, Wright B, Holmes E. Directly coupled high performance liquid chromatography and nuclear magnetic resonance spectroscopic with chemometric studies on metabolic variation in Sprague– Dawley rats. Anal Biochem 2001; 291:2245–252.

23.

Lenz EM, Greatbanks D, Wilson ID, Spraul M, Hofmann M, Troke J, Lindon JC, Nicholson JK. Direct characterisation of drug glucuronide isomers in human urine by HPLC–NMR spectroscopy: application to the positional isomers of 6,11dihydro-11-oxodibenz[b,e]oxepin-2-acetic acid glucuronide. Anal Chem 1996; 68:2832–2837.

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8 Multi- and Megavariate Data Analysis: Finding and Using Regularities in Metabonomics Data LENNART ERIKSSON and ERIK JOHANSSON

HENRIK ANTTI and ELAINE HOLMES


Biological Chemistry, Biomedical Sciences Division, Faculty of Medicine, Imperial College of Science Technology and Medicine, South Kensington, London, U.K.

1. INTRODUCTION 1.1. General Considerations Metabolites are the products and byproducts of the many complex biosynthesis and catabolism pathways that exist in humans and other living systems. Measurement of metabolites in human biofluids has often been used for the diagnosis of a number of genetic conditions, diseases, and for assessing 263

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exposure to xenobiotics. Traditional analytical approaches have been limited in scope, in that emphasis was usually placed on changes in the level of one or a few metabolites. For example, urinary creatinine and blood urea nitrogen are commonly used as parameters of renal function. Recent advances in (bio-)analytical separation and detection technologies, combined with the rapid progress in bioinformatics, have made it possible to measure much larger bodies of metabolite data (1). One prime example is the use of NMR-spectroscopy in the monitoring of complex timerelated metabolite profiles that are present in biofluids, such as urine, plasma, saliva, etc. In addition to NMRspectroscopy, there are several other analytical methods, which can produce highly characteristic metabolic signatures of biological samples, including MS, HPLC, GC=MS. All these methods generate large amounts of metabolite data and have been used to characterize biofluids, tissues, or cell cultures (2–4). The ongoing data explosion necessitates the use of appropriate analytical tools for extracting meaningful information from the large amounts of raw data. It is no longer efficient to analyze data by simply looking at them or by plotting them in simple graphs. More sophisticated, computer-based methods are needed if the data analysis is to be accomplished within a reasonable time. In this chapter, we shall study methods for extracting information from large tables of data. This is called multivariate data analysis, or MVDA for short and is particularly appropriate for mining and interpreting metabonomic, genomic, and proteomic data sets. More specifically, this chapter will focus on two multivariate projection methods which are useful: principal component analysis (PCA) (5) and partial least squares projections to latent structures (PLS) (6). 1.2. Pattern Recognition In the very many varied engineering, mathematical, and applied professions, the term pattern recognition (PARC) is often used in connection with MVDA to indicate how multivariate data analysis finds the typical ‘‘data pattern’’ for one

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or several classes of observations (e.g., type of rat, type of toxicity, etc.) (7). The ‘‘pattern’’ of one class represents information about the relations between the observations within the class: discerning which are similar, which are diverse, and which are atypical outliers. Information is also obtained about similarities and dissimilarities among the variables (descriptors, in this case spectral integrals). If the ‘‘patterns’’ between classes are different, they can be utilized to assign new observations to the classes, i.e., to classify new observations on the basis of the degree of similarity between their data and the manifested ‘‘class patterns.’’ The PARC can be generalized to the problem of finding patterns that express relations between blocks of variables measured on the same set of observations (or samples). In the simplest case there are two variable blocks, X and Y. This is also a generalization of regression and correlation. With various combinations of classification, discrimination, and block-relations, most questions put to a data table can be adequately addressed. Thus, PARC is an empirical but general approach to the analysis of multivariate data, empirical in the sense that few fundamental assumptions or models are needed to perform the analysis. One of the first methods of analyzing multivariate 1H NMR biofluid spectra was simple cluster analyses, such as hierarchical cluster analysis (HCA). 1.3. Projection Methods This article describes a remarkably simple approach to multivariate analysis based on so-called projection methods. This approach represents the observations (here: NMR-spectra of rats) as a swarm of points in a K-dimensional space (K ¼ number of variables), and then projects the point swarm down onto a lower-dimensional plane or hyperplane. The co-ordinates of the points on this hyperplane provide a compact representation of the observations, and the direction vectors of the hyperplane provide a corresponding representation of the variables. The projection approach can be adapted to a variety of data-analytical objectives, i.e., (i) summarizing and visualizing

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a data set, (ii) multivariate classification and discriminant analysis, and (iii) finding quantitative relationships among blocks of variables. This applies to any shape of multivariate data, with many or few variables, many or few observations, and complete or incomplete (containing missing entries) data tables. In particular, projections handle data matrices with more variables than observations very well, and can also accommodate noisy or highly collinear data. Spectroscopy, gene arrays, and two-dimensional proteomic gels are all methods that tend to generate variable-heavy data sets. With small modifications, projection methods can be made robust to outliers, deal with nonlinear relationships, and adapt to drift in multivariate process data. 1.4. Transition from Multi- to Megavariate Data Analysis The analysis of large data tables containing several measurements on the same sample is often called multivariate data analysis (MVDA). Traditionally, multivariate data analysis has implied the use of methods like multiple linear regression (MLR), linear discriminant analysis (LDA), canonical correlation (CC), factor analysis (FA), and principal component analysis (PCA) applied to independent variables, (i.e., variables that are totally independent and no underlying latent correlations between variables exist). In chemometrics, bioinformatics, metabonomics practice, however, we often assume that our systems are driven by inherent, latent, variables (e.g., metabolic pathways), which are few compared with the number of observed variables, K. Methods used here are PCA for overview, soft-independent modeling of class analogy (SIMCA) and PLS-DA for classification, and PLS and principal component regression (PCR) for latent variable regression. The latent variable models are philosophically different in objectives and formulation from the traditional multivariate models with independent variables. To distinguish between these two types of situations (with related data,


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Figure 1 Notation used in MVDA. The observations (rows) can be analytical samples, biological individuals (e.g., rats), chemical compounds or reactions, process time points of a continuous process, batches from a batch process, trials of a design-of-experiments (DOE) protocol, and so on. The variables (columns) might be of spectral origin, of chromatographic origin, or be measurements from sensors and instruments in a process.

models, and data-analytical methods), we have started to refer to the latter as megavariate. Megavariate data analysis models data in terms of multiple latent variables, to give results that are multivariate (8). This is a new nomenclature distinguishing between the situation where X is full rank and the more common megavariate situation where X has a much lower rank than both the number of variables (K) and the number of observations (N) as illustrated in Fig. 1.

2 DATA-ANALYTICAL METHODS 2.1. Pretreatment of Data Prior to MVDA, data are often pretreated, in order to transform the data into a form suitable for analysis, but also to reshape the data such that important assumptions are better fulfilled. In fact, preprocessing can make the difference between a useful model and no model at all (8). In this section, we will introduce different ways of scaling the data. A more general discussion on pretreatment of data is given in Sec. 5.2.

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2.2. Centering and Scaling 2.2.1. Rationale Behind Scaling Variables (here: chemical shift region integrals) often have substantially different numerical ranges. A variable with a large range has a large variance, whereas a variable with a small range has a small variance. Since, for instance, PCA is a maximum variance projection method, it follows that a variable with a large variance is more likely to be expressed in the modeling than a low-variance variable. The PLS is also sensitive to the choice of scaling. A simple example will further illustrate the concept of scaling. In connection with a preseason friendly game of football (soccer), the trainers of both teams decided to measure the body weight (in kg) of their players. The trainers also recorded the body height (in m) of each player. These data are plotted in two ways in Fig. 2(a) and (b). When the two variables are plotted in a scatter plot where each axis has the same scale—the x–and y-axes both extend over 30 units—we can see that the data points spread only in the vertical direction [Fig. (2a)]. This is because body weight has a much larger numerical range than body height. Should we analyze these data with PCA, without any preprocessing, the results would only reflect the variation in body weight. Actually, this data set contains an atypical observation (individual). This is much easier to see when the two variables are more appropriately scaled [Fig. (2b)]. Here, we have compressed the variation along the body weight axis and zoomed in on body height. There is a strong correlation between body height and body weight, except for one outlier I Figure 2 (a) Scatter plot of body weight vs. body height of 23 individuals. The data pattern is dominated by the influence of body weight. The two variables have been given the same scale. (b) Scatter plot of body weight against body height of 23 individuals. Now, the variables are given equal importance by displaying them according to the same spread. An outlier, a deviating individual, the referee of the game, is now discernible.


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in the data. This was impossible to see in the previous plot when body weight dominated over body height. We have, therefore, scaled the data such that both variables make the same contribution to the model. In order to give both variables, body weight and body height, equal weight in the data analysis, we standardized them. Such a standardization is also known as ‘‘scaling’’ or ‘‘weighting,’’ and means that the length of each co-ordinate axis in the variable space is regulated according to a predetermined criterion. The first time a data set is analyzed it is often a good choice to set the length of each variable axis to equal length. 2.2.2. Unit-Variance-Scaling There are many ways to scale the data, but the most common technique is the unit-variance (UV) scaling. For each variable (here: NMR region integrals), one calculates the standard deviation (sk) and obtains the scaling weight as the inverse standard deviation ð1=sk Þ. Subsequently, each column (variable) of X (i.e., the matrix of NMR-data) is multiplied by 1=sk. Each scaled variable then has equal (unit) variance. A simple geometrical understanding of UV-scaling is based on the equivalence between the length of a vector and its standard deviation (square root of variance) (8). Hence, the initial variance of a variable is interpretable as the squared ‘‘size’’ or ‘‘length’’ of that variable. This means that with UV-scaling we accomplish a shrinking of ‘‘long’’ variables and a stretching of ‘‘short’’ ones. By putting all variables on a comparable footing, no variable is allowed to dominate over another because of its length. One example of the value of UV-scaling can be seen in the analysis of NMR-spectra of urine obtained from rats treated with certain liver toxins. The excretion of particular patterns of bile acids can be highly diagnostic of cholestatic liver damage. However, without applying UV-scaling to the data, metabolites such as citrate, 2-oxoglutarate, and glucose will dominate the analysis because they are present in much greater concentrations than bile acids but carry less diagnostic information.


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Like any projection method, PCA and PLS are sensitive to scaling. This means that by modifying the variance of the variables, it is possible to attribute different importance to them. This gives the possibility of down-weighting irrelevant or noisy variables. However, one must not overlook the risk of scaling subjectively to give you the model you want. Generally, UV-scaling is the most objective approach, and is recommended if there is no prior information about the data. Sometimes no scaling at all would be appropriate, especially with data where all the variables are expressed in the same unit, for instance, with spectroscopic data. Later on, when more experience has been gained, more elaborate scaling procedures may be used. 2.2.3. Mean-Centering Mean-centering is the second part of the standard procedure for preprocessing. With mean-centering, the average value of each variable is calculated and then subtracted from the data. This improves the interpretability of the model and may also in certain cases remove some numerical instability. The mean-centering and UV-scaling procedures are often applied by default in commercial software, and the joint name ‘‘auto-scaling’’ is frequent (8). Note, however, that in some cases, such as multivariate calibration and classification based on spectral data, it is not necessarily advantageous to use this combination of preprocessing tools, and some other choice might be more appropriate (see further discussion below). 2.2.4. No Scaling and Pareto Scaling Sometimes, no scaling (but mean-centering) is the desired method for ‘‘scaling’’ the data. Usually, this option is deployed when all variables are expressed in the same unit, such as with spectroscopic data (8). Moreover, in recent years an alternative technique called Pareto scaling has become more common (9). Pareto scaling gives each variable a variance numerically equal to its initial standard deviation instead of unit variance. Here, the scaling

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p weight is 1= sk. Hence, Pareto scaling is intermediate between the extremes of no scaling and UV-scaling (8,9). 2.3. Principal Component Analysis 2.3.1. Introduction to PCA Principal component analysis (PCA) forms the basis for multivariate data analysis (7,8,10,11). As shown by Fig. 1, the starting point for PCA is a matrix of data with N rows (observations) and K columns (variables), here denoted by X. The PCA is used here in both examples. In the first example (Sec. 3), the rows are the NMR-spectra, and the variables are the chemical shift region integrals. When PCA is utilized in the second example (Sec. 4), the rows are the rats and the columns are the time points at which spectral measurements were carried out. Generally, the observations (rows) can be analytical samples, biological individuals (e.g., rats), chemical compounds or reactions, process time points of a continuous process, batches from a batch process, trials of a DOE protocol, and so on. In order to characterize the properties of the observations, one measures variables. These variables may be of spectral origin (NIR, NMR, MS, IR, UV, X-ray, . . . ), chromatographic origin (HPLC, GC, TLC, . . . ), or they may be measurements from sensors in a process (temperatures, flows, pressures, curves, etc.). PCA goes back to Cauchy, but was first formulated in statistics by Pearson, who described the analysis as finding lines and planes of closest fit to systems of points in space (see Ref. 5 for a historical account of PCA). The most important use of PCA is indeed to represent a multivariate data table as a low-dimensional plane, usually consisting of 2–5 dimensions, such that an overview of the data is obtained. This overview may reveal groups of observations, trends, and outliers. This overview also uncovers the relationships between observations and variables, and among the variables themselves. Operationally, PCA finds lines, planes, and hyperplanes in the K-dimensional space that approximate the data as well as possible in the least squares sense. It is easy to see that a


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Figure 3 The PCA derives a model that fits the data as well as possible in the least squares sense. Alternatively, PCA may be understood as maximizing the variance of the projection co-ordinates.

line or a plane that is the least squares approximation of a set of data points makes the variance of the co-ordinates on the line or plane as large as possible (Fig. 3). We will now explain how PCA works: initially, using a geometrical approach, followed by a more formal algebraic account. 2.3.2. Setting up K-dimensional space Consider a matrix X with N observations (e.g., NMR-spectra of rats) and K variables (e.g., chemical shift region integrals). For this matrix, we construct a variable space with as many dimensions as there are variables (the axes in Fig. 4). Each variable represents one co-ordinate axis. For each variable,

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Figure 4 (a) K-dimensional variable space. For simplicity, only three variable axes are displayed. The ‘‘length’’ of each co-ordinate axis has been standardized according to a specific criterion, usually unit-variance-scaling. The observations (rows) in the data matrix X can be understood as a swarm of points in the variable space (Kspace). (b) In the mean-centering procedure one first computes the variable averages. This vector of averages is interpretable as a point (here: in dark gray) in space. This point is situated in the middle of the point swarm (at the center of gravity). The mean-centering procedure corresponds to moving the origin of the co-ordinate system to coincide with the average point.


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the length has been standardized according to a scaling criterion, normally by scaling to unit variance. 2.3.3. Plotting the Observations in K-dimensional Space In the next step, each observation (each row) of the X-matrix is placed in the K-dimensional variable space. Consequently, the rows in the data table form a swarm of points in this space [Fig. 4(a)]. 2.3.4. The Effect of Mean-Centering The mean-centering involves the subtraction of the variable averages from the data. This vector of averages corresponds to a point in the K-space. The subtraction of the averages from the data corresponds to a re-positioning of the co-ordinate system, such that the average point now is the origin [Fig.4(b)]. 2.3.5. The First Principal Component After mean-centering and scaling to unit variance, the data set is ready for the computation of the first principal component (PC1). This component is the line in the K-dimensional space that best approximates the data in the least squares sense. This line goes through the average point (Fig. 5). Each observation may now be projected onto this line in order to get a co-ordinate value along the PC-line. This new co-ordinate value is known as a score. 2.3.6. Extending the Model with the Second Principal Component Usually, one principal component is insufficient to model the systematic variation of a data set. Thus, a second principal component, PC2, is calculated. The second PC is also represented by a line in the K-dimensional variable space, which is orthogonal to the first PC (Fig. 5). This line also passes through the average point, and improves the approximation of the X-data as much as possible.

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Figure 5 The first principal component, PC1, is the line which best accounts for the shape of the point swarm. It represents the maximum variance direction in the data. Each observation may be projected onto this line in order to get a co-ordinate value along the PC-line. This value is known as a score. The second principal component, PC2, is oriented such that it reflects the second largest source of variation in the data, while being orthogonal to the first PC. PC2 also passes through the average point.

2.3.7. Two Principal Components Define a Model Plane When two principal components have been derived, they together define a plane, a window into the K-dimensional variable space [Fig. 6(a)]. By projecting all the observations onto this low-dimensional subspace and plotting the results, it is possible to visualize the structure of the investigated data set. The co-ordinate values of the observations on this plane are called scores, and hence the plotting of such a projected configuration is known as a score plot. The score plot will show the similarities and dissimilarities between the observations (e.g., NMR-spectra of rats).


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Figure 6 (a) Two PCs form a plane. This plane is a window into the multidimensional space, which can be visualized graphically. Each observation may be projected onto this giving a score for each of the calculated dimensions (PC1, PC2). (b) The principal component loadings uncover how the PC-model plane is inserted in the variable space. The loading is described by the angle (a) between each variable and the principal component. Hence, for PC1 in a three-dimensional space, the loadings described are a1, a2, a3 and these are used for interpreting the meaning of the scores.

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2.3.8. The Loadings Show the Orientation of the Plane In a PC-model with two components, that is, a plane in K-space, we wonder which variables (e.g., chemical shift regions) are responsible for the patterns seen among the observations (e.g., NMR-spectra of rats). We would like to know which variables are influential, and also how the variables are correlated. Such knowledge is given by the principal component loadings. These loading vectors are called p1 and p2 (see further discussion in Sec. 2.3.10). Geometrically, the principal component loadings express the orientation of the model plane in the K-dimensional variable space [Fig. 6(b)]. The direction of PC1 in relation to the original variables is given by the cosine of the angles a1, a2, and a3. These values indicate how the original variables x1, x2, and x3 ‘‘load’’ into (¼contribute to) PC1. Hence, they are referred to as loadings. Of course, a second set of loading coefficients expresses the direction of PC2 in relation to the original variables. Hence, with two PCs and three original variables, six loading values (cosine of angles) are needed to specify how the model plane is positioned in the K-space. 2.3.9. Extensions to Higher-order Components Frequently, one or two principal components are not enough to adequately summarize the information in a data set. In such cases, the descriptive ability of the PC-model improves by using more principal components. There are several approaches that can be used to evaluate how many principal components are appropriate (5,12). One of these, cross-validation (CV), is discussed below (see Sec. 2.3.11.3.). 2.3.10. Summary of PCA By using PCA, a data table X is modeled as 0 þ T P0 þ E X ¼1x

ð1Þ

In the expression above, the first term, 1x¯0 , represents the variable averages and originates from the preprocessing


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step. The second term, the matrix product T P0 , models the structure, and the third term, the residual matrix E, contains the noise. The principal component scores of the first, second, third, . . . , components (t1,t2,t3, . . . ) are columns of the score matrix T. These scores are the co-ordinates of the observations in the model (hyper-)plane. Alternatively, these scores may be seen as new variables which summarize the old ones. In their derivation, the scores are sorted in descending importance (t1 explains more variation than t2, t2 explains more variation than t3, and so on). Typically, 2–5 principal components are sufficient to approximate a data table well. The meaning of the scores is given by the loadings. The loadings of the first, second, third, . . . , components (p1,p2,p3, . . . ) build up the loading matrix P. The loadings define the orientation of the PC plane with respect to the original X-variables. Algebraically, the loadings inform how the variables are linearly combined to form the scores. The loadings unravel the magnitude (large or small correlation) and the manner (positive or negative correlation) in which the measured variables contribute to the scores. 2.3.11. Additional PCA Diagnostics 2.3.11.1. Observation Diagnostics—Strong and Moderate Outliers PCA discovers strong outliers and moderate outliers. Conceptually, outliers are observations that are extreme or that do not fit the PCA-model. Outliers are both serious and interesting, but easy to detect. Strong outliers are found in plots of PC-scores and moderate outliers are found by inspecting the model residuals (13). By the term residuals we mean the Xvariation that was not captured by the PC-model, i.e., the variation which constitutes the matrix E in Eq. (1). Strong outliers are found in the scores. They have high leverage on the model, i.e., strong ‘‘power’’ to pull the PC-model toward themselves, and may ‘‘consume’’ one PC just because of their existence. The term leverage derives from the Archimedean principle that anything can be lifted out of balance as long as the lifter has a long enough lever. Leverage is a measure of

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the influence of an observation and is proportional to the distance of the observation from the center of the data. A diagnostic tool showing strong outliers is given by Hotelling’s T2 (5,8,13). This statistic is a multivariate generalization of Student’s t-test, and provides a check for observations adhering to multivariate normality. A definition of Hotelling’s T2 is given in Ref. 8. A data set may also contain moderate outliers, which are not powerful enough to shift the model plane and hence show up as outliers in a score plot. Moderate outliers are identified by the residuals of each observation. We here call the detection tool for moderate outliers DModX, a short-hand notation for distance to the model in X-space (8). DModX is based on considering the elements of the residual matrix E and summarizing these row by row. A value for DModX can be calculated for each observation. These values can be plotted in a control chart where the maximum tolerable distance (Dcrit) for the data set is given. Moderate outliers have DModX values larger than Dcrit. With process data, moderate outliers often correspond to temporary process upsets, but occasionally more persistent trends or shifts can be diagnosed. Finding outliers in metabonomics data implies that some NMR-spectra are different from the majority of spectra. The most common reason behind outliers is variation in the experimental conditions. Outliers can also be due to varying handling of the animals, and errors made during data transfer from one electronic device to another. However, mechanistically seen, the most interesting outliers are those which are related to unique metabolic profiles. For such animals, MVDA can pinpoint which chemical shift regions reflect their unique metabolic profile. 2.3.11.2. Variable Diagnostics—Which Variables are Well Explained? Apart from pooling the elements of the E-matrix row-wise, these elements may also be summarized column-wise to produce diagnostics related to the variables (here: chemical shift regions). One such diagnostic tool is called the explained


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variation of a variable, a quantity which ranges from 0 (no explanation) to 1 (complete explanation). It tells us the extent to which each variable is accounted for by the model. This is usually reported as the explained variation (R2) or explained variance (R2adj). The explained variance is simply the explained variation adjusted for the degrees of freedom (DF). The values of Rk2 are related to the loadings. For each com2 is proportional to how much the kth variable ponent, a, pak is modeled by this component. A more thorough description of these parameters is found in Ref. 8. Also observe that it is possible to calculate R2- and R2adj-values pertaining to the complete X-matrix, not just to the individual variables. 2.3.11.3. Model Diagnostics—How Many Principal Components are Really Needed? An important question is how many components should be included in the model? This question is linked to the difference between the degree of fit and the predictive ability. The fit tells how well we are able to mathematically reproduce the data of the training set. A quantitative measure of the goodness of fit is given by the parameter R2 (¼the explained variation). The problem with the goodness of fit is that with sufficiently many free parameters in the model, R2 can be made arbitrarily close to the maximal value of one (1.0). More important than fit, however, is the predictive ability of a model. This can be estimated by how accurately we can predict the X-data, either internally via existing data or externally through the use of an independent validation set of observations. The predictive power of a model is summarized by the goodness of prediction parameter Q2 (¼the predicted variation). Here, we use CV to estimate the predictive ability of the model with increasing number of components (see next section). The R2- and Q2-parameters display entirely different behavior as the model complexity increases (Fig. 7). The goodness of fit, R2, varies between 0 and 1, where 1 means a perfectly fitting model and 0 no fit at all. R2 is inflationary and approaches unity as model complexity (number of model

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Figure 7 The trade-off between the goodness of fit, R2, and the goodness of prediction, Q2. The vertical axis corresponds to the amount of explained or predicted variation, and the horizontal axis depicts the model complexity (number of terms, number of latent variables, etc.). At a certain model complexity, one gets the model with optimal balance between fit and predictive ability.

parameters, number of components, . . . ) increases. Hence, it is not sufficient to have a high R2. The goodness of prediction, Q2, on the other hand, is less inflationary and will not automatically come close to 1 with increasing model complexity. This is provided that Q2 is correctly estimated. 2.3.11.4. Cross-validation The approach to finding the optimal model dimensionality advocated throughout this chapter is called CV (12). Crossvalidation (CV) is a practical and reliable way to test the significance of a PC- or a PLS-model. This procedure has become standard in multivariate data analysis, and is incorporated in one form or another in most commercial software. However, CV is implemented differently in different packages, which may cause some confusion when comparing models developed by different packages. With CV, the basic idea is to keep a portion of the data out of the model development, develop a number of parallel models from the reduced data, predict the omitted data by


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the different models, and finally compare the predicted values with the actual ones. The squared differences between predicted and observed values are summed to form the predictive residual sum of squares (PRESS), which is a measure of the predictive power of the tested model. PRESS is computed as X ð2Þ PRESS ¼ ðxik xîk Þ2 In this work, CV is conducted for each consecutive model dimension starting with A ¼ 0. For each additional dimension, CV gives a PRESS, which is compared with the residual sum of squares (RSS) of the previous dimension. When PRESS is not significantly smaller than RSS, the tested dimension is considered insignificant and the model building is stopped. Normally, the performance a PC-model is evaluated by simultaneously considering the explained variation R2 (goodness of fit) and the predicted variation Q2 (goodness of prediction). As shown by Eqs. (3) and (4), these two statistics resemble each other: R2 ¼ 1 RSS=SSXtot:corr:

ð3Þ

Q2 ¼ 1 PRESS=SSXtot:corr:

ð4Þ

and they are both dimensionless. In the expressions above, SSXtot.corr. represents the total variation in the X-matrix after mean-centering. In the evaluation of the parameters R2 and Q2, there are a few noteworthy facts. The first is that without a high R2, it is impossible to get a high Q2. Generally, a Q2 > 0.5 is regarded as good and a Q2 > 0.9 as excellent, but these guidelines are of course heavily application dependent. Finally, the difference between R2 and Q2 must not be too large, and preferably not exceeding 0.2 – 0.3. 2.4. Partial Least Squares Projections to Latent Structures, PLS 2.4.1. Introduction to PLS PLS is a method for relating two data matrices, X and Y, to each other by a linear multivariate model (4,14–16).

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PLS is used here in both metabonomic examples, given at the end of this chapter. In the first example (Sec. 3), the rows are the NMR-spectra, and the X-variables are the chemical shift region integrals. The Y-matrix is an artificial matrix describing class membership of rats. In the second example, PLS is used to relate NMR-spectra (X) to urine sample times (Y), where each row corresponds to a spectrum (Sec. 4). PLS stands for projections to latent structures by means of partial least squares. It derives its usefulness from its ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. For parameters related to the observations (individuals, samples, compounds, objects, items), the precision of a PLS-model improves with the increasing number of relevant X- and Y-variables. This corresponds to the intuition of most experimentalists that many variables provide more information about the observations than just a few variables do. PLS can be seen as a particular regression technique for modeling the association between X and Y, but it can also be seen as a philosophy of how to deal with complicated and approximate relationships. 2.4.2. Preprocessing of Data As in any data-analytical application, data are usually preprocessed prior to using PLS. The PLS-modeling works best when the data are fairly symmetrically distributed and have a fairly constant ‘‘error variance.’’ Hence, variables that vary more than ten-fold are often logarithmically transformed before the analysis. In addition, data are usually centered and scaled to unit variance before the analysis. This is because in PLS any given variable will have an influence on the model parameters which increases with the variance of the variable. Scaling all variables to unit variance corresponds to the assumption that all variables are a priori equally important. Note, however, that spectral data are a special case where the combination Pareto scaling and mean-centering, or just mean-centering, is often employed (8).


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If a priori knowledge about the relative importance of variables (X or Y) is available, this should be used to scale the variables accordingly, giving important variables a slightly higher scaling weight than that corresponding to unit-variance-scaling; analogously, unimportant variables are given a slightly lower scaling weight. 2.4.3. Setting up the X- and Y-spaces In order to illustrate how PLS operates, we will consider a case in which there are three X-variables (K ¼ 3) and three Y-variables (M ¼ 3). For each matrix, X and Y, we construct a space with K and M dimensions, respectively. In these two spaces, each X- and Y-variable represents a co-ordinate axis with a length defined by its scaling, usually unit variance. Every observation in a data set may be understood as one point in the X-space and another point in the Y-space. Thus, with many observations, point-swarms with many members are formed in the X- and Y-spaces, as illustrated in Fig. 8.

Figure 8 A regression situation with K ¼ 3 X-variables and M ¼ 3 Y-variables. The length of each co-ordinate axis has been standardized by scaling to unit variance. The mean-centering procedure implies that the origins of the two co-ordinate systems will coincide with the average point (dark gray) in each cloud of points. Each observation is represented by one point in the X-space and another point in the Y-space.

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This figure displays the mean-centered data, and the origins of the two co-ordinate systems coincide with the average points of the data swarms. The two point-swarms have elongated shapes indicating the correlated distribution of points in each cluster. We wish to obtain a good description of these two point-swarms, and an understanding of the association between them. In other words, we would like to know whether there exists a relationship between the positioning of points in the predictor (X) space and the positioning of points in the response (Y) space. This can be elucidated by PLS-analysis. 2.4.4. Calculating the First PLS-Component The first PLS-component is a line in the X-space and another line in the Y-space (Fig. 9). These two lines are calculated such that they (i) well approximate the point-swarms in X and Y, and (ii) provide a good correlation between the positions of points along these lines in X and Y. The two lines

Figure 9 The first component of a PLS-model may be interpreted as two lines, one inserted in the X-space and the other in the Y-space. The orientation of these lines is regulated by the requirement that they should (i) well approximate the shapes of the two point-swarms and (ii) the scores t1 and u1 be maximally correlated. The observations projected onto the two lines give the projection coordinates (the ‘‘scores’’) t1 (for X) and u1 (for Y).


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intersect with the average points. By projecting the observations onto the two lines one obtains the scores t1 and u1, for X and Y, respectively (Fig. 9). The correlation between X and Y, in terms of the two score vectors t1 and u1, may be displayed in a scatter plot. The two score vectors are connected through the inner relation ui1 ¼ ti1 þ hi, where hi is a residual (Fig. 10). The slope of the dotted line in Fig. 20 is 1.0, and when there is perfect matching between the X- and the Y-data all the points are located on this diagonal. Conversely, when there is a weak correlation structure between X and Y, there is a considerable spread of points around the dotted line. The t1=u1 score plot in Fig. 10 is a visualization of the correlation structure between X and Y. In this score plot one can see outliers in the X-data, outliers in the Y-data, and outliers in

Figure 10 The projection co-ordinates, t1 and u1, in the two spaces, X and Y, are connected and correlated through the inner relation ui1 ¼ ti1 þ hi (hi is a residual). The slope of the dotted line is one (1).

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the relation between X and Y. Furthermore, when there are non-linearities between the predictors and the responses, these may also be detected by a curved relation between t1 and u1. 2.4.5. Adding the Second Component The second PLS-component may also be represented by two lines, one in each space, which pass through the average points (Fig. 11). In the X-space, this second line is orthogonal to the first one, whereas in the Y-space this may not necessarily be the case. These lines improve the approximation of, and correlation between, the positions of the X- and Y-planes as much as possible. Geometrically, a two-component PLS-model can be interpreted as planes in the X- and Y-spaces. By projecting the observations onto these planes, the PLS-scores t1 and t2 in X and u1 and u2 in Y are obtained (Fig. 12). Analogously to the first score vector pair (t1=u1), a plot of the second set of score vectors, t2 and u2, also visualizes the correlation structure (Fig. 13). Normally, the score vectors of the second component correlate less well than the first pair of latent variables. In fact, this is logical, as the first PLS-component captures the strongest source of variation in the data,

Figure 11 The second PLS component can be represented by two lines, one in each variable space. These lines improve the description and correlation of X and Y as much as possible.


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Figure 12 Two PLS components correspond to the insertion of model planes in the X- and Y-spaces. Upon projecting the observations onto these planes, the PLS-score vectors of the first model dimension, t1 and u1, and the second model dimension, t2 and u2, are generated.

i.e., the strongest ‘‘signal.’’ After the removal of the variation accounted for by the first component, weaker ‘‘signals’’ remain in the data and therefore the correlation between X and Y (in terms of t2 and u2) is usually weaker and less distinct.

Figure 13 The second pair of score vectors, t2 and u2, correlates, but usually less well than the first pair of score vectors (t1 and u1). This is indicated by the broader ‘‘correlation band’’ around the second component.

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2.4.6. Which Original Variables Contribute to the Formation of the Model Planes? Once a PLS-model has been established, it is of interest to make an interpretation of its meaning. This may be accomplished by considering the variable-related PLS-model parameters called weights. The weights for the X- and the Y-variables, which are denoted w and c, respectively, may be plotted together in the same plot. These weights are interpreted in much the same way as the PCA loadings (see Secs. 2.3.8 and 2.3.9), and show which variables contribute to the PLS-model, and which are not modeled at all. In principle, this means that the PLS weights reflect the relationships among all variables at the same time, and tell which are associated and which contribute unique information. Thus, with PLS, one obtains information on what X gives Y, or, how to ‘‘set’’ X to get a desired Y. This implies that in, for example, metabonomics modeling, it is possible to understand how the urinary profile has changed, and from thereon understand which organ(s) and physiological process(es) are involved. 2.4.7. Higher-order Components It is possible to include more than two components in a PLS-model. When this is done, we are no longer fitting two-dimensional planes in the X- and Y-spaces, but rather hyperplanes of three, four, . . . , dimensions. Conceptually, such hyperplanes are no different from uni-dimensional lines or two-dimensional planes, and the principles of projecting observations onto these hyperplanes and reading off the new co-ordinate values (the scores) are preserved. One may ask how many PLS-components are really necessary? One way to address this topic is through CV (see below). Another is plotting of successive pairs of latent variables. Not only will such plots give a good appreciation of the correlation structure, but they will also aid in determining the appropriate model complexity.


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2.4.8. Summary of PLS-Projections PLS-modeling of the relationship between two blocks of variables can be described in different ways. Perhaps the most straightforward way is that PLS fits two ‘‘PCA-like’’ models at the same time, one for X and one for Y, and simultaneously aligns these models. The objectives are (a) to model X and Y, and (b) to predict Y from X, according to: X ¼ 1 x0 þ TP0 þ E

ð5Þ

y0 þ TC0 þ G; Y ¼ 1 y0 þ UC0 þ F ð¼ 1 due to inner relation)

ð6Þ

In these expressions, the first terms, 1x¯0 and 1y¯0 , respresent the variable averages and originate from the preprocessing step. The information related to the observations are stored in the score matrices T and U; the information related to the variables are stored in the X-loading matrix P0 and the X-weight and Y-weight matrices W0 and C0 . The variation in the data that was left out of the modeling form the E and F residual matrices. The difference between PCA and PLS is that the former is a maximum variance least squares projection of X, whereas the latter is a maximum covariance model of the relationship between X and Y. A detailed account of PLS is given in Ref. 8. The X-weight matrix W contains the X-weight vectors wa, which show how the X-variables are linearly combined to form the score vectors ta. Hence, we understand which original variables dominate the new, latent variable ta. Xvariables that are highly correlated with the Y-variables get high weights. Similarly, the Y-weights ca inform us how the Y-variables are summarized by the score vector ua. In addition, one should observe that there are two versions of the X-weights, one denoted wa and the other wa. The w values relate directly to the X-matrix, whereas the w values refer to the residuals calculated in the previous dimension, Ea1, instead of the X-variables themselves. In summary, PLS forms ‘‘new x-variables’’, ta, as linear combinations of the old ones, and thereafter uses these new

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t’s as predictors of Y. Only as many t’s (components) are formed as are predictively significant (estimated via crossvalidation). For each component (a), the parameters, ta, ua, wa (and wa), pa, and ca are calculated by the PLS-algorithm. For the interpretation of the PLS-model, the scores, t and u, contain information about the observations (here: NMR-spectra of rats) and their similarities=dissimilarities with respect to the given problem and model. The weights w and c give information about how the variables (here: chemical shift regions) combine to form the quantitative relation between X and Y. Hence, these weights are essential for the understanding of which X-variables are important (numerically large w -values), which X-variables provide the same information (similar profiles of wa-values), the interpretation of the scores, t, etc. 2.4.9. PLS-Model Interpretation PLS provides many diagnostics which help in the model interpretation, and in the assessment of model performance and relevance. Foregoing sections have concerned the PLS-scores ta and ua, what they mean and how they can be used. In the current section, we consider the variable-related parameters, notably weights, coefficients, and VIP. VIP is an acronym for variable influence on projection. 2.4.9.1. PLS-Weights The PLS-weights w c give information about how the X-variables combine to form the scores t, the basis of the quantitative relation between X and Y. For a given PLS-model, one vector of X-weights wa and one vector of Y-weights ca are obtained for each model component (a). The PLS-weights can be plotted in scatter, line, or column plots. Moreover, it is possible to plot the X-weights (w ) alone, the Y-weights (c) alone, or both types of weights (w c) in the same graph. The line plot representation is prevalent in spectroscopic applications, since it displays the ‘‘peak-like’’ spectral structure modeled by each component.


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2.4.9.2. PLS-Regression Coefficients A PLS-solution given in the latent variable framework (with scores, weights, etc.) may be re-expressed as a regression model consisting of PLS-regression coefficients, BPLS, according to Y ¼ 1 y0 þ XBPLS þ F

ð7Þ

The relationship between the PLS-regression coefficients and the PLS-weights is given by: BPLS ¼ WðP0 WÞ1 ¼ W C

ð8Þ

The PLS-coefficients are of interest because they simplify the model interpretation when there are several components (>4–5) in the model. Their advantage is that the analyst obtains only one vector of concise model information per response, rather than several vectors of weights. The disadvantage of the coefficients is that information regarding the correlation structure among the responses is lost. This information is preserved by the PLS-weights. 2.4.9.3. The Variable Influence on Projection, VIP, Parameter Interpreting a PLS-model with many components and a multitude of responses can be a complex task. A parameter which summarizes the importance of the X-variables, both for the Xand Y-models, developed by Wold et al. in 1993 (15), is called the variable influence on projection, VIP. The details of VIP are given in Refs. 8,15. For the moment, it suffices to state that VIP is a weighted sum of squares of the PLS-weights, w, taking into account the amount of explained Y-variance in each dimension. Its attraction lies in its intrinsic parsimony; for a given model and problem there will always be only one VIP-vector. 2.4.10. Additional PLS-Diagnostics PLS offers a number of useful model parameters and diagnostic tools. Many of these tools are similar to those of PCA.

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2.4.10.1. Strong and Moderate Outliers Outliers may be either strong or moderate. In PLS (and PCA), the former are found by inspecting the scores, and the latter by looking at the residuals. An observation may be an outlier in X, in Y, and=or in the relationship between X and Y. Moderate outliers are seen in plots of DModX=DModY, which show each observation’s (e.g., NMR-spectra of rats) distance to the model in X=Y-space. DModX and DModY are based on a row-wise summation of the elements of the residual matrices E and F and are equivalent to the row residual standard deviation. For a one-dimensional PLS-model this leads to the formation of the ‘‘beer-can’’ like tolerance volumes in X and Y (Fig. 14). 2.4.10.2. Well Explained X- and Y-variables If the X-residuals in matrix E are summed column by column, it is possible to compute the explained variation (R2VX) of a

Figure 14 A tolerance volume enclosing a point-swarm can be used as a diagnostic tool to evaluate whether new observations (depicted here as the large circles) are similar or dissimilar to the training set members. In the plot, the dark-gray circle falls within the model tolerance, but the light-gray observation would be considered as deviating from the model and hence likely be a moderate outlier.


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variable. This quantity is often denoted as just R2, but here we use the notation R2VX. R2VX ranges from 0 (no explanation) to 1 (complete explanation), and reveals how predictors are explained by the model. Similarly, the sizes of the Y-residuals show which responses are well accounted for by the PLS-model. Quantitatively, this information is given by the explained variation, R2VY. For the necessary equations, please see Ref. 8. 2.4.10.3. Cross-validation In order to determine the appropriate number of components in a PLS-model, the technique of cross-validation is useful. As in PCA, CV is performed by dividing the data in a number of groups and then developing a number of parallel models from reduced data with one of the groups deleted. It should be noted that increasing the number of CV groups to N, i.e., the so-called leave-one-out approach, is not recommended, because the estimated Q2 then becomes too similar to R2. After developing the reduced model, the omitted data are used as a test set, and the differences between actual and predicted Y-values are calculated for these data points. The sum of squares of these differences from all the parallel models are used to form PRESS. This is a measure of the predictive ability of the model: X ð9Þ PRESS ¼ ðyim yîm Þ2 When CV is used in the sequential mode, PRESSa=SSa1 is evaluated after each component, and a component is judged significant if this ratio is smaller than around 0.9 for at least one of the y-variables [sharper bonds can be obtained from the results of Wakeling and Morris (17)]. Here, SSa1 denotes the (fitted) residual sum of squares before the current component (index a). The calculations continue until a component is nonsignificant. Alternatively, however, one can calculate PRESS for each component up to, say 10 or 15 components, and use the model which gives the lowest PRESS=(N A 1). This ‘‘total’’ approach is computationally much more taxing, and the practical difference from the ‘‘sequential’’ CV results is small.

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Both with the ‘‘sequential’’ mode and the ‘‘total’’ mode, a PRESS is calculated for the final model with the estimated number of significant components. This is often re-expressed as Q2 (the ‘‘cross-validated R2’’), a statistic which is similar to R2: R2 ðYÞ ¼ 1 RSS=SSYtot:cor:

ð10Þ

Q2 ðYÞ ¼ 1 PRESS=SSYtot:cor:

ð11Þ

In the expressions above, SSYtot.corr. represents the total variation in the Y-matrix after mean-centering and scaling. As stated previously, without a high R2 it is impossible to obtain a high Q2. Generally, a Q2 > 0.5 is regarded as good and a Q2 > 0.9 as excellent, but these guidelines are of course heavily application dependent. Differences between R2 and Q2 larger than 0.2–0.3 indicate the presence of many irrelevant model terms or a few outlying data points. 2.4.10.4. Standard Errors and Confidence Intervals Numerous efforts have been made to derive confidence intervals for PLS-parameters (see, for example, Refs. 18,19). However, most of these approaches have been based on conventional regression assumptions, treating PLS as a biased regression model. Only recently in the work of Burnham et al. (20–22) has the issue been investigated considering PLS as a latent variable model. One way to estimate standard errors and confidence intervals directly from the data is to use jack-knifing (23). This was actually recommended by Herman Wold (24) in his original PLS-work, and has recently been revived by Martens and Martens (25). The objective of jack-knifing is to estimate variability of model parameters. Interestingly, cross-validation—where the objective is to estimate the model complexity giving the optimal predictive power—produces results which can be fed directly to jackknifing. In this way, the various submodels generated by cross-validation are used to calculate the standard errors of the model parameters, which are then converted into


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confidence intervals via the t-distribution. Since the PLSparameters (scores, loadings, etc.) are linear combinations of the original data, they are approximately normally distributed and so jack-knifing works well in the estimation of confidence intervals.

3. RESULTS FOR EXAMPLE DATA SET I—A METABONOMIC INVESTIGATION OF PHOSPHOLIPIDOSIS 3.1. Background to Data-set Phospholipidosis is a condition which reflects derangement of normal phospholipid metabolism and can be induced by many different classes of drug in tissues such as liver, lung, brain, kidney, and endocrine glands. The condition can be difficult to detect and biomarkers are scarce (26). The first data set deals with male rats treated with the drugs chloroquine (an antimalarial) or amiodarone (an antiarrhythmic), both of which are known to induce phospholipidosis (26), here coded as ‘‘c’’ or ‘‘a’’. The drugs were administered to two different strains of rat, i.e., Sprague–Dawley and Fisher 344, here coded as ‘‘s’’ or ‘‘f ’’. Sprague–Dawley rats are a standard laboratory animal model, whereas Fishers rats are more susceptible to certain types of drug exposure and hence it is often easier to detect drug effects. In total, the data set contains N ¼ 57 observations (rats) and K ¼ 194 variables (chemical shift region integrals). These observations are divided into six groups (‘‘classes’’): control Sprague–Dawley (s), 10 rats; Sprague–Dawley treated with amiodarone (sa), 8 rats; Sprague–Dawley treated with chloroquine (sc), 10 rats; control Fisher (f), 10 rats; Fisher treated with amiodarone (fa), 10 rats; Fisher treated with chloroquine (fc), 9 rats. Urine samples were obtained from the rats treated with either chloroquine (dosed at 60 mg=kg=day i.p.) or amiodarone

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(dosed at 80 mg=kg=day i.p.) on day 21 and injected into an NMR-flow probe using the Bruker BESTTM flow injection system. The samples were measured on a 600 MHz NMR-spectrometer using a standard one-dimensional pulse sequence. Suppression of the water region was attained by using the ‘‘WET’’ pulse sequence (27). The acquired spectra were manually phase and baseline corrected prior to data reduction. The urine 1H NMR-spectra were reduced by summation of all the data points over a 0.04 ppm region. Data points between 4.5 and 6.0 ppm, corresponding to water and urea resonances, were excluded from data reduction and subsequent data analysis. Regions in the spectra associated with drug-related compounds (DRCs) were also exluded prior to data analysis, leaving a total of 194 NMR-spectral regions as variables for the multivariate modeling. A more elaborate account of the experimental conditions is found in the original literature source. 3.2. An Overview PCA-Model Generally, when working with spectral data it is recommended to work with Pareto-scaled data (8). This way of scaling the data can be seen as a compromise between UV-scaling (risk: noise is inflated for chemical shift regions of low signal amplitude variation) and no scaling (risk: only those chemical shift regions with large variation in signal amplitude will be seen). Hence, when overviewing the information in the first data set, PCA was applied to Pareto-scaled and meancentered NMR-data. For an overview model, usually only the two first components are extracted. In this case, these showed the performance statistics R2X ¼ 0.48 and Q2X ¼ 0.38. Fig. 15 shows the scores of these two components. We can see that all the chloroquine-treated animals are positioned in the top part of the plot, whereas the majority of the amiodarone-treated rats are found in the bottom part. All controls are located in the central, predominantly right-hand part of the plot. Hence, the second principal component reflects differences in the effects of the two drugs.


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Figure 15 Scores of the two first components of the phospholipidosis data set overview model. Filled circle ¼ controls of Sprauge– Dawley; filled triangle ¼ SD treated with amiodarone; filled diamond ¼ SD treated with chloroquine; open circle ¼ controls of Fisher; open triangle ¼ F treated with amiodarone; open diamonds ¼ F treated with chloroquine.

Another very interesting aspect is that the ‘‘f ’’-groups constantly tend to be ‘‘right-shifted’’ along the first principal component in comparison with the corresponding ‘‘s’’-groups. This make us interpret the first PC as a ‘‘difference-betweentype-of-rat’’-scale. In order to interpret the scores, we use the loadings. Fig. 16 displays a line plot of the second loading spectrum. This spectrum highlights the various chemical shift regions contributing to the formation of the second score vector. For instance, the chloroquine-exposed rats generally tend to have higher peaks at chemical shifts of succinate (2.42), taurine (3.26 and 3.42), etc., and lower peaks at shifts of creatine

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Figure 16 Line plot of loading vector p2 for the overview PCAmodel. This loading spectrum uncovers which chemical shift regions are responsible for the separation between the Fisher and Sprague– Dawley rats.

(3.06), glucose (3.70), hippurate (3.98, 7.58, 7.66, 7.86), etc. If a similar loading spectrum is plotted for the first loading vector, it is possible to identify which spectral variables reflect the major differences in NMR data due to strain of rat (Fisher or Sprague–Dawley). Moreover, it is of interest to examine the model residuals (see DModX plot in Fig. 17). The DModX plot reveals one very different ‘‘sc’’-rat with a DModX value exceeding the critical distance by a factor of 2. When tracing this information back to the previous score plot (Fig. 15), we realize that this animal is the remotely positioned sc-rat (marked with the open frame). This is an observation with unique NMR-data and its spectrum should be more carefully inspected to understand where the differences arise. These differences could


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Figure 17 Distance to model in the X-data (DModX) for the overview PCA model (Data Set I). There is one strongly outlying rat which displays a worryingly high DModX.

be due to some very interesting change in metabolic pattern, or be due to experimental variation in the handling of the rats, or perhaps a data transfer error. One way to pinpoint the likely cause for this discrepancy in DModX is through the loading plot or a contribution plot, but that option is not further exploited here. 3.3. PLS-Discriminant Analysis (PLS-DA) It is obvious from the above PCA model that the observations (rats) are grouped according to treatment in the score plot. However, knowledge related to class membership is not used to find the location of the principal components. The PC-model

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is calculated to approximate the observations as well as possible. It must be realized that PCA finds the directions in multivariate space that represent the largest sources of variation, the so-called principal components. However, it is not necessarily the case that these maximum variation directions coincide with the maximum separation directions among the classes. Rather, it may be that other directions are more pertinent for discriminating among classes of observation (here: NMRspectra of rats). It is in this perspective that a PLS-based technique, called PLS discriminant analysis (PLS-DA) becomes interesting (28,29). PLS-DA makes it possible to accomplish a rotation of the projection to give latent variables that focus on class separation (‘‘discrimination’’). The method offers a convenient way of explicitly taking into account the class membership of observations even at the problem formulation stage. Thus, the objective of PLS-DA is to find a model that separates classes of observations on the basis of their X-variables. This model is developed from a training set of observations of known class membership. In PLS-DA, the X-matrix consists of the multivariate characterization data of the observations. In order to encode a class identity, one uses as Y-data a matrix of dummy variables, which describes the class membership of each observation in the training set. A dummy variable is an artificial variable that assumes a discrete numerical value in the class description. The dummy matrix Y has G columns (for G classes) with ones and zeros, such that the entry in the gth column is one and the entries in other columns are zero for observations of class g. 3.4. PLS-DA of Groups ‘‘s’’ and ‘‘sc’’ In order to illustrate the utility of PLS-DA we are going to focus on the difference between group ‘‘s’’ (controls of Sprague–Dawley) and ‘‘sc’’ (SD rats treated with chloroquine). However, in so doing we must first eliminate the outlying ‘‘sc’’-rat. The PLS-DA requires homogenous groups devoid of outliers, otherwise inconsistent patterns may result.


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A PLS-DA model was calculated based on the 19 rats in the ‘‘s’’ and ‘‘sc’’-groups. All variables were mean-centered and Pareto scaled. This model contained two very strong components showing the performance statistics R2X ¼ 0.69, R2Y ¼ 0.94, and Q2Y ¼ 0.90. The X-score plot of t1 and t2 of this model is displayed in Fig. 18. Evidently, there is strong separation (‘‘discrimination’’) between the ‘‘s’’- and ‘‘sc’’groups. It is mainly the first component that is responsible for separating the two groups of rat from each other. The second model component picks up within class variation. The loadings for the first component (Fig. 19) indicate that chloroquine induced an increase in the urinary excretion of creatine (3.02, 3.94) and taurine (3.26, 3.42), which would infer damage to the liver. In addition, an increase in the regions corresponding to phenylacetylglycine (3.62 and 7.62) suggests that chloroquine is also causing phospholipidosis. Both of

Figure 18 PLS-DA t1=t2 score plot for the model contrasting the ‘‘s’’ and ‘‘sc’’ groups. Each point in the plot represents one rat. The two classes are well resolved in component 1.

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Figure 19 Line plot of the X-loadings of the first component of the PLS-DA model. This loading plot indicate chemical shift regions influential for the separation of the two classes along the horizontal direction in Fig. 18.

these lesions were independently confirmed by conventional histology. Thus, there is really no doubt that the chemical treatment of the rats induces a substantial and characteristic change in their NMR-profiles. 3.5. Disjoint PCA-Modeling of Groups ‘‘s’’ and ‘‘sc’’ In this paragraph, we would like to draw the attention to an alternative to PLS-DA, known as soft-independent modeling of class analogy, or SIMCA for short (7,8). SIMCA is a graphically oriented technique, and is applicable when clear groupings exist in the data, such as those seen among the observations (rats) in the first example. As discussed above, any data-analytical exercise usually starts with a PCA on the entire data set to get an overview. Provided that the data set is not pruned inappropriately, e.g., to artificially enhance a class separation, such an


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overview of the training set data gives valuable indications of class separation, trends, and outliers. Division into classes, accounting for time trends, and exclusion of outliers can then be carried out accordingly. It should be noted, however, that in any resulting subclass, the data should be selected such that each class of observations contains homogeneous data material. Subsequent to the initial data overview, in the SIMCA method each class of observations is modeled separately by disjoint PC-models. In order to unravel the appropriate dimensionality of each local PC-model, we recommend that cross-validation be used. Based on the residual variation of each class, one can compute the distance to the model (DModX) of each observation. It is also possible to compute a critical distance for each class model. This was discussed in Sec. 2.3.11.1. After the separate modeling of each class, the models are used to predict a likely class membership (‘‘classification’’) for new observations. An observation is classified in SIMCA according to the tolerance intervals of the different classes (as calculated from the residual distance to the model— DModX). Observations that do not fit any class are then considered as outliers, or perhaps as founders of a new, hitherto unseen, class. Furthermore, in regions where tolerance intervals overlap, the observations cannot be unequivocally assigned. These local PCA-models can be interpreted by inspecting loadings, scores, residuals, and contribution plots. Among other things, this will indicate which variables contribute to modeling class similarity (loading plot), and which variables do not (contribution plot in residuals of nonfitting observations). In order to illustrate the applicability of the SIMCA method, one local PCA-model was fitted to the ‘‘s’’-group and another to the ‘‘sc’’-group. These two models were used to infer class belongings of all the other rats. The results from the classification phase are summarized in Fig. 20. The plot in Fig. 20 is known as a Coomans plot, named after the Belgian chemometrician Danny Coomans, who in

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Figure 20 Coomans plot of SIMCA of the ‘‘s’’ and ‘‘sc’’ classes. This plot represents a scatter plot of DModX to the ‘‘sc’’ class model against DModX to the ‘‘s’’ class model. Open diamonds denote ‘‘s’’ rats; open circles denote ‘‘sc’’ rats; solid triangles represent all other rats.

the mid-80s demonstrated its great applicability (7,30). The essence of the Coomans plot is that class distances (DModX ’s) for two classes are plotted against each other in a scatter plot. By plotting also the critical distance, DCrit, for each model in the Coomans plot, four areas of diagnostic interest are created. In the lower left-hand part of Fig. 20 a region where prediction set samples (rats) that fit both models are found (no rats in this case). In the lower right-hand part and the upper left-hand part there are regions where those observations predicted to fit the ‘‘sc’’-model or the ‘‘s’’-model are found, respectively. Finally, we have the upper right-hand


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area where we find observations that do not conform to either of the models. These are all the ‘‘sa’’, ‘‘f ’’, ‘‘fc’’, and ‘‘fa’’, which consistently are found to be different from the ‘‘s’’ and ‘‘sc’’ rats. 3.6. Discussion of First Example The first example shows the power of NMR-data in combination with multivariate statistics to capture differences between groups of rats. Methodologically, it is very practical to commence the data analysis with an initial overview PCA of the entire data set. This will indicate groups, time trends, and outliers. Outliers are observations that do not conform with the general correlation structure. One clear outlier was identified among the ‘‘sc’’ rats. By way of example we have also shown how groupings spotted by an initial PCA may be further studied on a more detailed basis. Then techniques like PLS-DA and SIMCA are very useful. A necessary condition for PLS-DA to work reliably is that each class preferably is ‘‘tight’’ and occupies a small and separate volume in the X-space. Also, the number of modeled classes must not be too high. Experience shows that PLS-DA is useful with 2–4 classes, but when the number of classes exceeds four, it is usually more tractable to switch to SIMCA. Thus, in this presentation, we have focused on the differences between two classes, i.e., the ‘‘s’’ and ‘‘sc’’ rats. This is an analysis that will pick up drug-related effects of the chloroquine treatment. In order to find out exactly which variables (i.e., chemical shift regions) that carry class discriminatory power, one may consult plots of PCA or PLS-loadings, or contribution plots. A few of these possibilities were hinted at. It should be noted that one need not only compare ‘‘s’’ with ‘‘sc’’. Other possible comparisons focusing on drug effect are ‘‘f ’’ ) ‘‘fa’’, ‘‘f ’’ ) ‘‘fc’’, and ‘‘s’’ ) ‘‘sa’’. However, there are also other twists of the data analysis, which may reveal interesting information. For example, a comparison made as ‘‘f ’’ ) ‘‘s’’ would indicate rat strain differences and perhaps diet differences. And modeling arrangements like ‘‘fa’’ ) ‘‘sa’’ and ‘‘fc’’ ) ‘‘sc’’ might suggest species-dependent drug effects.

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Although both chloroquine and amiodarone induce phospholipidosis, they do not compare in Fig. 15 indicating that NMR-profiles are different for animals exposed to these two drugs. Biological data are complex, and toxicants or drugs rarely target a single organ or cell type. For example, in addition to inducing phospholipidosis, chloroquine also causes cellular necrosis in the liver, which accounts for the difference in mapping positions between amiodarone- and chloroquine-treated samples. Data filtering methods such as orthogonal signal correction can be used to focus on a single factor, such as phospholipidosis, and to exclude systematic variation arising from other biological phenomena. This has been described in other publications and is not covered in the present chapter (31). 4. RESULTS FOR EXAMPLE DATA SET II—DEFINING THE DYNAMIC SEQUENCE OF BIOCHEMICAL EVENTS FOLLOWING THE ONSET OF TOXICITY 4.1. Background to Data-set The second data set is a toxin data set containing exposure data for two compounds plus a control group. Ten rats were exposed to the hepatotoxin a-naphthylisothiocyanate (ANIT). Five rats were treated with a low dose (100 mg=kg i.p.) and five rats using a high dose (200 mg=kg i.p.). A second group of five rats was subjected to treatment by 200 mg=kg i.p. thioacetamide (TA), which is a known liver and kidney toxin. Finally, there was a control group comprising six rats. Urine samples were collected at six time points, including one predose measurement and measurements at 24, 31, 55, 79, and 103 hr after exposure. To account for changes in metabolic profiles, 209 NMR-variables (chemical shift regions) were acquired. The NMR-data were pretreated as described above for the first data set. In order to enable predictive validation of models, the parent data set was split into two parts. To get a sufficiently

Data source for data set II.


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large foundation for the model, we will use the 10 ‘‘ANIT’’ rats as training set, whereas the other two groups of rat will be used for external prediction. The advantage of this selection is that it allows assessment of whether NMR-spectra can discriminate between toxins going for different target organs, and also allows control rats to be compared with drug-exposed animals. The second data set can be understood as a (N J K) three-dimensional matrix built up by the ‘‘directions’’ rats (N) urine samples (J) NMR variables (K) (Fig. 21). In order to analyze this data table we will use methodology developed for batch statistical process control, BSPC (32,33), where each rat is regarded as an individual batch. The approach to BSPC used is reviewed in the next section. It is based on two levels

Figure 21 The three-way data table is unfolded by preserving the direction of the NMR-variables (NMR-shifts). This gives a two-way matrix with N J rows and K columns. Each row contains data points xijk from a single batch observation (rat i, urine sample j, variable k). If regression is made against urine collection time, the resulting PLS scores usually reflect linear (t1), quadratic (t2), and cubic (t3) relationships to this time.

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of batch monitoring, the lower observation level and the upper batch level. This leads to an easy and straightforward way of accounting for time trajectories. Pathological lesions are dynamic biochemical processes and metabonomic analysis of biofluids provides the unique opportunity of defining or monitoring the evolution of a pathological lesion, since a series of urine or plasma samples can be obtained over a specified time course with minimal damage to the organism. As will be seen below, the data set was divided into two parts, a training set and a test set. To train the BSPC models, we used the 10 ANIT-treated rats and to evaluate these models, we used a combined test set of the 11 TA-treated rats and control rats. 4.2. BSPC: A Method to Handle Three-way Data Tables At the lower observation level, the three-way data table is unfolded by preserving the direction of the NMR variables (Fig. 21). The resulting two-way matrix then has N (rats) J (urine samples) rows (observations) and K columns (NMRvariables). Hence, in this matrix, X, the observations are the individual urine samples collected for each rat at different subsequent time points, and not the whole rats. In the data set there are altogether 21 rats mapped by using either five or six urine collection times, resulting in a total of 121 observations. These observations are mapped by the 209 NMR-variables. The lower observation level modeling is here carried out using PLS to relate the NMR-spectral regions (X) to the urine collection time (single variable y). This analysis will capture trajectories of metabolic evolution, which can be interpreted in terms of PLS-model parameters (loadings, VIPs, etc.). Thus, the biomarkers contributing to the shape and direction of the trajectories, which can reflect the nature of the pathology in terms of the site or mechanism of damage, can be uncovered. At the batch level, the PLS-score vectors of the lower level model are re-arranged and used to account for the time-related metabolic properties of each animal. The three-


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way data table is unfolded by preserving the direction of the NMR-variables (NMR-shifts). This gives a two-way matrix with N J rows and K columns. Each row contains data points xijk from a single batch observation (rat i, urine sample j, variable k). If regression is made against urine collection time, the resulting PLS-scores usually reflect linear (t1), quadratic (t2), and cubic (t3) relationships to this time. The score values for each batch (rat) are arranged as row vectors underneath each other, giving a new matrix X that has the number of rows equal to the number of rats in the reference data set. From this new matrix, one calculates the averages and standard deviations (SDs) of the matrix columns, and subsequently control limits as averages 3 SD. The objective is to calculate a model over the whole batch (i.e., model the entire sequence of biochemical events in an ANIT-treated rat over the given time course) and to allow comparison on a rat-to-rat basis. This analysis will reveal which rats exhibit similar and=or different metabolic trajectory profiles, and also which rats that show a deviating behavior compared with the majority of the population. For example, by using this type of analysis, animals that are ‘‘fast’’ or ‘‘slow’’ responders to different drug treatments, or even those that display an idiosyncratic response can be easily detected (1). The bottom line is the ability to classify new rats as ‘‘conforming to’’ or ‘‘breaking’’ the correlation structure among the modeled rats, and to ascertain when deviation from ‘‘normality’’ occurs. In this work, the upper batch level is accomplished using PCA on the lower-level PLS-score vectors. We emphasize that the results presented below should be seen as a guide for BSPC-based modeling of time-dependent whole-animal metabolic processes. As such, we are focussing on the most central aspects of the data analysis. Much emphasis is given to the classification situation, i.e., the phase in which a model is used for prognosis of the metabolic response in new independent sets of rats. 4.3. Base Level PLS-Model To accomplish the lower-level model, the data for the 10 ANIT rats were reordered. The total number of observations (rows)

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in the training set was 55 (five high-dose rats urine sampled at six occasions þ five low-dose rats sampled at five time points). As the dependent y-variable, the sampling time was used. Prior to the data analysis, all variables were meancentered and Pareto scaled. According to cross-validation, the lower-level model was a five-component PLS-model describing 74% (R2X ¼ 0.74) of the variation in the spectral data and 92% (R2Y ¼ 0.92) of the variation in the time variable. The predictive power of the model was 77% (Q2Y ¼ 0.77). The X-score plot of the first two components is displayed in Fig. 22. There are 10 lines in this plot connecting the timed

Figure 22 The PLS score plot of the base-level model (second data set). This score plot represents 56% of the variation in NMR-data. Score lines indicating metabolic trajectory profiles of low-dose rats (dashed black lines) and high-dose rats (gray solid lines) are given. The left-hand ellipse indicates the region where all animals are positioned prior to dosing. Ideally, the trace of each rat should revert back to this area after exposure. It is clearly seen that within the investigated postdose time frame, none of the high-dose animals have this ability to recuperate; they all end within the right-hand ellipse. The low-dose animals have either reverted back to the normal area or are approaching it.


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urine samples of each rat. The left-most ellipse indicates the region where all animals are positioned in the predose situation. In other words, the model finds the low-dose (dashed lines) and high-dose (solid lines) animals metabolically similar prior to toxin exposure. After exposure, three of the low-dose animals have reverted back completely within the time frame of the investigation, and the other two animals are on their way back to the initial area describing the predose metabolic state. Conversely, however, none of the five high-dose animals have returned back to this area indicating a greater duration of effect in the high-dose rats. These differences show the power of the multivariate information in the NMR-spectra. The loading plot given in Fig. 23 indicates the most informative chemical shift regions. For instance, the regions 2.46, 2.58, and 2.74 (relating to urinary citrate and 2-oxoglutarate

Figure 23 Loading plot corresponding to Fig. 22. This plots shows that variables such as increased acetate, taurine, and glucose are good markers for high-dose ANIT-treated animals at 55 hr postdose.

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levels) are very characteristic for the predose metabolic configuration, whereas regions like 1.90 (acetate), 2.38, 2.42 (succinate), 2.50, 2.54, 2.66 (citrate), 3.22, 3.26 (trimethylamine-N-oxide), and 3.94, 7.54, 7.62, 7.82 (hippurate) are all indicative of conditions of the high-dose animals at 103 hr after exposure. The administration of ANIT to rats is known to induce a preliminary effect on the metabolism of bile acids, creatine, glucose, hippurate, and the tricarboxylic acid cycle intermediates. Here, one can observe that although many of the preliminary effects of ANIT have passed by 103 hr postdose, the tricarboxylic acid cycle intermediates (with the exception of succinate) and hippurate remain depleted. In addition, an increase in the excretion of acetate indicates that although there are no direct signs of hepatotoxicity remaining at this time, the animals are still not metabolically ‘‘normal.’’ Another observation from these data is that the citrate resonances appear slightly shifted in the samples from the high-dose ANIT group which results from an ANIT-induced change in urinary pH. 4.4. Classifying Individual Urine Samples The ability of this model to classify the timed urine samples of the 11 rats not used for model training was tested. It was found that these 11 rats did not separate from the 10 training set rats in the X-score space. However, in the prediction DModX plot (Fig. 24), there is no doubt whatsoever that the TA-treated animals are very different in their NMR-data compared with the ANIT-treated rats. Interestingly, the control group rats show greater similarity with the ANIT-dosed animals, as they generally tend to fit the lower-level model well. To be of value, metabonomic analysis must complement and enhance the more traditional methods of toxicity screening, either by providing biomarker information earlier, at lower levels of toxicity or more efficiently than other methods, or by enhancing knowledge of the time profile of toxicity. Therefore, a common practice, as followed in the current example, is to administer several doses of compound to the chosen animal model, ranging from an acutely toxic dose that can be confirmed by histopathology to a subtoxic dose. Here,


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Figure 24 DModX-plot for the lower-level PLS-model. It can be seen that the animals subjected to TA treatment do not fit at all the model based on the ANIT-treated animals. In other words, NMR-spectra for the TAs are radically different from the ANITs. Contribution plotting can then be used to understand how (i.e., in which spectral variables) a certain observation is different. The arrow indicates an animal exposed to TA and its urine sample collected 55 hr postdose.

the training set comprised animals treated with both a high (200 mg=kg) and low (100 mg=kg) dose of ANIT, thus the range of response between the high- and low-dose animals generates a model incorporating a high degree of variance. This accounts for the apparent inability of the ANIT model to distinguish between samples from control and ANITtreated animals at the lower level. The test samples obtained from TA-treated rats, on the other hand, were significantly different from those used to build the ANIT model. Two factors contribute to this observation; firstly, TA was administered at a high dose only (200 mg=kg) and thus induced a severe renal cortical lesion that was easily detected by histopathological examination. Secondly, the biomarkers of renal cortical toxicants include the increased excretion of a large range of amino acids, organic acids, and glucose, reflecting the inability of the damaged tubules to absorb such

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substances. Glucose alone generates many 1H NMR resonances covering approximately 8–12% of the spectrum (depending on how many regions are excluded on the grounds of drug metabolites or spectral artifacts), which exerts a high degree of leverage on the model. Thus, even when dealing with a comparable level of tissue damage in the liver and kidney, the markers for hepatotoxicity (bile acids, creatine, taurine, etc.) occupy relatively few spectral ‘‘bins,’’ in comparison with the markers for cortical nephrotoxicity (glucose, amino acids, and organic acids). Methods such as logical blocking, QUILTPLS analysis (34), and hierarchical extensions of PCA and PLS (see Sec 5.3), can be used to further advance the data analysis, but are considered beyond the scope of the current chapter. As a next step in the analysis, contribution plotting can be helpful to provide additional and more detailed information about previously observed patterns in scores and DModX. Hence, abnormally behaving observations can be placed under more careful scrutiny. This procedure may actually be thought of as placing a magnifying glass over a strange observation to try to resolve why it is different. As an example, Fig. 25 relates to the contribution plot for a urine sample

Figure 25 Contribution plot indicating in which spectral regions the observations highlighted in the previous figure are different. As seen, this mostly relates to the regions 3.46, 3.38, 3.22, (glucose), 1.48 (alanine), and 1.30 (lactate), which are present in higher concentrations in this TA-treated sample, as this animal exhibits a strong response to toxicity.


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collected 55 hr after exposure to TA. Chemical shift regions being very different for this animal are 1.30 (lactate), 1.48 (alanine), 2.1 (N-acetyl glycoprotein fragments), and 3.22, 3.38, 3.46, 3.7, 3.82 (glucose), reflecting the increased excretion of these compounds in TA-treated rats. 4.5. Upper Level PCA-Model of PLS-Descriptors On the upper level, the five PLS-score vectors of the lower-level model were rearranged. In the new X-matrix, the training set consisted of 10 rows, each corresponding to one ANIT-treated rat, and 50 columns (corresponding to the 5 PLS-vector 10 time points). A PCA of the training set gave a four-component model accounting for 79% of the variation. Some separation between the low and high doses of ANIT is apparent in the score space, but from the DModX statistics all 10 rats fell well below the critical distance indicating no outliers in the training set. 4.6. Classifying Whole Rats Next, the upper level PCA-model was applied to the 11 control and TA rats in the test set. Figure 26 includes the results from these predictions. It is obvious from Fig. 26 that the control group overlaps with the training set, while all TA-dosed rats are grouped outside the limit defined by the training set. Recall that the TA- animals were found to be different from the ANIT-treated rats already in the lower-level model. This was also clearly shown at the upper level in the DModX plot. However, also the controls are found different from the ANIT rats on the upper level. This was seen in a DmodX plot where all the rats from the control group had values clearly exceeding the critical limit defined by the ANIT-dosed animals. 4.7. Discussion of Second Example The second data set was included to exemplify how timedependent changes in levels of metabolites in biofluids may

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Figure 26 Predicted t1=t2 score plot of the upper level PCA model. Each point corresponds to one rat. A ¼ ANITs used for model training ( denotes high-dose animal); T ¼ TAs used for model testing; c ¼ control group. The TAs are situated in an area outside the area covered by the training set. Hence, they do not generally conform well with the model, i.e., they are different from the training set rats.

be monitored using a combination of NMR-spectroscopy and multivariate batch projection methods. Multivariate analysis of batch-wise data enables the correlation structure among measured variables to be explored. This is important not only for gaining an understanding of the underlying properties that dominate a batch process (here: time-dependent changes of metabolites in urine), but also for early fault diagnosis. However, not only is the relationship among all batch variables (here: chemical shift regions) at any time point of prime importance, but also so are the trajectories or time dependencies of all these variables. The development of various trajectories of batches may often serve as a fingerprint of each batch. Thus, by multivariately modeling average batch trajectory features, and deviations from normal batch evolution, it is often possible to separate good and bad batches from each other.


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Moreover, in addition to diagnosing whether a batch will end up as ‘‘accepted’’ (i.e., a rat whose urinary profile matches the training data) or ‘‘rejected’’ (i.e., a rat which does not conform to the model), it is of great significance in batch monitoring to predict ‘‘maturity’’ or ‘‘state’’ of new batches. In this context, this means that the PLS evolution charts can indicate the presence of fast or slow responders to specific toxic insult. In simple cases, the state of a batch is an uncomplicated linear or nonlinear function of time. However, in most cases batches develop (respond) differently due to varying influences of uncontrollable external factors. Additionally, as pointed out in Ref. 1, a careful model interpretation may also ‘‘provide a scale of the magnitude of response for each animal’’ (batch). To demonstrate the power of this technique, in the current example we chose to develop a model on the 10 ANITtreated rats. On the lower level, using the predicted DModX chart of the PLS-model, we were able to classify the TAtreated rats as fundamentally different from the training set. In other words, the PLS-model detected significant differences in the NMR data—which, in turn, point to the urinary profiles—induced by the treatment of a hepatotoxin or a toxin known to be causing both liver and kidney damage. Furthermore, on the top level, were also able to separate the control from the ANIT-group, again using the predicted DModX chart. This points to the fact that the differences in NMR-data between control and ANIT-treated rats are more subtle than those observed between animals dosed with TA and ANIT. Hence, operating on the different model levels means that ‘‘amplifiers’’ of different strength can be mounted on the model to detect strongly or weakly aberrant urinary profile behavior.

5. DISCUSSION 5.1. The Usefulness of Multivariate Projection Methods in Metabonomics There is a steady development in all parts of science and technology, including metabonomics, proteomics, and genomics, to

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use more and more variables to characterize molecules, reactions, metabolic processes, animals, and other ‘‘systems.’’ The reasons are obvious; firstly, we strongly feel that we know and understand more about our ‘‘systems’’ when we have measured many properties (e.g., chemical shift regions in NMR spectra) rather than a few. Secondly, ‘‘instrumental revolution’’ with computers, spectrometers, chromatographs, imaging equipment, and other electronic devices, provide the opportunities to get information-rich data for any investigated object, sample, individual, reaction, process, etc. It is remarkable that we only recently have learnt how to cope with this abundance of data. Traditional statistics has incorrectly instructed us that we must always employ fewer variables (K), than observations (N), otherwise we should enter into a jungle of unreal and spurious relationships. This alertness is correct if we are to treat each variable as precise and independent, i.e., having some unique piece of information. PCA, PLS, and similar projection methods, however, are based on other assumptions, namely that variables are correlated (collinear) and possibly also noisy and incomplete. These correlations are, in turn, modeled as arising from a small set of latent variables, where all measured (manifest) variables are modeled as linear combinations of the latent variables. In metabonomics science, the latent variables are often interpretable in terms of the systematic metabolic fluctuations characterized by high field NMR-spectroscopy of biofluids and tissues. The success of PCA and PLS indicates that these assumptions forming the basis of PCA and primarily PLS are more realistic than those of regression. Projection methods such as PLS and PCA have the advantageous property that the precision and reliability of parameters related to the observations improve with increasing numbers of relevant variables. This feature of projection methods is easily understood by realizing that the scores (ta and ua) are estimated as weighted averages of the X and Y variables, respectively. Any (weighted) average becomes more precise the larger the number of elements used as its basis. Analogously, the PCA and PLS variable-related parameters, i.e., loadings, weights, VIP, R2, Q2, regression coefficients,


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etc., become more precise the larger the number of observations, N. This because the loadings and weights and parameters derived thereof are linear combinations—weighted averages—of the N observation vectors. Hence, PCA and PLS can model data also when the number of X variables, K, exceeds the number of observations, N. Provided that the number of model components (A) is substantially smaller than N, and that the components all are significant according to cross-validation, PCA and PLS modeling works well also in this situation. It is important to note that PCA, PLS, and the like, are not statically locked to the array of problems sketched in this paper. On the contrary, many ‘‘add-ons’’ and ‘‘twists’’ to these methods exist and are aimed at facilitating=enriching both the preprocessing and data-analytical phases. Some of these amendments to the basic modeling set-up are discussed in Secs. 5.2. and 5.3. 5.2. Additional Preprocessing Tools 5.2.1. Block-Scaling One limitation of the scaling procedures discussed in Sec. 3.1 is that they do not consider whether variables are grouped in blocks of naturally related descriptors or the number of variables in each such block. If, for example, UV-scaling is used, a large block of variables (say, hundreds of NMR descriptors) will dominate over a smaller block of variables (say, exposure conditions of animals) for purely numerical reasons. This is often not wanted. One way of addressing this situation is to employ blockscaling (8). In this procedure, one may down weight blocks of variables in relation to a selected basis scaling procedure. The basis scaling method is generally UV-scaling, especially when variables are markedly different in nature and numerical range. However, in multivariate calibration, procedures like no scaling or Pareto scaling may well be used as the basis for block-scaling. Block-scaling can be done in many ways. In our experience, it is convenient to distinguish between soft and hard

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block-scaling (8). In soft block-scaling, each block of variables is scaled such that the sum of the variable’s variances (after completed scaling) equals the square root of the number of variables in that particular block. Here, the additional scaling weight used is 1=(kblock)1=4—where kblock represents the number of variables in a block—which is multiplied by the basis scaling weight. Hard block-scaling involves even further down weighting. With this approach, the variables in a block are scaled such that the sum of their variances is unity. Here, the additional scaling weight used is 1=(kblock)1=2. Blockscaling can convey several advantages, the greatest of these being to increase the ability to detect biomarkers of toxicity or disease that are present in lower levels than the metabolites that commonly dominate the NMR-spectra or assay in question. 5.2.2. Signal Correction and Compression Signal correction and compression are of great interest in multivariate classification and calibration, but may be used in many other fields, as well. Spectral data are often preprocessed (‘‘corrected’’) prior to data analysis, in order to enhance the predictive power of multivariate calibration models. This is because variation in X that is unrelated to Y may degrade the predictive ability of a multivariate calibration model. Common approaches for preprocessing of spectral data are first and second order derivation (35), multiplicative signal correction (MSC, also referred to as multiplicative scatter correction) (36), and standard normal variate (SNV) correction (37). Also, Wold et al. (38) a few years ago developed a novel filtering technique called orthogonal signal correction (OSC). In this latter approach, the objective is to ‘‘peel-off’’ from X (spectral data) variation that is mathematically independent of Y (response data). The OSC is a PLS-based solution. In contrast to other common methods (MSC, SNV, etc.) OSC uses Y to construct a filter of X. Furthermore, in the context of multivariate calibration, wavelet analysis is gaining more and more attention. Alsberg and coworkers highlighted a number of cases where wavelet


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analysis could be of interest, e.g., for denoising of IR spectra, for feature extraction in the classification of NIR-spectra, and for noise suppression and data compression of NIR-data (39). Trygg and Wold (40) also reported on the use of wavelets and PLS for multivariate calibration of compressed NIR-spectra. 5.2.2.1. First and Second Order Derivation A rapid and often used method for reducing scatter effects for continuous spectra is to use derivatives (35). The first derivative spectrum is the slope at each point of the original spectrum. It peaks where the original spectrum has maximum slope and it crosses zero where the original has peaks. The second derivative spectrum is a measure of the curvature at each point in the original spectrum. This derivative spectrum is more similar to the original spectrum and has peaks approximately as the original spectrum, albeit with an inverse configuration (35). The effect of the first derivative is usually to remove an additive baseline (‘‘offset’’), whereas the effect of the second derivative involves removal of a linear baseline. A problem with the above approach is that differencing may reduce the signal and increase the noise, thus producing very noisy derivative spectra. Realizing this risk Savitsky and Golay (SG) (41) proposed an improvement based on a smoothing approach SG derivatives are based on fitting a low degree polynomial model (usually of quadratic or cubic degree) piecewise to the data, followed by calculating the derivative and second derivative from the resulting polynomial at points of interest. 5.2.2.2. Multiplicative Signal Correction With multiplicative signal correction (MSC), each digitized spectrum (xi0 , row-vector in X) is regressed against the average spectrum (m), according to xik ¼ ai þ bimk þ eik. From each spectrum, one subtracts the intercept (ai) and divides by the slope (bi) to get the corrected data, according to xi,corr0 ¼ (xi0 ai)=bi. The result of MSC is that each ‘‘corrected’’ spectrum has the same offset and amplitude. With this formulation, we should realize that there is a risk of obtaining

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vectors a and b that are correlated to Y. Therefore, MSC may remove from X, information that is relevant for modeling and predicting Y. Nevertheless, this approach is particularly useful for NIR data and such like, where the baseline correction is inherently weak. 5.2.2.3. Standard Normal Variate Correction The mathematical formulation of Standard normal variate (SNV) is similar to that of MSC. In SNV, the parameters ai and bi are calculated as the average and the standard deviation of the ith row of X. Actually, this corresponds to row-centering and scaling (compare with column-centering and scaling discussed in Sec. 2.1.3). 5.2.2.4. Orthogonal Signal Correction and Some Extensions Orthogonal signal correction (OSC) is usually used to remove one component at a time from X based on the NIPALS algorithm (a standard method for extracting latent variables) (38). This has the advantage that the approach will cope also with moderate amounts of missing data, as do ordinary PCA and PLS. Prior to calculations, X and Y can be transformed, mean-centered, and scaled according to standard procedures. Details of the OSC algorithm can be found in Ref. 38. Recently, Trygg and colleagues have presented new ways to decompose the PLS solution into (a) components orthogonal to Y and (b) components correlated to Y. These approaches are called OPLS for orthogonal PLS (42) and O2-PLS for second generation OPLS (43). It is shown that by using OPLS=O2PLS one can derive the OSC solution in a more direct way, and hence OPLS=O2-PLS can be used to compute OSC in a different way from that described in Ref. 38. The results of these alternative OSC computations are, however, similar. Westerhuis et al. (44) have also recently proposed yet another OSC alternative called direct OSC. The above papers on OPLS and O2-PLS also contain an overview on the literature regarding these methods. Application of OSC to NMR-based metabonomic data has proved to be particularly useful for removing instrumental


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drift and inherent physiological variation that confound patterns of toxicity and stress (45). 5.2.2.5. Wavelet Analysis Wavelet analysis is useful for signal correction and compression. The theory of wavelet analysis can be made very elaborate and only brief account is provided here. Wavelets look like small oscillating waves, and they have the capability of investigating a signal according to scale, that is, bandpass of frequencies (39,40). The characteristic features of this approach are good compression and denoising of complicated signals. It has been shown that process fluorescence data measured on a sugar production plant could be compressed by 97% without loss of predictive power (from nearly 4000 spectral variables to 120 ‘‘wavelet’’ variables) (46). The wavelet transform uses a mother wavelet, that is, a basis function, with a certain scale (width of the analyzing function window) to investigate the time-scale properties of an incoming signal. By varying the width of this window, both sharp and coarse properties of the signal are captured. A narrow wavelet is used for detecting the sharp features, and a wider wavelet is useful for uncovering general signal properties. The mother wavelet can be selected from many different families of filters. The shape of the wavelet filter depends on the selected family and the order. More details are found in Refs. 39 and 40. Wavelet analysis is particularly useful for spectra that contain a mixture of overlapped broad and sharp components. For example, standard 1H NMR-spectra of plasma comprise of resonances from low MW metabolites such as amino acids and polyols (sharp resonances) overlaid with resonances deriving from higher MW metabolites such as triglycerides and lipoproteins (broad signals). 5.2.3. Transformation When a variable contains one or a few extreme measurements, which may influence model building unduly, there may be reason for transforming the raw data. Consider

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Fig. 27(a), which shows the histogram of a variable called Var1. One out of the 40 measurements in this variable is substantially larger than the others. If this extreme measurement is not manipulated in some way prior to data analysis, it will exert a large influence on the model and dominate over the other measurements. One way to make such a non-normal distribution more nearly normal is by transformation, for example, the logtransformation (47,48). Figure 27(b) displays the result of log-transforming Var1. Apparently, Var1 is approximately normally distributed after log-transformation. It is noted that a distribution of log (x) will not necessarily be perfectly normal, but will usually be much closer to normality than is the case for the untransformed data. Also note that sometimes data may contain very extreme observations (‘‘outliers’’), which may not be addressed satisfactorily by transformation. Then, other approaches such as trimming or winsorizing, whereby a percentage of the observations for a particular variable are excluded from one or both extremes of the range, may have to be tested (8). The log-transformation is not the only transformation one can think of. Other often used transformations are negative logarithm (‘‘neglog’’), logit, square root, fourth root, inverse, and power transformations (8). There is no doubt, however, that the log-transform is the most frequently applied transformation. This is because log-normal distributions are often encountered in nature, particularly when the variable studied has a natural zero, such as, retention times, weight, height, concentrations, etc., and ranges over one order of magnitude. 5.3. Some Extensions of PCA and PLS 5.3.1. Nonlinear PLS When any system or process is subjected to large changes, it appears nonlinear. In the present context, this means that the relation between X and Y becomes nonlinear. Also the relations between the X-variables may become nonlinear, as well as the relations between the Y’s. Even so, the X- and


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Figure 27 (a). Histogram of a nontransformed variable Var1. (b). Histogram of variable Var1 after log-transformation.

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Y-matrices can always be approximated by the bilinear PLSmodel. Hence, nonlinear situations can be described by PLSmodels, where the nonlinearities are expressed as nonlinear associations between the X-scores (ta) and the Y-scores (ua). These nonlinearities can be modeled as polynomials (quadratic, cubic, etc.), spline functions, or other nonlinear forms, e.g., bi-exponential. Numerous approaches have been published for the simultaneous estimation of the X-scores (T) and the parameters in a given type of nonlinear inner relation (49). The simplest polynomially nonlinear approach is to just expand the X-matrix with the squares or cubes of its columns, and then use this expanded matrix to model Y by PLS (50). A recently introduced method, GIFI-PLS, shows great promise for the future (34,51). It is based on the binning of continuous variables into categorical variables, followed by the expansion of the latter into sets of concatenated 1=0 dummy variables. This creates a flexible modeling set-up whereby nonlinearities, discontinuities, and other anomalies in the data are easily discovered. In general, however, great caution must be used in any type of nonlinear modeling, including that of nonlinear PLS. Since nonlinear models are much more adaptable and flexible than linear models, they easily fit outliers, noise, separate clusters, and the like, which results in very low predictive power of the model. A prudent use of cross-validation to avoid too many terms in the model is strongly recommended. Also, XY-score plots (ta vs. ua) provide diagnostics for the presence of nonlinearities (cf. Fig. 21). A nonlinear model is warranted only when strong curvature is seen in these score plots. 5.3.2. Hierarchical PCA and PLS Models In PCA and PLS models with many variables, plots and lists of loadings, coefficients, VIP, etc. become messy, and results are difficult to interpret. An interesting approach is to divide the variables into conceptually meaningful blocks, and then apply hierarchical multiblock PCA- or PLS-models. For example, in metabonomics such blocks may correspond to different


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spectral techniques, or, within the same technique, different spectral regions. The idea with hierarchical modeling is very simple. Take one model dimension (component) of an existing projection method, say PLS (two-block), and substitute each variable by a score vector from a block of variables. We call these score vectors ‘‘supervariables.’’ On the ‘‘upper’’ level of the model, a simple relationship, a ‘‘supermodel,’’ between rather few ‘‘supervariables’’ is developed. In the lower layer of the model, the details of the blocks are modeled by block models as block scores time block loadings. Conceptually, this corresponds to seeing each block as an entity, and then developing PLS models between the ‘‘superblocks.’’ The lower level provides the ‘‘variables’’ (block scores) for these block relationships. This blocking leads to two model levels; the upper level where the relationships between blocks are modeled, and the lower level showing the details of each block. On each level, ‘‘standard’’ PLS- or PC-scores and loading plots, as well as residuals and their summaries such as DModX, are available for the model interpretation. This allows an interpretation focussed on pertinent blocks and their dominating variables. For further details, reference is given in the literature (52,53). 5.4. Related Methods Before PLS, two methods were available for regression-like modeling with many and collinear X-variables, namely principal components regression (PCR) and ridge regression (RR). Naturally, one can also use variable selection and try to reduce the problem to one of ordinary multiple linear regression (MLR). The latter is, however, a poor approach that greatly increases the risk for spurious invalid models and very poor predictions of Y for new observations (54). In PCR, a principal component analysis (PCA) is first made of the X-matrix (properly transformed and scaled), giving as the result the score matrix T and the loading matrix P0 . Then, in a second step, a few of the first score vectors (ta) are used as predictor variables in a multiple linear regression

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with Y as the response matrix. In the case that the few first components of PCA indeed contains most of the information of X related to Y, PCR indeed works as well as PLS. This is often the case in spectroscopic data, and here PCR is an often used alternative. In more complicated applications, however, such as QSAR and process modeling, the first few principal components of X rarely contain a sufficient part of the relevant information, and PLS then works better than PCR. RR uses another approach to cope with near singularities of X in the regression problem. Here a small number, d, is added to all the diagonal elements of the variance covariance matrix of X (i.e., X0 X) before its inversion in the regression algorithm. This closely corresponds to the discarding of all principal components with singular values smaller than d, and indeed RR and PCR show very similar performance. Hence also RR has problems in complicated applications (14), such as QSAR, metabonomics, and process modeling, and is mainly useful in situations where ordinary regression ‘‘almost’’ works, i.e., rather few but correlated X-variables. Also, the RR solution often has a serious bias in the coefficients even at small values of d, making the interpretation of RR coefficients problematic. In addition, other methods such as neural networks (NN) are often tried in the analysis of chemical data. Since NNs are equivalent to a certain type of nonlinear regression, however, these are often less suitable for problems with many and collinear variables. Either a prereduction of the variables by selection or a PCA is needed for such problems, resulting in the same difficulties as discussed above for PCR and RR. However, probabilistic neural networks based on a Bayesian calculation of the probability distribution of objects overcomes some of these inherent problems, as described in the literature (55,56).

6. CONCLUDING REMARKS We have shown the ability of PCA and PLS to develop quantitative metabonomics models. PCA and PLS analysis of


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NMR-data creates one or several maps (i.e., score plots) that show trajectories of biochemical changes in biofluids induced by toxin exposure or disease. Through this technology it is possible (i) to detect target organs or pathways of dysfunction, (ii) to uncover likely chemical mechanisms of toxicity, and (iii) to identify useful biomarkers indicative of onset, development, and decay of abnormal animal health conditions. Depending on the objective of the investigation, a multivariate model can be tailored to ‘‘see’’ or ‘‘feel’’ different features hidden in the NMR-data. As shown by the first data set, a model can be trained to discriminate between toxins going for different target organs, but also taught to contrast control rats with drug-exposed animals. In order to facilitate the classification phase, the various score- and DModX-parameters of the PCA- and PLS-models may be displayed in control charts. As pointed out in Refs. 1 and 33, this will enable the identification of animals that respond slowly to intoxication compared with the majority of the population, and also those that respond quickly. Also, such charts may suggest magnitude and directionality of the response of each animal. Thus, in summary, there is really no doubt that the combination of urinary NMR-data and PCA=PLS offers a promising approach to addressing the mechanism and nature of pathological events. Within the next few years we foresee a general breakthrough for this rapidly developing discipline in the areas of toxicological screening and disease diagnosis. ACKNOWLEDGEMENTS Figures 1–14 are reproduced from Ref. 8 and are used with permission. REFERENCES 1.

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9 Use of Metabonomics to Study Target Organ Toxicity CRAIG E. THOMAS

ELAINE HOLMES

Investigative Toxicology, Lilly Research Laboratories, A Division of Eli Lilly and Company, Greenfield, IN, U.S.A.

Biological Chemistry, Biomedical Sciences Division, Imperial College of Science, Technology and Medicine, University of London, South Kensington, London, U.K.

DONALD G. ROBERTSON Drug Safety Evaluation, Pfizer Global Research and Development, Ann Arbor, MI, U.S.A.

1. INTRODUCTION Identifying target organ toxicity remains a primary objective of drug safety assessment. The more efficiently this can be done, the better. For example, can subchronic effects be detected with acute dosing, can the toxicity be detected in a 337

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less invasive manner than histopathologic assessment of collected tissues, etc. The bulk of the metabonomic literature over the past 5–10 years is devoted to studying target organ toxicity; either at the diagnostic or mechanistic level. Not surprisingly, the two most well-studied organs are the liver and kidney, while there exists a relative paucity of data on other organs as studied by NMR. This chapter will review the extensive literature on liver and kidney. Furthermore, recent efforts on using metabonomics to study drug-induced vasculopathies are discussed. This represents a unique opportunity to address a toxicity that hampers pharmaceutical drug development and for which current methods are intensive and invasive, and for which no reliable, robust biomarkers exist.

2. HEPATIC TOXICITY 2.1. The Liver as a Target Organ Hepatotoxicity continues to represent a major stumbling block for advancement of new chemical entities in human clinical trials (1). As the liver is exposed to absorbed xenobiotics and new drug candidates via portal vein perfusion and serves as the central point for metabolism, it is not surprising that this organ can be susceptible to multiple mechanisms of toxicity. Generally, acute liver injury manifests as cell death (necrosis) or lipid accumulation (steatosis). Direct injury to liver parenchyma can be readily detected by a combination of clinical chemistry and histopathology and, thus, monitored for in preclinical development. It is also possible to screen for hepatocellular damage in a variety of in vitro systems including isolated hepatocytes, liver slices, and cell lines with the caveat that these systems can have limitations with respect to metabolic capacity and, henceforth, bioactivation or detoxification (2). Because the liver is also designed to synthesize and secrete bile acids, drug-induced injury can lead to cholestasis that in itself can propagate additional hepatotoxicity. Not surprisingly then, certain drugs are also known to produce hepatic injury with features of both cholestasis and necrosis or apoptosis (3).

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In late stage preclinical development, or in clinical trials, termination of development due to hepatic injury becomes much less predictable, and more challenging to dissect mechanistically. In some instances, it is simply a matter of a ‘‘shrinking’’ margin of safety that can result from drug accumulation or enhanced metabolism to a reactive intermediate via enzyme induction. Chronic toxicity might also represent an inability to efficiently repair acute injury in the face of repeated insult. In man, liver injury is often idiosyncratic in nature and not always adequately predicted by studies in rodent and nonrodent mammals (1). Therefore, the ability to more accurately and rapidly assess this major liability early in the compound selection phase would represent a significant advance in drug development. If a technology with appropriate throughput provided insight into the mechanism of action driving the toxicity at an early stage, it is conceivable that an SAR can be developed around the hepatotoxicity. Alternatively, the identification of more precise and sensitive biomarkers for hepatic injury, whether direct or indirect injury, could improve on our ability to advance drugs through clinical development and minimize the occurrence of untoward effects. For purposes of this chapter, a biomarker will refer to a measurement that can be captured noninvasively and which is linked to a pathologic change. For example, the classic ‘‘biomarker’’ employed to monitor hepatic injury is serum transaminase levels, yet it is difficult to predict if a slight elevation in transaminases portends a progression to serious liver injury. In preclinical studies in animals, it is not unusual to see transient elevations in transaminases in blood with no histologic evidence of overt parenchymal injury. However, since it is of utmost importance to ensure patient safety in clinical trials, any suggestion of a hepatic liability can require that thousands of additional patients be monitored in order to adequately define the risk of exposure to a candidate drug, thereby greatly increasing the time and cost of development. As described below, metabonomics is promising as a tool to significantly increase the probability of identifying the liver as a target organ for drug-induced injury.

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2.2. Using Metabonomics to Study Acute Hepatic injury—Comparison to Clinical Chemistry and Morphologic Pathology Clearly, the pathogenesis of hepatic injury has multiple etiologies. However, in many instances, including microvesicular steatosis, nonalcoholic steatosis, and cytolytic hepatitis, perturbation of mitochondria has been shown to occur (3). As described elsewhere in this volume, biofluid NMR is an excellent tool to monitor multiple metabolic intermediates that are directly affected by the state of the mitochondrion. While the kidney is the target organ most intensely studied by high field NMR, metabonomic analyses of urine and tissue samples from animals treated with xenobiotics that induced varied types of liver injury have also been reported. One of the first published studies extending metabonomics to the liver was from Beckwith-Hall et al. (4) who studied three distinct hepatoxicants: a-naphthylisocyanate (ANIT), d-(þ) galactosamine (GalN) and butylated hydroxytoluene (BHT) by NMR. These three agents cause intrahepatic cholestasis, acute hepatitis, and centrilobular and periportal necrosis, respectively. In this study, the three toxicants were administered as a single dose to rats followed by 7 days of monitoring with conventional clinical chemistry, urinary metabolites via 600 MHz 1H-NMR, and histopathology of liver tissue. Perhaps not surprisingly, the NMR spectra identified a number of urinary metabolites that changed similarly amongst the compounds (Table 1). These included decreases in urinary excretion of citrate, 2-oxoglutarate, and succinate; changes that are often associated with toxicity to other target organs and appear to signal generalized toxicity, irrespective of the organ (5,6). More unique to the three hepatotoxicants was an increase in taurine, creatine, and acetate. The elevation in urinary taurine is consistent with the findings of Timbrell et al. (7,8) who demonstrated a similar increase for a variety of liver toxicants using methods other than NMR. Early 1H-NMR work also revealed hypertaurinuria in association with hepatotoxicity (9). Overlapping, but not all-inclusive, changes included increased


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Table 1 Major Metabolic Changes Identified for Hepatotoxicity Using ANIT, Galactosamine and BHT Increased Metabolite changes observed for all three toxicants Acetate Creatine Taurine Metabolite changes observed for two of three toxicants Alanine (A,G) Bile Acids (A,G) Glucose (A,B) Lactate (A,G)

Decreased

Citrate 2-Oxoglutarate Succinate

N-methyl nicotinate (G,B) Hippurate (G,B)

Table lists urinary metabolites identified as changed in response to ANIT (A), galactosamine (G), or BHT (B). Several metabolites were either increased or decreased in common across all three toxicants. For metabolites affected by two toxins, those increased were most similar for ANIT and galactosamine while those decreased were particular to galactosamine and BHT. (Adapted from Ref. 4.)

bile acid excretion with GalN and ANIT, while glycosuria was associated with BHT and ANIT. The most significant distinction between the compounds was best illustrated by using principle components analysis (PCA) to analyze time related changes in the NMR spectra from urine. The magnitude of the trajectory (position in PC1 vs. PC2 relative to time) for BHT indicated a less severe injury that was corroborated by histopathology evaluation. The position in movement along PC1 relative to PC2 for the compounds also highlighted differences or similarities in metabolic profile at various timepoints. For example, the mean position (PC1 vs. PC2) for the rats treated at 24–96 hr postdose with ANIT mapped closely to the GalN treated rats at 24–72 hr postdose. Inspection of the PCA maps revealed that bile acids were similar urinary components at those timepoints and dissociated the effects of these two toxins from BHT. The ability to separate the compounds by pattern recognition provides an opportunity to identify unique biomarkers

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for the particular pathologic lesion observed. In the case of GalN, there was increased urinary excretion of betaine, urocanic acid, tyrosine, threonine, and glutamate. While bile acid elevation was common to both GalN and ANIT, the amino aciduria was associated primarily with GalN. It is also important to note that the differences in metabolite profile were consistent with differing pathologic injury as shown by histologic examination. Correlations between histology, clinical chemistry, and urinary NMR spectra were also noted; for example, with ANIT, the maximum yield of urinary bile acids coincided with maximal elevation in plasma ALP and bilirubin levels, bile duct proliferation, and cholangitis. Overall, this study provided strong evidence that even with simple pattern recognition methods, it is possible to separate toxicants by unique groups or a ‘‘pattern’’ of biomarkers. The work of Beckwith-Hall has been more recently confirmed and extended by Waters et al. (10) who performed NMR analysis on liver, urine, and plasma of ANIT-treated rats. Clinical chemistry=hematology and histopathology evaluations were also performed to provide an overall integrated metabonomics study of ANIT-induced liver injury. Critical to the evaluation was a complete evaluation of all these parameters at 3, 7, 24, 31, and 168 hr postdose allowing a detailed picture of the time dependence of the insult and repair to emerge. The earliest noted changes were an increase in plasma and urinary glucose; in agreement with the corresponding drop in hepatic glucose and glycogen noted histologically. Also emerging early were an elevation in liver and plasma lactate consistent with increased glycogenolysis and glycolysis. The commensurate depression of succinate, citrate, and 2-oxoglutarate implied a general increase in energy metabolism. There were also reported changes in lipid metabolism and storage. As early as 24 hr postdose, there was an elevation in lipids, such as triglycerides in liver, plasma, and urine, which fell below control levels by 168 hr and could be explained by either a drug-induced steatosis, triglyceride accumulation as a response to toxic injury, or an impairment of hepatic apolipoprotein formation. The slight lag in plasma lipid elevation relative to the liver agrees with the histo-


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pathologic evidence of cholestasis. While the overall timedependent trajectory of injury suggested a return toward a ‘‘normal, control state’’ beginning at about 72 hr postdose, there were specific changes in lipid profiles that were noted late. The decrease in hepatic lipid content at 168 hr postdose was associated with increases in trimethylamine-N-oxide, betaine, phosphocholine, and choline. This can be explained by catabolism of accumulated lipids to intermediate species. As these alterations occurred late, and coincident with bile duct hyperplasia, these components could be considered as biomarkers for the bile duct cell proliferation. As with the previously described work using ANIT (4), NMR evaluation of the urine revealed bile-aciduria and glycosuria; as well as marked elevations in taurine and creatine that are currently accepted as markers of hepatic injury. While the source of taurine is not established for many hepatotoxicants, the known dependence of ANIT toxicity on intraheptic recycling of a GSH–ANIT conjugate suggests a possible mechanism. The increase in taurine may be a protective mechanism that prevents cysteine buildup, in response to the decrease in GSH biosynthesis that occurs following GSH liberation via dissociation of the drug conjugate. The induction of hepatotoxicity in this study, as judged by urinary NMR analysis, was also confirmed by the marked elevations in plasma transaminases, glutamate dehydrogenase, and sorbitol dehydrogenase. This concordance of NMR data with the methods of clinical chemistry and histology data was consistent throughout the study. Furthermore, the careful staging of timepoints allowed a clear picture of the etiology of hepatic injury and recovery. While this study utilized all the technologies (clinical chemistry, histopathology, NMR) at all the timepoints, it must be recognized the tremendous resources that would be consumed to do this on a routine basis. The ability to noninvasively monitor urinary changes easily, and for relatively little cost, highlights one of the distinct advantages of metabonomics. However, as for any new and unproven technology, careful validation of the technology is required necessitating the conduct of detailed and laborious studies, such as that just described, in order to establish a strong link

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between metabolite changes and the current ‘‘gold standards’’ of morphologic pathology and clinical chemistry. 2.3. Metabonomic Studies of Dose-Dependent Hepatotoxicity Most metabonomic studies, including the aforementioned, were conducted using a single dose. In the arena of drug discovery and development, important decisions require a clear understanding of dose-dependent effects and margin of safety based on systemic exposure to the drug candidate. Two relatively recent studies have addressed whether biofluid NMR can detect dose-dependent effects using several well-studied hepatotoxicants. Robertson et al. (11) evaluated the feasibility of using high field NMR and statistical paradigms for pattern recognition to screen for toxicants which affected the liver or kidney. In this study, both a high and a low dose of the hepatotoxicants ANIT and CCl4 were employed. Again, the metabonomic results were compared to histopathology and clinical chemistry in an ongoing effort to validate the NMR technology. Clinical chemistry changes were restricted to the high-dose animals only, peaked at 24–48 hr and returned to control levels by Day 4 for both toxicants. Microscopically, ANIT (10 mg=kg) showed minimal bile duct proliferation while the dose of 100 mg=kg resulted in additional features including increased numbers of Kupffer cells, fibrosis, and necrosis in two of four treated rats (Fig. 1). At 0.1 mL=kg CCl4, only minimal hepatocellular vacuolation was noted, but 0.5 mL=kg caused mild to marked necrosis and Kupffer cell proliferation that was significant at Day 4, but had returned to normal at Day 10. Both CCL4 and ANIT showed the common feature of a decrease in urinary excretion of 2-oxoglutarate, citrate, and hippurate. On Day 1, the trajectory for the two doses of ANIT was similar in magnitude and direction indicating similar biochemical effects at the two doses (Fig. 1). Subsequently, however, the low-dose animals returned rapidly to control regions of the trajectory plot, while the high-dose animals had maximal injury on Day 2 corresponding to


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Figure 1 Principle components analysis of urine spectra from rats treated with ANIT. Male Wistar rats were treated with a single oral gavage dose of ANIT at 10 and 100 mg=kg and urine collected pretest and in 24 hr intervals thereafter for 4 days. The numbers depict sample days and the letters denote an individual animal. Individual animal data from the same sample days are grouped and highlighted by the shaded polygons. Thus, each polygon represents the inter-animal variability for each day, while the distance of each polygon from the appropriate pretest polygon is a measure of the magnitude of the effect. Only pretest and Day 1 are shown for the 10 mg=kg group. The corresponding mean serum total bilirubin levels for each group are also shown in parentheses. For the micrographs, ANIT at 10 mg=kg showed minimal bile duct proliferation while at 100 mg=kg additional features included increased numbers of Kupffer cells, fibrosis, and necrosis. (Figure adapted from Ref. 11.)

the maximum serum bilirubin elevation. Likewise, the trajectory analysis for CCL4 at the two doses demonstrated a more severe and longer lasting metabolic disturbance at the high dose.

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The findings reported by these investigators demonstrated that the time course of injury and recovery was similar when comparing NMR to clinical chemistry data. It was also of interest to note that animals that appeared to be outliers from the clinical chemistry data, based on severity or temporal effect, were also judged to be outliers by PCA of the NMR spectra. This tight agreement between the NMR technique and the clinical chemistry was stronger than between NMR and histology, but it must be considered that tissues were obtained for microscopic examination only on Days 4 and 10. Nonetheless, this study has several important connotations. First, it further solidifies the strong correlations between conclusions based on clinical chemistry findings and those from NMR data. As toxicology studies frequently use serum markers to monitor and quantify toxicity, and these parameters are also often used clinically, this is further proof for the potential application of metabonomics both in animals and man. Secondly, it is one of the first illustrations of the sensitivity of the NMR to distinguish dose-dependent effects in a manner reasonably consistent with histology. The PCA of ANIT effects demonstrated a separation from controls for both doses, while the low-dose CCl4 animals could not be distinguished from controls. Accordingly, the only detectable histologic change in the low-dose CCl4 treated animals was a slight depletion of glycogen. Finally, this work demonstrated that it was possible to rapidly and easily distinguish the two hepatoxicants from the two renal toxicants, following a single dose, using NMR and simple pattern recognition techniques. A second study, which also touched upon the issue of dose, investigated hydrazine toxicity (5). Similar to what was described for ANIT, Nicholls et al. combined 1H-NMR analysis of urine and plasma with traditional clinical chemistry and histopathology of the liver. Again, this emphasizes the critical need at this juncture to continue to test and validate metabonomic technology against the currently accepted methods that are used to make critical decisions on safety of drug candidates. Hydrazine, a well-known hepatotoxicant, was dosed at 75, 90, and 120 mg=kg and caused midzonal hepatic fat vacuolation at 48–72 hr postdose, and which had


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resolved by 7 days. The NMR analysis of the urine revealed clear dose-dependent effects as judged by the magnitude of the changes on PCA. At 75 and 90 mg=kg, the trajectories were maximal at 24–32 hr, but by 152 hr were similar in position to those of control rats as judged by PC1 vs. PC2, thus indicating recovery. At 120 mg=kg, the animals were removed from the study early owing to poor health, yet it was clear that these animals were more affected, particularly in the direction of PC2, as compared to the two lower dose groups. Detailed examination of the loading plots showed the usual, toxicant-induced decreases in Krebs cycle intermediates citrate, succinate, and 2-oxoglutarate, as well as trimethylamine-N-oxide, fumarate, and creatinine. Interestingly, the decrease in 2-oxoglutarate occurred earlier than changes in citrate and succinate suggesting an impact on pathways other than just the tricarboxylic acid cycle. Consistent with the work with other hepatotoxicants (4,8), urinary levels of taurine and creatine were elevated as was excretion of threonine, N-methylnicotinate, tyrosine, b-alanine, citrulline, Na-acetylcitrulline, and arginosuccinate. In the plasma, there was a general trend of elevated amino acids including glycine, isoleucine, valine, lysine, arginine, histidine, and threonine. Low molecular weight substances elevated in both plasma and urine included alanine, citrulline, tyrosine, and creatine. There was also an overall reduction in plasma lipids, particularly in the region of the spectra associated with long chain CH2 groups of fatty acids and terminal methyl groups. A specific examination of the dose–response relationship for specific metabolites was conducted. This is significant, as demonstration of dose-dependency would help to solidify the metabolites as biomarkers of hydrazine toxicity, hepatotoxicity, or both. To some extent, the elevations in both urinary and plasma tyrosine were dose-dependent. The most striking finding was the dose-dependent effect of hydrazine on levels of 2-aminoadipate (2-AA) in both biofluids. This potential biomarker had been previously shown to be associated with hydrazine toxicity (12), but in this work 2-AA was followed over the entire 7 days of the experiment, and was shown to be the major discriminant indicative of a compound-induced

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effect. The effect of hydrazine on both 2-AA and tyrosine can mechanistically be explained via aminotransferase inhibition and pyridoxal 50 -phosphate sequestration. Increases in alanine in liver and plasma by hydrazine in earlier studies had been suggested to reflect transaminase inhibition (13). Furthermore, 2-AA has been reported to be a neurotoxicant causing seizures and convulsions (14) and its elevation may explain the reported neurologic effect of hydrazine. Shown in Fig. 2 are results from a study in which Sprague–Dawley (SD) rats were dosed with hydrazine at 30 and 90 mg=kg. Data are presented for predose and at 24 and 48 hr after dosing. It is apparent that there is a dose-dependent effect for a number of metabolites. This includes 2-AA which is detectable at 24 hr at 90 mg=kg, but not until 48 hr at 30 mg=kg. Overall,

Figure 2 600 mHz 1H-NMR spectra of urine samples from hydrazine-treated rats. Male, SD rats were given a single dose of hydrazine at 30 or 90 mg=kg. Urine was collected prior to, and in 24 hr intervals after dosing. Dose-dependent effects for a number of metabolites are evident with respect to magnitude of the change or, in the case of 2-amino-adipate, the effect is observed at an earlier timepoint at the high dose.


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these studies clearly demonstrate the potential power of high field biofluid NMR to understand toxicity mechanistically and to provide biomarkers of intoxication. 2.4. Phospholipidosis In addition to the aforementioned work on well-known hepatotoxicants, several recent publications have focused on a particular pathologic condition, rather than a specific toxicant, namely phospholipidosis (PLD). Phospholipidosis is generally associated with subchronic or chronic treatment and is characterized by the accumulation of phospholipid within cells; the most characteristic finding being the appearance of ‘‘foamy’’ macrophages. Whilst observed with multiple chemical structures, the classic inducers of this phenomenon are cationic amphiphilic drugs of various classes including antidepressants, antiarrythymics, antianginals, and others (15,16). The current requirement to characterize this feature by electron microscopy and=or biochemical measurement of tissue phospholipid content does not lend itself to facile or early identification of this issue early in drug safety evaluation. Thus, metabonomics has been investigated for its ability to provide biomarkers of this condition. Nicholls et al. (17) studied five drug candidates by NMR; two of which had been shown to cause mild PLD in lung and liver. After a single dose of the drug, urine was collected for 48 hr, at which time rats were sacrificed for histologic examination. Two of the compounds resulted in the appearance of foamy alveolar macrophages with evidence of hepatic lipid accumulation. The urinary profiles for the animals demonstrated a clear difference between these two compounds and the three which showed no morphologic evidence of PLD. The spectra of these two compounds were distinguished by decreases in urinary citrate and 2-oxoglutarate, which have already been shown to be nonspecific markers of xenobiotic exposure. However, there was a clear elevation in phenylacetylglycine (PAG) which is not a commonly observed metabolite and it was suggested that these three metabolites together may represent a set of biomarkers signifying this condition. What is puzzling

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is that it is difficult to understand the linkage between the formation of PAG and the mechanism of toxicity; this is in need of further investigation. Nonetheless, another group has also reported on the appearance of PAG in association with PLD (18). We have also utilized metabonomics in an effort to develop a ‘‘screening’’ method for this toxicity. In general, we observe an increase in PAG when compounds such as amiodarone are utilized (Fig. 3). While the endogenous level of PAG in rats is generally low, there are variations in control, nontreated animals. Currently, our belief is that endogenous levels of PAG reflect variations in diet and fed vs. fasted conditions, but this remains to be ascertained.

2.5. Magic Angle Spinning (MAS) of Liver Tissue To date, the progress made in using biofluid NMR to characterize hepatotoxicity is encouraging. The ability to assign biomarkers specifically to hepatic injury will depend upon powerful chemometric methods that compare and contrast amongst various target organ biofluid profiles. Another opportunity to associate metabolites with liver damage is to directly measure metabolite changes in the liver using magic angle spinning-NMR (MAS-NMR) as described more fully in Chapter 5. Bollard et al. (19) have demonstrated the ability of MAS-NMR with an 800 MHz instrument to identify various classes of biological molecules in intact liver tissue. Not surprisingly, the success of this technique is highly dependent upon tissue sample preparation (20). One-dimensional (1D) NMR was capable of resolving substances of generally less than 1000 Da. Using multiple 2D methods, it was possible to assign resonances to low molecular weight substances such as glucose, alanine, glutamate, glycine, and others; as well as to observe signals from glycogen. As glycogen depletion is often one of the more subtle manifestations of liver injury, these data suggest that NMR can detect at least one of the pathologic hallmarks of toxicity. It must be cautioned, however, that as with any new technology, the results must not be over-interpreted. In many toxicity studies, glycogen


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Figure 3 600 mHz 1H-NMR spectra (d 1.5–4.5) of urine samples from amiodarone-treated rats. Fisher 344 rats were given a single oral gavage dose of amiodarone at 500 mg=kg and urine was collected prior to dosing and in 24 hr intervals thereafter. Evident by 24 hr was a reduction in several metabolites, including the Krebs cycle intermediates succinate, 2-oxoglutarate, and citrate. Phenylacetylglycine (PAG), which is less often observed in urine, was detectable at 24 hr postdose, with a greater concentration at 72 hr postdose.

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depletion is a relatively short-lived finding and is not ultimately linked to any evidence of hepatocellular injury. In many instances, glycogen depletion can signify a decrease in food consumption associated with acute compound administration. As an aside, it must be considered that in acute toxicity studies, inappetance is often noted which would naturally be expected to influence many intermediary metabolites, in addition to glycogen. Thus, a future area of investigation should be to study the impact of food and=or water deprivation on urinary metabolite profiles and how to model these changes in relation to a direct compound effect that results ultimately in tissue injury. A very recent study described NMR and pattern recognition studies on liver extracts and intact livers from rats treated with ANIT (21). Aqueous extracts of liver following ANIT treatment showed progressive decreases in glucose and glycogen, while signals for bile acids, choline, and phosphocholine were elevated. Additionally, an increase in the cytoprotective agent glutathione was detected at 24 hr postdose. The lipid fraction (chloroform:methanol extract) revealed elevated triglyceride levels. Using MAS-NMR, which required little sample preparation, it was possible to distinguish similar alterations. What was not revealed by MAS-NMR was the increases in GSH and lactate, and not all timepoints showed the elevations in choline and phosphocholine. This may reflect that extraction of the lipids from the membrane into solution allowed isotropic motion enabling measurement similar to liquid 1H-NMR. When PCA was performed with the combined data of both aqueous and organic extracts, the trajectory plot indicated a recovery by 168 hr postdose; the same was not true for the MAS-NMR. However, the variation in the data was less for MAS-NMR as compared to 1H-NMR. In spite of these differences, the late (168 hr) changes accounting for the separation from control animals by 1H-MAS-NMR were commensurate with the bile duct hyperplasia noted histologically. Thus, 1H-MAS-NMR provided an opportunity to screen potential target organs with minimal resources devoted to sample preparation.


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While MAS-NMR is expected to further develop our ability to study target organ toxicity, it must also be recognized that it is possible to see metabolite changes in tissues which are not injured, as judged by morphologic pathology. This concept was nicely illustrated in the work of Garrod et al. (22). In this study, the renal papillary toxicant 2-bromoethanamine (BEA) was administered as a single dose to SD rats with kidney (cortex and papilla) and liver tissue obtained at 2, 4, 6, and 24 hr postdose. The most pronounced finding was an elevation in glutaric acid which was attributed to an inhibition of mitochondrial fatty acyl CoA dehydrogenases. While the renal papilla is clearly the target organ for BEA, glutaric acid was found in all three tissue samples suggesting that BEA induced an overall mitochondrial defect. The other remarkable change in the papilla included a marked depletion of renal osmolytes that may signal a homeostatic response to polyuria. The liver also had increases in triglycerides, lysine, and leucine in addition to glutaric acid. Thus, while it could be considered that glutaric acid represents a biomarker for renal papillary injury, this study highlights that changes within a given tissue do not necessarily implicate that tissue as a target organ; if a target organ is defined as only those organ(s) showing pathologic or functional alterations. While it is prudent to interpret metabonomic changes carefully when rendering judgements on the safety or efficacy of drug candidates, it is clear that the attributes of biofluid NMR make it suitable for rapid evaluation of new chemical entities at an early stage of assessment. In particular, the ability to monitor for compound effects in a nonbiased fashion is desirable at an early stage where limited, or no, in vivo data exist. While this same advantage can also be problematic in assigning metabolite alterations to a specific target organ as discussed above, it can be expected that this will become less of an issue with further work. For example, the association of elevations in taurine and creatine with liver injury is reasonably well documented (4,5,7,8). During the course of a study with a new chemical entity being evaluated as a potential drug candidate, we observed early elevations in taurine and creatine by urinary NMR. Previous histopathology findings

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with other members of this structural class had clearly shown renal papillary necrosis (RPN) as the dose limiting toxicity. However, based on these NMR data, in a subsequent study, the livers from animals treated with this agent were examined by light microscopy at 4 and 168 hr postdose. Consistent with the urinary NMR results, evidence of a drug effect was present at the early, but not late, timepoint. These data demonstrate nicely the linkage between the NMR findings and histopathology and document the sensitivity of biofluid NMR for detecting target organ effects.

2.6. Chemometric Analysis of HepatotoxinInduced Urinary Metabolite Changes The concepts of multivariate statistics and pattern recognition methods are described in detail elsewhere within this volume. However, as it is clear that data analysis and the development of robust models to describe target organ toxicity will be critical to the eventual acceptance of metabonomics as a tool for drug safety evaluation, several studies will be touched upon herein. From the work described in preceding sections, it is evident that there can be similarities in metabolic changes for differing toxicants when judged simply by PCA. This is certainly true for liver and kidney which are by far the most well-studied organs. In an effort to better describe the metabolic changes associated with liver or kidney injury, Holmes et al. (23) used hydrazine and HgCl2 as model toxicants to provide data for chemometric analysis. In addition, both SD and Han-Wistar (HW) rats were used with each toxicant. Chemometric analysis was able to distinguish control urine spectra from the two strains with the HW rat urine having more acetate, lactate, and taurine, and less hippurate, as compared to SD rat. Drug treatment produced organic acids, amino acids, and sugars as biomarkers of HgCl2 administration, while taurine, b-alanine, creatine, and 2-AA were found following hydrazine treatment. Soft independent modeling of class analogy (SIMCA) data analysis was employed to build predictive models from a training set of 416 samples


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according to toxicity type and strain. One hundred and twenty-four samples were used to test the models and 98% of the samples were correctly classified as control, hydrazineor HgCl2-treated. Furthermore, the method was sufficiently robust to correctly classify 79% of the time the control for the strain. These data represented an important advance as they demonstrated the great improvement afforded by model building, as compared to the more simplistic PCA, and built upon other, earlier investigations by this group using PCA and SIMCA (24). More recent work from the same investigators has further demonstrated the power of appropriate data analysis in the use of metabonomic data (25). In this study, 13 toxins or drugs affecting liver or the kidney were used. The toxins targeted the kidney cortex or glomerulus and induced hepatic injury of varying etiology including cholestasis, steatosis and necrosis as judged by histopathology. Again, both HW and SD rats were treated with different doses depending upon the dose required to elicit the toxicity for each compound, urine was collected over 7 days, and organs taken for histopathology at 48 and 168 hr postdose. The 1H-NMR spectra were data-reduced and analyzed using a probabilistic neural network (PNN) approach with 583 samples making up the training set and 727 used as the test set for validation. Using this method, the 13 classes of toxins could be distinguished from one another, including strain difference, in greater than 90% of the samples. Not only was the model able to classify the samples, it was also able to delineate the metabolites that dictated the classification. In some cases, there was a time dependence for classification. For example, the early phase of BEA-induced changes is dominated by glutaric acid which was classified with other compounds as a mitochondrial poison. Classification for BEA at later timepoints was with compounds causing renal papillary toxicity as defined by changes in trimethylamine-N-oxide, dimethylglycine, and creatine. From these two studies, it can be surmised that there exists tremendous promise for using biofluid NMR to rapidly and reliably identify and classify toxicants according to target organ, including regional effects, as well as mechanism or

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pathologic features such as cholestasis. While this in and of itself represents a significant competitive advantage for drug hunting, it also presents the opportunity to subsequently develop non-NMR methods to screen for particular toxicities during preclinical lead optimization. Additional methods for analyzing metabonomic data are also currently being evaluated. Data obtained from study of urine collected from ANIT-treated rats have been evaluated using batch statistical process control, otherwise referred to as batch processing (BP) (26). This method, normally used to monitor industrial processes, is based on partial least squares (PLS) and has the advantage that each rat is considered a batch. The technique provides two levels of data analysis; at one level, PLS regression against time permits the toxin-induced metabolic effects to be assessed. A second level of analysis revolves around PC-based analysis of lower level PC scores and allows a means for representing the total sequence of metabolic effects for a particular rat (batch). The two levels of loadings are inter-related and generate a more complete picture of xenobiotic-induced metabolic derangements. In the ANIT study, a model defining the mean urine profile for 7 days following a single 100 mg=kg dose was generated and compared to the control group. All ANIT-treated animals could be shown to deviate from control rats and changes were consistent with analysis by other methods. The advantage of BP was that it provided a facile way to visualize the response to ANIT on an animal-byanimal basis, as well as the net variation in metabolite excretion profiles.

2.7. Metabonomic Studies of Hepatic Injury in Man The most widespread use of NMR with humans has been in the study of inborn errors of metabolism (27). While the patient-to-patient variation in urine composition can make interpretation more problematic than in rats fed a consistent and well-defined diet, the method has been used successfully


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(for a description of major differences in rodent vs. human urine as judged by NMR, see Ref. 28). The NMR has also been used to study the progress of patients receiving renal transplants (29) or diagnosed with glomerulonephritis (30,31). The NMR technique has been used to study the metabolism of acetaminophen in man which is largely excreted in the urine in conjugated form. Overdosing with acetaminophen has been shown to cause centrilobular necrosis, subsequent to glutathione depletion. While the NMR readily detects differences in the drug conjugate profile following ingestion of toxic doses, evaluation of the spectra for endogenous metabolites is also informative. In both fatal and nonfatal cases, levels of lactate, tyrosine, alanine, and other amino acids were elevated pointing to a perturbation of hepatic transamination reactions (32). In a situation of the extreme overdose, elevations in amino acids in plasma were likewise noted. Therefore, urinary NMR profiles can be used to detect drug effects in human urine and can potentially identify target organs such as the liver. While there have been a few isolated reports documenting NMR analysis of urine following accidental poisoning, there are at present no detailed investigations specifically addressing the use of urinary metabonomics to study hepatotoxicity in humans. The study of bile can provide an indirect assessment of hepatic function and this has been done in several instances in humans. NMR spectroscopy of bile is challenging owing to the complexity of the matrix and the broad resonances due to the presence of bile acids in mixed micelles with cholesterol and phospholipids (28). One approach to overcome this is to lyophilize and reconstitute the sample in water; however, loss of certain unstable components during processing cannot be discounted. Powell et al. (33) have suggested the use of bile to monitor hepatic function in humans and have studied the bile composition from patients with primary biliary cirrhosis of the liver and with hepatobiliary diseases including cancer. It is reasonable to expect that as models, and associated biomarkers for hepatic injury in animals become more developed; additional use of the technique in the clinic will follow.

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2.8. Current Limitations in the Use of Metabonomics to Study Liver Injury Relative to the kidney as a target organ, the available data for specific hepatotoxins are scarce. It is readily apparent that most hepatotoxicants, and toxins in general, lead to disturbances in metabolites influenced by mitochondrial function such as citrate, succinate, and 2-oxoglutarate. This is perhaps not wholly surprising considering the central role the mitochondrion plays in cellular metabolism and in providing reducing equivalents that are essential in xenobiotic metabolism and detoxification. It is clear from several studies that increases in urinary taurine and creatine are often associated with hepatotoxicity and not with other target organs (4,5,7–9). At present, however, it remains somewhat tenuous to ascribe changes in these two metabolites solely to hepatic injury. For example, cadmium has been shown to decrease citrate, 2-oxoglutarate, and succinate which occurs coincident with an elevation in creatine (6). At first glance, one might suspect hepatic injury, however, the liver is not affected as judged histologically and creatine was shown to arise via direct release from the seminiferous tubules. An added complication might be that urinary taurine has been shown to increase with age in laboratory rodents (34). This would render control for age, a criticality in the study of hepatotoxicity. Much success has been achieved in developing models for regional effect of toxicants in the kidney. In the liver, as described in the introduction to this chapter, there are also marked differences in the location and nature of liver damage. For drug development, it will be valuable to be able to delineate site and mechanism as this may impact significantly on the level of concern and the safety margin that must be achieved to deliver success in the clinic. The recent described studies with SIMCA and PNN offer hope that robust models that reliably predict liver injury can be developed since Holmes et al. (24) were able to classify liver toxins as cholestatic, steatotic, necrotic or ‘‘other.’’ Therefore, much additional effort to study hepatotoxins with multiple etiologies and resultant pathologic sequela is warranted.


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Another challenge, and potential pitfall, of metabonomics in the study of liver injury is that certain pathologies such as fibrosis would not be expected to be associated with metabolic derangements until significant tissue damage had occurred. As most metabonomic investigations currently use only a single dose, it can be anticipated that the expansion of the method to encompass more chronic pathologies will necessitate the use of multidose studies. Inherent in this is the continued need to link the metabolic changes with histopathology findings. In spite of these current challenges, considerable progress has been made in the use of metabonomics to study hepatocellular injury or liver function and further experimentation will continue to affirm the importance of this method in drug safety assessment.

3. RENAL TOXICITY 3.1. The Kidney as a Target Organ The kidney is particularly susceptible to toxicants, and nephrotoxicity represents one of the major causes of attrition in drug discovery and development. The vulnerability of this organ to xenobiotic-induced toxicity lies in a combination of contributory factors. The kidneys receive 25% of the total cardiac output and are thus exposed to high concentrations of toxins circulating in the blood, which are concentrated by tubular reabsorption and the counter current multiplier system in the Loop of Henle. Additionally, the renal cortex possesses an extremely high level of metabolic activity places a high oxygen demand on the tissue, thereby rendering the tissue susceptible to ischemic damage. Although many toxicants affect multiple tissues, others are specific to particular regions of the nephron. The kidney is a highly heterogeneous organ and is comprised of structurally and functionally distinct regions. Over 20 morphologically distinct cell types exist within a single nephron accounting for the differential distribution of enzymes and other endogenous metabolites throughout the kidney. Although certain chemicals, such as mercury and lead, are directly toxic to the kidney, more

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commonly a relatively inert parent undergoes biotransformation into a reactive metabolite; e.g., acetaminophen which is metabolized to para-aminophenol (35). Conventional assessment of renal toxicity and function includes measurement of the glomerular filtration rate, urinary flow rate, blood urea nitrogen, plasma creatinine, inorganic urinary electrolytes, urinary glucose, and clearance of para-aminohippuric acid (36). These tests of renal function are not specific, and therefore have limited diagnostic potential. In addition, the kidney has a high capacity for compensating for tissue damage and can mask the effect of a nephrotoxin until the onset of severe toxicity. Even after major intervention such as surgical removal of one kidney, within a short time the remaining kidney will hypertrophy to such an extent that the conventional clinical assays of renal function appear normal (37). Detection of enzymuria provides a more specific measure of renal pathology. For example, antibiotics are known to cause elevations in lysosomal enzymes such as N-acetyl glucosaminidase (NAG) and b-galactosidase, whilst metals such as mercury and cadmium induce an increase in urinary activity of the brush border membrane enzymes of the proximal tubule, including alkaline phosphatase, lactate dehydrogenase, and g-glutamyl transaminase (38,39). However, enzymuria is at best transitory. Metabonomics offers a more efficient means by which to characterize renal lesions, providing an effective screening tool for a wide range of low MW metabolites. Nephrotoxicity has been extensively studied using metabonomic technology and some of the toxins studied to date by metabonomic technologies are listed in Table 2, together with the major characteristic biomarkers associated with each nephrotoxin. 3.2. Proximal Tubular Toxicity Traditionally the proximal tubule has been divided into the pars convoluta and pars recta, but more often the terms S1, S2, and S3 are now used to define the structurally and functionally distinct regions of the proximal tubule (Fig. 4). By far the most common type of nephrotoxicity is proximal

Proteins ("), glycoproteins ("), dicarboxylic acids ("), hippurate (#), creatine ("), citrate (#), 2-oxoglutarate (#) Glucose ("), hippurate (#)

Glomerulus

TMAO ("), dicarboxylic acids ("), hippurate (#), creatine ("), citrate (#),

Renal medullary

2-oxoglutarate (#)

Glucose ("), amino and organic acids ("), hippurate (#), creatinine (#), citrate (#), 2-oxoglutarate (#) Glucose ("), amino and organic acids ("), hippurate (#), creatinine (#), citrate (#), 2-oxoglutarate (#)

Renal cortex S2=S3

Renal cortex S1

Main metabolic perturbations

Examples of Metabonomic Studies on Nephrotoxins

Region

Table 2

2-Chloroethanamine hydrochloride, Propyleneimine (58)

Cisplatin (42) 2-Bromoethanamine hydrochloride (50)

Hexachlorobutadiene (40) 1,1,2-Triflchloro-3,3,3-fluoro-1propene (41) S-(1,2-dichlorovinyl)-l-cysteine (41) Para-aminophenol (11,40)

Uranyl nitrate (64)

Sodium chromate (40,42) Cephalosporins (44,63) Mercury chloride (44)

Adriamycin, Puromycin (54)

Toxin (ref)

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Schematic diagram of a nephron.

tubular toxicity since a high level of blood flow to the kidney is delivered to the cortex. Moreover, the pars recta or S2=S3 region of the proximal tubule is generally most affected by toxicants as this portion of the proximal tubule has a greater capacity for active secretion of compounds than the convoluted portion of the proximal tubule. Damage to the proximal tubule is often manifested by failure to reabsorb solutes from the lumen of the nephron leading to high urinary concentrations of glucose, amino acids, and organic acids, amongst other metabolites. Toxins that predominantly target the S3 portion of the proximal tubule include HgCl2, hexachlorobutadiene, 1,1, 2-trichloro-3,3,3-trifluoro-1-propene (TCTFP), maleic acid, para-aminophenol, and uranyl nitrate. The urinary fingerprint generated by each of these toxins is remarkably similar (Fig. 5) and includes increased levels of glucose, amino acids


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Figure 5 Stackplot of 600 MHz urine spectra obtained from animals 48 hr after treatment with S3 renal cortical nephrotoxins. The spectra show the similarity of biochemical perturbation profiles; mercury II chloride (HgCl2), uranyl nitrate (UN), hexachloro-1,3-butadiene (HCBD) and 1,1,2-trichloro-3,3,3-trifluoro1-propene (TCTFP).

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such as alanine, glutamate, glutamine, isoleucine, leucine, lysine, threonine, tyrosine, and valine, and organic acids such as b-hydroxybutyrate and lactate. These urinary changes are indicative of reduced capacity of the proximal tubule to reabsorb such compounds. Other metabolic perturbations following the onset of renal cortical toxicity are more characteristic of mitochondrial effects or reduced muscle turnover and include a reduction in urinary concentrations of citrate, succinate, 2-oxoglutarate, hippurate, and creatinine (6,40–42). In particular, resonances from glucose, lactate, alanine, and b-hydroxybutyrate tend to dominate the urine spectral profiles after the administration of S3 proximal tubular toxicants. However, although there is a generalized pattern of dysfunction relating to S3 renal toxicity, the urinary perturbations observed also contain features that are specific to individual toxins. Because such a large portion of the 1D urine spectrum contains resonances from glucose, amino acids and organic acids and the tricarboxylic acid cycle intermediates (>25% of the aliphatic integral region), it can be difficult to detect multiple toxicity signatures in the urine profiles where a strong S3 proximal tubular response occurs. Metal species such as Hg2þ, U2þ, and Pb2þ all induce renal tubular damage by a combination of ischemia and=or direct cytotoxicity, in addition to targeting other organs and tissues. However, it is the tubular necrosis signature that usually dominates the spectral profile. Fluoride has been shown to induce widespread damage in the proximal tubule and also gives rise to a typical S3 urinary metabolite pattern (42,25). Likewise, cisplatin, which affects both the proximal and distal tubule, produces a predominantly proximal tubular profile (40,42). Although most proximal tubular toxins act upon the straight portion, certain nephrotoxins such as chromium and to some extent, cephalosporins, are more specific to the S1 or convoluted portion of the nephron (43). The metabolic pattern induced by chromate ions is distinct from that of the S3 tubular toxins and is dominated by glucose in the absence of gross amino acid and organic aciduria (42). In addition, although some depletion


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in citrate and succinate resonances are evident, the effect is not as marked as that observed with classic S3 toxins which can totally obliterate the tricarboxylic acid cycle intermediates from the spectral profile within 24 hr of a single treatment. A study investigating the nephrotoxicity of three cephalosporins also found a strong metabonomic signature for necrosis of the proximal convoluted tubule. Cephaloridine, cefoperazone, and cephalothin were administered to male New Zealand rabbits. All three compounds induced glycosuria and resulted in a depletion of urinary hippurate concentrations within 48 hr (44). However, only cephaloridine treated animals displayed histological evidence of necrosis, which would again indicate that metabonomic technology is, at least in some instances, more sensitive than conventional measures of renal toxicity. Similar conclusions have been drawn for other target organs. In support of this observation, work performed by Robertson et al. (11) showed that metabonomic technology was able to detect similar biomarkers of toxicity for the renal cortical toxin para-aminophenol at both high (150 mg=kg) and low (15 mg=kg) doses, whereas conventional histopathology and clinical assays were not able to differentiate the low dose from control. Although the focus of metabonomic technology originated from acute toxicity screening studies, recent studies have illustrated the scope of the technology in investigating more subtle pathologies such as those arising from environmental pollution. One such study employed MAS-NMR spectroscopic techniques (see Chapter 5) in order to directly investigate the effects of exposure to low levels of cadmium in the kidneys (45). Early evidence of cadmiuminduced nephrotoxicity was detected as altered renal lipid, glutamine, and glutamate, levels. 3.3. Renal Medullary Toxicity Model toxins that specifically target the renal medulla are rare. Many analgesics, such as ibuprofen, aspirin, phenacetin, mefenamic acid, and indomethacin have been reported to induce toxicity in the Loop of Henle (46) and are known to have a synergystic action, particularly when combined with

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caffeine. The chemical structures and physico-chemical properties of these compounds are quite diverse but they are all thought to produce an analgesic effect via inhibition of prostaglandin synthesis. A causal relationship exists between long-term intake of analgesics and RPN, and interstitial nephritis (47,48). The diagnosis of analgesic nephropathy is difficult, since the development of this condition is relatively silent until advanced stages of renal failure have occurred. Clinical symptoms of RPN are nonspecific and include loss of urinary concentrating ability, electrolyte wastage, increased blood urea nitrogen, and increased serum creatinine, which makes diagnosis of this condition difficult using conventional renal function assays. Model compounds such as BEA, 2-chloroethanamine (CEA), ethyleneimine, and propyleneimine (PI) have been used to model non-steroidal anti-inflammatory (NSAID) papillary damage since analgesics generally require longterm administration before papillary lesions develop. However, as with analgesic mixtures, although these model compounds predominantly affect the renal papilla, very often they also induce a secondary cortical damage that can obscure the underlying papillary necrosis. Additionally, rats are multipapillate, whilst humans are unipapillate, thereby further confounding extrapolation of data from model studies to human RPN. Various mechanisms of analgesic-induced RPN have been proposed including direct cellular toxicity, enhanced by the concentrating effect of the countercurrent multiplier system in the Loop of Henle, ischemia, inhibition of prostaglandin synthesis, free radical formation, and immunologic response. However, it is most likely that the pathogenicity of analgesic-induced RPN is multifactorial (48). BEA, CEA, and PI are believed to induce toxicity via the formation of the aziridine intermediate which is extremely reactive (49). All these compounds cause a reduction in citrate, 2-oxoglutarate, and succinate, in conjunction with a decrease or perturbation in the levels of trimethylamine Noxide and dimethylglycine, which are thought to act as nonperturbing renal osmolytes (Fig. 6). Additionally, an increase in creatine is also commonly a feature of drug-induced RPN.


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Figure 6 1H-NMR urine spectra obtained from a SD rat after a single i.p. dose of 150 mg=kg BEA.

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Other notable effects of BEA administration include increased excretion of lactate, alanine, and glucose (Fig. 6) which result from a secondary insult to the renal cortical tubules. In the case of both BEA and CEA, a major contribution to the spectral profile from 8 to 24 hr p.d. derives from resonances from dicarboxylic acids such as glutaric and suberic acid. Magic angle spinning NMR spectroscopy can be used to obtain metabolite profiles for small sections of intact tissue (10–20 mg) and has been used to measure intact renal papillary and cortical tissue following BEA administration in order to connect urinary biomarkers to pathological events. A time course of spectra over a 24 hr period (Fig. 7) shows the depletion of renal osmolytes such as betaine, TMAO, myoinositol, sorbitol, and glycerophosphocholine in the papilla together with an increase in creatine and glutamate levels and a change in the composition of the triglyceride resonances at d 1.26. Thus, perturbation of renal osmolytes and creatine correlated across both urine and papillary tissue profiles. The appearance of dicarboxylic acids in the urine at 6–8 hr postdose was deemed more likely to be related to a generalized mitochondrial dysfunction rather than a specific papillary lesion as these dicarboxylic acids were also found to be

Figure 7 1H-NMR-MAS spectra of renal papilla tissues were obtained from (a) control, and (b) BEA-treated rats 24 hr p.d. showing perturbation of the renal osmolyte profile.


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elevated in intact liver (Fig. 8) and renal cortical samples, as well as in papillary tissue from BEA-treated animals. 3.4. Renal Glomerular Toxicity The renal glomerulus is the primary site of action for many chemicals. However, compounds that specifically target the glomerular apparatus are also uncommon. Certain chemicals induce a change in the permeability of the glomerular basement membrane resulting in the leakage of proteins into the nephron. Destruction of the glomeruli will inevitably result in a buildup of debris in the tubules, which then induces a series of urinary perturbations similar to the characteristic proximal tubular signature. Typically, the renal glomerular signature includes a broadening of certain spectral features caused by proteinuria. Toxins such as puromycin aminonucleoside, adriamycin, penacillamine, and gold-based antiarthritic drugs have been reported to induce lesions in the glomeruli (50–53). Puromycin aminonucleoside reproducibly induces glomerular toxicity around 3–7 days after the administration of a single dose of 150 mg=kg (54). A series of metabolic patterns can be observed in Fig. 9a,b corresponding to initial effects of puromycin in the liver (manifested as increased levels of urinary creatine, taurine, and PAG). Also evident were effects in the renal tubules (slight glycosuria) followed by a marked spectral change over the latter time periods reflecting the glomerular lesion characterized by a broad envelope of resonances from proteins superimposed with sharper resonances deriving from glycoprotein fragments. Inspecting the spectral profiles by PCA allows the similarity of spectra to be represented efficiently and can help to unravel temporal patterns within the data. In the case of puromycin treated animals, the mean PCA trajectory shows three inflections corresponding to the metabolic status, or the stage of toxicity (Fig. 9). Metabonomic analysis has also been used successfully to follow adriamycin and to identify markers of both cardiotoxicity and renal glomerular toxicity (42).

Figure 8 1H–1H total correlation MAS spectra of (a) renal papilla, and (b) liver. In each case, a spectrum from a control animal (black) has been overlayed with a spectrum from a BEA-treated animal (red). Thus, metabolites that are present only in the BEA-treated sample appear as red crosspeaks only.

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Figure 9 Effect of puromycin aminonucleoside on urinary metabolites. (a) Stackplot of 600 MHz 1H-NMR urine spectra obtained from a rat after the administration of puromycin aminonucleoside, and (b) mean PCA trajectory plot showing deviation of trajectory from predose and control position, and progression with time.

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Figure 9 Continued

3.5. Species and Strain Differences in Response to Nephrotoxins Certain species and strains of laboratory animals are known to be susceptible to nephrotoxicity. Literature suggests that the Fischer 344 rat strain is particularly sensitive to renal toxicants (55), and that the Gunn rat is more susceptible to analgesic models (56). In contrast, desert dwelling rodents such as Mastomys natalensis and the Mongolian gerbil have proved to be more resistant to nephrotoxins, including HgCl2, BEA, PI, CdCl2, and phenylamine (49,57–60). Indeed, the basal metabolic composition of biofluids from healthy control animals differs between species and even between strains (see Chapter 10). Therefore, it is not surprising that there are significant differences in urinary profiles of Fisher 344 rats and M. natalensis in comparison to the SD rat, a standard laboratory strain, after the administration of a single dose of PI (58). Histopathology confirmed that Mastomys were more resistant to PI in comparison with SD rats, but did not find any differences in susceptibility between the SD and the Fischer 344 strains (58). Metabonomic technology provides a good platform for comparing the response of different laboratory species to xenobiotics. One such comparison employed


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probabilistic neural networks to assess the response of SD and HW rats across several liver and kidney toxins. Although the resultant response was found to be similar for both strains, the technology was sensitive enough to distinguish between the strains (25). 3.6. Direction of Metabonomic Research in Nephrotoxicity Studies One of the main strengths of metabonomic analysis is that information is generated for a wealth of low MW metabolites simultaneously; thereby facilitating the assessment of multiple biomarkers for specific pathologies. For example, as mentioned earlier, creatinuria is commonly a feature of hepatic injury and is particularly diagnostic in the presence of perturbed taurine and PAG levels. However, creatinuria in association with changes in the renal osmolytes indicates renal medullary dysfunction. Hence metabonomic data can also provide a framework for understanding the biochemical consequences of toxicity. Advances in computational algorithms and data filters are making metabonomic analysis more sensitive and hence more applicable across a wide range of biomedical disciplines (see Chapter 8). In terms of investigating nephrotoxicity, these tools can be used to ‘‘filter out’’ extraneous biological effects in order to focus on more subtle pathologies, or to remove the dominating effect of S3 tubular toxicity. The use of trajectories or BP methods (61) allows the dynamic nature of toxic lesions to be taken into account. The ability to describe the evolution of lesions provides a more accurate mapping of the similarities and differences between nephrotoxins with respect to site or mechanism of action. Following a single moderate dose of toxin, indices of onset progression and regression phases of lesions can be monitored and metabolic markers of both degeneration and regeneration elucidated (62). A ‘‘simple’’ PCA trajectory plot is shown in Fig. 10 for two renal cortical toxins, a renal medullary toxin, a renal glomerular toxin, and a hepatotoxin. The trajectories are constructed by connecting the mean response (as measured

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Figure 10 Three-dimensional plot showing the mean trajectories for groups of animals treated with regional toxins. The regions and toxins were: renal glomerular (puromycin aminonucleoside ;), renal medullary (2-bromoethanamine hydrochloride c), liver (hydrazine, ) and two proximal tubular (HgCl2 & and hexacholro-1,3-butadiene G). Note the different directions of the trajectories for each type of tissue lesion and the similarity of the two renal cortical trajectories indicating a common metabolic state.

in the first three principal components) of a group of animals in chronological order for each compound. The direction and the magnitude of the trajectory from the origin, or predose position, can be translated into information pertaining to the nature and extent of pathology. Moreover, as with simple PCA maps, the corresponding loadings can be calculated to indicate which spectral metabolites have the highest leverage, or are the strongest biomarker set, for each toxicity type.

4. VASCULAR TOXICITY 4.1. Challenges for Assessing Vascular Toxicity In the preceding sections, it has been described how metabonomics is complimentary to, or provides advantages over,


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currently existing, well-accepted endpoints for renal and hepatic toxicity. Metabonomics may offer, in fact, its highest level of application for those toxicities that cannot be reliably screened noninvasively or by simple blood tests. In these cases, the only reliable way to demonstrate toxic liability is by microscopic examination. By definition, these toxicities require the laborious effort of conducting in vivo toxicity studies followed by histopathologic assessment. Most often these studies involve large numbers of animals to convince the investigator that the presence or absence of the lesion at any particular timepoint is representative of the animal’s true response to the drug. Of course, one also has to be concerned about the time course of the pathology itself. When is an adequate time interval to assess the toxicity? Should multiple sacrifice times be included in the study design? How many animals per timepoint need be examined? Is there a sex difference, etc.? Clearly, it is these types of toxicities where metabonomics screening can present a tremendous advantage over traditional approaches. Vascular injury represents an example of such a problematic target toxicity and recent work using metabonomics as a new tool to study vasculopathies are discussed below.

4.2. Drug-Induced Vascular Injury Drug-induced vasculopathies, in particular those groups of vascular toxicities frequently, but perhaps inappropriately, called ‘‘vasculitis’’ are one example of a toxicity of great interest to the pharmaceutical industry that is in dire need of a technique for rapid, non-invasive assessment. The vasculitides are a significant problem because they are associated with several classes of pharmaceutical agents including phosphodiesterase type 3 (PDE3) inhibitors (65), PDE4 inhibitors (66), endothelin receptor antagonists (67,68), adenosine agonists (69), dopamine (DA1) receptor antagonists (70), and potassium channel openers (71). Beyond the range of therapeutic classes, species difference in response to these agents, further complicates their evaluation. In dogs, the lesion tends

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to primarily affect cardiac arteries, while in rat the mesenteric vasculature appears to be especially sensitive. Figure 11 presents a micrograph of a typical rat mesenteric vascular lesion. Not all compounds affect both species and, in those that do, one species may be more sensitive (based on systemic exposure) than the other. Clearly, much can be gained if these lesions can be assessed by something short of vascular microscopic assessment. Although vascular lesions in dogs are of as much interest as vascular lesions in rats, early screening efforts have focused on the rodent species simply because it is much easier from a logistics standpoint. Furthermore, certain rat mesenteric vasculitides, particular those induced by PDE3 and

Figure 11 Cross-section of mesenteric artery from a rat treated with CI-1018 for 3 days. Animals dosed at 750 mg=kg showing marked necrosis of the media with hemorrhage and mixed inflammatory cell infiltrates in the media, adventitia, and perivascular tissue.


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PDE4 inhibitors, are dose limiting for clinical studies. These findings have become significant hurdles in the development of these compounds. 4.2.1. CI-1018 The initial evaluation of metabonomic technology for use in assessing vasculitis was conducted using the PDE4 inhibitor, CI-1018 (72), a compound that had previously been demonstrated to produce a mesenteric vasculopathy in rats (73). Doses of at least 750 mg=kg are required to induce lesions in males within a 4-day time period, and routinely at least 1500 mg=kg is administered to induce vascular lesions in males. Females are a bit more sensitive, probably due to toxicokinetic considerations rather than a true sex difference in sensitivity. Despite the massive doses required to induce these lesions, the compound is fairly well tolerated and typically 100% survival can be anticipated over a 4-day dosing period. Even at the high-dose levels, incidence of vascular lesions in males is generally less than 50% at the 1500 mg=kg dose level (females tend to have 75–100% incidence at doses of 750 mg=kg and above) (74). Another drawback to the compound is that when administered orally, the vehicle typically contains polyethylene glycol (PEG) to ensure uniform suspensions. In practice, PEG is readily eliminated in rat urine and typically produces a major signal in the NMR spectrum in the region from 3.6 to 3.8 ppm that must be eliminated for further statistical analysis (72). Despite these drawbacks, CI-1018 proved to be a fairly useful model of drug-induced vasculopathy because of, not despite; it has relatively low potency in inducing this untoward effect. When groups of Wistar rats were administered from one to four doses of CI-1018 ranging from 250 to 3000 mg=kg, 11 rats (across doses) were found to have microscopic evidence of mesenteric vasculopathy. Thirtyseven rats across all doses did not have any microscopic evidence of vascular lesions. Urine collected daily during the study (pretest though termination) was assessed for NMR spectral changes using PCA. Results of that data

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clearly indicated that 8 of 11 rats, later identified as having vascular lesions, had distinct urine NMR spectra as revealed by the principal component map. Importantly, 36 of 37 rats without lesions had no such distinction. The lack of potency (for producing the vasculopathy) worked in favor of the technology in this setting because some rats dosed as high as 3000 mg=kg for 4 days, had no evidence of vascular lesions. Importantly, urine samples from those animals were not significantly different from control samples indicating that the urine spectral changes induced in animals with vascular lesions were not simply a reflection of unrelated high-dose effects of the compound (e.g., efficacy or other target organ toxicity). Figure 12 presents the PCA map obtained from one of the experiments in that study. 4.3. Advantages of Metabonomics for Assessing Drug-Induced Vascular Toxicity Some of the advantages of metabonomic technology have already been identified. The fact that vascular toxicity can be assessed noninvasively is certainly a significant attraction. The possibility of using the technology similarly in the clinical setting also makes the approach quite appealing. Less obvious

I Figure 12 PCA analysis of urine from 36 of 48 rats treated with CI-1018. Data are plotted as the first three principal components (PC1, PC2, and PC3). Open circles represent control and pretest samples. Solid circles represent samples from animals treated with CI-1018 (all doses combined). Letters indicate samples from the eight animals with vascular lesions (a–h). Numbers after letters refer to sample day. The large circle identifies clustering that separates animals with lesions from animals without lesions. X ¼ sample from 1500 mg=kg animal without vascular lesion falling outside the control cluster. Y0 and Y1 represent pretest and Day 1 samples from outlier animal—see text for explanation. (a) Plot oriented for best visualization of all data. (b) Plot rotated 45 around PC3 axis showing distinct pattern separation of samples from animals G and H from other samples. Both animals had profound ketonuria.


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is the fact that less bulk drug need be used to screen for these toxicities in vivo since each individual animal can be monitored from pretest through onset to peak and reversal of toxicity, obviating the need for satellite groups for time course studies. The data from the CI-1018 experiment described above demonstrated another advantage of metabonomics technology, specifically, the ability to identify concurrent effects within a group and even within an individual animal. Several animals within the study had clear evidence of ketonuria. Originally, the authors concluded that the ketonuria was simply a manifestation of toxicity-induced inappetence, a common cause of ketonuria. Later findings led the authors to question that assumption (74), but the fact remains that an effect completely separate from the vascular effect was readily apparent. The ketonuria observed in these animals may have mechanistic relevance and provide an avenue for further research that would have been otherwise unknown. 4.4. Metabonomics and Vascular Toxicity: Issue of Concern 4.4.1. The ‘‘Usual Suspect’’ Question One of the pressing questions that almost universally arises, when evaluating PCA analyses of NMR spectral data, is what exactly is driving the pattern separations. In other words, what are the biochemical changes in the urine responsible for the pattern separation. The hope, of course, is that these biomolecules may serve as biomarkers. Unfortunately, this is seldom as easy as it sounds. In the study cited above, the authors concluded that the major changes in urinary biochemical composition included the increases in the urinary ketone bodies, acetoacetate, 3-hydroxybutyrate, and acetone due to the ketonuria previously mentioned. Changes associated with pattern separation due to vascular lesions in the absence of ketonuria included decreases in citrate, 2oxoglutarate and succinate, fumarate and hippurate, as well as increases in formate. Representative spectra from the experiment are presented in Fig. 13. While these might be


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Figure 13 600 mHz 1H-NMR spectra of urine samples from normal and CI-1018 treated animals. Vertical scales were manually adjusted to provide a constant urea peak height and key metabolites in the urine are labeled. Bottom trace: Normal pretest urine (from animal D on Fig. 2). Middle trace: Urine from same animal after 3 days of treatment administration of 1500 mg=kg CI-1018 (D3 in Fig. 2). Top trace: Sample from animals treated for 3 days with 3000 mg=kg CI-1018, with profound ketonuria (G3 in Fig. 2) Inset: The upfield region of the top trace, plotted with a 15-fold reduction in vertical scale.

considered biomarkers of an effect, it is unclear as to what exactly they are biomarkers of. In particular, decreases in Krebs cycle intermediates have been noted with toxins as different as ANIT and BEA. How specific can these changes then be? In fact, these and several other metabolic intermediates are so frequently the drivers behind PCA separations of various toxicants; they have been dubbed, somewhat tongue in cheek, as ‘‘the usual suspects.’’ This example demonstrates how one of the technology’s biggest strengths can also be a

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significant weakness. The nondiscriminating nature of the analysis means any aspect of an animal’s pathophysiologic response to an exogenous compound is potentially observable. While this is an extremely powerful advantage when screening novel compounds with unknown toxicity profiles, it also means that frequently changes will be reflected in the urinary spectra that are unrelated to the toxicity of concern and which complicate the ability to interpret the data from a mechanistic standpoint. This should not be unexpected and the problem is equally applicable to both proteomic and toxicogenomic approaches. What is the genotype of a ‘‘sick’’ animal? When an animal loses weight or is unkempt in its appearance (urine scald, fecal staining, etc.), exhibits tremors or is hypoactive; do these effects produce altered gene transcription? What circulating proteins are quantitatively changed? Unfortunately, the questions are somewhat ‘‘chicken and egg’’ since it is difficult to discriminate cause from response with these nonspecific indices of toxicity—but that is precisely the point. When attempting to associate specific biomolecular changes (or gene transcript or protein changes for that matter) in urine or other tissue with target organ toxicity, these changes have to be interpreted in light of any and all indirect effects of the toxicant. These not only include any secondary toxicities the compound may have, but also include the indirect metabolic consequences of the toxicity of interest. For example, genes associated with oxidative stress may be mechanistically related to a vascular toxicity and the vascular effects themselves then induce hypoactivity and inappetence, which secondarily induce a gene response to these clinical effects. Clearly, unless you can temporally differentiate the genetic response, or differentiate based on severity, it becomes difficult to determine which genes are linked directly to the toxicity and which are secondary. The same holds true for metabolic responses; it should not be terribly surprising that rats can have similar clinical responses to a variety of toxicants and this common response will be reflected by similar biochemical flux through key metabolic pathways. However, toxicants having similar components driving PCA separations clearly have distinct spectral differences such that though


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different from control spectra, they are also different from each other (11). 4.4.2. Spectra=Toxicity Relationships The discussion in the previous section raises the second most common question asked when examining a series of compounds, all of which produce similar end stage pathologies. Many, if not all, PDE3 and PDE4 inhibitors induce mesenteric vasculitis in rats. If we assume the mechanism of vascular pathology is the same within this class of compounds, which seems a reasonable assumption, and the endpoint pathology is the same then should not the metabolic response be the same and all PDE4 inhibitors produce similar urinary spectral profiles? Figure 14 presents spectra obtained from two PDE4 inhibitors, CI-1018, and rolipram. It does not take PCA analysis to demonstrate that not only are these urinary NMR spectra produced by animals treated with these compound different from control, they are also quite different from each other. How can this be? The answer is familiar to anyone who has run similar type studies assessing either pharmacological or chemical structure activity relationships with regard to class toxicities. Though compounds within a chemical or pharmacological class frequently produce similar toxicities they are seldom identical with respect to all actions on animal physiology. Temporal response and severity frequently vary, clinical signs may differ, and sometimes secondary toxicities vary within the class. Certainly toxicokinetic responses are usually different to some extent. Therefore, it should not be surprising that at any given timepoint urinary spectra may differ quite remarkably among toxins producing, what would otherwise be considered, a similar response. The trick of course is identifying those key components of the urinary spectra that are reproducibly associated with the toxicity of interest, while separating out and discounting those effects that are secondary and only indirectly related to the target lesion. It is this arena where current efforts are focused in an effort to tease out those biomolecular changes that will have utility as biomarkers of vascular toxicity.

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Figure 14 600 mHz 1H-NMR spectra of urine from rats treated with rolipram or CI-1018. Shown are the spectra of pretest (control) urine sample (bottom trace) and urine from the rats treated for 3 days with rolipram (middle trace) or CI-1018 (top trace). Despite similar mesenteric vascular lesions with both rolipram and CI-1018, the NMR spectral changes induced by rolipram are notably different than changes induced by CI-1018.

4.5. Metabonomics and Mechanisms of Vascular Pathology The initial work demonstrating the utility of metabonomic technology to assess vascular toxicity raised a significant question. Were the biomolecular changes expressed in the urinary spectra a reflection of the mechanism of vascular toxicity or were they simply a reflection of the concurrent inflammation that is the hallmark of these types of lesions? This question is of great significance as the search for biomarkers


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for vascular toxicity has been largely restricted to markers of inflammation, which may not be specific for drug-induced vascular lesions alone (75–77). Furthermore, many drugs that induce vascular toxicity are being developed for inflammatory disease indications, which would greatly complicate the use of inflammatory biomarkers for assessment of vascular lesions. To address this particular question, an experiment was undertaken in which rats were pretreated with dexamethasone for a day and then concurrently administered CI-1018 and dexamethasone for four consecutive days at a dose previously demonstrated to produce vasculitis in 75–100% of animals receiving the compound (74). Additional groups of rats were given either vehicle, CI-1018 or dexamethasone alone to serve as appropriate controls. The data are summarized in Fig. 15. In the study, 6=6 CI-1018 treated animals had a detectable pattern shift during the course of the study in accordance with results noted previously (72). Interestingly, five=six animals treated with dexamethasone have vascular lesions characterized by minimal medial smooth muscle necrosis and degeneration without concurrent inflammatory cell infiltrates. One of the six dexamethasone=CI-1018 treated animals had no evidence of vasculitis. Perhaps most important was the fact that even in the absence of an inflammatory component of the lesion, the NMR spectral patterns were similar to those observed with CI-1018 alone, shifted relative to control with the five animals exhibiting medial lesions having shifted NMR spectral patterns, while the one unaffected animal had no observable pattern shift. These data suggested that the urinary spectral patterns induced by CI-1018 were not simply a reflection of the concurrent inflammatory process, but rather more directly related to the etiology of the lesion. These data raise a whole series of interesting questions; for example, how can a very focal lesion in one vascular bed induce micro- to milli-Molar changes in urinary biomolecular components? Mechanistically, this may indicate a yet-to-be-described intermediate metabolic component of the lesion (perhaps associated with ketonuria?). Another mechanistic question that arises when looking at the metabonomic data is the temporal nature of the NMR

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Figure 15 PCA plot from female rats treated with 750 mg=kg CI1018, with or without concurrent dexamethasone treatment. Numbers above symbols indicate day of sample collection (Day 1 ¼ pretest). Note distinct spectral separation of Day 3–5 (48–96 hr postdose) samples from animals treated with CI-1018 from control or pretest samples. Although dexamethasone markedly suppressed the inflammatory component of vascular lesions, vascular pathology was still evident and NMR spectral patterns differed little from animals treated with CI-1018 alone.

pattern shifts. Almost without exception, the onset of NMR spectral pattern shifts occur 48–72 hr after initiating dosing even if only a single dose is used to initiate the lesion. Furthermore, the onset of these changes generally precedes overt microscopic evidence of vascular pathology. Additionally, NMR pattern shifts have been observed to occur at doses of rolipram lower than those that induce overt vascular pathology (78). These data taken together have significant mechanistic ramifications that would not otherwise be available. The significance of these changes and the role they play in the etiology of the vascular lesions still need to be elucidated.


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4.6. Conclusions Metabonomic technology has already made an impact in the area of assessing and understanding drug-induced vascular pathology. As with any new technology, the questions generated by any set of experiments are frequently more important than the questions answered. Future work will focus on identifying those unique biomolecular changes associated with vascular toxicity that may serve as potential biomarkers. Furthermore, the biomolecules themselves will aid in the generation of testable hypothesis with regard to the etiology of the lesion. Already ‘‘panomic’’ experiments are underway which link transcriptomic, proteomic, and metabonomic technologies to generate a complete picture form gene to protein to phenotype. It is hoped that utilization of all three technologies in unison will be synergistic, rather than additive, with regard to our understanding assessment of drug-induced vascular toxicity.

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Waters NJ, Holmes E, Williams A, Waterfield CJ, Farrant RD, Nicholson JK. NMR and pattern recognition studies on the time-related metabolic effects of a-naphthylisothiocyanate on liver, urine, and plasma in the rat: an integrative metabonomic approach. Chem Res Toxicol 2001; 14:1401–1412.

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Espina JR, Shockcor JP, Herron WJ, Car BD, Contel NR, Ciaccio PJ, Lindon JC, Holmes E, Nicholson JK. Detection of in vivo biomarkers of phospholipidosis using NMR-based metabonomic approaches. Magn Reson Chem 2001; 39:559– 565.

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Bollard ME, Garrod S, Holmes E, Lindon JC, Humpfer E, Spraul M, Nicholson JK. High-resolution 1H and 1H–13C magic angle spinning NMR spectroscopy of rat liver. Magn Reson Med 2000; 44:201–207.

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Waters NJ, Garrod S, Farrant RD, Haselden JN, Connor SC, Connelly J, Lindon JC, Holmes E, Nicholson JK. Highresolution magic angle spinning 1H NMR spectroscopy of intact liver and kidney: optimization of sample preparation procedures and biochemical stability of tissue during spectral acquisition. Anal Biochem 2000; 282:16–23.

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Garrod S, Humpher E, Connor SC, Connelly JC, Spraul M, Nicholson JK, Holmes E. High-resolution 1H NMR magic angle spinning NMR spectroscopic investigation of the biochemical effects of 2-bromoethanamine in intact renal and hepatic tissue. Magn Reson in Med 2002; 45:781–790.

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Holmes E, Nicholls AW, Lindon JC, Connor SC, Connelly JC, Haselden JN, Damment SJP, Spraul M, Neidig P, Nicholson JK. Chemometric models for toxicity classification based on NMR spectra of biofluids. Chem Res Toxicol 2000; 13: 471–478.

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10 Physiological Variation in Laboratory Animals and Humans M.E. BOLLARD, E.G. STANLEY, Y. WANG J.C. LINDON, J.K. NICHOLSON and E. HOLMES Biological Chemistry, Biomedical Sciences Division, Imperial College, University of London, South Kensington, London, U.K.

1. INTRODUCTION Various physiological factors influence the metabolic composition of the biofluids and tissues of living organisms. Both internal and external stimuli result in small metabolic adjustments in order to preserve homeostatic equilibrium in organisms. The metabolite profiles of the tissues and body fluids provide a fingerprint of the metabolic status of an animal and the expressed phenotype is a product of many genetic and environmental events. Factors such as diet, temperature, 397

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hydration state, hormonal cycles, metabolic rate, allostatic load, age, gender, and circadian rhythms all interact to influence the metabolism of an organism in a dynamic manner. In order to interpret and understand the metabolic consequences of pharmacology, pathology, or genetic modification, it is first necessary to define ‘normality’ in healthy organisms and to establish the breadth of normal physiological variation. In general, pathological and toxicological effects on metabolite profiles are greater than pharmacological effects, with physiological variation causing even more subtle perturbations in comparison. Nevertheless, these subtle effects have a diverse range of both intrinsic and extrinsic sources, which affect many biochemical pathways resulting in characteristic metabolite variation in biofluids and tissues. These biochemical pathways may also be involved in toxification or detoxification of xenobiotics making it necessary to understand their contribution to defining ‘‘normality’’. Nuclear magnetic resonance (NMR) spectroscopy together with pattern recognition (PR) techniques provide an efficient tool with which to investigate the inherent metabolic variability in control populations of experimental animals and humans. A 1H NMR spectrum of a biofluid is extremely complex, consisting of thousands of well-resolved signals, the intensities of which reflect the concentration of metabolites present in the sample. NMR spectroscopy enables the simultaneous monitoring of a wide range of low molecular weight endogenous and exogenous metabolites, and provides a method for identifying organic compounds by virtue of the influence of the global and local chemical environment of the proton moiety. Thus, the 1H NMR spectrum of a biofluid or tissue sample provides a multidimensional fingerprint of an organism, which can be changed by a disease or toxin, or as an effect of the nutritional state or lifestyle of the animal. The PR methods can be used to reduce the complexity of the data, allowing the examination of sequentially collected urine or other biological samples over a given time-course to establish changes in profile and highlight the dynamic metabolic status of an organism (1). Previous chapters have described some of the principles and practices of analyzing NMR data

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with chemometric and bioinformatic tools to provide a means of characterizing and predicting a range of pathologies (see Chapter 9). Here, we illustrate the use of NMR-based metabonomic analysis to characterize the more subtle metabolic perturbations associated with physiological variation. The NMR spectra are exquisitely sensitive to detecting physiological variability in a ‘‘normal’’ population and the subsequent use of PR can generate characteristic patterns of biochemical perturbations describing a particular physiological or pathological state. The NMR–PR techniques have facilitated investigations into a range of extrinsic factors in the rat and mouse, such as diurnal variation, which is controlled by an artificial light–dark cycle in the laboratory (2,3). In addition, this metabolic profiling approach has been used to relate the metabolic signature or metabotype of a biofluid to differences in genetic composition of organisms and can be used to interpret the functional consequences of genetic modification (4). In order to differentiate between physiological and pathological responses in animal models and humans, we must first construct multivariate boundaries of normality. An estimated 3–5% of experimental animals are not healthy prior to inclusion in toxicological studies and as a result may show anomalous responses to toxins (5). The identification of these individuals can improve the sensitivity of the analysis and increase the interpretability of subsequent PR models incorporating toxicological or disease-related data. Such control models in humans, representing ‘‘normal’’ populations, are particularly useful in identifying individuals, who were non-compliant during clinical trials. The ability of NMR–PR based techniques to distinguish between various normal physiological states illustrates the power and sensitivity of this approach in detecting subtle changes in the endogenous metabolite profiles of the urine, and for investigation into biochemical and physiological rhythms. Within a laboratory environment, many of the external influences that may cause variability in caged animals, such as food intake, room temperature, and light intensity can be controlled. However, even in experiments where the animals are genetically homogenous and the environmental conditions

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are carefully controlled, metabolic differences between animals have been observed. Disparity in the microenvironment of an animal, such as hormonal fluctuations during the estrus cycle, level of activity and biological rhythms are more difficult to control and are reflected in the urinary and plasma 1 H NMR spectral profiles. Humans are exposed to a much greater diversity of environmental conditions and thus present a significant analytical challenge in terms of characterizing their normal metabolic profiles. Standard PR tools such as principal component analysis (PCA), hierarchical clustering, and neural network analysis have been effective in differentiating between pathological states and in indicating the presence and nature of physiological variation. However, since multiple environmental and genetic factors contribute to the overall metabolite profile of tissues and biofluids, the consequences of a single intended intervention can be difficult to disentangle from the other inherent sources of biological variability that influence the spectral profile. Urinary composition is able to change readily in response to many parameters, such as diurnal variations in intermediary metabolism, circadian rhythms, hormonal influences, stress, dietary components, the overall state of nutrition, stage of food intake, or levels of exercise and does this without exerting a detrimental effect on an organism. Other biological matrices such as plasma and tissues are under tighter homeostatic control and although metabolic challenges to the organism will result in a perturbation of the system, the changes in metabolite profiles are generally more subtle and harder to detect. In addition to the wide range of standard chemometric tools available, more sophisticated strategies including data filtering algorithms or data restructuring (e.g., logical blocking or QUILT analysis) and the application of differential scaling factors can be used to aid the deconvolution and removal of unwanted biological variation or ‘‘noise.’’ This allows us to focus on the metabolic effects of a single stimulus such as a drug or disease process (6). Some of these techniques are described in Chapter 8. This chapter aims to emphasize the importance of considering the extent and nature of physiological variance prior to interpreting data from toxicological or clinical studies in


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animals and humans. It is essential to determine normality if we wish to facilitate differentiation between physiological and pathological responses and ascertain the degree of pathological response.

2. PHYSIOLOGICAL VARIATION IN LABORATORY ANIMALS 2.1. Inter-Animal Variation The metabolic composition of urine from same-sex animals is known to vary according to the health status of a particular individual, as a result of genetic variability or as a response to stress. Such inter-animal differences can affect the metabolism of a drug or its toxicity. For instance, a significant degree of inter-rat variability is observed in urinary profiles after galactosamine treatment at toxic levels resulting in differing magnitudes of response between animals (7,8). More surprisingly, however, are the inter-animal differences found in supposedly homogeneous populations of rats maintained under identical environmental conditions. Recent toxicological studies in the Sprague–Dawley (SD) control rat and B6C3F1 mouse documented greater inter-individual variability of urinary profiles between rats than between mice (9). In the work carried out on physiological variation in female SD rats, urine from a group of 10 animals was sampled over a 10-day period (2). From PCA of the data, the scores from the first two PCs, which accounted for 66% of the variance in the data were tabulated, and the mean values and standard deviation ellipses for each rat were mapped using the mean integral values of urinary spectral data as input variables (Fig. 1). In general, samples from individual animals overlapped within one standard deviation. Several animals showed a higher degree of dissimilarity from other rats, for instance, rat 8 excreted relatively low concentrations of tricarboxylic acid cycle (TCA) intermediates and hippuric acid, and higher concentrations of taurine, dimethylglycine, creatinine, and glucose than the other rats in terms of urinary spectral

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Figure 1 PC1 vs. PC2 scores plot of mean-centered urinary spectral data standard deviation ellipses for each female SD rat sampled during several estrus cycles, illustrating the partial separation of individual rat data.

profile. Studies have shown that citrate, taurine, hippuric acid, and the renal osmolytes have a high inter-individual variation in control animals (2,10–12). The normal physiological levels of hippuric acid in the urine are known to be highly variable due to alterations in the gut microbial contents as a result of external factors such as stress or a change in diet (13,14). Citrate excretion is thought to be affected by numerous factors including nutrition, alterations in acid–base balance, hormones, calcium, and renal metabolites (15,16). 2.2. Age-Related Differences The age of experimental animals is known to influence their susceptibility to certain toxins and in many cases young


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animals have been shown to be more sensitive and show increased duration of drug action than more mature animals. The reason for this lies in the development of drug metabolizing enzymes, which can also be dependent upon the substrate, species, strain, and gender of the animal (17). In other instances neonates are less susceptible to drug toxicity due to differing hepatic metabolism of drugs. For example, in a study carried out by Moser and Padilla (18) into the biochemical toxicity of chlorpyrifos in young (postnatal Day 17) and adult (about 70 days old) rats, the magnitude of the age-related differences decreased as the rats matured. In addition, the onset of maximal effects was delayed in the young rats; recovery occurred more quickly and immature rats showed no gender-related differences in toxicity. The ageing rat undergoes numerous physiological changes that are reflected by physical and biochemical changes in the animal, resulting in a difference in the proportions of endogenous metabolites excreted in the urine. For instance, the quantity of aromatic metabolites in the urine is known to vary according to age of the animal in addition to the influences of diet and gut microflora (14,19). The effect of aging on urinary profiles is clearly illustrated in Fig. 2 whereby rats aged 12–13 weeks could be clearly separated from 7–8-week-old rats (20). Metabolite patterns in the urine are dependent on the stage of development of the animal, e.g., in the case of the rat, the kidneys do not reach full development, and therefore, a mature pattern of glomerular filtration, until the third month of age (19). The levels of sex hormones in the plasma are age-related in the rat. The mechanism of age-associated alterations in plasma sex hormone levels and their affect on drug metabolizing enzyme activities were studied in male and female Fischer 344 (F344) rats of ages ranging between 3 and 30 months (21). Plasma testosterone levels, as well as the activity of the rate limiting enzyme required for testosterone production in the testes, decreased with senescence. Imprinting neonatal female and male rats with either testosterone or estrogens, respectively, has been shown to alter the activity of certain hepatic enzymes to reflect the male and female liver type (22).

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Figure 2 PC plot of urine samples from 8-week-old (open squares) and 13-week-old (solid circles) rats showing distinct clustering of samples from animals differing in age by only a few weeks.

2.3. Gender Differences Studies have shown that the metabolite profiles of female urine samples differ significantly from those of males, for instance, sex-related differences in the elimination of citrate in the urine have been determined (10,23). In the work carried out on gender differences in the Han-Wistar (HW) rat, male urinary profiles were found to be more variable than females, particularly in the urinary excretion of the TCA cycle intermediates (24) and, in general, male rats have a greater metabolic activity than females. This results in a significantly greater exposure and hence prolonged pharmacological activity for many drugs in female rats compared with males (25). However, males tend to be more susceptible to toxins and, therefore, are generally preferred for toxicity testing (26). For instance, chloroform produces liver damage in male and female mice but renal injury only in males (26).


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The toxicity of cadmium and mercury salts in the rat is known to be more marked in males than in females (27). A striking effect of cadmium treatment in the rat is a sudden and large reduction in the TCA cycle intermediates in the urine. This effect is more pronounced and persistent in males and has been attributed to inhibition of renal carbonic anhydrase resulting in tubular acidosis (27). Cadmium toxicity also induces creatinuria in male rats and has been related to testicular toxicity (27). Gender-related differences in cocaine toxicity are well documented in the rat with lower doses and plasma concentrations required to induce toxic signs and symptoms in male rats than in females (26). There are, however, cases where female rats show a greater response to toxin treatment than males. For instance, female rats are known to exhibit a higher occurrence of d-galactosamine-induced fatty liver than males (28). In a study of male and female HW control rats, where urine samples were collected during the light cycle of the day, clear differences were observed in the urinary 1H NMR profiles between genders (24) (Fig. 3). Female rat urines comprised higher levels of N-acetyl glycoproteins, dimethylglycine, and certain bile acids, whilst male rat urine samples contained elevated levels of a sulfate-conjugated chlorogenic acid metabolite, meta-hydroxyphenylpropionic acid (mHPPA) and 2-oxoglutarate. The effect of gender on biochemical composition of the urine can be clearly illustrated using PCA (Fig. 4). In Fig. 4, male and female HW rat urinary spectral data are clearly separated in PC5 accounting. This separation of the data was enhanced by partial least squares discriminant analysis (PLSDA) to predict the gender with greater than 99% accuracy. Gender-related differences in sulfotransferase enzyme activities have been documented for amines and alcohol substrates in a number of species (29). The higher levels of m-sulfate-conjugate of m-HPPA in male urine samples compared with those of female rats may be of importance in drug metabolism investigations where sulfation is a major route of detoxification mechanism as in the case of paracetamol (24). The elevated levels of bile acid metabolites in female rat urine samples compared with that of

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Figure 3 The 600 MHz 1H NMR spectra (d9.0–0.5) of control urine samples collected from male (lower spectrum) and female (upper spectrum) Han Wistar rats. KEY: DMG, dimethylglycine; TMAO, trimethylamine-N-oxide; HoD, deuterated water; m-HPPA, metahydroxyphenylpropionic acid; PAG, phenylacetylglycine; NACs, Nacetyl-glycoprotiens; u1, unknown.

males reflect the increased rate of cholesterol and bile acid synthesis and metabolism in females (24). Separation of male and female plasma samples from HW rats has also been achieved using PCA (Fig. 5). This separation was attributed to lower concentrations of plasma lipoproteins in female rats compared with their male counterparts (Fig. 6). This difference is possibly related to a protective role of estrogenic hormones (30). It has been postulated that such sex-linked differences are under the influence of a sex-linked chromosome, most likely the X-chromosome (31). From clinical chemistry measurements, male rats have elevated concentrations of the enzymes alkaline phosphatase (ALP), alanine aminotransferase (ALT), alpostate aminotransferase (AST) glutamate dehydrogenase (GDH), and plasma triglycerides, whilst females have higher concentrations of total proteins (TPR) and albumin (23,32,33). Sex-related differences in toxicity have been related to differences in hepatic drug metabolism. The expression of


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Figure 4 Scores maps showing (a) PC1=PC5 scores and (b) t[1]=t[2] PLS-DA scores derived from the 1H NMR spectra of control urine samples showing separation of samples based upon gender of Han Wistar rats.

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Figure 5 PC1=PC2 scores maps derived from the single-pulse 1H NMR spectra of plasma samples showing gender-related separation.

sex-specific cytochrome P450s in rats and mice is regulated by growth hormone, thyroid hormone, and sex hormones (34). For instance, the sex-specific cytochrome P450s CYP2C11, CYP2C13, and CYP3A2 are expressed in males whereas CYP2C12 is expressed in females (35). Male rats secrete growth hormone in a rhytmic manner, whereas, growth hormone secretion in the female rat is ‘‘continuous’’ and is mimicked by the expression of certain cytochrome P450 enzymes. However, gender-related variation in drug metabolism between the sexes is exaggerated in the rat compared with other species such as the mouse (36). 2.4. Species Differences Not surprisingly control urine samples from different species can be separated by metabonomic analysis, enabling evaluation of biomarkers across species. For instance, the excretion of glycine conjugates in the urine has been shown to be


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Figure 6 The 600 MHz single-pulse 1H NMR spectra d5.5–0.5 of blood plasma samples collected at 48 hr postdose from a control male rat (lower spectrum) and a control female rat (upper spectrum). Abbreviations: 3HB, 3-d-hydroxybutyrate; HDL, high density lipid; HOD, residual water resonance; LDL, low density lipid; NAC, N-acetylglycoprotein; OAC, O-acetylglycoprotein, VLD, very low density lipid

species-dependent (37). The glycine conjugation of benzoic acid to hippuric acid and its subsequent urinary excretion occurs in primates, rodents, and rabbits, however benzoic acid is excreted unchanged or as the glucuronide conjugate by insects, birds, and reptiles (37). Similarly, the excretion of phenylacetic acid as the parent compound or as the glutamine, glycine, or taurine conjugate is species-dependent, for example, it is excreted as phenylacetylglutamine in humans (38) and phenylacetylglycine in rats (39). Figure 7 shows a PCA plot of urinary data from humans, rats, rabbits, and mice illustrating the inter-species differences in urinary profiles, where each species occupies a discrete area on the scores map (19). So far, the majority of metabonomic publications have been concerned with toxicology and physiological

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Figure 7 PC plot of combined NMR spectra from untreated animals and humans showing clear separation of all species.

variation in the rat, however, this is likely to change as the technology increases in popularity. Distinct species variations in the metabolic adaptation of small animals according to diet and growth strategy are known to affect the metabolic composition of urine (40). The metabolic profiles of three wild mammals (bank vole, shrew, and wood mouse) with different natural diets have previously been studied using 1H NMR and statistical PR, and compared with that of the SD laboratory rat (40). The four species were clearly separated by their urinary metabolite profiles. For instance the bank vole contained higher amounts of aromatic amino acids in its urine compared with the laboratory rat, whilst rats contained higher levels of hippuric acid (Fig. 8), which is known to be effected by diet, age, and gut microflora (13,18). Rat urine was also more homogenous in composition compared with the wild animals, containing less amino acids and TCA cycle intermediates and appeared to have less inter-group variation. This is not surprising as the laboratory strains of rat are in-bred to increase physiological homogeneity. The


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Figure 8 The 600 MHz spectra from bank vole (a), wood mouse (b), and rat (c). Bank vole urine contained a variety of aromatic compounds whilst rat urine could be distinguished by a relatively high concentration of hippuric acid. Key: A, lactate; B, alanine; C, acetate; D, glutamate; E, succinate; F, citrate; G, creatinine; H, creatine; I, TMAO; J, glucose and sugar containing region; K, urea; L, tyrosine; M, tryptophan; N, hippurate; O, urocanate; P, phenylalanine.

lower concentrations of amino acids found in the rat urine may indicate that wild animals are not able to metabolize fully the high protein content of laboratory chow and may express lower transaminase activity than rats (40). In addition, all three wild animals had higher concentrations of plasma triglycerides compared to the laboratory rat. This may have toxicological implications associated with increased half-life of lipophilic xenobiotics in wild animals compared with the rat (40). The pharmaceutical industry is continually looking for model species or strains that best mimic the human for toxicological screening or disease evaluation. Studies into inter-species differences in response to toxins between laboratory

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animals have been carried out by Holmes et al. (41,42) by comparing the SD and F344 rat and the Multimammate desert Mouse (Mastomys natalensis). The multimammate desert mouse has a large proportion of long looped nephrons relative to other strains of mouse and therefore, like most desert species is more efficient at concentrating urine (43). Analysis of the 1H NMR spectra of the urine from Mastomys and F344 rats revealed that there were higher levels of creatine, succinate, N-acetylglycoprotein, pyruvate, betaine, glycine, and other amino acids in control Mastomys urines compared with control F344 rat urine samples. The latter excreted greater amounts of 2-oxoglutarate and trimethylamine-N-oxide than the Mastomys. The relatively high levels of organic and amino acids in the urine of Mastomys may be a result of their greater capacity to conserve water by concentrating the urine (42). Species differences between the Mastomys and the F344 rats following treatment with the nephrotoxins HgCl2 (41), 2-BEA (44) and propyleneimine (42) were also observed, with the former proving to be more resistant to nephrotoxicity than the F344 rat strain. This was postulated to be related to renal medullary:cortical ratios and the elevated concentrations of the renal osmolytes, which have been shown to protect against osmotic stress in the kidney (41). Similarly, the Syrian hamster, which like the Mastomys also has a greater capacity for urine concentration than the rat, is documented to be less susceptible to chemically induced renal papillary necrosis (45). One of the major differences in the response of the Mastomys to BEA and propyleneimine treatment compared with the F344 rat was the induction of taurinuria (42,44), Taurine is known to protect renal medullary cells from osmotic stress (46) and, therefore, may accumulate in the inner medulla of the Mastomys, protecting it from nephrotoxins (42). A metabonomic approach employing multivariate statistical or PR analysis of 1H NMR urine spectra was applied to investigate the between species differential biochemical responses of rats and mice to hydrazine exposure (8). Several metabolic responses to hydrazine were common to both the rat and mouse, including elevated levels of urinary 2-aminoadipate and creatine and depletion of the TCA cycle


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intermediates. Metabolic trajectories were mapped in PC space using the mean spectral data at each time point to provide a way of monitoring the progression of and recovery from the toxic lesion. The difference in direction and shape of the trajectories between the rat and mouse reflected the distinct pattern of toxin induced changes in the metabolic profile, which may prove to be an indication of variation between the exact mechanisms of toxicity between the two species. The differences in response of the rat and mouse to hydrazine treatment were supported by histopathology (8). 2.5. Strain Differences Differences in response to xenobiotics between strains of commonly used experimental animals have been well documented. Strain differences in rats are known to effect metabolism and variations in enzyme activities in the liver and kidney have been well documented. For instance, variations in tryptophan metabolite excretion and enzyme activities have been found between two strains of albino mice and also between three strains of rat (Wistar, Gunn, and SD) (47,48). The susceptibility of male mice to chloroform induced nephrotoxicity is known to vary between the different strains (49). The urinary profiles of SD and F344 rats are similar except that the SD rat has slightly higher urinary concentrations of glucose and amino acids. The sensitivity of the F344 rat to nephrotoxin exposure is well documented (50–52) and hence this particular strain of rat is commonly used in mechanistic nephrotoxicity studies (52,53). In contrast, the SD rat is not considered to be particularly sensitive to nephrotoxic insult (54) and, as such, consideration of such differences in sensitivity between strains is, vital prior to carrying out toxicity studies. Metabonomics has previously been utilized successfully in determining the difference between HW and SD rats (1,10,41,42,44,55–58). The two strains are very similar metabolically and genetically, and are both routinely used for toxicity screening. However, using PCA, NMR urinary profiles of the two strains could be partially separated in metabolic

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Figure 9 The PCA plot of PC1 vs. PC2 from mean-centered datareduced 1H NMR spectra of 900 control urine samples from HW ( ) and SD (& ) rats.

space (Fig. 9). Despite the degree of overlap, the strain of rat could be predicted correctly 86% of the time using the supervised classification method SIMCA (Soft Independent Modeling Classification Anology). From visual inspection of the 1H NMR spectra of urine samples from the two strains, HW rats were determined to have higher levels of acetate, lactate, and taurine, whilst SD rats had elevated levels of hippuric acid (Fig. 10). Further work by Holmes et al. (56) observed distinct differences between the metabolic profiles of control urine from SD and HW laboratory rats using probabilistic neural networks (PNN). Strain-related differences in metabolic response to a number of liver and kidney toxins could also be characterized using this approach. Metabonomic methodology has successfully been applied to differentiate morphologically indistinguishable


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Figure 10 The 600 MHz 1H NMR spectra of whole rat urine (A) Han-Wistar and (B) Sprague–Dawley animals. Abbreviations: DMG, dimethylglycine; HOD, residual water; m-HPPA, m(hydroxyphenylpropionic acid); NAGs, N-acetylglycoproteins; 2OG, 2-oxoglutarate; TMAO, trimethylamine-N-oxide.

but genetically different species of earthworm from analysis of tissue extracts and celomic fluid using 1H NMR spectroscopy and multivariate statistics (59). Similarly, this approach has been utilized successfully for the determination of the metabolic differences between two strains of laboratory mice, the AlpK:ApfCD (white) and C57BL107 (black) mice. By PCA, it was possible to separate the two strains and to predict the strain of the mouse in 98% of cases using PLS-DA (Fig. 11). From comparison of the 1H NMR spectra of their

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Figure 11 The PC scores plot derived from the 1H NMR spectra of urine samples obtained from Alpk:ApfCD and C57BL10J mice.

urine, the white mice had noticeably higher elevated levels of 2-oxoglutarate, citrate, trimethylamine-N-oxide, and guanidinoacetic acid whilst black mice had higher levels of taurine, creatinine, dimethylamine, and trimethylamine (Fig. 12). Perturbations in renal osmolytes were postulated to be the result of strain differences in enzyme activity. The ability to predict phenotype or genotype has obvious applications to the study of genetic polymorphism and genetic modulation (transgenics). 2.6. Transgenic Models The number of different species with sequenced genomes is on the increase. Using NMR spectroscopy together with pattern recognition analysis, it is possible to derive metabolic profiles or metabotype of the biofluid, tissue, extract, or intact tissue from a genetically modified animal (60). Using this approach, the mdx mouse, a model of Duchenne muscular dystrophy has


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Figure 12 The 500 MHz 1H NMR spectra of typical urine samples obtained from (a) Alpk:ApfCD mouse and (b) C57BL10J mouse. Key: 2OG: 2-oxeglutrate, TNA trimethylamine-N-oxide.

been investigated (61,62). Samples of cardiac and brain tissue from the mdx mouse were shown to have distinct metabolic profiles compared with tissue from control mice. By calculation of the metabolite ratios in the two tissue types, the separation between mdx and control mice observed by PCA was attributed to elevation of the taurine levels relative to both creatine and phosphocholine. Taurine has previously been reported as a biomarker for dystrophic tissue in skeletal muscle (63,64) and is thought to be an adaptive response to a loss of dystrophin (61). Duchenne muscular dystrophy has also been investigated in intact tissue using 1D and 2D high resolution magic-angle-spinning (MAS) NMR coupled with PR (63). Changes in the intensity and shape of lipid resonances together with increases in lactate and threonine were observed. The variation in lipid composition in mdx skeletal muscle has previously been identified in vivo and in intact biopsy samples (65,66).

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2.7. Hormonal Effects Female rats tend to be less widely used for toxicity studies partly because of variations in hormonal status related to the estrus cycle. Female mice housed together will stop cycling if no males are present but will resume their estrus cycle on exposure to male mouse pheromones (67). This effect is known as the Whitten effect and can be used to synchronize female estrus cycles or for timing mating (67). The Whitten effect is less marked in female rats than in mice. Group housed female mice tend to stop cycling and display either pseudopregnancy (Lee-Boot effect) or anestrus (67). Sex hormones directly influence morphological and functional aspects of the kidney proximal tubule (68), for instance in the rat, pregnancy is associated with an increased glucose filtration rate and a decreased urine flow rate (69). However, hormonal effects on urinary composition are subtle compared with differences observed due to inter-animal or strain differences (2). Hormones are also known to effect the composition of plasma, for instance serum levels of calcium are independent of food consumption and are instead regulated by the hormonal glands (75). Estrus cycle-related perturbations in metabolic urinary profiles have been investigated using metabonomic technology. A control population of female SD rats was sampled twice daily (am and pm) for 10 days (2). The stage of the estrus cycle at each urine collection time point was determined by vaginal cytology. The rats progressed through at least one complete estrus cycle every 3–4 days. This cycle comprises of four distinct stages of, namely: proestrus, estrus, metestrus and diestrus. The partial separation of the different stages of the cycle observed by PCA of these data (Fig. 13) was attributed mainly to the levels of citrate, trimethylamine-N-oxide, creatine, creatinine, taurine, glucose, and N-acetyl glycoprotein resonances at each of the four stages (Fig. 14). Sex-related differences in the elimination of citrate may be linked to estrogen levels (9). Menstruation is known to effect the N-oxidation of trimethylamine in females resulting in a fall in the urinary trimethylamine-N or trimethylamine ratio (70). In addition,


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Figure 13 The PC1 vs. PC2 scores plots of mean-centered data from NMR spectra of urine from a rat sampled during the light period of the day, for 10 days, over two estrus cycles. (a) A locus of data points with time. (b) Separation of each stage of the estrus cycle. ¼ estrus, ` ¼ diestrus, & ¼ metestrus.

steroid hormones have previously been shown to influence flavin-containing monogenase activities in rodents and man (72). Food intake in the mature female is lower during estrus than during diestrus, sometimes by as much as 6 g=day,

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Figure 14 The 600 MHz 1H NMR spectra (d4.5–0.5) of urine samples collected from a female rat during three stages of the estrus cycle (a) diestrus, (b) estrus, and (c) metestrus. Key: DMA, dimethylamine; DMG, dimethylglycine; 2-OG, 2-oxoglutarate; TMAO, trimethylamine-N-oxide.


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which may explain the differences in glucose levels between the different stages of the estrus cycle (72). 2.8. Diurnal Effects Rats are generally nocturnal, feeding, drinking, and mating at night. Therefore, the difference in their activity levels falls into a diurnal pattern and this is reflected in their urinary profiles both in terms of biochemical composition and the volume of urine excreted. This effect needs to be considered where split collections are taken in the laboratory for metabonomic analysis or clinical chemistry measurements, for instance, 0–8 and 8–24 hr postdose, however, is negated if samples are collected continually over 24 hr. Time-dependent variations in pharmacological activities have previously been reported. For instance, a time-dependent variation in the diuretic effect of furosemide according to light–dark cycle and time of food intake has been observed (73). Diurnal effects on the metabolic composition of control urine have been investigated in the SD rat using a metabonomic approach. Light and dark cycle samples could be separated using a simple PC model (Fig. 15). Urine samples collected during the day were found to have lower levels of taurine, hippuric acid, and creatinine together with elevated levels of glucose, succinate, dimethylglycine, glycine, creatine, and betaine (Fig. 16). The effects of diurnal variation on control rat urinary profiles using 1H NMR spectroscopy and statistical pattern recognition have also been documented in the HW rat (74). Diurnal changes related to hormonal action may also affect serum endogenous metabolite levels in the rat. For instance, plasma concentrations of phosphate and calcium are inversely related with calcium levels being greater during the light-cycle (75). Gavaghan et al. (3) have recently investigated the diurnal variation in metabolism between two different phenotypes of mice, AlpK:ApfCD (white) and C57BL107 (black) mice. Samples collected am contained higher levels of creatine, hippuric acid, trimethylamine, succinate, citrate, and 2-oxoglutarate, and lower levels of trimethylamine-N-oxide, taurine,

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Figure 15 The PC3 vs. PC5 scores plot of mean-centered data from NMR spectra of female rat urine sampled during several estrus cycles, illustrating the separation of day from night-time urine samples. & ¼ night, ` ¼ day.

spermine, and 3-hydroxy-iso-valerate relative to pm samples. The increase in urinary excretion of the TCA cycle intermediates in pm urine samples may reflect the increased metabolic activity of mice during the night due to their nocturnal habits. 2.9. Water Deprivation The effects of restricting the water supply to male rats have previously been studied by Clausing and Gottschalk (76). Rats were either given water ad libitum or 10 ml restricted water supply. Water restricted animals had decreased urine volume, food consumption, body weight development and organ weights, together with impaired renal function. Therefore, reduced water intake may be a confounding factor in studies into the effects of nephrotoxins on rats. In recent studies, where SD rats were deprived of water for 48 hr, separation of


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Figure 16 The 600 MHz 1H NMR spectra (d4.5–0.5) of urine samples collected from a female rat during (a) the night and (b) the day Key: DMG, dimethylglycine; 2-OG, 2-oxoglutarate; TMAO, trimethylamine-N-oxide.

48 hr urine samples from predose and controls was observed from PCA of urinary NMR data (unpublished data). This was attributed to elevated levels of creatinine and depleted levels of taurine, 2-oxoglutarate, succinate, citrate, and hippuric acid. The decrease in the levels of 2-oxoglutarate, citrate, and succinate may be due to the inhibition of mitochondrial respiration. Alterations were also observed from clinical chemistry measurements, including increased urinary albumin, sodium, osmolality, and glucose, and decreases in potassium and urine volume. 2.10. Fasting Moderate food or caloric restriction has historically been linked to increased longevity and decreased disease incidence (77). In a study carried out by Levin et al. (77), whereby rats were fed ad libitum or given 75%, 50%, or 25% of the amount of feed consumed by controls, the severely restricted group

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developed bone marrow necrosis, thymic atrophy, and mild testicular degeneration. In the mildly and moderately restricted groups changes were considered adaptive and nondetrimental. Food restriction during the first few years of life in the rat decreases proteinuria, increases para-aminohippuric acid transport and reduces the incidence and severity of renal lesions, therefore delaying age-related detrimental changes in kidney function (78,79). In a recent study, groups of SD male rats were food restricted to 25% and 50% of the ad libitum fed control group, for 2 weeks or were 100% restricted for 1 day only (unpublished data). All restricted animals showed depleted levels of urinary TCA cycle intermediates and relatively small increases in creatine, due to muscle breakdown, along with decreases in creatinine and taurine. In the 100% restricted group, a decrease in trans-aconitate concomitant with an increase in phenylacetylglycine was observed in the 0–48 hr urine samples, with animals recovering by 72 hr. The 100% food restricted group of animals appeared to recover fully after 72 hr. In work carried out by Rikimaru et al. (80), the excretion of creatinine in the urine of rats, per unit of skeletal muscle mass, was promoted by food deprivation. Previous work has been carried out to identify serum profiles that reflect changes in food intake in both male and female rats where animals received food ad libitum or were food restricted by 35% (81). Both hierarchical cluster analysis (HCA) and PCA distinguished the dietary groups of origin for male rats to greater than 85% accuracy using 56 known metabolites and females to 94% and 100% accuracy, respectively, for 63 identified metabolites. 2.11. Dietary and Gut Microfloral Influences In animals, volatile fatty acids produced from microbial fermentation in the gut contribute directly to metabolism. In the ruminant and rat, most of the propionic acid in intermediary metabolism originates from microbial fermentation in the gut (82). Phipps et al. (12,13) have shown that changes to a rat’s diet can alter the ratio of hippuric acid and chlorogenic acid metabolites (such as meta-(hydroxyphe-


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nyl)-propionic acid (m-HPPA) and 3-hydroxy-cinnamica (3HCA)), excreted in the urine (13,83). These changes were attributed to variations in the composition of the diet and redistribution of gut microflora responsible for the metabolism of plant phenolics and aromatic amino acids. Further statistical analysis of a large homologous population of rats fed on a standard rat diet where all batches were within the manufacturers specification, identified two subpopulations of rat in the first two PCs (Fig. 17). These two subpopulations were attributable to the presence of either chlorogenic acid metabolites or hippuric acid (Fig. 18). In previous studies, a steady increase in chlorogenic acid metabolite excretion is accompanied by a concomitant decrease in hippuric acid excretion and vice versa, reiterating the possibility of a redistribution of the gut microflora (13,83). In the work carried out by Nicholls et al. (84), the urinary metabolite profiles of germ free rats were compared before and after exposure to a ‘normal’ environment (Fig. 19). A number of changes were observed over the first 17 days including an increase in glucose, decrease in the TCA cycle intermediates and increases in trimethylamine-N-O, hippuric acid, phenylacetylglycine, and m-HPPA. At 21 days, the urinary profiles resembled that of control animals. The metabolic changes observed are indicative of the colonization and redistribution of gut microflora and the varying health of the animal. In earlier work carried out by Goodwin et al. (85), increases in the urinary excretion of benzoic acid, phenylacetic acid, and m-HPPA and p-HPPA were observed after fecal inoculation of germ free rats. The gut microbial composition has a significant effect on the urinary metabolite profile and hence should be taken into consideration when interpreting the effects of orally dosed drugs. Some of the changes observed in the urine after drug treatment may be a result of metabolism by organisms in the gastrointestinal tract. 2.12. Temperature Effects Exposure to cold triggers a number of mechanisms, which reduce heat loss, such as vasoconstriction of blood vessels

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Figure 17 Principal components scores plot (PC1 vs. PC2) of mean-centered data derived from 1H NMR spectra of control whole rat urine showing two subpopulations of urine samples.

under the skin. To increase heat production, output from the thyroid gland is increased, which in turn speeds up metabolism. Mice are known to be more sensitive to temperature changes than rats. The effect of a drop in temperature on endogenous metabolism was inadvertently observed in a small number of mice. These animals were found to have elevated levels of several endogenous metabolites including glucose and some amino and organic acids (Fig. 20). Increasing carbohydrate and fat metabolism has previously been observed by Panin et al. (86) on exposure of HW rats to cold (þ5 ). Heat stress can be caused by hot-boxing of rats at 40 C, prior to blood sampling from the tail vein and has several metabolic consequences including elevation of blood lactate, with concomitant decreases in plasma triglyceride and glucose (87).


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Figure 18 The 600 MHz 1H NMR spectra of whole rat urine representative of (a) samples from subpopulation B containing high levels of hippuric acid and (b) samples from subpopulation A containing high levels of 3-HPPA and other plant phenolic metabolites.

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Figure 19 Stack plot of 500 MHz 1H NMR spectra (9.5–5.5) of whole rat urine at selected time points after removal of a germ free environment.


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Figure 20 1H NMR spectra of urine from a control mouse suffering from hypothermia.

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2.13. Sleep Deprivation The effects of sleep deprivation have been well documented in the rat (88,89). In rats subjected to total sleep deprivation, all animals died within 11–21 days (88). No anatomical cause of death was determined although animals showed signs of lesions to the tail and paws and weight loss despite increased food consumption. The weight loss was attributed to increased energy expenditure. In later studies carried out by Rechtschaffen et al. (89), total sleep deprivation caused similar symptoms together with decreased body temperature and perturbations in the plasma hormones, norepinephrine, and thyroxine. Changes suggested that sleep may be necessary for thermoregulation. Studies into the effect of sleep deprivation and recovery sleep on plasma corticosterone in the rat concluded that stress is not a major contributor to this condition (90). 2.14. Stress and Acclimatization Stress induces both biochemical and physiological responses in laboratory animals. Physiological stress occurs within normal physiological limits whereas overstress or distress may occur which is detrimental to biological processes and requires some form of biochemical and biological response. All animals respond in some way to the presence of humans and within any study stress effects can be caused by restraint, temperature, noise, food deprivation, and procedural induced fear or distress, for example, when dosing. Rapid removal of large volumes of blood can lead to hypovolemic shock, hence the tight guidelines over the number of times blood can be sampled over the course of a study. Acclimatization is the term given to describe the ability of a particular organism to adapt to a particular environmental stress, by prolonged exposure to that stress, without enhancement by genetic modification (91). Within toxicology studies, acclimatization generally refers to the adaptation of each animal to the novel environment of a metabolism cage, where they are housed individually. As rats are generally sociable animals, solitary confinement and environmental constrictions (such as no bedding to avoid contamination of urine samples)


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may cause stress effects in these animals (67). In a study carried out by Stanley et al. (23), male HW rats (n ¼ 18) were divided into two groups. One group (n ¼ 10) experienced acclimatization to metabolism cages for three separate 7-hr periods prior to continual housing for 72 hr, whereas the remaining group (n ¼ 8) experienced no acclimatization period. The animals experienced regular light–dark cycles of 12 hr light followed by 12 hr dark and urine samples were collected at 6 hr intervals across the study. The 1H NMR urinalysis coupled with PR showed that 50% of the rats in the unacclimatized group showed elevation of urinary glucose at 54 and 60 hr compared with the normal control rats (Fig. 21). Glucose levels returned to within the acclimatized control range by 72 hr, as shown by the mean scores metabolic trajectories from PCA of these data (Fig. 22). The unacclimatized rats also showed a sig-

Figure 21 The 600 MHz single-pulse 1H NMR spectra (d4.5–1.5) of urine samples collected at 54 hr from (A) acclimatised male Han-Wistar rats and (B) unacclimatized male Han-Wistar rats. Key: 2-OG, 2-oxoglutarate; CAMS, chlorogenic acid metabolites; TMAO, trimethylamine-N-oxide; DMG, dimethylglycine; NAC, N-acetyl glycoproteins.

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Figure 22 Comparison of the mean metabolic PC1=PC2 trajectories derived from 1H NMR spectra of urine samples collected from acclimatized and unacclimatized male rats.

nificant increase in water consumption compared with the acclimatized animals at 54 hr (Dunnett’s test, p < 0.05). Previous investigations have shown that rodents exposed to environmental constraint often exhibit polydipsia and glycosuria as a precursor to the development of diabetes (92).

3. PHYSIOLOGICAL VARIATION IN HUMANS Metabonomic studies involving human subjects pose more complex problems due to the greater influence of intrinsic factors including genetics, ageing, gender, and menopausal status. Extrinsic factors are also more numerous in human subjects compared with laboratory animals and include a wider range of dietary variation, socio-economic status, artificial hormones, smoking, stress, exercise, and fitness levels. A particular problem we have found with human studies is the high degree of non-compliance including alcohol consumption and taking of over-the-counter medication such as paracetamol, ibuprofen, or aspirin. All these substances have


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characteristic metabolite excretion patterns (93,94), which often mask resonances from the endogenous metabolites we are interested in. Nevertheless, the use of metabonomics in clinical investigations is becoming popular due to the rapid, multicomponent and relatively inexpensive analysis that can be performed on each sample. Much success has been had with diagnosis of the severity of atherosclerosis from plasma metabolite profiles using metabonomic technology (95). In the proceeding sections, we will introduce some of the physiological factors that we consider when performing human clinical investigations. 3.1. Inter-Subject Variation In general, the composition of human urine in healthy individuals may be considered as an expression of an individual’s metabolism. In a study carried out by Zuppi et al. (9), urine from 50 normal subjects were analyzed by 1H NMR spectroscopy. From quantification of peak heights expressed as mmol=mol creatinine, the mean values calculated for a series of urine samples from the same individual showed low standard deviations. In contrast, when the urines from all 50 individuals were compared quantitatively, high standard deviations were found as a result of inter-person variability. The greatest metabolite variability between subjects was found in the concentration of hippuric acid, as was the case for intra-individual and inter-day variability. This is most likely due to differences in gut microflora between individuals and even within the same individual over time. Other urinary metabolites, which varied between individuals, included acetate, citrate, lactate, and glycine (a precursor of hippuric acid). The concentrations of trimethylamine and trimethylamineoxide in the urine are also known to vary between individuals as a result of dietary influences (see Sec. 3.5), differences in gut microflora (96), enzyme activity, and gender (10,97). 3.2. Gender Sex hormones are known to have control over the morphology of the kidney and hence to effect urinary metabolite profiles

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Figure 23 The PC1 vs. PC2 scores plot of NMR spectroscopic urinary data from healthy male and female subjects.

(28,68), for instance in humans, glycosuria is common in pregnant females (98) and an increase in citrate concentration in female urine may be related to estrogen levels. In a study carried out by Hodgkinson (22) on 29 normal male and female subjects, women were found to excrete more citrate than men. However, it is possible that this may originate from blood and epithelial cells, which have been found in female urine. In a recent study carried out in healthy male and female subjects where urine samples were collected every morning over 2 weeks, PCA of the spectral data showed separation in PC1 (Fig. 23). This was attributed to elevated levels of citrate and glycine in female urine samples. Glycine is a precursor to hippuric acid, therefore, its variation in urine may be related to the gut microflora. Male samples contained higher creatinine levels, possibly related to the higher muscle content of the male body. 3.3. Water Deprivation and Water Loading Previous studies in human subjects have been carried out into the effect of fluid deprivation, with urine sample collection


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occurring 2 hr after fluid deprivation, 1 hr after fluid restoration and 1 hr after fluid loading in each subject. Using the unsupervised learning method, non-linear mapping (NLM), it was possible to clearly distinguish between samples collected from water-deprived subjects and those collected during the water-restored state. In addition, samples collected after water loading could also be partially separated from urines obtained after water-deprivation. Separation of these classes of data was attributed to alterations in the levels of citrate, hippuric acid, trimethylamine-N-oxide, and 3-Dhydroxybutyrate. 3.4. Fasting In a study carried out by Bales et al. (99), urine samples were collected from healthy male subjects after an overnight fast and then at regular intervals during and after further fasting for a total of 48 hr. From 1H NMR analysis of urine samples, an increase in the excretion rates of acetylcarnitine, acetoacetate, 3-D-hydroxybutyrate, acetone, creatinine, and sarcosine was observed 24 hr after the commencement of fasting, together with depletion in urinary hippuric acid (Fig. 24). Within 2 hr of food consumption, there was a dramatic fall in the rate of excretion of acetoacetate, acetate and acetylcarnitine and several hours later only small amounts of these metabolites were excreted. The rate of excretion of 3-d-hydroxybutyrate, however, did not return to control levels until 10 hr after fasting stopped. In earlier studies carried out by Hoppel and Genuth (100,101), carnitine, acetylcarnitine, and 3-d-hydroxybutyrate were detected in both urine and plasma samples after fasting. Using 1H NMR spectroscopy, studies into the effect of fasting on plasma metabolite levels detected a depletion in the mobile pool of fatty acids as well as a gradual decrease in glucose and increase in the ketone bodies (102). 3.5. Diet A diet rich in fish is known to result in the excretion of high concentrations of trimethylamine and trimethylamine-

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Figure 24 The 400 MHz 1H NMR spectra of urine samples collected after 12, 35 and 48 hr of fasting from a healthy male subject. The rise in intensity of acetoacetate, 3-D-hydroxybutyrate, acetone, and acetylcarnitine can be observed.

N-oxide in the urine (97). In a study of normal subjects given a meal of trimethylamine-N-oxide containing fish, trimethylamine-N-oxide appeared rapidly in the plasma and urine, suggesting that trimethylamine-N-oxide is efficiently cleared by the healthy kidney (18). Consumption of a fish meal will cause


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such samples to become outliers when performing multivariate data analysis methods (unpublished data) due to the high intensity of the trimethylamine-N-oxide resonance (Fig. 25). This can be overcome by removing the region corresponding to trimethylamine-N-oxide or exclusion of the sample from the model. Other sources of trimethylamine and trimethylamine-N-oxide in the body may be the degradation and recycling of biliary phospholipids as well as bacterial metabolism (97,103). Diets rich in carbohydrates, such as an Italian diet, cause an increase in excretion of citrate, lactate, alanine, and glycine (104). Work carried out by Zuppi et al. (104) into the influence of diet on urinary endogenous metabolite profiles used a group of 25 normals from Rome and 25 normals

Figure 25 The PC1 vs. PC2 scores plot of NMR spectroscopic urinary data from healthy male and female where one subject is an outlier due to consuming a fish meal.

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from Svaldbard (Norway). The subjects were studied during the same seasonal period to negate the effect of different light–dark cycles. Those subjects from Rome were submitted to a high carbohydrate diet whilst the Svaldbard group ate a diet higher in lipids but lower in carbohydrates and included a large amount of preserved food. The lower concentration of alanine, lactate, and citrate in the subjects from Svaldbard may be related to the lower carbohydrate diet in this group. In addition, these individuals also had higher levels of hippuric acid and lower levels of glycine in the urine. This was postulated to be due to the high levels of benzoic acid, a precursor of hippuric acid, found in preserved food. In a study carried out in healthy male and female subjects, where morning urine samples were collected each day over two weeks, from PCA of the NMR spectroscopic data, separation of meat eaters from vegetarians was observed in PC1 (Fig. 26). This was attributed to increased levels of hippuric acid, lactate, and citrate in vegetarians and elevated creatine in meat eaters (Fig. 27). The dietary contribution to creatinine excretion in human urine has been estimated to be approximately 240 mg=day (103) and is known to increase with increased meat consumption (105). 3.6. Exercise Holmes et al. (10), have previously carried out studies into different forms of mild physiological stress. Urine samples were collected from subjects 1 hr after exercise or from at an equvalent time subjects. Spectra of urine samples collected after exercise showed a higher concentration of lactate than those from controls most likely as a result of anaerobic respiration in these individuals. Zuppi et al. (9), have also observed elevated lactate in human urine samples after exercise. 3.7. Stress Stress describes the way in which the body copes with various stressors from a wide range of sources including physiological=oxidative stress causing real tissue and=or cellular


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Figure 26 The PC1 vs. PC2 scores plot of NMR spectroscopic urinary data from healthy male and females separated according to diet.

damage such as that observed due to ageing, exercise, disease, and pain (106). Psychological stress influences include fear, distress, and sleep deprivation. Acute stress such as that observed with factors such as public speaking (107) are short lived and the body should recover fully but if this stress becomes sustained or chronic this can have detrimental effects on the psychological and physiological well-being of the subject. This can result in depression, hypertension, cardiovascular disease, gastrointestinal problems irritable bowel, dyslipidemia, insulin resistance, and diabetes. All stressors induce the ‘‘fight or flight’’ response by triggering release of catecholamines such as adrenaline from the adrenal medulla and stimulation of glucocorticoid (cortisol) hormone production. The latter also exhibit circadian rhythm

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Figure 27 The 600 MHz 1H NMR spectra (d0.4–4.6) of urine from a meat-eating and vegetarian female subject.

and are at the maximum at the start of light=wakefulness. This leads to rapid mobilization of glycogen and triacyglycerols from stores, increased oxygen delivery to the brain, heart, and skeletal muscle as well as increased metabolic rate. The biochemical consequences of stress are the stimulation of gluconeogenesis leading to increased blood glucose and decreased amino acids and effects on lactate and pyruvate metabolism. Increases in the production of ketone bodies such as 3-d-hydroxybutyrate and acetone reflect breakdown of glycerol from fats. Many of these metabolites can be routinely detected by NMR of biofluids making metabonomics an ideal platform for monitoring stress responses especially those associated with chronic stress which forms part of the etiology of many diseases. Work is currently being carried out in the metabonomic field to develop a further understanding of the biochemical mechanism of chronic stress in animal models and in various disease=illness states in man.


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11 Environmental Applications of Metabonomic Profiling JACOB G. BUNDY Biochemistry Department, University of Cambridge, Cambridge, U.K.

The current major challenges for environmental toxicologists and ecotoxicologists include the need to develop a mechanistic understanding of the toxic action of pollutants at a molecular level, and to understand how molecular and cellular events affect higher order (population and ecosystem) functioning (1). Postgenomic technologies will be vital in order to address these questions, and metabonomics could play a major role in helping do so. This chapter will summarize some of the advantages and disadvantages of NMR-based metabonomic profiling as applied to ecotoxicology, and review the progress that has been made to date. Recent reviews on the use of NMR in plant science are available elsewhere (2), as well as the topic of plant 453

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metabolomics (3). There are fewer published studies on microbes, and these tend to concern standard laboratory organisms such as Saccharomyces cerevisiae and Escherichia coli (4–8). Hence, the subjects of metabonomics in plants and in microbes will not be covered.

1. DIFFERENCES TO CLINICAL STUDIES The clinical applications of metabonomics are addressed elsewhere in this book. What is sufficiently different about the application of metabonomic techniques to environmental problems that warrants a separate chapter? The basic approach—generation of metabolic profiles via high-resolution NMR spectroscopy, and multivariate pattern recognition (PR) to help interpret the multiple changes occurring in these spectra—is identical. However, there are a number of differences that will affect, e.g., study design, usefulness of the results obtained, etc., and it is important to mention these here.

1.1. Goals One of the major differences between clinical and environmental metabonomic studies is caused by the endpoints of interest—human medicine is individual based, whereas ecotoxicology is concerned with populations, and hence assessing the risk to sensitive species and ecosystems. The additional factors of exposure, bioavailability, and food-chain transfer become important. A simple adoption of a clinical study design to an environmental setting might well be regarded as of low value by some ecotoxicologists, however, well executed the metabonomic aspects of the study. Fortunately, these problems need not be solved in isolation. The use of biomarkers in ecotoxicology and pollution monitoring is an active research field, and metabonomics fits well into this area—it can be thought of as providing a series of small-molecule biomarkers of toxic stress. Thus, current

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thinking in biomarker research can be used to help interpret metabonomic studies with maximum value to real environmental issues. The use of biomarkers (here considered as biochemical marker molecules only) has a number of exceptional advantages for ecotoxicological assessment. The foremost advantage is that the biomarker provides an integrated measure of the actual response of an organism to a pollutant (9–11). This is of importance firstly in taking account of actual exposure: total environmental concentrations are not likely to be representative of the amounts bioavailable to an organism. Pollutants may also be released in pulses, and chemical sampling may miss episodes or hotspots of contamination (9). Secondly, biomarkers may be useful in taking into account an organism’s actual state of health: for instance, there may be a biological response to a toxic insult that results in either resistance or tolerance, either at an individual or population level. Clearly, the effects of a certain level of a pollutant may be very different, depending on whether the animals are from a na€ve or previously exposed population, and relying on simple chemical concentrations may well be misleading. Thus, if biomarkers could be used to give an indication of biological effect, i.e., fingerprinting a particular stressor, this could be of great value (11,12). Morgan et al. (13) define biomarker strategies as falling into three groups: (i) application of biomarkers at different levels of complexity as part of a rapid toxicity screening program; (ii) development of mechanistic links between molecular biomarkers and higher order (functional) biological levels; and (iii) use of multivariate-profiling techniques to define characteristic fingerprints or profiles of biological stress=harm that are more specific than single biomarker responses. Handy et al. (9) state that, given the current state of the art in biomarker research, the most potentially useful approach is the application of suites of biomarkers at the molecular, cellular, and physiological level. Metabonomics clearly falls into this third group, although it is easy to imagine potential applications in, say, the rapid profiling of chemicals for regulatory test purposes against required test organisms.

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1.2. Sample Size and Time Course Metabonomics is described as the ‘‘time-resolved’’ measurement of response to a toxic insult (14), and many studies show the high value of being able to follow the course of biological responses to a toxin through time (15). This can easily be done when repeated samples may be taken from the same individual (for example, urine samples). More often than not, however, it is impossible to obtain repeat samples from an organism of environmental relevance, and destructive sampling is the only possible method. Often, test species are so small that it would be essential to combine many individuals simply to obtain a single spectrum, for example, with the classic aquatic test organism Daphnia magna, or with any of the isopod species frequently used for soil toxicity testing (16). It is possible to obtain a reasonable 1H spectrum based on a tissue extract of as little as 5 mg tissue dry weight, using a modern high-field spectrometer and a standard 5 mm probe. (Sensitivity could also be increased and hence sample requirement reduced by using more sensitive, higher field instruments, or by cryogenically cooled probes; microvolume probes and magic-angle-spinning probes also require less sample.) However, it is preferable to work with larger quantities of tissue, e.g., 20 mg dry weight or more, if available. Clearly, there will be some situations where repeated samples can be taken—for example, urine from wild mammals or blood plasma from fish large enough to allow such sampling—but this is likely to be an exception. It is possible to follow a time course by sacrificing different individuals at different time points, and in fact this is more ‘‘realistic’’ in terms of modeling toxic response at the population level, although perhaps less easy to interpret for mechanistic information. However, the numbers of animals required for destructivesampling studies has meant that environmental metabonomic studies have tended not to cover many time points. 1.3. Variation and Statistical Models Clinical drug toxicity studies are typically carried out on relatively homogeneous rat or mouse populations, which are


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kept under ideal, reproducible conditions. This minimizes the obscuring of actual treatment effects by other confounding factors—diet, environmental effects such as temperature, different populations and differential survival, genetic inhomogeneities, age, life-cycle stage, etc. Environmental studies are, in general, less well controlled. One approach to this problem is to mimic clinical studies by focusing solely on laboratory experiments, which are deliberately as ‘‘unnatural’’ as possible, and diet, surroundings, and interactions between individuals are all specified. Alternatively, more ‘‘realistic’’ studies may use either microcosm=mesocosm exposures to mimic environmental effects; expose laboratorysourced organisms in situ to be recovered at a later date; or collect autochthones from regions of concern, e.g., contaminated sites or along a known gradient of environmental contamination. These more ecologically relevant studies will also require testing in parallel laboratory experiments, in order to demonstrate that environmental biomarkers are indeed directly produced by specific toxins or stressors. There is also the added complicating factor that observed metabonomic effects may be caused either by changes at the individual level—by affecting regulation of a suite of genes related to stress, metabolism, etc.—as is observed in laboratory studies, or by changes at the population level, i.e., actual adaptation or selection for differential populations (17,18). Thus, increased baseline variability is characteristic of environmental metabonomic studies, and additional environmental factors are likely to complicate interpretation of field data. Even biomarkers that are often thought of as having ‘‘known’’ mechanistic interpretations turn out to be remarkably difficult to interpret in a field context—for example; the response of metallothionein (MT) levels to cadmium (Cd) is a classic, highly studied example. Metallothionein levels are also affected by other heavy metals, other xenobiotics, seasonality, and are known to vary in fundamental biochemical processes, e.g., at different points in the cell cycle (19–21). Thus, it is surprisingly difficult to make any kind of reliable prediction of the presence=absence of environmental toxicants, even

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when laboratory studies show clear dose–response behavior of biomarkers=metabonomic profiles. In ecotoxicology, an increase in the overall variability of a biochemical=biological trait has been proposed as an indicator of stress, even if there is no significant increase=decrease in the mean of that trait (e.g., Ref. 22). Recently, it has also been pointed out that the variability of certain parameters may also significantly decrease as a consequence of exposure to toxic stressors (23). These approaches have not yet been taken into account for environmental metabonomic studies, but might well be useful for future work. 1.4. Life-cycle Sensitivities Laboratory acute toxicity tests may test only exposure during a specific life-cycle phase. Rapid acute tests often use adult organisms, because of the relative experimental ease in obtaining animals and running the tests. However, adulthood is often the least sensitive life-cycle stage; reproduction and development may often be far more sensitive to chemical exposure. Fortunately, it is often possible to perform laboratory exposures which test sensitivity during reproduction and development, and standard toxicity test protocols have been devised. There is, therefore, clear potential for metabonomic studies which assess biochemical responses during sensitive reproduction=development phases. Such studies are only just beginning to be carried out (24). 1.5. Need for Field Validation The above sections have introduced some of the additional complications entailed by applying metabonomic techniques to environmental problems and ecologically relevant test organisms. Because of these complications, it is usually regarded as essential in ecotoxicology that field sampling of authentically exposed organisms is carried out. Field sampling of indigenous populations means that sensitivity at different lifecycle stages is, to an extent, taken into account—even if only adult organisms are collected, they would have been exposed at all stages during development. An equally important issue


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is that extraneous environmental factors are automatically taken into account. Hence, the likely high baseline variability of metabonomic profiles of control (unexposed) organisms will not be so problematic if it can be shown that exposed=stressed stressed profiles are clearly separable in metabolic space.

2. CHARACTERIZATION OF BASELINE DATA BY NMR SPECTROSCOPY An obvious prerequisite for metabonomic studies on environmental species is the ability to take and profile a suitable biological sample. As discussed above (Sec. 1.2), small sample size may often be limiting, and in this case whole-body extracts may be the only way to obtain enough tissue to allow NMR analysis. This is clearly not ideal, and if it is possible to take more specific samples, e.g., specific organs=tissue types or biofluids, this option should certainly be tested. Metabonomic analysis can be carried out by treating the spectrum purely as a digitized vector, and in this case, there is in theory no need for any prior knowledge of the metabolites to be found in the samples. But in practice, it is useful to first assign at least the major metabolites that can be found in a particular sample type; assignment of the minor resonances of a spectrum can then be directed by chemometric identification of resonances as belonging to biomarker compounds (25). A number of different organisms have been studied, falling into a range of taxonomic groups. The different studies are reviewed briefly below; for convenience, a list of the different species and relevant references is given in Table 1. In some cases, individual resonances have been assigned from the NMR spectra, and a compilation of all of the observed metabolites is presented in Table 2. It should be noted that the absence of an assigned metabolite does not imply that the metabolite was not present, as the list is based solely on published data. Other magnetic resonance approaches have been used for analysis of environmental samples—in particular, there are many examples of the use of 31P NMR spectroscopy in comparative physiology and biochemistry studies (26), including

Eisenia veneta (tissue extracts, coelomic fluid)

Aporrectodea caliginosa, A. longa, A. icterica, Dendrodrilus rubidus, Dendrobaena octaedra Schistocerca gregaria (desert locust; hemolymph)

E. veneta þ L. terrestris

Starvation over seven day period Freeze treatment

3-Trifluoromethylaniline; 12 fluorinated monoaromatic anilines and phenols

Eisenia andrei, Lumbricus rubellus

Terrestrial invertebrates: earthworms

E. fetida þ E. andrei þ E. veneta (coelomic fluid)

Copper; zinc and other heavy metals resulting from pollution from smelter

Species

Taxonomic group

Toxicants=other stressors tested

Laboratory

Laboratory: filter paper exposures and soil microcosms. Semifield: mesocosms. Field scale: indigenous (LR) and alien (EA) cultures exposed in situ Laboratory: filter paper exposures and soil microcosms. Semifield: mesocosms Both laboratory and wild populations sampled Laboratory: treated on filter paper Laboratory, using wild-collected populations

Setting

45

35

32

25,39

33,34

31,36,63

Reference

Table 1 Summary of Some Different Species of Environmental Relevance that have been Profiled by NMR for Metabonomic Studies

460 Bundy

Vertebrates: fish

Vertebrates: mammals

Marine invertebrates

Insecta

Terrestrial invertebrates: other

Arion subfuscus (slug), Oniscus asellus (isopod; woodlouse), Porcellio scaber (isopod; woodlouse), Glomeris marginata(millipede) Manduca sexta (tobacco hornworm; hemolymph) Culex pipiens Haliotis rufescens (red abalone; foot muscle, hepatopancreas, digestive gland) Sycionia ingentis (ridgeback prawn; hepatopancreas); Strongylocentrus purpuratus (purple sea urchin; eggs); Clethrionomys glariolus (bank vole; kidney biopsy, urine) Apodemus sylvaticus (wood mouse; kidney biopsy and urine), Crocidura suaveolens (white-toothed shrew; kidney biopsy) Mastomys nataliensis (multimammate desert rat; urine). Parophrys vetulus (English sole; liver and gill) Oryzias latipes (Japanese medaka) Laboratory: exposure by direct contact

47

Laboratory Trichloroethylene

62

Laboratory: exposure by oral route

24,58

59

2-bromoethanamine

47

Laboratory

61

46 47,53

Laboratory Collected from field

Laboratory: exposure via food Laboratory

43

Laboratory

Cadmium

Withering syndrome (caused by bacterial infection)

36

Collected from field

Environmental Applications 461

Alanine Arginine Asparagine Aspartate Glutamate Glutamine Glycine Histidine Methylhistidine Isoleucine Leucine Lysine Methionine Ornithine Phenylalanine Proline Hydroxyproline Serine

x

x

x

x x

x x

x

x

x

x

x

x

x x

x

x

x

x x x

x x

x x

x x x

x

x

x

x x x x x x

x

x x

x

x x x x x x x x x

x x x x x

x x

HR

Terrestrial invertebrates—earthwormsc

Terrestrial invertebrates—otherd

x

x x x x x x x

x

x x

x x

x

x x x

x

x x

x

x

x x x

x x

x x x x x

x

x x x

x

x x

x

x x

x x x x x x x

x x x x x

x

x

x

x x x x x x x x x x x

x x x x x x x x x x

x

x

x


x

x


x

x

x x x x x x x

x x

x

x

x x x

x

x

x

x

x x

x

x x

x

x

x

x x

x

x

x x x

x

x

x

x

x x

x x x x

x

SI LR LT EA EV EV DO DR AI AC AL AS OA PS GM MS SG cf

Marine invertebratesb

CS CG CG PV PVI SP u k g

Vertebratesa

Table 2 List of Metabolites that have been Assigned in NMR-based Studies of Environmental Organisms. Presence of Assigned Metabolite Indicated by ‘‘x’’ (Absence of ‘‘x’’ may not Necessarily Indicate Absence of Metabolite)

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x

x

x

Acetate Acetoacetate Ascorbate butyrate a-Hydroxybutyrate b-Hydroxybutyrate Citrate Formate Fumarate GABA Hippurate 4-Aminohippurate a-Ketoglutarate Lactate Malate Malonate Pyruvate Urocanate Succinate Betaine Carnitine Choline

x

x x x x

x

x x

x x x x

Threonine Tryptophan Tyrosine Valine

x

x

x

x x x

x x

x

x

x x

x x

x

x

x

x x

x

x x

x

x

x

x

x x x x

x x x

x

x x x

x x x x x

x

x x x x

x x

x

x

x x

x

x x

x

x

x

x

x

x x x x

x x

x

x x

x

x x x x

x

x

x

x

x

x x x x

x x x

x

x x

x

x x x x

x

x x x x

x x

x x

x

x

x

x

x x x

x

x

x

x

x

x

x

x x x

x

x

x x x

x

x

x

x

x x x

x

x

x

x

x x x

x

x x

x

x

x

x x

x

x

x

x

x

x

x

x

x

x

x

x x

x

x

x x

x

x

x

x

x x

(Continued)

x

x

x

x

x

x


Fucose

Creatine Creatinine Dimethylamine GPC Homarine Taurine Hypotaurine N-methyl taurine TMAO Adenine Adenosine ADP ATP Inosine IMP NMN N-methyl nicotinamide Uridine UMP

Table 2 (Continued )

x

x

x

x x

x

x

x

x

x x

x x x x

HR

Terrestrial invertebrates—earthwormsc

Terrestrial invertebrates—otherd

x

x

x x

x

x

x

x

x

x

x

x x x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x x

SI LR LT EA EV EV DO DR AI AC AL AS OA PS GM MS SG cf

Marine invertebratesb

CS CG CG PV PVI SP u k g

Vertebratesa

464 Bundy

x

x x

x x x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x x

x

CS—Crocidura suaveolens, blood. CG u—Clethrionomys glareolus urine. CG k—C. glareolus kidney, by MAS. PV g—Parophrys vetulus gill. PV l—P. vetulus liver. b SP—Strongylocentrus purpuratus, eggs. HR—Haliotis rufescens, muscle. Sycionia ingentis, hepatopancreas. c LR—Lumbricus rubellus. LT—L. terrestris. EA—Eisenia andrei. EV—E.. veneta. EV cf—E. veneta coelomic fluid. DO—Dendrobaena octaedra. DR—Dendrodrilus rubidus. AI—Aporrectodea icterica. AC—A. caliginosa. AL—A. longa. d AS—Arion subfuscus. OA—Oniscus asellus. PS—Porcellio scaber. GM—Glomerulus marginata. MS—Manduca sexta, hemolymph. SG—Schistocerca gregaria, hemolymph.

a

Glucose myo-Inositol Maltose Sucrose Trehalose Glycerol Glycerol-3-phosphate HEFS Lombricine Putrescine Sarcosine Urea Phosphatidylcholine Lipid triglycerides


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two-dimensional (2D) experiments for the identification of a greater number of phosphorylated metabolites than are usually observed by 1D spectroscopy (27). However, because 31 P NMR reports on a much smaller proportion of free metabolites than 1H NMR, only 1H NMR-based studies are described below. 2.1. Terrestrial Invertebrates—Earthworms Earthworms are an important ecological group of soil animals. They usually form the major portion of animal biomass in soils that are suitable for the support of earthworms, and play a major role in soil functionality by increasing organic carbon turnover rates through mixing of soils and comminution of vegetable matter (28–30). They contribute to soil fertility and help to support populations of soil microorganisms, and are regarded as a key ecological group. Consequently, there is a long history of ecotoxicological testing using earthworms, and more earthworm species have been characterized by 1H NMR spectroscopy than any other taxonomic grouping. Gibb et al. (31) analyzed aqueous tissue extracts of Eisenia andrei and Lumbricus rubellus using one-dimensional (1D) and (2D) 1 H–1H Correlated Spectroscopy (COSY) and homonuclear Jresolved (JRES) spectroscopy. The COSY experiment provides increased structural information—1D resonances appear along the diagonal, and off-diagonal cross-peaks are seen between resonances which exhibit through-bond J-coupling, i.e., usually between 1H nuclei which are separated by no more than two or three bonds. The JRES experiment simplifies the crowded 1D proton spectrum by presenting the splitting caused by J-coupling in a second dimension, i.e., effectively rotating the multiplet into a second dimension and thus greatly reducing NMR signal overlap. A series of small metabolites were identified, including sugars, organic acids and bases, osmolytes, nucleosides, and amino acids (Table 2). Some resonances of the compound 2-hexyl-5-ethyl-3-furansulphonate (HEFS) were mistakenly assigned as ethanol; based on our later experiments with earthworms, it is also likely that the resonances assigned to sucrose were in fact from maltose.


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Warne et al. (32,33) increased the number of earthworm species that have been characterized to four, adding E. veneta and L. terrestris. These species were also studied using aqueous tissue extractions. The metabolites observed were very similar to those observed by Gibb et al. (31). Bundy et al. (34) identified HEFS in E. veneta tissue extracts; this metabolite is present in all worm species that have yet been tested (11 species—Table 1, and E. nordenskioldi, unpublished data). Bundy et al. (35) characterized another five species—three Aporrectodea species, A. icterica, A. caliginosa, and A. longa; Dendrodrilus rubidus; and Dendrobaena octaedra. These were studied using an initial acetonitrile=water tissue extraction, followed by lyophilization and reconstitution in 2H2O. The acetonitrile=water step was included with the intention of precipitating macromolecules and halting postextraction metabolism. The most obvious differences that this made to the metabolite profiles were, firstly, that HEFS was present in much higher proportion—frequently HEFS was the most abundant metabolite present; and secondly, that adenosine, but not inosine, was observed in control worms. Recently, we have acquired spectra of perchloric acid extracts of L. rubellus tissue; extraction in ice-cold perchloric acid is a standard technique for simultaneously extracting small-molecule metabolites and halting enzymatic activity. These give extremely different profiles to those obtained by aqueous extractions (31,36). The major differences include much smaller relative concentrations of free amino acids and free sugars; greatly increased relative concentration of HEFS; observation of resonances from a new compound tentatively assigned as lombricine=phosphoryllombricine (resonances at d4.26, d4.00, and d3.48). This assignment is made on the basis of similarity of the resonances to those of l-serine ethanolamine phosphodiester (37), which is a component of lombricine (except that lombricine contains a d-serine residue, not l-serine (38)), and their very rapid (within 10 sec) alteration following extraction if PCA is not used to halt metabolism (unpublished results). This very rapid metabolic change would be expected of phosphagens.

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It is also possible to extract coelomic fluid (CF) from earthworms. This can be collected by invasive puncture, but for the Eisenia genus, much better results are given by a mild electrical stimulus to the worm (39). We have found that collection by an invasive puncture gives spectra with many contaminating signals from free amino acids. The CF contains an entirely different set of metabolites to those observed in whole-body tissue extracts; an initial characterization has been made of the CF of E. veneta (39). In contrast to the tissue extracts, the CF spectra are dominated by signals from organic acids (Table 2). In addition, the fluid appears to contain several aromatic metabolites in relatively high concentration: E. veneta CF contains nicotinamide mononucleotide. E. andrei and E. fetida CF contain many resonances from several so far unknown aromatic compounds (25). An alternative way of presenting overall species similarity in terms of spectroscopic profiles is by direct analysis of the binned spectral data. Figure 1 shows a comparison of five

Figure 1 Hierarchical cluster analysis (using Ward’s method of linkages and Euclidean metric) of 600 MHz 1H NMR spectral data of tissue extracts, showing overall earthworm species similarity: Aporrectodea species form a separate cluster.


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earthworm species’ spectral profiles by hierarchical cluster analysis [the maximum number of species available studied under identical experimental conditions (35)]. The three Aporrectodea species form a cluster, as expected. Species identification is often tricky for soil invertebrates, and may cause some problems in earthworm ecotoxicology, where some ‘‘species’’ are in fact species complexes (40). Metabonomic toxicity analysis offers potential additional benefits in being able to compare individual similarity and similarity between strains=species. Clearly, it would not be possible to perform chemotaxonomic identification based purely on a na€ve use of metabolite profiles—the observed metabolites will change depending on the physiological state of the organism, and overall profiles might well be affected more by ecotype than by genetic similarity— but it would be an interesting area for future study. 2.1.1. Terrestrial Invertebrates—Other Soil-dwelling Species Gibb et al. (36) also profiled four other soil invertebrates that are either used or could potentially be used for ecotoxicity testing—a slug, a millipede, and two crustaceans (woodlice species). It is surprising how superficially similar the metabolite profiles are from these different species in terms of compounds present (Table 2). In fact, this is only an indication of the fact that a few primary metabolites are found across these different species, rather than a true species comparison. Gibb et al. (36) also used hierarchical cluster analysis to demonstrate that more realistic physiological relationships could be established (although in this case certain species were represented only by a single individual). 2.1.2. Terrestrial Invertebrates – Insect Hemolymph Insects of sufficient size provide an opportunity to sample specific biological compartments. Manduca sexta is a serious agricultural pest (41), and has been widely studied, with much known about its biochemistry. It is therefore an obvious

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choice for further metabonomic investigations that may well be complementary to existing knowledge. Thompson (42) identified putrescine and trehalose in the 1H NMR spectra of hemolymph of the 5th instar larvae of M. sexta, in addition to investigating energetic metabolism by 31P NMR. Phalaraksh et al. (43) confirmed the assignment of these two compounds, and assigned a further 17 metabolites visible in 1D and 2D spectra (Table 2). A presumed unassigned sugar was also detected, with an observed doublet (J ¼ 7.5 Hz, dH 5.14 ppm, and dC 103 ppm) typical of sugar anomeric protons (44). Lenz et al. (45) have similarly characterized the hemolymph of final-instar nymphs of Schistocerca gregaria. (A point of methodological interest is that because the samples obtained were small in volume, 20–100 mL, a microprobe was used to maintain sensitivity. The adoption of microprobes, and in addition cryoprobes, will remove some of the problems associated with the analysis of small volumes that are typical of environmental samples.) They also observed intriguing differences in spectra depending on whether the nymphs had been raised in solitary or social conditions: solitary nymphs had decreased ethanol and acetate, and increased putrescine and trehalose concentrations. It is interesting that 1H NMR spectroscopic profiling has also shown that final-instar larvae of the mosquito Culex pipiens pallens have increased tissue extract concentrations of metabolites that are end products of fermentative metabolism, including ethanol and acetate (46). Possibly, the differences in the two groups are related to completeness of developmental stage. 2.2. Marine Invertebrates Fan et al. (47) used a combination of both GC–MS and 1H NMR spectroscopy for identification and absolute quantitation of 41 different metabolites. Spin-lattice (T1) relaxation times were measured for these metabolites to permit calculated corrections for differences arising from incomplete relaxation of the magnetization. Three marine invertebrate species and extracts of tissues were studied (Table 2): foot


471

muscle tissue from Haliotis rufescens (red abalone), hepatopancreas from Sycionia ingentis (ridgeback prawn), and eggs from Strongylocentrus purpuratus (purple sea urchin). (A vertebrate species was also profiled, 2.3, as well as five plant species, the results of which will not be discussed here.) The study was specifically designed to profile osmolytes. Total correlation spectroscopy (TOCSY) and 1D nuclear Overhauser effect (NOE) difference spectra were used to help confirm NMR assignments. The NOE acts through space and not through bonds, unlike the COSY and TOCSY experiments, and NOE experiments can therefore be valuable in helping assign metabolites which possess isolated singlet resonances, e.g., from N-methyl protons as found in some osmolyte metabolites. Osmolytes were indeed found in high concentrations: betaine was the highest-concentration metabolite in both S. ingentis hepatopancreas and H. rufescens muscle, although it was not detected in S. purpuratus; taurine was abundant in all the three invertebrate species, especially in S. ingentis and H. rufescens; alanine, glutamate, aspartate, proline, and arginine were also found in abundance in S. ingentis hepatopancreas; and arginine was high in concentration in H. rufescens muscle. The most abundant metabolite in S. purpuratus eggs was glycine (47). These compounds are all known to be present in marine invertebrates as osmolytes in order to cope with salinity stress (e.g., 48–51). However, arginine=phosphorylarginine is the major phosphagen for the majority of invertebrate species, equivalent to creatine in vertebrates (52), and thus it is likely that its high concentration in S. ingentis hepatopancreas and H. rufescens muscle is because of its requirement as an energy store. A more recent metabonomic study has focussed on H. rufescens, confirming that the spectra are dominated by glycine and betaine (53). Muscle tissue extracts were analyzed in addition to hemolymph and digestive gland tissue extracts. A number of new metabolites were assigned with including the osmolytes N-methyltaurine and dimethylglycine, and the unusual zwitterionic aromatic compound homarine (1-methyl-2-pyridine carboxylic acid). Homarine is also believed to function as an osmolyte (54).

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2.2.1. Vertebrates—Fish The Japanese medaka (Oryzias latipes) is a well-characterized model species for developmental biology; 34 separate stages during embryogenesis can readily be discerned by light microscopy, as the developing embryos are transparent. The advantages of having an easily manipulable system in which embryogenesis can be observed means that there are several developmental environmental toxicology studies which have used O. latipes (e.g., 55–57). The sensitivity to pollutants is at a maximum during development. Thus, animal models of development that can be used in toxicity tests are valuable. Viant (24) and Viant et al. (58) have profiled changes in O. latipes eggs both during normal development and after exposure to trichloroethylene. At the time of writing, these experiments were still ongoing and a full assignment of observed peaks was not yet made, and hence this species is not included in Table 2. However, a brief discussion of toxicity-influenced changes on spectral profiles is included in Sec. 3.2.2. 2.2.2. Vertebrates—Mammals Griffin et al. (59) compared metabolite profiles from three different wild mammals, Clethrionomys glareolus (bank vole), Apodemus sylvaticus (wood mouse), and white-toothed shrew (Crocidura suaveolens), chosen because of their different ecophysiological niches (respectively, herbivorous, gramnivorous, and insectivorous). Laboratory rats (Sprague–Dawley) were also included to permit comparison to a typical laboratory organism. A particular feature of this study is that biofluid (urine and blood plasma) spectra were complemented by magic-angle-spinning (MAS) spectra of intact renal cortex kidney tissue samples. Metabolite profiling by MAS 1H NMR spectroscopy means that animal tissue samples can be analyzed directly with no need for any extraction methods—thus potentially minimizing artifacts introduced by the selection process, whether of selective extraction or chemical conversion of metabolites (60). It has also been suggested that MAS may be useful insofar as MAS-visible metabolites still


473

remain in tissue residues even following conventional liquid extraction techniques (61). Clethrionomys glareolus urine had high concentrations of aromatic compounds (relative to creatinine) compared to A. sylvaticus and the laboratory rat, including hippurate and the aromatic amino acids phenylalanine, tryptophan, and tyrosine. Rat urine was also lower in TCA cycle components. Principal components analysis of the autoscaled urine data (after binning of the spectra into 0.04 ppm regions) completely separated the rat samples from the other two rodent species along PC 1; it is worth noting that the rodents were all fed the same diet (laboratory chow), so the observed differences were not caused by the trivial reason of different dietary intake. Higher concentrations of the aromatic amino acids were also reported in C. glareolus blood plasma as compared to A. sylvaticus. Even clearer differences between the laboratory rat and the wild mammals were observed in MAS renal cortex spectra: in particular, lipid triglyceride signals dominated the wild mammal spectra compared to the laboratory rat samples. It is tempting to speculate that this may be because there is a greater selection pressure for the wild animals to build up fat reserves when provided with ample food, as they might have need of energy reserves in times of food scarcity. In conclusion, there were clear differences observed between the biochemistry of the laboratory rat and the three wild mammal species. The rat spectra were also observed to have less individual-to-individual variation than the wild mammal spectra, which is a natural consequence of the greater heterogeneity expected of wild populations. The baseline data collected in this study have been used in further toxicological studies on the effects of cadmium (Cd) and arsenic (As) on C. glareolus (cf. Sec. 3.4.1). Holmes et al. (62) studied the multimammate desert mouse (Mastomys natalensis): the responses of urinary spectroscopic profiles to the model kidney toxin 2-bromoethanamine were compared to those of the laboratory rat. Mastomys natalensis was selected because, as a desert animal, it has a specialized kidney structure to reduce water

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losses. A complete assignment of the urinary spectra was not attempted, but specific biomarker compounds were identified in the urine. The spectra were not analyzed using multivariate methods, but by visual identification of changes in resonances. The results are discussed briefly in Sec. 3.2.1.

3. TOXICOLOGICAL AND RELATED STUDIES In this section, we review those studies that have used metabonomic methods—nonselective profiling of toxicity-induced alterations in metabolic status by multivariate analysis of 1 H NMR spectra—applied to environmental organisms and environmental problems. 3.1.1. Worms—Metal Exposures Several studies have been carried out with earthworms. Gibb et al. (31) used 1H NMR spectroscopic metabolic profiling of two species (L. rubellus and E. andrei) to detect metabolite changes caused by mesocosm exposure to soil spiked with copper at up to 160 mg kg1. Eisenia andrei is a small, rapidly-reproducing epigeic earthworm usually found in compost heaps that is widely used in regulatory toxicity testing because of ease of maintaining cultures, despite its lack of relevance to soil exposures. Lumbricus rubellus is a more ecologically relevant soil organism, typically found in soil litter layers. Histidine (integrated separately and expressed as an internal ratio relative to tryptophan) was found to be copper-responsive in L. rubellus but not in E. andrei. A dose–responsive increase in histidine=tryptophan ratios was observed. The conjecture was made that this might be a direct physiological response by L. rubellus, and that intracellular free histidine levels might be upregulated in order to reduce cytotoxicity by chelation of copper ions. A subsequent field study (63) has been carried out at a site contaminated with mixed heavy metals from smelting works, where the principal contaminant of concern (based upon comparison of field concentrations with laboratory-derived toxicity data) is


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known to be zinc. Laboratory cultures of E. andrei exposed on-site in buried nylon mesh bags were compared to autochthonous earthworms (L. rubellus and L. terrestris). Unfortunately, practical difficulties in covering the full gradient of contamination reduced the potential value of the results. Laboratory-reared E. andrei were exposed for a three-week period at three sites; there were only two survivors from the site closest to the contamination source, reducing the statistical power of any analysis. There were some interesting results from the indigenous earthworms that were collected. Lumbricus rubellus was not found at the site closest to the pollution source; L. terrestris was found at this site, but was not found at the control site furthest from the smelter. Thus, neither indigenous species could be used to cover the entire contamination gradient, again reducing the value of the results. However, histidine was again observed to be affected in both Lumbricus species: histidine concentrations were increased by a small but significant level in L. rubellus populations taken at sites of intermediate pollution, but were dramatically decreased in L. terrestris populations taken at the most polluted site. Because neither species was found over the entire contamination gradient, it was difficult to conclude whether these differences represented a true specieslevel biochemical difference in response to metals. But the results do confirm that histidine—previously identified as a copper-responsive biomarker in L. rubellus—is implicated in earthworms’ biochemical responses to metals. Some of the differences observed in the field study may be because the primary pollutant was zinc, not copper; future additional multispecies and multicontaminant microcosm exposures would be valuable in settling these possibilities. One point to be borne in mind is that some of the free amino acid concentrations were probably produced by protein hydrolysis under the sample extraction conditions used (cf. Sec. 2.1). Thus the histidine response may either be due to a result of changes in intracellular free histidine concentrations, or of changes in the total amino acid pool—for example, histidine-rich copper-binding proteins may have been upregulated (64).

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3.1.2. Earthworms—Organics Exposures, Laboratory Tests A parallel effort has also been made to delineate the changes in biochemical space induced by organic pollutants in earthworms, using E. veneta as a model (32–34,39). Initial studies were carried out with single compounds and a modified filterpaper contact test (OECD). The contact test involves inserting a piece of filter paper into a glass vial such that it surrounds the entire interior of the vial, adding 1 mL of an aqueous solution of the test compound, and then introducing a worm (in the original test, an individual of E. andrei would be exposed for a 48 hr period). This contact test is severely limited as an ecological test system; for example, it gives no idea of how a compound’s toxicity will be affected by its bioavailability in soil. It is, though, an easily controllable test system appropriate for carrying out preliminary studies, particularly useful when one wishes to ensure exposure of the earthworm to a compound (65). Warne et al. (33) reported the effects of a model aromatic pollutant, 3-trifluoromethylaniline, on spectroscopic profiles of aqueous extracts of E. veneta. The compound was dosed using the filter-paper contact test using range-finding concentrations (1000, 100, 10, 1 and 0.1 mg cm2). The 1000 and 100 mg cm2 level caused complete mortality, placing the compound into the ‘‘very toxic’’ category (66). Principal component analysis using autoscaled data was used to interpret overall toxic effects of the data, whereas individual biomarkers for each dose level were identified by calculating the Pearson’s correlation coefficient for each binned variable with a class variable for treatment. The only observed biomarker compound at sub-lethal levels was HEFS (although not assigned in the paper), which was increased in worms dosed at 10 mg cm2. Of particular interest was that worms exposed at sub-lethal concentrations were all significantly separated from control worms, even at the lowest dose levels of 1 and 0.1 mg cm2, showing that the overall spectral patterns were sufficient to distinguish the effects of 3-trifluoromethylaniline even though there were no classic single-molecule biomarkers identified.


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A second study with E. veneta exposed to organic pollutants via the filter paper contact test used three compounds: 4-fluoroaniline, 2-fluoro-4-methylaniline, and 3,5-difluoroaniline (34). As for Ref. 33, aqueous extracts were analyzed by 1D 1 H NMR spectroscopy, and PCA used as a pattern recognition (PR) technique on the data-reduced spectra. Two distinctly different biochemical effects were observed for two groups of compounds: 4-fluoroaniline caused an almost total reduction in maltose concentrations, whereas the other two compounds, 2-fluoro-4-methylaniline and 3,5-difluoroaniline, were associated with a reduction in HEFS concentrations. A multivariate technique such as metabonomics might be especially useful in environmental monitoring in helping to assign sources of environmental stress. The unexpected and large biochemical differences between the toxic effects of similar compounds (all being monoaromatic fluorinated anilines) demonstrate the value of a non-selective biomarker monitoring approach. The use of coelomic fluid as an alternative source for metabonomic experiments has also been demonstrated (39). Coelomic fluid of E. veneta presents an entirely different profile, unsurprisingly, to that of whole-body extracts. The spectra are dominated by peaks from organic acids. The compound 3-fluoro-4-nitrophenol was shown to affect the metabolite profiles. Principal components analysis of mean-centered data showed separation of dosed from control worms along the third principal component: malonate and acetate were negative biomarkers, and succinate and a resonance assigned to trimethylamine-N-oxide were positive biomarkers of exposure to 3-fluoro-4-nitrophenol. The reasons for the change in these specific metabolites are not clear; however, 3-fluoro-4-nitrophenol is likely to act as an uncoupler of oxidative phosphorylation [on the grounds of structural and presumed chemical similarity to the known uncoupler 3-trifluoromethyl-4-nitrophenol (67,68)], and thus fluctuations in the concentrations of Krebs cycle intermediates and other organic acids are not surprising. Part of the rationale for collection and analysis of coelomic fluid was that it receives waste metabolites en route to excretion in urine via the nephridiopores (30), and

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thus it was expected that effects of a toxic chemical disturbance would be clearer in the coelomic fluid than in wholebody extracts. In fact, the changes in the spectral profiles were small compared to the dramatic changes often observed in mammalian urine spectra (69–71). Further experiments have confirmed that toxin-induced changes in coelomic fluid are very small, even at lethal levels (unpublished data). Hence, it appears that earthworms’ homeostatic control over the coelomic fluid at the small-molecule level may be greater than previously believed. 3.1.3. Earthworms—Organics Exposures, Soil Tests It is vital to include exposures in realistic environmental matrices in ecotoxicity testing. We have exposed E. veneta to two model organic compounds (3-fluorophenol and 3-trifluoromethylaniline) in soil microcosms at concentrations up to 100 mg kg1 soil dry weight. The compounds were mixed with the soils as aqueous solutions, and worms added to the microcosms 24 hr after spiking of the soils. The microcosms were destructively sampled at seven and 28 days, and coelomic fluid samples were taken from the worms by electrical stimulus. The worms were then snap-frozen and extracted in an acetonitrile=water mixture. No effects of any kind could be determined for the compound 3-fluorophenol, even though this was the most toxic of the two (100% mortality at 100 mg kg1, and 5% mortality at 75 mg kg1, whereas 3-trifluoromethylaniline caused no mortality at any dose level). Neither PCA nor partial-least-squares regression showed any significant grouping of affected animals (unpublished results). Life-cycle parameters (cocoon production and cocoon hatching) were monitored as a sensitive environmental endpoint, but unfortunately cocoon production was absent or low even in control microcosms, possibly because of a seasonal effect. This lack of NMR-detectable differences is an example of how even a highly controlled laboratory exposure, far simpler than an actual field experiment, may not reproduce biomarker effects seen in a simpler system, and illustrates the


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need for controlled experiments at all levels of complexity (e.g., laboratory, microcosm, field). Care must be taken not to over-interpret the results—for instance, if a (hypothetical) field study of an organic contaminant had shown metabolite differences, this might be caused by indirect effects on food supply or environmental physical conditions. For the current example, there are several reasons why metabonomic effects may not have been observed: the interaction of organic pollutants with soils is extremely complex (e.g., 72,73); and it is possible that the contaminants in the soil had been degraded or rendered unavailable by microbial action, as it has been shown that similar monoaromatics can be rapidly degraded by soil microbial communities (74). 3.2. Other Invertebrates Metabonomic pathophysiological assessment has also been extended to marine invertebrates. Red abalone (Haliotis rufescens) are subjected to a withering disease, most probably caused by a bacterial infection. Haliotis rufescens is an important commercial species, so it is of considerable interest to investigate the occurrence and effects of this disease. Healthy, withered, and stunted (i.e., suffering from an intermediate form of withering syndrome) abalone had hemolymph, digestive fluid, and HClO4 tissue extracts of foot muscle analyzed by 1D 1H NMR spectroscopy (53). The spectra were integrated into 0.005 ppm bins, log-transformed, and analyzed by PCA. There was a complete separation of the samples within principal component space for all the three sample types (hemolymph, digestive fluid, and muscle extract): healthy abalone were separated from withered abalone along PC1, and from stunted abalone along PC2. The observed metabolite changes included decreases of free amino acids and adenylates in the stunted=withered abalone. This was suggested to be most probably because withering syndrome in abalone involves starvation. The large pools of oxidizable amino acids in marine molluscs are known to be used as a cellular energy source (75), and starvation has been shown to decrease free amino acid concentrations (76). It was

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also observed that homarine was greatly increased in diseased animals; the interesting suggestion made was that this increase served to maintain overall osmolyte concentrations, which would otherwise be diminished by the decrease in free amino acids. 3.2.1. Vertebrates—Mammals The multimammate desert mouse (M. natalensis) was selected for a study of the kidney toxin 2-bromoethanamine hydrobromide (BEA), and compared to the Fischer 344 rat (62). The rationale was that M. natalensis, like other desert organisms, has a highly specialized kidney structure in order to maintain water relations in a desert environment, and might therefore prove to be a sensitive model for studying BEA, which causes renal papillary necrosis. Nuclear Magnetic resonance was used as a profiling tool; PR methods were not applied directly to the spectral data. Urinary collections were made up to 96 hr postdose to enable to follow the progress of the effects of the toxic insult through time. Overall, the response of M. natalensis was similar to that of the laboratory rat: urinary succinate and a-ketoglutarate levels were decreased in the 0–24 hr time period, followed by increased a-ketoglutarate for the duration of the experiment. Glutarate and adipate were also increased by BEA treatment. However, there were some NMR-observable differences in the species response to BEA; M. natalensis exhibited a sustained increase in taurine concentrations, which was not observed in the rat. Ethanol was also observed in postdose M. natalensis urine, but not in rat urine. The NMR-profiling results were also compared to urinary enzyme activity assays (lactate dehydrogenase, g-glutamyltransferase, and alkaline phosphatase). The response in both species was similar, except that alkaline phosphatase activity was elevated in M. natalensis but not in the rat for 48 hr following BEA treatment. Clear differences were shown by histopathological analysis: the laboratory rat was much more sensitive to BEA, with much higher levels of cellular damage caused at the same dose level.


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Effects of both Cd (as CdCl2) and As (as As2O3) on the bank vole C. glareolus have been studied by MAS profiling and metabonomic analysis (61,77). Male voles were exposed to approximately 6 mg Cd g1 body weight for 14 days, resulting in a final mean Cd concentration in kidney tissue of 8.4 mg g1 dw (77). Renal cortex samples were taken for MAS analysis, using T2-edited (CPMG) sequences. Spectra were data reduced into 0.04 ppm bins, and analyzed by autoscaled PCA. Controls were completely separated from Cdexposed samples (n ¼ 5 in each case) along PC 2; Cd exposure caused decreases in the levels of leucine=isoleucine, glutamate, glycine, and taurine levels, and increases in lipid levels (variables assigned to lipid allyl protons and –COCH2– moieties). Reported literature values of renal Cd concentrations that cause harm to bank voles following dietary Cd exposure are broadly congruent with the results reported by this study: 38 mg g1 dw was reported to cause ‘‘severe testicular and renal injuries’’ (78). Histopathological changes (focal degeneration of proximal tubular cells) were caused by values of 25–40 mg g1 wet wt but not 12–16 mg g1 wet wt (79). If an 80% tissue water content is assumed, this approximates to changes at a 5–8 mg g1 dw but not 2.4–3.2 mg g1 level. Thus, metabonomic analysis detected biochemical changes at approximately the lowest tissue concentrations that have been shown to cause cellular damage in C. glareolus (79). Dietary exposure to Cd causes a decrease in tissue Fe levels in C. glareolus, and supplementation with dietary Fe reduces the apparent toxicity of Cd (79,80). It has, therefore, been suggested that Cd toxicity is indirectly manifested in bank voles, by depression of tissue Fe and Fe-associated oxidative processes, including probably mitochondrial processes (80). It is not yet clear how this may be related to the metabonomic results. Cadmium has also been shown to cause an unexpected decrease in lipid peroxidation in C. glareolus (81), and thus certainly has the potential to affect lipid metabolism, which may be related to the changes observed in lipid resonances. The effects of dietary exposure to As (28 mg g1 in food for a 14 day period) on C. glareolus kidney tissue were also

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studied by MAS 1H NMR metabolite profiling, and compared to effects on the wood mouse A. sylvaticus (61). Pattern recognition techniques were not applied for data analysis. The results in this case were less clear cut than for Cd exposure; the main effect observed was broadening of the resonance at d 1.30 in C. glareolus. This resonance was assigned to be the methylene protons of lipid triglycerides. No discernible effects were seen for A. sylvaticus. Some further intriguing effects were discerned by diffusion-weighted MAS NMR spectroscopy of the tissue samples: the apparent diffusion coefficient of the slowest-diffusing water pool—assigned to intracellular water—in C. glareolus was increased by As exposure. It was suggested that this might have been caused by rupture of the smallest cell fraction, indicating renal damage. It is unlikely that calculation of apparent diffusion coefficients by NMR spectroscopy will ever be used for environmental site assessment, but it is an example of the latent information potentially obtainable by using NMR as an analytical technique. 3.2.2. Vertebrates—Fish The Japanese medaka (O. latipes) provides a frequently used system for studying developmental environmental toxicants. Trichloroethylene (TCE) is a very common environmental pollutant and a known disruptor of development (82,83), and was therefore chosen as a model compound. Developing embryos were exposed to TCE throughout development, and then analyzed by NMR spectroscopy immediately before hatch. Eggs were exposed in a static nonrenewal system in sealed jars for a seven-day period postfertilization (stages 12–34, the penultimate stage prior to hatch). One hundred eggs were combined to provide enough sample for NMR, and extracted with 6% HClO4. One-dimensional spectra taken at 500 MHz were binned into 0.005 ppm regions, and log-transformed mean-centered data analyzed by PCA (58). Initial results show a very clear effect of TCE on the embryo spectral profiles: there is a clear relationship between TCE concentration and an axis based on PCs 1 and 2 (Fig. 2). The controls are


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Figure 2 Effect of trichloroethylene on biochemical profiles in developing medaka embryos: plot of nominal trichloroethylene concentrations against data from principal components analysis of 500 MHz 1H NMR spectra, 2.5 Hz bin width. Error bars ¼ SD (n ¼ 4), dashed lines indicate 95% confidence limits.

completely separated from the dosed embryos for all dose levels, even at the lowest nominal dose of 1 mg l1, and the highest doses are clearly shown to cause greater changes than the lower doses. It is of particular interest that metabonomic changes were observed at these low concentrations, which were considerably lower than the doses which caused a delay in hatching (a common indicator of developmental effects), i.e., the metabonomic biomarkers appeared to be highly sensitive even at levels well below the observed gross phenotypic effects. Future work will examine the effects of TCE throughout the course of development. Metabonomic analysis has been used to provide a ‘‘developmental trajectory’’ for O. latipes, i.e., the changes in metabolite profiles occurring during normal embryogenesis can be plotted on a PCA scores plot (24). Metabolism during development, as shown by these metabonomic changes, is strictly controlled and highly reproducible

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between individual replicates. This developmental trajectory will provide a baseline for the examination of future developmental toxicants: for example, it should be possible to distinguish at what developmental point metabolic disturbances are induced.

4. CONCLUSIONS AND FUTURE IMPLICATIONS 4.1. Unusual Metabolism Some problems occur in the metabolic profiling of nonmodel organisms. One of these is the appearance of unusual and unassigned metabolites. It is impossible to state how many new or unusual and hence unassigned metabolites may be detected in a metabolic-profiling study of a ‘‘new’’ organism. There are not enough previous examples to be able to predict with any degree of confidence whatsoever what proportion of metabolites in a sample from a new organism may be genuinely novel. For example, in the course of work within our own group, we have discovered that every earthworm species that we have yet tested (11 different species to date from five different genera) contains the aromatic compound HEFS. This compound was previously identified in earthworms, although the incorrect structural isomer was given (84). However, this previous discovery was not known to us when we were attempting to assign the observed resonances in the NMR spectra, and hence the situation was the same as if the compound had never been characterized. Thus, 1D and 2D 1H and 13C NMR spectroscopic data and high-resolution mass spectrometry were needed to determine the structure (34). This compound bears no clear relation to any class of biochemicals and its biogenesis is not obvious. Its function in earthworms is unknown, although the extremely high concentration of up to 0.1% total wet weight (84) may indicate that it has some kind of structural or other physiological role. We conjecture that it may protect against dehydration—a regular peril faced by earthworms—possibly by stabilizing membranes. Strongly amphiphilic metabolites


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have been shown to help maintain water relations in other species (85,86). 2-hexyl-5-ethyl-3-foransolfonate is a good example of a completely new metabolite being (re)discovered consequent to observation of unassigned 1H resonances. Figure 3 shows the 1H NMR spectrum of an HClO4 extract of L. rubellus, and shows that the spectrum is dominated by

Figure 3 500MHz 1H NMR spectrum of tissue extract (whole organism) of the earthworm L. rubellus, showing large number of unassigned and nonstandard metabolites. Metabolites are labeled directly on the spectrum. A: high-frequency region of spectrum; vertical scale is expanded tenfold relative to B. Resonances from HEFS and formate are not represented to their full height. B: low-frequency region of spectrum. Gly: glycine. Glu: glutamate. Gln: glutamine. MeHis: methylhistidine. Ala: alanine. Val: valine. Tyr: tyrosine. U1: unknown compound, but possibly related to lombricine=phosphoryllombricine. Selected other unassigned resonances are marked by ‘‘?’’.

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resonances from compounds that are not found in similar spectra from vertebrates. Similarly, the aromatic compound homarine found in abalone was initially assigned on the basis of the structural information implicit in the NMR spectra (53), and it was only subsequently realized that this compound had already been identified in marine invertebrates (54). These examples show the value of 1H NMR spectroscopy as an analytical technique for nonmodel organisms: the nonselective nature of the technique means that even previously unknown metabolites are detected, and the high level of structural information given by the spectra means that unknown and=or unassigned metabolites can be quickly recognized as such, together with many clues as to their possible chemical class and structure. 4.2. Baseline Variability This is a key issue for genuine environmental studies (i.e., involving field and=or mesocosm studies), and may well be noticeable even in laboratory studies on nonmodel organisms, which are likely to be genetically and hence metabolically more heterogeneous than typical laboratory rat and mouse strains. Even well-characterized univariate biochemical markers such as metallothionein or heat-shock protein levels have been found to be highly variable in field tests. Metabonomic profiles will be affected by a wide range of physical and environmental factors, including temperature and water stress, salinity, seasonality, nutrient status, and life-cycle stage (87). Any realistic use of metabonomics for assessment of field samples will require a consideration of these factors. Identification of suitable controls is important in those situations where there are clear and known sources of contamination; alternatively, in situ exposure of test animals will address many of these problems, but tells us nothing about the actual health of the indigenous organisms and ecosystems. It is possible that the multivariate response of metabolite profiles could, in the long run, actually prove advantageous: if enough background data were acquired, the NMR spectra could be used to fingerprint not


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only exposure to pollutants, but also the degree of other biotic=abiotic stresses. 4.3. Prior Knowledge Proteomics and transcriptomics both require prior knowledge of sequence data, and are thus currently limited to a relatively small set of species. Nuclear magnetic resonance-based metabonomic profiling has a unique advantage in having no requirement for prior knowledge—any sample can be analyzed with equal facility. Novel biofluids may offer specific problems, e.g., may have a high macromolecule content or be difficult to collect, but tissue extracts can be readily prepared (assuming sufficient biomass is available) and good spectra can very easily be obtained. This is certainly one of the major advantages of metabonomics over other ‘‘omic’’ techniques, e.g., transcriptomics=proteomics—the use of a model organism is not essential. This is particularly useful for environmental applications, as there are many existing bioassays using many different species. It also opens up the possibility of using metabonomic profiling for systematics, in the broader sense of comparison of biological functions across species=strains=populations rather than the restricted sense of identifying and categorizing species. An example of such a biological function might be sensitivity to a specific toxin or pollutant. If the 1H NMR spectra are treated solely as fingerprints, then there is genuinely no need for prior knowledge. However, it is usually of interest to know what metabolites are contributing to the resonances that vary significantly between samples or treatments. Thus, every new sample type (e.g., different species, different tissue, or biofluid) may require an initial assignment of its 1H NMR-observable resonances, which might seem a major task. In practice, the situation is likely to be somewhere between these two extremes: new samples will generally benefit from initial spectral assignment, but this may not be too onerous given that the primary metabolites are the same even across extremely varied phyletic groups (Table 2). It is certainly true that

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‘‘unusual’’ species may well contain unexpected and unassigned metabolites, which then require a large amount of time and effort for characterization (e.g., Ref. 34). But even if the observed resonances cannot all be assigned, it is still possible to gather valuable biochemical information (6), e.g., by comparing profiles between different organisms, or by comparing effects of chemicals to those of pollutants of known mechanisms of toxic action. Thus, we have been able to show that sibling earthworm species, apparently occupying the same ecological niche, are in fact highly differentiated at the biochemical metabolite level, even though we have not yet identified the aromatic metabolites that differ between the species (25). 4.4. Lack of Success The application of metabonomic methods to environmental problems is, in reality, still at the potential stage. There are no examples of completely new or unexpected discoveries or insights into biochemical function or mechanism that have been produced by metabonomic methods. There are certainly no examples of actual application of these methods to risk assessment or environmental monitoring; use in a regulatory or other setting is hampered by the lack of knowledge of baseline data variability for suitable species. Metabonomics may be thought of as multivariate profiling of a suite of smallmolecule biomarkers; for a biomarker or suite of biomarkers to be useful and accepted as a monitoring tool, it is essential that there is a clear mechanistic understanding of how the biomarker response is related to the chemical stressor (9). To date, there is no completely reliable predictive model that can be used to generate falsifiable hypotheses about changes in metabolism that could be tested by metabonomic methods, even for the simplest and best-understood model organisms, although attempts are being made to construct such models (6,88,89). Consequently, it is not surprising that there are currently no examples of metabonomic studies using environmentally relevant organisms where there are unequivocal mechanistic links between metabolite-level changes and


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specific chemical effects. This may be unsurprising, but is still problematic for the adoption of metabonomic techniques for ecotoxicity assessment. Nonetheless, the techniques have been tested and validated in a number of different organisms, including vertebrates and invertebrates, and in settings ranging from artificial exposure in laboratory systems to collection of autochthonous populations from contaminated field sites. Earthworms have been tested with both metals and organics, in laboratory and field tests, and furthermore the physiological effects on metabonomic profiles (starvation, freezing) have also been investigated. Analysis of earthworm tissue extracts=coelomic fluid could quite plausibly be used for soil ecotoxicity assessment. Specific sites that are known to be contaminated with hotspots, or point sources of toxins, could be assessed by comparison with appropriate controls. In conclusion, some of the most important problems facing ecotoxicologists include: a. Assessing the effects of mixture toxicity, in particular, interactions between different chemical stressors. b. Determining the degree of ‘‘health’’ of the organisms found in an ecosystem—are pollutants that are present actually stressing the organisms? c. Determining subtle sublethal endpoints, in particular, effects on reproduction and development. All of these are difficult problems, and addressing them will require much effort and input from different disciplines. Modern postgenomic technologies may well transform the ability and speed with which these scientific questions can be answered. Metabonomics has not yet been widely adopted for ecotoxicological assessment, and there are many problems with doing so, as discussed above. But it has certain crucial advantages—such as applicability to any species, without requiring prior sequence information; potential for rapid and high-throughput testing; and direct relationship to organism-level biological functionality—that could render a metabonomic approach highly informative and of immense value.

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12 Current Challenges and Future Developments in Metabonomics Technology DONALD G. ROBERTSON Departments of Worldwide Safety Sciences, Pfizer Global Research and Development, Ann Arbor, MI, U.S.A.

1. PERSPECTIVE In 2002, I attended a conference at which metabonomics was the subject of an all-afternoon session. The format was in four presentations with an extended period of interactive Q&A after each session. One of the most heated discussions arose between an avid supporter of the technology (not myself) and a member of the regulatory community over whether metabonomics was an ‘‘old dog with new tricks’’ or a ‘‘new dog’’. The regulatory representative took the ‘‘old dog’’ view 499

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with the metabonomics advocate expressing indignation as to how this cutting edge technology could be considered as an old dog of any sort. It was a highly entertaining and amusing half-hour debate. As I reflect back on that debate, it becomes quite apparent to me that both discussants were ‘‘right’’ at least in their own perception—it was simply a matter of perspective. Clearly metabonomics technology will allow us to do some things we currently do, but much more simply, faster or cheaper. In vivo toxicity screening and target organ identification are routinely conducted today, but metabonomics may enable us to do these much more rapidly and cost effectively. This example might be considered a new trick for an old dog—a faster and simpler way to do what we have traditionally done in in vivo studies with classical histological and clinical pathology assessment. However, the ability to non-invasively, yet repeatedly assess toxicity, in some cases prior to any traditional manifestation of toxicity, certainly is something more than a ‘‘new trick.’’ It opens up a whole new avenue of scientific thought (some might say a can of worms) about what toxicity is and how we should properly assess it for a meaningful evaluation of risk.

2. THE POWER OF THE METABONOMIC APPROACH IN TOXICOLOGY Metabonomics (as well as many other ‘‘omic’’ sciences) makes real the possibility of true systems biology assessment. The question is—are we ready for such an assessment? The preceding chapters should make it abundantly clear that we are pushing back the boundaries of when we can identify a ‘‘response’’ be it toxic or otherwise to an extremely sensitive level. In many cases, this leads us to some of the most fundamental questions in the field of toxicology. When does an effect become ‘‘toxicity’’? Are there thresholds of systemic response that are indicative of entering a toxic state vs. ‘‘normal’’ adaptation? For that matter when does normal adaptive response become abnormal (i.e., toxicity)? Complicating matters even further is the fact that some dose–response curves

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may be ‘‘U’’ shaped. It is well recognized that the lack of certain vitamins or trace elements is just as harmful (read that toxic) as too much of the very same molecule. However, the manifestations of the toxicities at either end of the dose– response curve are very different. This concept has been extended to non-nutritional substances by the concept of hormesis (1), a premise well recognized in radiation biology. Chemical hormesis suggests that certain compounds, traditionally thought of as toxic, may actually have beneficial effects at low doses. The traditional approach for managing such thorny questions is to avoid dealing with them all together. We might not know how to define toxicity but we will know it when we see it. We simply monitor for classical signs of toxicity (e.g., abnormal clinical pathology and=or histopathology) and we know the dose is too high when these manifestations become evident. Figure 1 provides a simple diagrammatic representation of the course of a candidate therapeutic agent through the body of an organism. When drugs behave the way we like them to, the compound enters the body undergoing disposition and transformation, produces a biochemical change, which may lead to a physiologic change and in some cases a morphologic change. Depending on the target, any one or all of these responses may be part of the desired effect. However, each level of response can lead to an untoward response as well. For example, inhibition of a particular kinase may produce the anti-inflammatory result we desire in our target tissue of the joint, but the same inhibition in a non-target tissue can result in toxicity. Lowering cholesterol may be a admirable goal, except lowering cholesterol too much may produce a plethora of pathologies (2). The same is true of physiological response such as blood pressure. This concept, frequently called exaggerated pharmacology, is well understood and in many instances can be anticipated. If anticipated, these untoward responses can be appropriately monitored to establish a reasonable therapeutic index. However, secondary or indirect responses to compounds (or their metabolites), which are frequently unknown, are much more problematic. To address these potential toxicities, we have to essentially survey the

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Figure 1 Simplified diagrammatic representation of the course of a candidate therapeutic agent through the body of an organism. The right side indicating positive and desired outcomes the right side the negative possible outcomes. What is evident is that there may be many common responses between efficacious and a toxic outcome.

entire animal for inappropriate physiologic or morphologic response to a compound. The trick of course, is to ensure that the unknown target is actually obtained at necropsy and the appropriate biochemical or physiologic assay is in place to assess the potential toxicity. Obviously, we can do neither with any certainty so we employ range-finding studies to


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narrow down the dose ranges for definitive studies and identify target organs. We can then subsequently make sure the appropriate assays are in place—assuming any are available. What does all this have to do with metabonomics? Metabonomics enables the potential of gaining significantly more information from fewer animals about a broader range of endpoints without any a priori knowledge of target organ(s). Metabonomics can be done non-invasively, the time course of effect from pretest baseline, through efficacious response to initiation of toxic response and reversal to baseline can all be obtained from a single animal. Moreover, complicated responses, involving different targets and different time courses, can be readily identified (though not so readily deconvoluted—see below). Looking at Fig. 1, we can see that metabonomics, along with other ‘‘omic’’ technologies, allows us to assess events from the onset of the initial biomolecular response to the death of the animals. The toxicologist now has to figure out what to do with that information. In other words, we will need to address the ‘‘thorny’’ questions. The pressing needs for dealing with these questions are exemplified by some of the issues currently facing toxicogenomics advocates as they interact with regulatory bodies. Gene changes such as increased expression of protooncogenes in large format transcriptomic or otherwise unexplainable findings have lead to a great deal of soul-searching within the industry as to how best to use this kind of data for assessing safety (3). With regard to metabonomics, what would regulatory bodies do with studies in which traditional no-effect doses (no histologic or clinical pathologies) have metabonomic profiles consistent with hepatotoxicity evident at higher doses? Will that mean the loss of a no-effect dose and perhaps a reduced or eliminated therapeutic index? This would be a clear disincentive for any metabonomics application with clinical relevance. Will we have to have a mechanistic understanding of all metabonomic data prior to its use at regulatory agencies? While clearly this would be desirable for all involved, in reality it will be a long time coming. These and other key questions will need resolution before the full potential of metabonomics technology can be realized.

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3. METABONOMICS AS AN ‘‘OMIC’’ TECHNOLOGY 3.1. ‘‘Omics’’ as a Tool of Systems Biology John Lindon et al. described the origin and definition of metabonomics as a word (this volume, Chapter 1). An even broader net finds metabonomics in the family of ‘‘omics’’ technologies, which include toxicogenomics (or transcripotomics) and proteomics as well as a host of other derivations of these technologies, many of which have been given their own names. What is common about these approaches is that they are more system than analyte oriented. The role of metabonomics as a systems biology tool has been recently reviewed (4) and its inclusion as a sister science to toxicogenomics and proteomics has been well recognized (5–7). A systems approach has significant advantages to the toxicologist, but these advantages do come with a price. The advantages include the possibility of systemic toxicity evaluation from a single sample. In one sense, biological and medical sciences have come full circle with regard to diagnosis of disease and=or toxicity. Before the advent of modern medicine, the only useful information physicians frequently had to diagnose ailments was, overt effects or clinical signs (fever, inappetence, rash, malaise, etc.) or the most crude ‘‘biochemical’’ assessments (urine smell or taste). The postulated cause of these effects was as either unknown or highly imaginative (misalignment of ‘‘humors’’, etc.). Even though the proposed etiology may have been eventually disproved, the methodology of associating a pattern of clinical changes with a specific ailment has survived to today. Physicians frequently make tentative diagnoses on presentation of a series of clinical findings alone, without necessarily having to understand the mechanistic link of each clinical finding to the suspected disease. As most clinical signs are systemic manifestations of what may be a very focused disease or toxicity, they are very much akin to ‘‘omic’’ data in that they can reveal the response of an organism to a toxic or disease insult in toto. However, omic data have the significant advantages of being objectively measured, sensitively quantified (though measurement may be relative) and most


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importantly they can provide mechanistic insights. This is particularly important for the preclinical toxicologist who cannot ask their subjects to tell us how they feel. We can now assess an animal in distress via a peripheral sample (regardless of the target), frequently before any clinical manifestations become evident and in many cases before morphological changes are evident. In some cases, this could be more sensitively and precisely accomplished by incorporation of an appropriate biochemical measurement in the study protocol, but that would presume the toxicologist knew what to look for before the study started. This is most often not the case. The forgoing discussion should make apparent a significant drawback that has plagued the toxicologists’ use of ‘‘omic’’ data. The problem with systems biology is that it is systems biology. This tautology emphasizes the fact that when using omic data, one must consider the entire systemic response of an animal to the toxin, not just how the omic data reflect (or not) changes relevant to the target of interest. This was brought home to us in some early metabonomic evaluation work where we noted that 13-week old rats had quite a distinct metabonomic profile compared to 8-week old rats (8). While fascinating, it was certainly not what we were interested in for evaluating the utility of the technology for assessing renal and hepatic toxicants. Though age differences are easily handled by proper study design, indirect effects or secondary target organ effects of a toxin within an individual animal cannot be so easily compensated for. For example, if an animal loses weight in response to a hepatotoxic insult, the weight loss itself will have profound systemic manifestations at the gene, protein and intermediate metabolite level independent of and confounding to the effects induced by the target organ pathology. How do you separate those effects from one another? It can be done, but it is not a trivial task and clearly represents one of the biggest challenges to the toxicologist attempting to use omic data for safety evaluation. However, the assessment of safety involves the organism as a whole. We are not only interested in renal safety or liver safety or the effect on any individual target organ. Our eventual goal is to extrapolate our preclinical findings to poten-

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tial effects on the quantity and quality of life of the human population who may be exposed to the drug. If the goal of the toxicologist is to assess a therapeutic index for a candidate novel therapeutic, a simple, but comprehensive descriptor of toxicity (and potentially efficacy as well) that takes into account all potential targets, could be extremely powerful. The converse to this argument is that it will be difficult to deconvolute a specific target biomarker from a set of data that reflects the entire systemic response to a compound. As pointed out for other omic technologies—metabonomics data present a significant threat for serious misinterpretation (9). Metabonomics does not excuse the toxicologist from the conduct of high quality science and the critical examination of the generated data. 3.2. The ‘‘Panomics’’ Approach One of the plethora of omics related terms to arise in the past few years is the term ‘‘panomics.’’ Other terms have been used, to mean the same thing, but panomics can be considered as an omics of omics. An obvious panomics approach is utilizing transcriptomics, proteomics, and metabonomics on the same study enabling pursuit of toxic effects from the gene=transcript level through protein expression to phenotypic biomolecular expression. This approach engenders an inherent synergy of these technologies by following the logical biologic progression from initial biochemical response to a toxic stimulus to overt toxicity, taking into account any cascading biochemical or physiological responses along the way. To those experienced with proteomic and transcriptomic data, this may seem a bit of a stretch as the temporal and quantitative discordance between gene expression and protein expression has been well recognized (10). However, as depicted in Fig. 2, metabonomics may allow for normalization of what could otherwise be uninterruptible results. Three theoretical and highly stylized data sets from a ‘‘panomics’’ experiment are represented. Results a–c are theoretical transcripts, results d–f represent theoretical proteins, and results g–i represent theoretical metabolites. Panel A represents the ideal situation where a response to a chemical insult induces


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Figure 2 Highly stylized experimental outcomes from hypothetical panomics experiments. " ¼ Increased expression compared to baseline, # ¼ decreased expression compared to baseline, ¼ no change from baseline. See text for further explanation.

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an upregulation of transcript a, no change in transcript b and a decrease in transcript c, 4 hr after exposure. This is followed in temporal sequence by subsequent and similar changes in proteins at 8 hr and metabolite expression at 12 hr. Panel B, though still highly stylized, represents a more realistic set of data in which four animals are sampled at each time point giving what appears to be varying results in transcript, protein, and metabolite expression. In actuality, the expression profiles are identical to panel A if individual animal temporal variation is understood as demonstrated in panel C. The only difference between panels C and B is that the results have been normalized by response and not by time. This is simply a practical extension of observations put forward by Nicholson et al. (11). This ability to understand individual animal variation is a powerful advantage metabonomics can bring to panomics analyses. There are, of course, a number of caveats. Metabonomics is not a real time analysis (at least not yet), so rapid changes would be difficult to normalize. Additionally, if serum is the biofluid being investigated, the limitation of sample number over time would not be any different than serum proteomic analysis. If urine is being used, the limitation becomes the timely availability of a volume sufficient for analysis. Despite these limitations, the ability to understand toxicity in the context of individual animal response, not artificially imposed sampling times, represents one of the greatest advantages metabonomics brings to the toxicologist.

4. SHORT TERM NEEDS FOR METABONOMICS AS A SCIENCE While advances in NMR and MS instrumentation have been discussed in earlier chapters, there still remain needs for the technology outside analytical concerns. Though metabonomics in concept (12,13) has been around longer than metabonomics as a term (14), it is still a relative technological newcomer in the armamentarium of the toxicologist. One of the most pressing needs for this technology within the toxicology community is wider utilization that will bring


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greater acceptance. The initial slowness in uptake of metabonomics was largely due to the steep capital investment required to conduct NMR-based metabonomics. Frequently, the slowness in up take was not due to unavailability of NMR facilities, but in the unfamiliarity of toxicologists with NMR spectroscopists and vice versa. Nuclear magnetic resonance spectroscopists and toxicologists, though usually employed by most medium to large-size pharmaceutical or academic concerns, typically travel in different circles, discouraging collaborative efforts. As metabonomics gains awareness in the greater scientific community, the advantages of collaboration of these two groups have become selfevident, leading to an ever-increasing pace of expansion of the technology. The consortium for metabonomic toxicology (COMET) served as a model of cross discipline collaboration for the purpose of evaluating metabonomics as a tool to evaluate preclinical toxicology within the pharmaceutical industry (15). The consortium for metabonomic toxicology certainly demonstrated that toxicologists and NMR spectroscopists could get along, but provided synergistic energy with minimal turf-guarding concerns. Another development has been the recent expansion of MS-based metabonomics (16,17). Mass spectrometry as a technique is widely available and is certainly more familiar to toxicologists. As these approaches gain acceptance, the pool of metabonomics practitioners will grow substantially. A byproduct of wider use of the technology will be definitive case studies of metabonomics derived biomarkers and mechanistic work. Although some initial pharmaceutically relevant metabonomics derived biomarker work related to markers for phospholipidosis has been published (18,19), many more examples will be required before skeptics will be convinced of the utility of the technology for this application. An even greater body of work exists describing the utility of metabonomics in mechanistic studies (20–23). However, the work has been conducted by relatively a few laboratories. Wider utilization will lead to wider acceptance of this powerful tool for mechanistic work. A corollary to panomics studies is the use of integrated metabonomics, metabonomics of var-

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ious biofluids and tissues, which brings many advantages to the toxicologist interested in both mechanistic and biomarker applications (21,24). A significant limitation to widespread utilization of this technology is the relative paucity of MAS capable instrumentation (see Chapter 5). As the benefits of the approach become evident, wider utilization of MAS as part of an integrated metabonomics assessment within toxicity studies will gain wider use. Beyond the needs indicated above, one of the most pressing requirements for the technology, and for all omic technologies for that matter, are appropriate informatics tools. Omic technologies generate data at a faster rate than any nonsilicon based life form can assimilate. While data visualization tools and multivariate statistical packages are now available (see Chapter 8), there is yet no established informatics tools that will take metabolic findings from pattern to pathway with the ability to link in panomics data. Although the principles of such a system have been described (25), and initial reports of combined toxicogenomic proteomic investigation are appearing in the literature (26,27), there is still a paucity of truly ‘‘panomic’’ toxicity studies. Despite this absence, many groups are working towards this goal, and it can be envisioned that true panomic studies will soon be appearing.

5. CAUTIONARY NOTE The previous section specified to one of the greatest needs of metabonomics as the need for expanded use of the technology. However, this advice should be heeded. One of the current problems with other omic sciences is the proliferation of platforms, vendors, and junk science. One need only go to any major scientific meeting to be deluged with pitches for the latest platforms, CROs and software packages that will solve all omic related problems. One is frequently amazed as to what passes for ‘‘validation’’ for people selling these approaches. For some, anything producing the desired and expected result in one study can be considered validated. For many toxicolo-


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gists conducting experiments in industrial and academic laboratories, experimental data frequently leave us trying to understand why things did not work or what the meaning is of data that were generated. However, when reviewing ‘‘validation studies’’ in sales pitches from vendors trying to sell their latest omic wares, we are more often left asking the question of why their experiments did work. It is difficult to understand why animals given carbon tetrachloride, for example, have gene responses only along the expected paths of hepatic toxicity and oxidative stress related effects. Is this truly all that happened? Should not the fact that the animals lost weight or that several had frank renal lesions play a part in the response. If not why not? While this is a hypothetical illustration, there have been too many examples of such wok being pitched to the scientific community. We are being told what we want to hear—modern day toxicological snake oil. This has lead to a backlash of sort, particularly in the realm of toxicogenomics in the industrial sector, such that any ‘‘predictive’’ toxicogenomic screens are now looked at with a rather jaundiced eye. This is tragic, because there is real potential for such work, but too many people doing shoddy work oversold it too quickly. What makes this particularly worrisome for those of us in the pharmaceutical business, is that the regulatory community has taken notice of developments within the omics sciences (6,28) and a major blackeye now would hurt all those trying to push the omic sciences forward within the arena of drug safety evaluation. The same fate could befall metabonomics technology if we are not careful. It will be the responsibility of metabonomics practitioners to thoroughly evaluate the shortcomings of the technology as well as its advantages, making the scientific community as aware of the former as well as the latter. If the science is truly as good as we think, it will easily withstand the crucible of rigorous peer review. As we play our roles as reviewers of manuscripts, advisors on committees, and purchasers of services, we will need to make clear what is good science and what is self-serving non-sense.

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6. CONCLUSION Metabonomics has clearly come of age as a science. This volume only touched on major applications of the technology including environmental (29) and clinical (30–32) applications that could (and probably will be) the subject of future volumes all by themselves. However, the focus of this volume was toxicological applications, and even within that narrowed scope, metabonomics has gone beyond the ‘‘emerging technology’’ stage and is entering the realm of routine practice. Not all applications of the technology are at the same stage, but the growth rate in publications is expanding at a rapid pace (Fig. 3) clearly suggesting the science is gaining growing acceptance. Although toxicological applications are certainly expanding with the science, there is still a lot of room for growth. The potential for rapid screening technology

Figure 3: Cumulative publication rate of papers containing metabonomics or metabolomics as a key word. Data obtained from MedLine search covering the years 1996–2003.


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development, biomarker discovery, and the use of the technology as a powerful tool for understating basic mechanisms of toxicity will all serve to make metabonomics an indispensable technique for toxicologists in the 21st century.

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Robertson DG, Breider MA, Milad MA. Preclinical safety evaluation of avasimibe in beagle dogs: an ACAT inhibitor with minimal adrenal effects. Toxicol Sci 2001; 59:324–334.

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Castle AL, Carver MP, Mendrick DL. Toxicogenomics: a new revolution in drug safety. Drug Discov Today 2002; 7:728–736.

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Nicholson JK, Wilson ID. Understanding ‘global’ systems biology: metabonomics and the continuum of metabolism. Nat Rev Drug Discov 2003; 2:668–676.

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Fiehn O. Combining genomics, metabolome analysis, and biochemical modeling to understand metabolic networks. Comp Funct Genom 2001; 2:155–168.

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Aardema MJ, MacGregor JT. Toxicology and genetic toxicology in the new era of ‘‘toxicogenomics’’: Impact of ‘‘-omics’’ technologies. Mut Res 2002; 499:13–25.

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Reo NV. NMR-based metabolomics. Drug Chem Toxicol 2002; 4:375–382.

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Robertson DG, Reily MD, Sigler RE, Wells DF, Paterson DA, Braden TK. Metabonomics: evaluation of nuclear magnetic resonance (NMR) and pattern recognition technology for rapid in vivo screening of liver and kidney toxicants. Toxicol Sci 2000; 57:326–337.

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Lewis LL. Key challenges for toxicologists in the 21st century. Trends Pharmacol Sci 2001; 22:281–285.

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Gygi SO, Rochon Y, Franza BR, Aebersold R. Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 1999; 19:1720–1730.

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Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 2002; 1:153–162.

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Nicholson JK, Higham D, Timbrall JA, Sadler PJ. Quantitative 1H NMR urinalysis studies on the biochemical effects of acute cadmium exposure in the rat. Mol Pharmacol 1989; 36:398–404.

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Gartland KP, Anthony ML, Beddell CR, Lindon JC, Nicholson JK. Proton NMR studies on the effects of uranyl nitrate on the biochemical composition of rat urine and plasma. J Pharm Biomed Anal 1990; 8(8–12):951–954.

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Nicholson JK, Lindon JC, Holmes E. ‘‘Metabonomics’’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999; 29:1181–1189.

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Lindon JC, Nicholson JK, Holmes E, Antii H, Bollard ME, Keun H, Beckonert O, Ebbels TM, Reily MD, Robertson DG, Stevens GJ, Luke P, Breau AP, Cantor GH, Bible RH, Niederhauser U, Senn H, Schlotterbeck G, Sidelmann UG, Laursen SM, Tymiak A, Car BD, Lehman-McKeeman L, Colet J, Loukaci A, Thomas C. Contemporary Issues in Toxicology: the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol Appl Pharmacol 2003; 187:137–146.

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Plumb RS, Stumpf CL, Gorenstein MV, Castro-Perez JM, Dear GJ, Anthony M, Sweatman BC, Connor SC, Haselden JN. Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development. Rapid Comm Mass Spectrom 2002; 16:1991–1996.

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Pham-Tuan H, Kaskavelis L, Daykin CA, Janssen HG. Method development in high-performance liquid chromatography for high-throughput profiling and metabonomic studies of biofluid samples. J Chromatogr 2003; 789:283–301.

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Nicholls AW, Nicholson JK, Haselden JN, Waterfield CJ. A metabonomic approach to the investigation of drug-induced phospholipidosis: an NMR spectroscopy and pattern recognition study. Biomarkers 2000; 5:410–423.


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19.

Espina JR, Shockcor JP, Herron WJ, Car BD, Contel NR, Ciaccio PJ, Lindon JC, Holmes E, Nicholson JK. Detection of in vivo biomarkers of phospholipidosis using NMR-based metabonomics approaches. Magn Reson Chem 2001; 39:559–565.

20.

Beckwith-Hall BM, Nicholson JK, Nicholls AW, Foxall PJD, Lindon JC, Connor SC, Abdi M, Connelly J, Holmes E. Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chem Res Toxicol 1998; 11:260–272.

21.

Waters NJ, Holmes E, Williams A, Waterfield CJ, Farrant RD, Nicholson JK. NMR and pattern recognition studies on the time-related metabolic effects of a-naphthylisothiocyanate on liver, urine, and plasma in the rat: an integrative metabonomic approach. Chem Res Toxicol 2001; 14:1401–1412.

22.

Slim RM, Robertson DG, Albassam M, Reily MD, Robosky L, Dethloff LA. Effect of dexamethasone on the metabonomics profile associated with phosphodiesterase inhibitor-induced mesenteric vascular lesions in rats. Toxicol Appl Pharmacol 2002; 183:108–116.

23.

Mortishire-Smith RJ, Skiles GL, Lawrence JW, Spence S, Nicholls AW, Johynson BA, Nicholson JK. Use of metabonomics to identify impaired fatty acid metabolism as the mechanism of a drug-induced toxicity. Chem Res Toxicol 2004; 17:165–173.

24.

Coen M, Lenz EM, Nicholson JK, Wilson ID, Pognan F, Lindon JC. An integrated metabonomic investigation of acetaminophen toxicity in the mouse using NMR spectroscopy. Chem Res Toxicol 2003; 16:295–303.

25.

Lu B, Lawton MP. Data integration in the new era of toxicogenomics. Toxicol Sci 2003; 72(S1):98.

26.

Ruepp SU, Tonge RP, Shaw J, Wallis N, Pognan F. Genomics and proteomics analysis of acetaminophen toxicity in mouse liver. Toxicol Sci 2002; 65:135–150.

27.

Heijne WHM, Stierum RH, Slijper M, van Bladeren PJ, van Ommen B. Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach. Biochem, Pharmacol 2003; 65:857–875.

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MacGregor JT. The future of regulatory toxicology: impact of the biotechnology revolution. Toxicol Sci 2003; 75:236–248.

29.

Bundy JG, Spugeon DJ, Svendsen C, Hankard PK, Osborn D, Lindon JC, Nicholson JK. Earthworm species of the genus Eisenia can be phenotypically differentiated by metabolic profiling. FEBS Lett 2002; 521:115–120.

30.

Brindle JT, Antti H, Homes E, Tranter G, Nicholson JK, Bethell HW, Clarke S, Schofield PM, McKilligin E, Mosedale DE, Grainger DJ. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMRbased metabonomics. Nat Med 2002; 8:1439–1444.

31.

Brindle JT, Nicholson JK, Schofield PM, Grainger DJ, Holmes E. Application of chemometrics to 1H NMR spectroscopic data to investigate a relationship between human serum metabolic profiles and hypertension. Analyst 2003; 18:32–36.

32.

Moolenaar SH, Engelke UFH, Wevers RA. Proton nuclear magnetic resonance spectroscopy of body fluids in the field of inborn errors of metabolism. Ann Clin Biochem 2003; 49: 16–24.

Index

2-Bromoethanamine, 353 2-Bromoethylamine, 211 Renal papillary pathology, 211 NMR urinalysis, 211 2-oxoglutarate, 405

Accelerator mass spectrometry, 37 Acetaminophen, 357 Acute renal failure, 138 Administration of furosemide, 138 Acylcarnitine excretion, 136 Intracellular carnitine insufficiency, 136 Adaptive response, 49 Induction of cytochrome P450 isozymes, 49 Age-related Differences, 402 Alpha-CH proton, 235 Analgesic, 366

Analog-to-digital signal, 239 Anestrus, 418 Arsenic-induced hemorrhage, 182 Diffusion-weighted 1H MAS NMR spectroscopy, 182 Aselective biomarkers, 40 32P-postlabeling assay, 40 Automated phasing, 202

Base-line distortion correction algorithms, 202 Baseline variability, 457 b-Blocker practolol, 248 b-lactam ring, 9 Bile formation, 146 Bile, 146, 357 Bile salts, 146 Biliary cirrhosis, 357 Hepatobiliary diseases, 357 Bile-aciduria and glycosuria, 343

517

518 Biliary toxicants, 211 Bioinformatics, 55 Biomarker of lipid peroxidation, 40 Malondialdehyde, 40 Biomarkers of exposure, 31 Biomarkers of effective dose, 32 Biomarkers of internal dose, 31 Biomarkers, 12, 27, 454 Biomarkers of exposure, 28 Biomarkers of response, 28 Biomarkers of susceptibility, 28 Biomolecular NMR, 77 Block-scaling, 321 Blood plasma levels, 138 Renal papillary damage, 138 Blood plasma lipoprotein content, 124 Malignancy, 124

Cadmium toxicity, 405 Capillary electrochromatography, 255 Capillary electrophoresis, 255 Carbon tetrachloride, 213 Cardiovascular diseases, 8 Catabolism, 343 Cell culture supernatants, 2 Cell lysis, 156 Centrilobular hepatocellular necrosis, 214 Hepatocellular mitosis, 214 Hepatocellular vacuolation, 214 Centrilobular hepatotoxicity, 213 Centrilobular mononuclear infiltrates, 214 Cephalosporins, 364 Chemical noise, 107 Chemometrics analyses, 5 Chlorpyrifos, 403 Cholestasis, 343 Choline containing metabolites, 187 Duchenne muscular dystrophy, 187

Index [Choline containing metabolites] Multiple sclerosis, 187 Chromophores, 4 Consortium for metabonomic toxicology, 509 Consortium on metabonomic toxicology, 20 Coomans plot, 305 Correlation spectroscopy, 88 Cortical nephrotoxicity, 316 Cortical tubular epithelial necrosis, 214 Creatinuria, 373, 405 Cross-validation, 278

Data, 6 Degree of renal failure, 126 Degree of shielding, 80 Detoxification mechanism, 405 Deuterium isotope shift, 123 Dexamethasone, 385 Diabetes, 125 Administration of insulin, 125 Diabetic mouse model, 209 Diet, 435 Diffusion-ordered NMR spectroscopy, 87 Dipolar couplings, 16 Direct infusion mass spectrometry, 103 Diurnal Effects, 421 Diurnal rhythm, 204 Diurnal variation, 399 DNA adducts, 35 DNA repair, 56 Drug discovery-screening paradigm, 218 Drug safety assessment, 337 Duchenne muscular dystrophy, 416

Ecotoxicology, 454 Electronic spectroscopy, 4

Index Endogenous analytes, 201 Endogenous low mass metabolites, 225 Endogenous metabolites, 3 Enzymuria, 360 Estrus cycle, 204, 418 Diestrus, 418 Estrus, 418 Metestrus, 418 Proestrus, 418 Exaggerated pharmacology, 501 Exercise, 438

Fast acetylators, 61 Fasting, 424 Fibrosis, 359 Flavin-containing monogenase activities, 419 Flow-probes, 240 Food restriction, 424 Para-aminohippuric acid transport, 424 Proteinuria, 424 Renal lesions, 424 Fourier transform, 4 Fourier transformation, 85 Free induction decay, 85 Free induction decays, 202 Exponential decay function, 202 Fast Fourier transformation, 202 Frequency domain spectra, 202 Functional NMR-based metabonomics laboratory, 75

Gas chromatography, 4 Gel-electrophoresis, 12 Gender Differences, 404 Gender, 433 Genomics, 2 Glomerulonephritis, 357 Glutathione conjugates, 34 Glycine conjugation, 409

519 Glycogenolysis and glycolysis, 342 Glycosuria, 341 Gut microfloral communities, 8 1

H magic-angle spinning, 16 H-NMR spectroscopy, 196 Han-Wistar Zucker rats, 236 Heat shock=stress proteins, 49 Hemoglobin adducts, 36 Hepatotoxicity, 316 Hepatotoxicity, 343 Hydrazine, 346 Hepatotoxin carbon tetrachloride, 207 Hepatotoxin, 213 Heteronuclear single quantum coherence, 91 Hierarchical cluster analysis (HCA), 265, 424 Hierarchical clustering, 400 High resolution magic-anglespinning (MAS) NMR, 417 High-resolution NMR spectroscopy, 454 High-throughput screening efforts, 195 Histopathologic assessment, 208, 338 Homeostasis, 3 Homonuclear Hartmann-Hahn experiment, 88 Hormonal effects, 918 HPRT mutation frequency, 51 Hyperplasia, 343 Hypertrophy, 360 1

In vivo toxicologic studies, 208 Influence vectors, 10 Infrared spectroscopy, 103 Instrument noise, 107 Inter-individual variability, 401 Internal variability, 205 Inter-Subject Variation, 433 Invasive procedure, 197 Invasive, 30

520 J-resolved experiment, 87

Karplus equation, 83 Ketonuria, 380 Kidney proximal tubule, 418 Kidney toxicity, 217 Krebs cycle, 215, 347 Kupffer cells, 344

Latent information, 107 Lee-Boot effect, 418 Leverage, 279 Light–dark cycle, 399 Linear relationship, 78 Magnetic field strength, 78 Nuclear magnetic moment, 78 Observation frequency, 78 Lipophilic xenobiotics, 411 Lipoprotein, 12 Atherosclerosis, 12 Liquid chromatography, 4 Liquid-liquid extractions, 238 Liver and kidney, 338 Idiosyncratic, 339 Necrosis, 338 Steatosis, 338 Liver toxicants, 211 Loadings, 278 Logical blocking or QUILT analysis, 400 Lung damage, 46 CCl6 protein, 46 Lung disease, 51 Breath analysis, 51

Magic angle spinning, 79, 350 Magic-angle-spinning (MAS) spectra, 472 Magnetic resonance imaging, 76 Magnetic resonance spectroscopy, 76 Mapping space, 18 Markers of liver dysfunction, 45

Index [Markers of liver dysfunction] Levels of certain bile acids, 45 Mass spectral data, 231 Mass spectrometer, 245 Mass spectrometry, 4, 173 Mean-Centering, 271 Measurement of enzyme activity, 47 Megavariate data analysis, 267 Metabolic axes, 8 Metabolic control analysis, 7 Metabolic disturbances, 126 Renal function, 126 Metabolic profiling, 77 Metabolomics, 3 Metabonomic literature, 230, 338 Vasculopathies, 338 Metabonomic technology, 196, 499 Metabonomic-based techniques, 180 Metabonomics, 2, 110, 173, 453 NMR-based metabonomic profiling, 453 Plant metabolomics, 454 Meta-hydroxyphenylpropionic acid, 405 Methylamine metabolism intermediates, 209 Molecular mobility, 84 Molecular self-diffusion coefficients, 79 MS, HPLC, GC=MS, 264 MS instrumentation, 508 Multidimensional fingerprint, 398 Multifocal tubular basophilia, 214 Multimammate Mouse, 412 Multivariate data analysis, 264 Multivariate pattern recognition (PR), 454 Multivariate projection methods, 264 Partial least squares projections, 264 Principal component analysis, 264

Index Nalgene cages, 200 Negative electrospray ionization, 231 Neural network analysis, 400 Neuronal ceroid lipofuscinosis, 209 Neurotoxicant, 348 NMR spectroscopy, 30 NMR spectrum, 78 NMR, 508 NMR-spectroscopy, 264 Non-invasive evaluation, 196 Noninvasive, 30 Nuclear hyperplasia, 214 Nuclear magnetic resonance (NMR) spectroscopy, 75 Nuclear magnetic resonance, 4 Nuclear Overhauser effect, 90

521 Peak toxicity, 205 Phospholipidosis, 297, 349 Amphiophilic drugs, 349 Plasmaglucose, 12 Diabetes, 12 Plasma, 116 Point-swarms, 285 Principal component analysis, 400 Principal components, 203 Probabilistic neural networks, 414 Projection approach, 265 Protein–ligand interactions, 79 Proteome, 3 Proteomics, 2 Proton–proton NOE, 90 Pseudopregnancy, 418

Quantitative signal distortion, 131 Omic technologies, 503 Proteomics, 504 Toxicogenomics, 504 Orthogonal signal correction, 322 Osmolarity, 200 Oxidations and reductions, 232 Oxidative DNA damage and lipid peroxidation, 40 8-hydroxy-2-guanosine, 40

Panomics, 506 Paraaminophenol (PAP), 206 Pareto scaling, 271 Partial least squares discriminant analysis, 405 Partial least squares model, 185 Patent information, 108 Pattern recognition method, 203 Multivariate analysis, 203 Principal component analysis, 203 Pattern recognition, 14, 196, 264 Pattern separations, 380 PCA plot, 204, 409 Peak parking, 242 Peak picking, 242

Radiochromatogram, 248 Raman spectroscopy, 103 Real time analysis, 508 Red blood cell NMR spectroscopy, 128 Refrigerated metabolism racks, 200 Renal carbonic anhydrase, 405 Renal functional patency, 213 Renal inner medulla, 135 Renal medullary:cortical ratios, 412 Renal toxicity, 359 Nephrotoxicity, 359 Proximal Tubular Toxicity, 360 Renal Glomerular Toxicity, 369 Proteinuria, 369 Renal Medullary Toxicity, 365 Vascular Toxicity, 374 Vasculitis, 375

Sample preparation requirements, 200 Scaling, 270

522 Score plot, 276 Scores, 276 Screening paradigm, 197 Screening study design, 208 Seminal fluid, 142 Seminal vesicle fluid, 142 Serum, 116 Signal-to-noise ratio, 249 Sleep deprivation, 430 Slow acetylators, 60 Solid phase extraction chromatography, 15 Solid phase extraction, 233 Species Difference, 408 Spin–spin coupling, 249 Sprague–Dawley rats, 207 Sprague–Dawley (SD) control rat, 401 Standard normal variate correction, 322 Standard tox endpoint, 206 Steatosis, 47 Strain Differences, 413 Stress and acclimatization, 430 Stress, 438 Superconducting solenoid, 94 Supervised classification method SIMCA, 414 Sym-endogenous metabolites, 9

Tautology, 505 TCA cycle intermediates, 404 Temperature effects, 425 Tensor interactions, 175 Tetramethylsilane, 81 Therapeutic index, 501 Time slicing, 241 TOtal Correlated SpectroscopY, 228 Toxicology, 500 Dose–response curve, 501 Toxicity, 500

Index Transcriptomic, 6 Transcriptomic data, 6 Transcriptomics, 2 Gene-chip technologies, 2 Transgenic models, 416 Tricarboxylic acid cycle intermediates, 208 Triple-quantum filtered COSY, 88 Trypan Blue exclusion assay, 179 Tubular acidosis, 405 Tubular nephrotoxin, 206 Tumor suppressor genes, 63

UDP-glucuronosyl transferase, 9 Ultraviolet spectroscopy, 4 Unit-Variance-Scaling, 270 Urinary biomarkers, 216 Urinary NMR profile, 208 Urinary profiling, 219 UV chromatogram, 243 UV chromophores, 76

Vaginal cytology, 418 Variability, 204 Variable ionization efficiency, 4 Vasculitis, 220

Water deprivation, 422 Water Loading, 434 Water resonance, 133 Water resonance frequency, 133 Wavelet analysis, 325 Weighting, 270 Whitten effect, 418 Wister rats, 208

Xenobiotic metabolites, 8, 232 Xenobiotic toxicity, 140 Xenobiotics, 219, 226, 398

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Fire Toxicity

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Toxicity Testing for Assessment of Environmental Agents: Interim Report

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Small-scale Freshwater Toxicity Investigations: Volume 2 - Hazard Assessment Schemes

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Reviews in Food and Nutrition Toxicity, Volume 2 (Reviews in Food and Nutrition Toxicity)

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Reviews in Food and Nutrition Toxicity, Volume 3 (Reviews in Food and Nutrition Toxicity)

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Metals, Fertility, and Reproductive Toxicity

The Handbook of Metabonomics and Metabolomics

Competence Assessment in Dementia

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Uncertainty Modeling in Dose Response: Bench Testing Environmental Toxicity

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Toxicity Pathway-Based Risk Assessment: Preparing for Paradigm Change: A Symposium Summary

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Demographic Toxicity: Methods in Ecological R Assessment

Toxicity

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Fire Toxicity

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Toxicity Testing for Assessment of Environmental Agents: Interim Report

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Small-scale Freshwater Toxicity Investigations: Volume 2 - Hazard Assessment Schemes

Male-mediated Developmental Toxicity (Issues in Toxicology)

Metabolomics Metabonomics and Metabolite Profiling

Metabolomics: Metabolites, Metabonomics, and Analytical Technologies

Reviews in Food and Nutrition Toxicity, Volume 2 (Reviews in Food and Nutrition Toxicity)

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Reviews in Food and Nutrition Toxicity, Volume 3 (Reviews in Food and Nutrition Toxicity)

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