Frontiers in Drug Design and Discovery, Volume (2)

Frontiers in Drug Design & Discovery Bentham Science Publishers Ltd. http://www.bentham.org/fddd Volume 2, 2006 Conte...

Author: ur-Rahman | Atta- Caldwell | Gary W. Choudhary | M. Iqbal

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Frontiers in Drug Design & Discovery Bentham Science Publishers Ltd. http://www.bentham.org/fddd

Volume 2, 2006

Contents Editorial: Biomakers to Biosensors: Technology and Applications to Improve the Drug Discovery Process G.W. Caldwell, Atta-ur-Rahman and M.R. D’Andrea and M.I. Choudhary

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Strategies of Biomarker Discovery for Drug Development X.J. Lou, S.M. Belkowski, J.M. Dixon, B. Hertzoh, D. Horwitz, S.I. Ilyin, D. Lawrence, D. Polkovitch, M. Towers and M.R. D’Andrea

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Protein and Antibody Microarrays: Clues Towards Biomarker Discovery K.Usni-Aoki, M. Koy, M. Kawai, M. Murakami, K. Imai, K. Shimada and H. Koga

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The use of Biomarkers to Detect Cervical Neoplasia and to Diagnose High-Grade Cervical Disease D.P. Malinowski

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New Developments in the Field of Protein and Metabolism Assays Aimed at Drug Discovery Processes K. Narasimhan, P. Sukumar and M. Choolani

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Proteomic Screening for Novel Therapeutic Targets in Kidney Diseases V. Thongboonkerd

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Aptamer-Based Technologies as New Tools for Proteomics in Diagnosis and Therapy V. de Franciscis and L. Cerchia

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Recent Developments in Proteomics: Mass Spectroscopy and Protein Arrays V. Kulkarni and M. Rao

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NMR Spectroscopy Based Metabonomics: Current Technology and Applications C.A. Daykin and F. Wulfert

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NMR-Based Metabonomics of Urine from an Exploratory Study of Ciprofibrate in Healthy Volunteers and Patients with Type 2 Diabetes Mellitus G.C. Leo, E.J. van Hoogdalem and M.B.A. van Doorn

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Chromatography Mass Spectrometry Based Metabonomic Analysis Method G.W. Caldwell, W. Lang, G.C. Leo, J.A. Masucci, W. Jones and A. Mahan

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OWLS—A Versatile Technique for Drug Discovery S.E. Ramsden

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Cell-Based Biosensors in Proteomic Analysis S.E. Kintzios

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Current Approaches in Natural Biopolymer-Nanoparticle Hybird Functional Materials: From Drug Delivery to Bio-Detection Application R. Brayner

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Spectroscopic Analysis of Cell Physiology and Function M. Riley, I.M. Fernandez and P. Lucas

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Encapsulates Biomolecules Using Sol-Gel Reaction for High-Throughput Screening K. Sakai-Kato, M. Kato, N. Utsunomiya-Tate and T. Toyo’oka

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Modeling of Environmentally Sensitive Hydrogels for Drug Delivery: An Overview and Recent Developments H. Li, R. Lou and K.Y. Lam

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Polyelectrolyte Nanocapeules – Promising Progress in Development of New Drugs and Therapies S. Krol, A. Gliozzi and A. Diaspro

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Contributors

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Subject Index

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Editorial

Frontiers in Drug Design & Discovery, 2006, Vol. 2 i

Editorial: “Biomarkers to Biosensors – Technologies and Applications to Improve the Drug Discovery Process” Moving forward in the 21st century, the discovery and development of ethical therapeutics “drugs” remains rich with opportunities but hampered by challenging technological and financial obstacles. The Frontier in Drug Design and Discovery series is dedicated to pharmaceutical scientists around the world who seek to bring affordable, effective and safe drugs to patients. The first volume brought together experts to discuss the advantages and limitations of screening techniques used in the drug discovery process to identify potential drug candidates. While screening techniques have advanced steadily in the last decade, a large number of these drug candidates fail in clinical studies because we cannot accurately predict how effective or how safe these candidates will behave in humans based solely on animal studies. In the second volume, experts discuss new technological and conceptual approaches to accelerate and to improve the predictability of the discoveries made in the laboratory into clinical testing. Twenty years ago, it was common to have a wealth of background information available on potential drug targets thanks to years of academic and pharmaceutical basic research. In today’s drug discovery world, therapeutic targets are typically poorly understood at the conception of projects. Moreover, the understanding of the concordance of efficacy and toxicity of pharmaceuticals observed in animals with that observed in humans is usually lacking. The debate in the pharmaceutical industry on how to proceed ahead on projects without a wealth of background information has focused on the use of innovative biomarker strategies, establishing proof of mechanism in human subjects, market differentiation, and efficiently terminating the development of unsuccessful projects. The areas of bioinformatics, genomics, proteomics, and metabolomics are leading the way to identify the details of the machinery that make up a living cell and thus, establish a solid scientific platform for biomarker and biosensor discoveries. Scientists are now embarking on an endeavor to discover how these biomarkers and biosensors can be used to understand the complex behavior that underlies the development and the progression of diseases. It is our firm belief that the investigation of the effects of drugs and the nature of disease will become ever more feasible because of advances in biomarker and biosensor research. We have selected authors to contribute their expertise to assemble a one-stop reference book to discuss the full range of biomarker and biosensor programs. X.J. Lou and colleagues have contributed a chapter outlining practical guidelines for the discovery, validation and use of biomarkers to accelerate the drug discovery process. These guidelines have the potential to shorten timelines and costs for developing drugs. The chapter by K. Usui-Aoki and colleagues give a well-balanced review of the status of protein and antibody microarray technologies. The surface plasmon resonance-based antibody microarray approach looks very promising for clinical applications. The discussion of combining protein/antibody and cDNA microarray data clearly illustrates the difficulties of validating large data sets. D.P. Malinowski introduces the reader to the use of biomarkers for the detection of cervical carcinoma and for the diagnoses of high-

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Editorial

grade cervical disease. These biomarkers appear promising in molecular diagnostic applications to detect malignant cells in both histology and cervical cytology specimens. K. Narasimhan and colleagues have written an excellent chapter describing the use of high-throughput proteomic and metabolic assays to accelerate the drug discovery process. Novel applications of chromatographic, electrophoretic, immunologic and mass spectrometry technologies are used to analyze the proteome and the metabolome for clinical benefit. The chapter by V. Thongboonkerd gives an overview of gel-based, surface-enhanced laser desorption ionization, and liquid chromatography coupled to tandem mass spectrometry proteomic methods. The chapter focuses mainly on applying these techniques to renal and urinary proteomics to define novel therapeutic targets in kidney diseases. V. De Franciscis and L Cerchia have written a chapter reviewing the use of specific nucleic acid-based compounds “aptamers” as biosensors for protein detection. The aptamers protein detection approach looks very promising for a large number of applications in all parts of the drug discovery process. A. Kulkarni and M Rao have prepared a chapter reviewing different mass spectrometry approaches being used to identify and characterize proteins from diverse sources and recent developments in protein array technology. C. Daykin and F. Wülfert introduce the reader to nuclear magnetic resonance (NMR) spectroscopy data handling, data analysis strategies and metabonomics as a tool for understanding the development and the progression of diseases at the metabolic level. NMR based-metabonomics contains a wealth of information on the endogenous biochemical processes in living cells. G.C Leo and colleagues present the results from a urinary NMR based metabonomic study of ciprofibrate in healthy volunteers and patients with type 2 diabetes mellitus. The study showed that supervised statistical analysis is able to separate ciprofibrate treated versus placebo treated male and female subjects. G.W. Caldwell and colleagues have prepared a chapter reviewing chromatography mass spectrometry based metabonomic strategies applied to diverse areas such as fermentation, bacteria, plants and animals. J.J. Ramsden presents an interesting chapter on the use of optical waveguide lightmode spectroscopy to measure binding and dissociation between biomolecules, protein conformational changes, and interaction of drugs with lipid membranes. The potential uses of this technique for drug design and discovery are discussed. S.E. Kintzios introduces the reader to cell based biosensors in proteomic analysis. The chapter by R. Brayner review the current approaches for hybrid functional materials based on natural biopolymers such as proteins and polysaccharides and nanoparticles consisting of metals, oxides, quantum dots for drug delivery and biosensors applications. M. Riley and colleagues present an interesting chapter on the use of infrared and Raman spectroscopy to evaluate biochemical pathways of mammalian cells. A critical review of equipment, experimental approaches, and analytical methods is provided for drug screening applications. K. Sakai-Kato and colleagues have written an excellent chapter describing how biomolecules can be immobilized on capillary-, microchip-, and microarray-based analytical systems using the sol-gel reactions. This technology has potential applications as biosensors and screening systems. H. Li and colleagues give an overview of the

Editorial

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mathematical modeling for simulation of environmentally sensitive hydrogels. The chapter discusses the properties and performance of the hydrogels as drug delivery systems. S. Krol and colleagues review the use of encapsulation techniques for living cells and artificial tissues. In total, we hope that these chapters will provide a well-rounded overview of the application of today's Frontier technologies and how they can be applied to do Drug Design and Discovery.

Gary W. Caldwell Atta-ur-Rahman Michael R. D’Andrea M. Iqbal Choudhary

Frontiers in Drug Design & Discovery, 2006, 2, 5-22

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Strategies of Biomarker Discovery for Drug Development Xiang Jian Lou, S.M. Belkowski, James M. Dixon, Brenda Hertzog, Dan Horowitz, Sergey I. Ilyin, Danielle Lawrence, D. Polkovitch, Meghan Towers, Michael R.D’Andrea* Drug Discovery, Target Validation, Johnson & Johnson Pharmaceutical Research and Development, LLC, Welsh and McKean Roads, Spring House, PA 19477-0776, USA Abstract: Drug development is a long and costly process. Although the length of drug development may vary depending on the target class, attrition is the main contributor to the financial burden. Therefore, various biological markers (biomarkers) are needed to determine compound efficacy and dosing. Biomarkers provide comprehensive information about the molecular network of the target and lead compound in vivo. Such information has the potential of shortening time of development and reducing costs by facilitating the decision of what lead compound should move forward at early development stages. This review article focuses on the strategies for biomarker discovery by giving readers a practical guideline to approaches that are used for the discovery, validation and use of biomarkers to accelerate the process of drug discovery.

INTRODUCTION Latest calculations estimate that the costs for bringing new medication to the market is approximately $600-800 million, which is largely due to high attrition rates [1]. Hence, it is essential to improve success rates in order to maintain the long-term economic viability necessary to provide novel health care solutions to unmet medical needs [1]. New and more accurate, knowledge-based decisions are required to advance or stop a lead candidate compound as early as possible in the discovery process. Many types of biomarker measurements help support key decisions throughout the drug discovery process, from pre-clinical evaluations to regulatory approval. This, of course, is a huge commitment of time and resources. It is thought that pre-clinical animal model data should help steer or direct biomarker efforts as the candidate compound is scheduled for first in human studies. Therefore, it is key to assess drug exposureresponse relationships such as specificity and potency toward the molecular target, potential toxic side effects and therapeutic applications. This is an impressive challenge for biomarkers and can consume as much time validating potential (translatable) preclinical biomarkers for the human condition, as it would take to validate targeted-based compounds.

*Corresponding author: Tel: 215-628-5619; Fax: 215-540-4887; E-mail: [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.

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Biomarker discovery and validation has been extensively discussed in the literature for both disease diagnosis and prognosis, but no guideline of this process has been provided for drug development. Fundamentally there are three types of biomarkers important in drug development: disease-related, target-related and toxicity biomarkers. However, since drug development is a long process involving multiple stages of in vitro, in vivo animal and human testing, various forms of the above three types of biomarkers can be used in each stage. Biomarker names can be quite confusing. To facilitate understanding, we wish to define our use of biomarker terminology. We define diseaserelated biomarkers used in the preclinical stage (in vivo animal testing) as animal efficacy biomarkers, while target-related biomarkers are referred to as pharmacodynamic (PD) biomarkers. When target-related biomarkers are also disease related, the term of efficacy or PD biomarkers are often interchangeable. In the clinical stage (clinical trials phase I, II, III), both target-related and disease-related biomarkers are referred to as human efficacy biomarkers and toxicity biomarkers are called human toxicity biomarkers. The biomarker discovery process can be envisioned as a rolling wheel without an actual start or end (Fig. 1). In this wheel, a drug target can be selected from diseaserelated biomarkers discovered either for diagnosis or for target selection. Once a target is selected, target-related biomarkers can be discovered by altering the amount or activity of the target. Target-related biomarkers can often be used to screen lead compounds and to elucidate the mechanism of action (MOA) of a compound in model systems at the stage of lead generation and optimization. When either target-related biomarkers are specific for the target and are present in the animal model or disease-related biomarkers are present in the animal model, then they can serve as PD and/or animal efficacy biomarkers for validating compounds at the preclinical stage. In the best-case scenario, these animal efficacy and PD biomarkers might also translate to potential clinical biomarkers to accelerate phase II and III clinical trials. In the case of toxicity, biomarkers for the preclinical stage (animal toxicity biomarkers) also have the potential to serve as human toxicity biomarkers. On the other hand, human efficacy and toxicity biomarkers can also translate back to early stage in vitro or animal models for the measurements of efficacy and toxicity. Before a biomarker strategy is formulated, information obtained from the literature coupled with any pre-clinical data will help to determine what technologies are best suited for biomarker discovery. We prefer to initiate a biomarker development plan when the target is proposed. In addition to traditional molecular biology and biochemistry technologies such as polymerase chain reaction (PCR), mutagenesis and Western blotting, recently developed high-throughput technologies provide essential tools for large-scale differential analysis. These novel techniques are categorized as genomics, proteomics and metabolomics. Genomic technologies allow genome-wide analysis of disease-unique gene expression profiles or polymorphisms. The most commonly used gene expression profiling technologies include DNA microarrays [2-5], quantitative PCR [6-8], serial analysis of gene expression [9,10] and representative difference analysis (RDA) [11,12]. The polymorphism analysis technologies mainly involve genome-wide linkage mapping using either sequencing [13] or high-density single nucleotide polymorphism (SNP) microarrays [14].

Strategies of Biomarker Discovery

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Proteomic and metabolomic approaches screen for the disease-specific expression patterns for proteins and metabolites, respectively. The most commonly used differential protein analysis are two-dimensional electrophoresis (2D-GE), surface enhanced laser desorption time-of-flight mass spectrometry (SELDI-TOF), liquid chromatography/mass spectrometry (LC-MS/MS), multidimensional protein identification technology (MudPIT, which couples 2D-LC to MS/MS), isotope-coated affinity tag (ICAT) and laser capture microdissection (LCM) coupled with reverse phase protein arrays (for a review see [15]). As opposed to genomics, the advantage of proteomics is that an identified protein biomarker is itself a functional end point and thus a true signature of a disease state.

Fig. (1). The Biomarker discovery wheel shows that many aspects of biomarker research originate from previous studies to impact future studies and decisions. Although the wheel presentation may appear to be an oversimplification, biomarkers support or validate four fundamental areas: target selection, lead validation and optimization, and pre-clinical and clinical situations. All together, the information obtained from each of these areas should help advance drug discovery compounds to the clinic.

There are a multitude of differences between genomic and proteomic techniques. For example, unlike PCR and reverse transcriptase related methods in genomic analysis, no proteomic technology for amplification detection exists. Also, genomic methods are deemed more quantitative than their proteomic counterparts. While proteomic analysis

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has been considered the more valuable technique, it is also the most challenging biomarker discovery technology. The range between the most and least expressed proteins in a proteome may exceed ten orders of magnitude [16]. Proteins exhibit a wide range of post-translational modifications and various truncated or proteolytically degraded forms. Finally, proteins, unlike DNA and RNA, do not have complementary binding partners with high-specificity. Most of the protein-protein or protein-DNA interactions are highly sensitive to experimental conditions. Regardless of these challenges, proteomic analysis has delivered many potential disease-specific biomarkers [17,18]. Differential metabolite measurements are usually performed using NMR and mass spectrometry. Details of metabolomics are discussed in several sections of this book. Although designing a strategy for biomarker discovery depends on the drug development stage the biomarker is meant for and the biological properties of the target, ultimately, it is the question at hand that determines the most suitable technology to address the need. The remainder of the chapter will discuss such strategies for each stage of drug development. TARGET SELECTION STAGE Traditionally, most new drug candidates are launched through one or more of the following four approaches: (1) to discover or select a new drug target, (2) to design a drug based on the increased knowledge for a known target, for example a new drug targeting a receptor with increased specificity and affinity, (3) to modify the chemical structure of a known compound (4) to search for biological activity of large numbers of natural products, banks of previously discovered chemical entities, large libraries of peptides, proteins, nucleic acids and other organic molecules. Major attention is now being given to the discovery and selection of entirely new targets for drug therapy since a good target is fundamental to the success of drug development [19]. A good target should be specific to disease conditions and can be enhanced or disrupted by either small molecules or antibodies. The interruption of the target should not do harm to other physiological conditions, specifically it will not cause significant side effects. Target selection cannot start from the clinical endpoints in human subjects because some disease conditions take more than 10 years to reach significant clinical endpoints. More importantly, the test of target interruption cannot be performed directly on human subjects. Therefore, target discovery and selection usually rely on the measurement of disease-related and target-related biomarkers in model systems. Furthermore, if a disease-related biomarker is proven to cause the disease (under certain conditions) and this disease-related biomarker is “drugable”, namely it can be specifically enhanced or interrupted, this disease-related biomarker can be a drug target. Disease-related biomarkers are generally discovered through two approaches: unbiased differential analysis of disease vs. normal samples and pathway-based candidate searching. Recently developed high-throughput technologies for genomics (vide supra ), proteomics (vide supra ) and metabolomics studies provide essential tools for the differential analysis approach. Each technology can convey different information and have different technical capabilities. These technologies by themselves or in combination with other methods have generated many successful examples of diseaserelated biomarker discoveries [20-22]. For example, using oligonucleotide-based DNA microarrays, Le Page et al. identified two differentially expressed genes encoding



cytokine IL-8 and FGF-2 as potential biomarkers by comparing primary cultures of 39 ovarian cancer specimens with 11 normal ovarian epithelia. The increased expression levels of these two epithelial ovarian cancer biomarkers were confirmed by quantitative PCR and immunohistochemistry (IHC) using an independent tissue array representing different grades and pathologies of ovarian disease. To date, they have shown by ELISA that the levels of these two cytokines are elevated in epithelial ovarian cancer patients and a combination of these two biomarkers with CA125 can detect epithelial ovarian cancer with a higher specificity than CA125 alone [23]. Pathway-based approaches usually design pathway-biased tools according to existing knowledge. These pathway-biased tools are then used to search for biomarkers [22,2426]. For example, it has been hypothesized that abnormal DNA methylation is one of the major mechanisms of imbalanced gene expression in cancer. Therefore, methylationdetection arrays may be used for cancer biomarker discovery [27,28]. More often, pathway-based approaches are combined with unbiased “omics” differential analysis. The recent discovery of a type II collagen peptide as a biomarker for osteoarthritis (OA) is one example of such a combination [29]. In order to determine OA biomarkers, the authors first generated antibodies against the collagen degradation product in the presence of MMP-13 (collagenase-3). Collagen degradation is reported to be elevated in OA. Transgenic over-expression of MMP-13 in hyaline cartilages and joints induced cartilage changes in mice characteristic of human OA [30]. By using these antibodies as bait, they discovered peptides in urine that are elevated in OA patients. These peptides have been applied, in a small clinical setting, as biomarkers for OA recognition and MMP inhibitor treatment and have provided favorable results [29]. Many biological and pharmacological pathway examples are available on the websites of various consortiums and databases [31-33] (www.BioCarta.com). Target-related biomarkers are usually discovered through functional enhancement and interruption of the target in in vitro or animal models. The most commonly used functional enhancement and interruption methods include: (1) over-expressing (transgenic) and knocking out target genes in animals, (2) virus-mediated knocking in (transfection), (3) small interfering RNA (siRNA) mediated knocking down of the target genes in cell culture systems and (4) modification by tool compound or bioactive molecule induced stimulatory or inhibitory effects [34]. Target-related biomarkers are then discovered in these biologically manipulated model systems using the similar unbiased differential analysis and/or pathway-specific approaches (vide supra). Once discovered, a causal relationship can be confirmed by another round of functional enhancement and interruption. A causal relationship is not necessary for the validation of biomarker, but will enhance the merit of the biomarker. For example, GREB 1 is a potential down-stream biomarker for estrogen receptor (ER) in breast cancer. This is because GREB1 was induced by beta-estradiol in the ER-positive endometrial cell line ECC-1 and MCF-7 cells respectively [35,36] and the suppression of GREB 1 using siRNA blocked estrogen induced growth in MCF-7 [35]. If the target is an enzyme, the down stream biomarkers can be first searched for among its substrates and/or products, such as phosphorylated products (in the case of kinases) and digested products (in the case of proteases). In addition to facilitating target selection, many disease-related biomarkers such as low-density lipoprotein (LDL), high-density lipoprotein (HDL), C-reactive protein (CRP) and CD40 also become drug targets (for review papers, see [37,38]). Due to the

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heterogeneity of many diseases such as atherosclerosis, cancer and Alzheimer’s disease it is important to first examine whether a putative target from the literature or a potential disease-specific biomarker obtained using approaches discussed above is abnormally expressed in a representative sampling of disease tissues. A causal relationship between the target and disease-related biomarkers also needs to be confirmed by any of the knocking in or knocking down model systems. More importantly, correcting the abnormally functioning targets should only bring disease-related biomarkers back to normal levels instead of causing damage to other parts of the body. Knowledge integration through correlation of data obtained using different technologies and systems approach of understanding the biological networks should have synergistic effects on target selection [39] in addition to biomarker discovery [40]. The “omics” technologies provide the means to comprehensively monitor the molecular network of biomarker, target and disease processes. Drug discovery through a validated target with biomarkers and model systems indicating normal and perturbed networks has shown the potential of providing improved therapy [41] and/or accelerating the process of drug development [13,42,43]. In the latter case, through a population-based, genome-wide study involving hundreds of heart attack patients and their family members in Iceland, deCODE genetics discovered variations in the gene encoding 5-lipoxygenase activating protein (FLAP) that doubles the risk of the disease [13]. The same genetic variation was also found to confer a significantly increased risk of stroke in the Scottish population [43]. It has been known for many years that important down stream biomarkers of 5-lipoxygenase are leukotrienes (for a review, see [44]). These are potent drivers of inflammation and may contribute to the instability and rupture of atherosclerotic plaques. Since plaque rupture is the event immediately preceding most heart attacks, a drug that can down-regulate the activity of the leukotriene pathway may offer a highly targeted and effective means of reducing risk of heart attack. Well-tolerated anti-leukotriene reagents have also been discovered for the treatment of asthma (for reviews see [45]). One of the reagents is DG031 developed by Bayer AG. DG031 inhibits leukotriene synthesis through its binding to FLAP. deCODE genetics therefore licensed DG031 from Bayer AG. Because of the extensive safety and clinical data already gathered on the DG031, it only took deCODE genetics a little over one year to bring DG031 to phase II clinical trial from target discovery. In the phase II trial, DG031 showed the effect of decreasing the levels of target leukotriene B4 and heart attack related biomarkers such as CRP and myeloperoxidase [42]. Regardless of the limitations of this trial, the deCODE genetics FLAP example provides an exciting example of translating genomic findings and biomarker measurements to target selection and clinical application. LEAD VALIDATION AND OPTIMIZATION STAGE Once lead compounds are developed or selected, biomarkers are used to validate (or invalidate) the efficacy of these early lead compounds in animal models. Although many of the disease- and target-related biomarkers discussed previously could be directly used in this stage, this is a critical stage where a biomarker strategy must be in place to provide decision tree analyses as the compound progresses. A myriad of issues should be considered in the development of a biomarker plan for this stage, such as (1) is the MOA determined for the target and/or compound, (2) are there downstream profiles that can confirm receptor inhibition or activation, (3) can these effectors be assayed in fluids or biopsies and (4) are these pre-clinical endpoints translatable to the clinic? The biomarker strategy is dependent upon answering these questions. As an example, oncological



targets affect cell proliferation, differentiation and death; however, in an effort to improve specificity beyond the less selective chemotherapeutical and radiological approaches, new therapeutic approaches should be implemented to characterize the MOA for these anticancer agents in preclinical and in clinical trails. Although, in theory, it is required to understand a compound’s MOA, if there are selective assays that support the progression of a compound through the drug discovery process map and there is ample in vivo evidence, the compound will continue to progress. In fact, it may take years and numerous studies to fully validate a compounds MOA. Such is the case for the application of bisphosphonates (BPPs) and a newergeneration BPP, zoledronic acid, in the management of advanced, metastatic bone disease. Skeletal metastases are a common form of cancer metastasis, ranking in frequency behind only that of liver and lung [46]. Further complications include pain, hypercalcemia, pathologic fractures and spinal cord compression, which negatively impact the patient’s quality of life and function [46]. BPPs have been recently used to effectively manage and prevent bone pain and skeletal complications in a number of malignancies, such as pamidronate in multiple myeloma [47] and clondronate in breast cancer [48]. Although BPPs provided a novel therapeutic intervention back in 1996, it is the subsequent work by a myriad of reports that continue to elucidate the MOA of the BPPs, which inhibit osteoclast function and bone resorption [49]. After binding to mineralized bone, BPPs are subsequently released and osteoclasts internalize the compounds leading to apoptosis and decreases resorption [50,51]. Continuing research embellish these earlier findings by expanding on the understanding of the MOA for the BPPs as it is reported that TGF-β and Il-6 may play roles in the mechanisms underlying bone metastasis and resorption, and the BPPs may also have a direct anti-angiogenic effect [51,52]. This example, which is nicely reviewed in Saba and Khuir [46], presents a situation that precludes the need to clearly elucidate the MOA of a particular compound to advance the compound to a clinical setting. Understanding the pathway of the target activation can also help generate a predictable biomarker plan in the event that off-target effects are adverse and toxic. For example, our lab demonstrated the cellular impact of a compound on the intended biomolecule in vivo [53]. We developed an in vivo assay to validate the pharmacodynamic activity of JNJ-10198409. This compound is a relatively selective inhibitor of platelet-derived growth factor receptor tyrosine kinase (PDGF-RTK) in tumor tissues after administering the compound orally in a nude mouse xenograft model of human LoVo colon cancer. Since we were aware of the signaling pathway of PDGFRTK, we developed a novel assay to quantify the in vivo anti-PDGF-RTK activity of the inhibitor in tumor tissue by determining the phosphorylation status of phospholipase Cγ1 (PLCγ1), a key downstream cellular molecule in the PDGF-RTK signaling cascade. We used two antibodies, one specific for the total (phosphorylated and unphosphorylated forms) PLCγ1 (pan-PLCγ1) and the other, specific for phosphorylated form of PLCγ1 (ph-PLCγ1) to immunohistochemically detect their expression in tumor tissues. Computer-assisted image analysis was then used to directly compare the ratio of phPLCγ1 to pan-PLCγ1 immunolabeling intensities in serial sections (5 µms) of tumors obtained from vehicle- and compound-treated tumor bearing mice. Our data demonstrated statistically significant, dose-dependent differences in the ph-PLC/panPLC ratio among the four treatment groups (vehicle, 25, 50, and 100 mg/kg b.i.d.).

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These results confirmed the ability of this compound to suppress PDGF-RTK downstream signaling in tumor tissues in vivo. Not only are drug developing companies assessing the relationship between the compound and its target in the animal models, but they also have to extrapolate the human experience from these preclinical in vivo models and to determine if any of the preclinical biomarkers are translatable to the human condition. A recent example describes the application of biomarkers of the anticancer activity of R115777 (Zarnestra) in an in vitro model of human breast cancer [54]. In this study, they illustrated the inhibition of HDJ-2 farnesylation, up-regulation of RhoB, inhibition of VEGF and MMP-1 secretion in addition to other markers in cultured cells. These markers were consistent with the MOA and they demonstrated that the compound did interfere with tumor growth, survival and angiogenesis pathways in breast cancer models with low or over expressed HER2/neu receptor [54]. These studies highlight the need to elucidate the MOA of a particular compound for biomarker discovery efforts. MOA studies support the selection of therapeutic agents, appropriate models of efficacy and experimental design, as well as rational characterization and prediction of non tumor (host) effects [55]. For example, in the field of cancer research, about six physiological changes that lead to malignant growth are represented among targeted therapies such as those pathways involved in growth, angiogenesis and metastasis [56]. Elucidating the MOA of these targeted pathways will not only enable the stratification of patients and cancer subtypes, but will lead to the identification and characterization of a variety of new targets and biomarkers for anticancer therapies [55]. PRECLINICAL STAGE Preclinical biomarkers can be discovered and employed in the PD/PK (pharmacokinetics) model often used in drug discovery. PD describes the intensity of a drug effect in relation to its concentration in the body fluid [57] resulting in response-time profiles [58]. PK describes the time course of the concentration of a drug in a body fluid (preferably plasma or blood) that results from the administration of a certain dose [57] resulting in drug concentration-time profiles [58]. Simply put, PK is “what the body does to the drug” and PD is “what the drug does to the body” [59]. Many target- and diseaserelated biomarkers discussed before can be used at this stage to evaluate the PD effect in animal. PK/PD models are a vital part of drug development. They play an important role in the selection of drug candidates and provides useful information to investigators in the early “go/no-go” decision making process. In these animal models, a simplified version of a true biological process is performed in a non-clinical environment and the data are then used to predict the possible drug effect in humans. Animal species, dose and dosing intervals, as well as mode and duration of administration, are all important factors of a successful PK/PD model [60]. A study performed by Barrett et al. [61] using agonist-induced platelet aggregation as a biomarker to predict the efficacy of GP IIb/IIIa receptor antagonists, selected the guinea pig over the rat and dog because of their platelet characteristics. Guinea pig platelet functions resemble those of human platelets and their platelet membrane glycoproteins exhibit similar homology to human GP IIb/IIIa and GP Ib/IX. This species had a



considerable advantage over the others and was the optimum choice for this type of study. Once the animal species has been selected, a mechanism-based modeling, currently the preferred methodology for PK/PD correlations [62], can be setup. The biomarkers in these model systems should mimic the biochemical and molecular changes seen in the disease process [63]. An example is the suppression of Factor IX activity and the prolonged response of activated partial thromboplastin time (aPTT) in Cynomolgus monkeys [64]. This modeling system was useful in describing the dose-dependent effects of a novel humanized anti-Factor IX monoclonal antibody for anti-coagulant therapy [64]. This model approach is generally applicable in predicting the effect of similar therapeutic monoclonal antibodies. In another example, Van der Graaf et al. applied a mechanism-based operational model of agonism to evaluate the adenosine A1 receptor-mediated in vivo effect of N6-cyclopentyladenosine analogs in rats [65]. Using heart rate as the biomarker, measures of agonist affinity and efficacy were determined. Once established, PK/PD relationships can also be used to evaluate and optimize various dosage forms and drug delivery systems [62]. Based on animal studies, the next step is to predict and evaluate in vivo potency and intrinsic activity of the compound, as well as any other drug interactions, in humans [62]. A well-chosen biomarker can translate between species and allow this determination to be made. The use of EEG effect intensity as an endpoint for determining EC50 values for various opioids in rats was employed by Cox et al. [66]. Others have shown that concentration-EEG effect relationships of synthetic opioids obtained in this study have also been found in humans [67,68]. This PD characterization of synthetic opioids may be useful in the design of new compounds at the preclinical stage. Another important effort of biomarker discovery in the preclinical phase is the determination of toxicity biomarkers. The attrition rate of compounds due to toxicity is very high and if this can be determined earlier in preclinical drug discovery then money and time can be saved. Determining toxicity biomarkers in preclinical studies can be quite difficult. These issues may not be apparent in initial drug discovery experiments because short-term dosing may not reveal or produce toxicity signatures. In many cases it is not until later development when chronic dosing is performed that these effects are apparent. Many of the toxic effects of compounds occur unexpectedly and are unpredictable in nature. The first sign of these toxic effects are often determined by observing a change in serum chemistry, hematological parameters or microscopic morphology [69]. Liver toxicity is one of the most common forms of drug-induced system damage. The liver is exposed to a wide variety of metabolic, toxic, microbial and neoplastic insults that are potentially hepatotoxic. Therefore, compounds that have a distinct type of toxicity due to their chemistry should be suspected for their capability to induce these toxicities. Knowledge of specific toxicities led to the development of free radical toxicity based models such as the one developed for pyrogallol [70]. Unfortunately, not all toxicities are predictive based on the chemistry of the compound. A toxicity marker must be developed for a class of drug that may act in a particular way. Due to the high number of different toxicities that can arise, Steiner et al. developed an algorithm that can outline the type of toxicity being induced by a compound based on hepatic gene expression in rats [71]. Using this algorithm, investigators were able to accurately predict the hepatic toxicity of hydrazine and coumarin while showing the lack

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of hepatic toxicity after administration of gentamicin. This readout can identify the potential toxicity that an uncharacterized compound may exhibit. Some compounds may have expected potential organ specific toxicities. One approach is to look at toxicity biomarkers defined for a predicted target organ. In the case of drug induced heart toxicities, markers described in humans could also be used in preclinical studies. Serum troponins have been described as reliable markers for acute myocardial infarction in humans [72]. A high degree of similarity between the levels of cardiac troponins found in animal studies and humans provides strong evidence that they may play a role in bridging biomarkers for both preclinical and clinical studies in monitoring drug-induced cardiac injury [73]. Unfortunately, there are also many other types of toxicity that may not be as straightforward making a standard marker quite elusive because the damage may not occur in organs that are expected to undergo toxicity. Therefore an alternate approach is to look for biomarkers associated with physiological and pathological changes in a toxic condition in areas of potential damage based on MOA of the compound. A description of this approach is the study performed by Searfoss et al. In this study a target specific compound, functional gamma secretase inhibitor, was tested in rats at high doses. Adipisin was found to be expressed in the intestine, an organ known to be a potential site of injury based on the MOA of the compound, and was directly correlated with the damage to the tissue [74]. The adipisin was excreted by the rats making this an easily assessable and tissue specific toxicology biomarker. Discovery of both efficacy and toxicity biomarkers can be performed with the use of computer aided drug design (CADD). Studies have shown the ability to use PK/PD data from one species and apply this predicted outcome to another species [75]. One such example was rat PK/PD data that was modeled to a rhesus monkey. Comparison of the positron emission tomography (PET) experiment in the rhesus monkey confirmed the model to be relevant. Modeling of this nature could extrapolate data and biomarker use for both efficacy and toxicity from preclinical to clinical setting with far better accuracy. CLINICAL STAGE One of the ultimate goals of biomarker discovery is to identify a biomarker that is practical for use in clinical studies. These markers would have to acquire samples by minimally invasive or non-invasive procedures and give correct information about the state of a disease (disease-related biomarkers) or action of a compound (human efficacy biomarkers). General approaches for disease-related biomarker discovery has been discussed in the target selection stage. One of the potential problems in disease-related biomarker development is the accuracy of the biomarker. An example of disease-related biomarkers can be illustrated by the screening of men for prostate-specific antigen for early prostate cancer. Unfortunately, it is not usually wise to depend on the information of a single biomarker because of the possibility of obtaining false positive and false negative data. To that end, e-cadherin, an important cell-cell adhesion protein, has been linked to the malignant progression of adenocarcinomas including prostate cancer [76-78]. Most excitingly, a soluble 80-kDa fragment of e-cadherin detected in the serum was significantly increased during prostate cancer progression [79]. As with many



biomarkers, e-cadherin is not specific to prostate cancer as decreased e-cadherin expression is associated with poor survival in bladder cancer patients [80,81]. In spite of these contrary findings, increased serum levels of soluble e-cadherin was also detected in patients with early relapse of superficial bladder tumors [82]. Although some diseases provide the patient symptoms, other diseases may provide the primary health care physician insight into the patient’s well being. Conditions such as ovarian cancer do not provide overt clues until they are presented with a late stage disease. Once the disease has developed into a late stage disease, the survival decreases from 95% at stage I (surgical and chemotherapeutic intervention is not needed) to approximately 20-25% five-year survival despite appropriate treatment [83]. Therefore, there is a need to diagnose a disease such as ovarian cancer much earlier. Unfortunately, the pathology is confined to the ovary and the patient rarely becomes symptomatic, in contrast to the cancers of the breast, prostate and colon. Furthermore, the ovary is anatomically more difficult to assess during routine examinations. Even imaging frequently cannot distinguish between a benign and a malignant condition. The need for, and the development of, reliable biomarkers, especially in the serum, is a priority. Since it may not be realistic to envision a discovery of a single ovarian biomarker, perhaps a combination of biomarkers may hold accuracy to diagnose ovarian cancer earlier. One such study by Rapkiewicz et al. [83] was able to correctly identify all cases of earlystage ovarian cancer (n=116) based on their proteomic signature. These experiments were based on the premise that pathological changes in tissue such as the ovary, will be presented in a particular and potentially diagnostic protein biosignature pattern in the serum [84-86]. Three of those biosignatures were identified as glyoxaolase I, RhoGD1a and FK506 binding protein, all of which have been implicated in oncogenesis pathways that include cell apoptosis, DNA synthesis and mutagenesis [87,88]. In concert, Ye and colleagues [89] reported the identification of the haptoglobin-α subunit in sera of patients with ovarian cancer for the use as a potential serum biomarker for the diagnosis of the disease [89]. Although a specific early stage ovarian cancer biomarker has yet to be identified and validated, it may be that a panel of many biomarkers, such as those described above, may provide enough diagnostic potential to alert the physician to perform additional testing. Although, as mentioned above, various technologies can be used to assess biomarkers in tissue and bodily fluid samples in clinical settings, relevant biopsies may not always be feasible. Noninvasive imaging approaches offer significant advantages in monitoring the action of a compound in both preclinical and clinical settings. Noninvasive imaging techniques include light imaging, computed tomography (CT), magnetic resonance imaging (MRI), PET as well as different combinations of these modalities. Even though, all of these approaches can facilitate preclinical and clinical activities, in this chapter we focus on PET. PET provides an opportunity to noninvasively evaluate in vivo PK, PD and PK/PD as well as the response to therapy if relevant clinical paradigm is established. Applications of PET for PK/PD analysis are particularly important for drugs with central MOA. Previously published studies reported on the application of PET to establish relationship between drug plasma concentration, central nervous system (CNS) receptor occupancy and efficacy to facilitate dose selection [90, 91]. This information helps to de-risk clinical development by providing better prediction of dosing to be used in the phase II and/or III. It also aids in reducing the cost and duration of phase II clinical trials. A significant number of

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CNS-related PET tracers has been developed and validated. A list of validated PET tracers (albeit not complete) is available at http://www.snidd.org/. Co-development of new CNS drug and related PET tracer(s) offers an efficient biomarker strategy, but requires substantial investment of time and effort if previously validated tracers are not available for the target of interest. It should be noted that, even though PET-enabled studies are fairly easy to understand from a conceptual point, logistics of PET applications in the clinic are fairly challenging and subject to FDA regulation in US. Among many other requirements, clinical deployment of a novel PET tracer requires submission of an IND (Investigational New Drug) to the FDA. An IND needs to be supported by appropriate toxicity and safety data. PET tracer precursors have to be manufactured under GMP (Good Manufacturing Practices) compliant conditions. Monitoring of patients’ radioactive exposure and ensuring overall compliance with GCP (Good Clinical Practice) are also very important. PET tracers have fairly short half-lives and consequently require PET studies to have very effective coordination between radiochemistry lab and personnel administering the tracer and performing the scan. Postscan data processing and interpretation is also very important [92-96]. Quite a few PET tracers are routinely used in current medical practices and include: FDG ([18F]fluorodeoxyglucose), FLT (3'-Deoxy-3'-[18F]fluorothymidine) and FDOPA (6-[18F]fluoro-L3,4-dihydroxyphenylalanine). These and other already validated tracers are available for both preclinical and clinical studies from local cyclotron facilities as well as from Siemens cyclotron network (http://www.ctimi.com/cti_petnet) and can be used with relative ease for preclinical and clinical applications. For example, Alzheimer’s and other dementias significantly alter brain metabolism early, even before manifestations of mild cognitive symptoms [97-99]. Clinical FDG-PET detects this altered metabolism and provides noninvasive diagnostic assessment. In Oncology, tumor glucose utilization can be assessed by FDG-PET as a measure of tumor metabolism. FDG-PET can provide valuable diagnosis, staging and prognosis information for many types of tumors. In the study by Weber et al. 40 patients (3 female, 37 male, age 55 ± 11 years) with locally advanced adenocarcinomas of the esophagogastric junction and preoperative chemotherapy (cis-Platin, 5FU, Paclitaxel) had FDG-PET prior to and 14 days after initiation of therapy. This study demonstrated a strong correlation of changes in FDG-uptake with histopathological tumor regression and patient survival [100]. Whole Body FDG-PET imaging could also predict the outcome for patients with lymphoma following chemotherapy treatment [101]. In fact, predictive values generated by FDG-PET are superior to those of conventional imaging. Pharmacogenetic and pharmacogenomic biomarkers may also be very important at the clinical stage. Using pharmacogenetic and pharmacogenomic biomarkers to select the appropriate patient group may significantly decrease the cost of drug development as clinical trials focused on the right population (responders without adverse reaction) may decrease the trial size and increase the success rate [102]. However, there is still debate about the cost-effectiveness of genetic screening before drug trials. Nevertheless, details on pharmacogenetic and pharmacogenomic biomarkers are beyond the scope of this review article. BIOMARKER VALIDATION Validation of key biomarkers is critical for their use in preclinical studies or to promote their advancement into clinical studies. The goals of validation, among others,



are to determine the fitness of a marker in distinguishing normal from treated samples or normal from diseased samples in a specific manner. In addition, validation aims to determine the sample availability of the marker (i.e. located in blood or secretions) and translatability of the marker into other models or into the clinic. Specificity of a biomarker is key in allowing its use for assessing treatment efficacy and understanding the potential mechanism of action of the compounds being used. Validation of accessibly of the biomarker is important to assure the user of a highly reproducible and stable result. It is also essential to validate the translation of a marker from preclinical to clinical studies to give the user confidence that the biomarker is suitable for the model system/clinical study. An excellent example of each of these validation steps is the validation of adipocyte complement-related protein of 30 kDa (Acrp30) expression for PPARγ activity. PPARγ agonists such as rosiglitizone were found to induced Acrp30 mRNA expression in 3T3L1 preadipocytes [103]. This observation in cell cultures was then extended into a preclinical mouse model. The distribution of Acrp30 was found to be primarily in adipose tissue and qPCR analysis of white adipose tissue of db/db mice showed an increase in Acrp30 message expression after rosiglitizone treatment. This was further validated by examination of the protein level in plasma and a correlative increase in circulating levels of this protein was found in treated animals [103]. The increase was found to be specific to PPARγ as PPARα agonists did not increase message or protein levels in the mouse model. Therefore, this marker had been defined as an adipose specific marker that could be used as a read out of metabolic activity. Having shown specificity and an easily accessible source as well as a cause and effect relationship to the biologic endpoint, the biomarker was assessed for its usefulness in clinical studies. The increase in circulating Acrp30 after rosiglitizone treatment was confirmed in human studies and it was found that PPARα agonists did not affect the expression of Acrp30 showing its specificity as a clinical biomarker. These data together validate the use of this biomarker, PPARγ agonists, in both pre-clinical and clinical studies [103]. In the case just outlined, the protein biomarker was determined to be available in plasma. Validation of biomarker availability is quite easily performed if working with plasma or urine. However, not all relevant biomarkers are present in an easily accessible source. If the site of action of a compound is limited to the analysis of biopsies it is important to validate the availability and reproducibility of the biopsy material. Boyle et al. hypothesized that message levels of key cytokine mRNAs in the synovium were a good indicator of rheumatoid arthritis (RA) [104]. However, the levels of these cytokines in synovium were not easily standardized or reproduced. They found that IL-6 message levels measured from biopsied synovial tissue of RA patients using qPCR were increased over osteoarthritis patients and could be standardized using peripheral blood mononuclear cells (PBMC) [104]. This allowed a high reproducibility in the patient samples removing drift from sample storage and handling. Prior to this methodology, synovial tissue could not accurately depict the state of RA in patients even though it was the immediate site of action of the disease. Validation of the assay conditions led to the use of synovium as a convenient source of patient samples to monitor RA. As the previous examples show, the value of a biomarker is increased with each validation step. This leads to use of the biomarker in more levels of drug development with increased confidence of the outcome leading to better go/no-go decisions.

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SUMMARY The value of biomarkers cannot be over emphasized. Although the mainstream definition of a biomarker includes those biological markers that will have value in the clinic, it is clear to us that the term, biomarker, represents a conglomerate of markers obtained to address questions and validations that arise throughout the entire drug discovery process (Fig. 1). Each will answer a particular question, and may not carry through to the next process, while others may translate from animal models to human trials. It is also possible that particular biomarker(s) in a Phase I study will not support subsequent Phase II studies. In closing, it is our approach to first define the question or hypothesis, assemble the technological platform, obtain and analyze the data to ultimately help advance the lead compound to the clinical setting. ABBREVIATIONS PD

= Pharmacodynamic

MOA

= Mechanism of action

PCR

= Polymerase chain reaction

RDA

= Representative difference analysis

SNP

= Single nucleotide polymorphism

2D-GE

= Two-dimensional electrophoresis

SELDI-TOF = Surface enhanced laser desorption time-of-flight mass spectrometry LC-MS/MS = Liquid chromatography/mass spectrometry MudPIT

= Multidimensional protein identification technology

ICAT

= Isotope-coated affinity tag

LCM

= Laser capture microdissection

IHC

= Immunohistochemistry

OA

= Osteoarthritis

siRNA

= Small interfering RNA

ER

= Estrogen receptor

LDL

= Low-density lipoprotein

HDL

= High-density lipoprotein

CRP

= C-reactive protein

FLAP

= 5-lipoxygenase activating protein

BPPs

= Bisphosphonates

PDGF-RTK = Platelet-derived growth factor receptor tyrosine kinase PLCγ1

= Phospholipase Cγ1



PK

= Pharmacokinetics

aPTT

= Activated partial thromboplastin time

CADD

= Computer aided drug design

PET

= Positron emission tomography

CT

= Computed tomography

MRI

= Magnetic resonance imaging

IND

= Investigational New Drug

GMP

= Good Manufacturing Practices

GCP

= Good Clinical Practice

FDG

= [18F]fluorodeoxyglucose

FLT

= 3'-Deoxy-3'-[18F]fluorothymidine

FDOPA

= 6-[18F]fluoro-L-3,4-dihydroxyphenylalanine

Acrp30

= Adipocyte complement-related protein of 30 kDa

RA

= Rheumatoid arthritis

PBMC

= Peripheral blood mononuclear cells

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Protein and Antibody Microarrays: Clues Towards Biomarker Discovery Kazue Usui-Aoki1,3,*, Motoki Kyo2, Makoto Kawai1, Masatoshi Murakami1, Kazuhide Imai1, Kiyo Shimada1, Hisashi Koga1, 3 1

Chiba Industry Advancement Center, 2-6 Nakase, Mihama-ku, Chiba 261-7126, Japan; 2 Biotechnology Frontier Project, Toyobo Co., Ltd., 10-24 Toyo-cho, Tsuruga, Fukui 914-0047, Japan and 3Kazusa DNA Research Institute, 2-6-7 Kazusa-Kamatari, Kisarazu, Chiba 292-0818, Japan Abstract: The recent advancement of proteomics technologies has provided us a variety of approaches for protein-expression profiling. Among these approaches, protein and antibody microarrays are promising new ones for biomarker discovery. Although at present they have several limitations with respect to sample preparation, sensitivity, specificity, and so on, protein and antibody microarrays will no doubt become a standard adjunctive method in the actual clinical scene. With this in mind, we have been establishing a novel system for antibody microarray in which surface plasmon resonance (SPR) technology is utilized for the signal detection. Up to 400 real-time antibodytarget bindings could be measured simultaneously within a single hour. Although SPR is assumed to be an expedient technology for protein and antibody microarrays, here we describe its advantages and disadvantaged compared to other detection technologies. This review focuses on the technological aspects of these two methods and a discussion of their clinical usefulness. We further emphasize the interpretation of the protein and antibody microarray results in combination with the results of DNA microarray and intracellular pathways mainly constructed from data on protein-protein interaction.

INTRODUCTION Such as western blot and immunohistochemical analyses are robust but lowthroughput methods for investigation of protein expression. A high-throughput method for rapid screening of expression against hundreds of proteins in complicated biofluids is necessary for deep understanding of disease processes on a proteomic level. As is known from comparative expression studies, mRNA and protein expression levels do not always correlate [1-3]. A protein expression level is often modulated depending on several kinds of post-transcriptional and -translational processes. Therefore, detailed expression information on multiple proteins is required to appreciate a complicated biological phenomenon. High-throughput methodologies, which allow

*Corresponding author: Tel: +81-438-52-3919; Fax: +81-438-52-3918; E-mail: [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.

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fast, direct and quantitative detection of hundreds of proteins, are also required. The current state of protein profiling technologies, such as 2D-PAGE combined with mass spectrometry, must still cope with significant drawbacks before they are able to fulfill all the needs for high-throughput proteomic analysis. Antibody microarrays are a solid-phase technology that can be used to screen expression of multiple proteins concurrently [4]. Several antibody-based techniques have been developed and used to profile protein expression [5-9]. The present platform for antibody microarrays utilizes the technologies developed for either DNA microarrays [5, 9] or sandwich immunoassay techniques [10]. The use of two differentially labeled extracts is similar to conventional DNA microarray analysis and allows for pair-wise comparisons. This kind of dual label system can be surveyed in the literature and is now commercially available [5,9]. Micro-sandwich immunoassay technique, another representative approach to antibody microarrays, requires two antibodies to capture and detect a target protein, but its sensitivity and specificity are superior to those of other techniques. We attempted to develop a high-throughput antibody based protein detection system that could be used to screen protein expression patterns in several biological fluids. For this purpose, we utilized surface plasmon resonance (SPR) technology and established an SPR-based antibody microarray system [11]. For SPR biosensors, target molecules are immobilized on a gold-coated chip, unlabeled biological samples are loaded, and the angle change of reflected light is measured [12-15]. The angle change indicates the change of the target protein in different biological samples. Two key advantages of SPR biosensors are the ability to use unlabeled samples, in which native protein conformation is preserved, and the ability to continuously monitor binding kinetics. On the other hand, the SPR biosensors have insufficient sensitivity (especially compared with sandwich immunoassay), and consequently require more protein for loading samples. In addition, there has been no available detector for multiple SPR-signals. To exploit the advantages and overcome the disadvantages of this method, we developed a novel platform for the antibody microarrays based on SPR technology. In this system, up to 400 real-time antibody-target bindings could be measured simultaneously within a single hour. We first provide an overview of protein and antibody microarray technologies and then introduce our system and most recent improvements to the system; finally, we discuss how to validate the antibody microarray results. OVERVIEW OF HIGH-THROUGHPUT PROTEIN EXPRESSION ANALYSIS Currently, array formats for protein analysis fall into two major classes, protein arrays, Fig. (1A) and antibody arrays, Fig. (1B-D). In the protein array format, numerous recombinant or purified proteins (reverse-phase protein microarrays) are immobilized on the solid phase. Patient samples such as serum from autoimmune disease are incubated onto the microarray, and the bound ligands (e.g., autoantibody) are detected by direct or indirect labeling. In the antibody format, numerous antibodies are immobilized onto the substratum as specific capture molecules, and endogenous protein levels related to patient’s pathophysiological status are comprehensively monitored. Although technologies established for DNA microarrays have been adapted to protein and antibody microarrays, protein and antibody microarrays are faced with significant challenges that have yet to be solved by the establishment of DNA

Protein and Antibody Microarrays


microarray technologies. The most serious obstacle is the vast range of protein concentrations in different biological fluids. For example, the range of protein concentration in any cell is more than 1010; this range can not be covered by preexisting methodologies. Furthermore, PCR-like direct amplification methods do not exist for proteins. Consequently, chemical enhancement of the detected signals is an obligatory step for protein and antibody microarrays [16-19]. Expected sensitivity is at least at the femtomolar level within acceptable background. Moreover, the labeling and chemical enhancement methods must have linear reactions and be reproducible to insure reliable quantitative analysis. The elimination of natural contamination of reagents for chemical enhancement (biotin, peroxidases, alkaline phophatases, fluorescent proteins, and so on), should also be attempted so as to minimize background activity.

Fig. (1). Classification of protein/antibody microarray platforms. (A) Schematic illustration of protein microarray format. Numerous recombinant or purified proteins (reverse-phase protein microarrays) are immobilized on the solid phase (red). An analyte containing a specific ligand (e.g., antibody; black) is incubated onto the microarray. The bound ligands are detected by direct or indirect labeling (yellow stars). (B-D) Schematic illustration of protein microarray formats. In a sandwich immunoassay format (B), unlabeled targets (red) are captured by immobilized antibodies (black) and then detected by labeled detection antibodies (green). In a direct labeling format (C), fluorescently labeled targets (red with yellow stars) are captured by immobilized antibodies (black) and the signals correlate with the amount of captured targets that are directly detected. In our SPR-based format (D), unlabeled targets (red) are captured by immobilized antibodies (black), and then SPR arises when light is reflected from the thin layer of gold at the surface. When targets in the sample bind and concentrate at the surface, the incident angle of the reflected light changes and an SPR signal is detected.

Protein Microarray Protein microarrays should be useful in screening ligands (e.g., antibodies) in sample fluids as mentioned before, Fig. (1A). Protein microarrays can also analyze other interactions with peptides, small molecules, oligosacchaloides or DNA as well as proteins. Several different platforms for protein microarray are already available for clinical use; we only introduce “reverse-phase” protein microarrays in this short review article. Because most potential molecular markers and targets are proteins, proteomic profiling is expected to yield more direct answers to functional and pharmacological

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questions than does transcriptional profiling. Reverse-phase protein microarrays represent a technology uniquely suited to screening a broad range of molecular markers or pathway targets in large numbers of samples simultaneously in a high-throughput manner [20-22]. An advantage of reverse-phase protein microarrays is their highthroughput capabilities using low sample volumes. Typical reverse-phase protein microarrays for patient biopsy materials are printed with nL-µL of whole cell lysate onto immobilized support materials to allow for probing with specific antibodies. This advantage is perfectly matched for molecular profiling of clinical patient samples. Frequently only a small amount of patient material is available for molecular analysis. Nishizuka et al., developed a reverse-phase protein lysate microarray system with the 60 human cancer cell lines used by National Cancer Institute to screen for new anticancer agents (NCI60) [23]. To identify molecular markers, they also developed a multistep protocol starting with NCI60. The first step of this protocol is the selection of candidate markers based on differential transcript expression levels with cDNA microarrays [24]. To confirm and quantify the protein expression level of these candidates, reverse-phase protein microarrays were utilized. Finally, they identified two best candidates, villin for colon cancer cells and moesin for ovarian cancer cells [24]. Villin appears at last as useful as the currently used colon marker cytokeratin 20 [25]. Reverse-phase protein microarrays, prepared using simple procedures and standard microarray equipment, represent a powerful tool for the discovery of new biomarkers. Antibody Microarray Antibody microarrays are a high-throughput technology concurrently used to screen for protein expression and allow the identification and the quantification of a large number of target proteins from a minute amount of a sample within a single experiment. Antibody microarrays based on traditional sandwich immunoassay, Fig. (1B) [4, 26], do not require direct labeling of proteins contained in sample fluids. Instead of protein labeling, detection antibodies recognizing an epitope opposite to immobilized antibodyrecognizing sites are labeled with a fluorescent dye. The obvious limitation of this assay is the requirement of having two non-overlapping accessible epitopes and two validated antibodies for a target molecule. Nevertheless, antibody microarrays based on sandwich immunoassay are the most sensitive and specific assay. For example, the IL-6 assay demonstrated sensitivity at 2pg/mL [27]. Especially in the cytokine field, this type of assay is rapidly expanding. On the other hand, direct labeling of proteins contained in sample fluids is a less sensitive but more convenient method for comprehensive analysis of protein expression without complete sets of paired antibodies, Fig. (1C). All of the proteins in the sample are labeled with either a fluorophore or a hapten tag such as biotin, Fig. (1C). Direct labeling has several advantages. One is that the paired antibodies essential for antibody microarrays based on sandwich immunoassay are not required. Thus, it is easier to expand array-contents as soon as a single antibody to a target becomes available. Another advantage is in the adaptation of the differential display technique, a commonly used technique in DNA microarrays. More specifically, a mixture of two samples labeled with different tags is incubated on the same microarray. Co-incubation of the sample in question with a control sample makes it possible to provide internal normalization of the target proteins. A disadvantage of the direct labeling method is the potential interruption of antibody-antigen interactions depending on excess labeling at



the epitope and/or its surrounding area. The background signal arising from direct labeling is another disadvantage of this method. Because all proteins in a sample are labeled, including highly sticky proteins, strong nonspecific binding of these proteins to antibodies or to the microarray substrate may cause measurable interference. Nonspecific binding could also reduce detection sensitivity to target proteins and thus deteriorate the accuracy of the data. The background in the direct labeling method can be reduced by manipulation of surface chemistries [28] or through further improvement in blocking and/or washing protocols. Although much effort has been exerted to improve the direct labeling method, the protocol’s optimization has not been accomplished SPR-BASED ANTIBODY MICROARRAY The availability of high quality, specific antibodies is the limiting factor, and starting point, for successful utilization of antibody microarray technology [4]. Unfortunately, high-quality antibodies are currently available for only a small percentage of the known proteins involved in signal networks and gene regulation. The generation of large comprehensive libraries of fully characterized specific antibodies is a significant challenge for the new generation of antibody microarrays. Taking into consideration this difficulty, we have been conducting a project comprehensively generating antibodies against mouse KIAA proteins and performing their validation [29, 30]. Using our libraries of antibodies, we established a novel antibody microarray system in which surface plasmon resonance (SPR) technology is utilized for signal detection, Fig. (1D), [11]. Up to 400 real-time antibody-target bindings could be measured simultaneously within a single hour. This rapid detection was achieved by a direct readout of the bindings using SPR technology. Protein Apurified polyclonal antibodies were spotted onto a Bare Gold Affinity Chip in 20 X 20 formats as shown in Fig. (2A). All antibodies used here had their titers checked by ELISA and their specificities checked by several immunological techniques, including western blot and immunohistochemistry. Some of these data are freely available through our InGaP database, a comprehensive database of gene/protein expression profiles of mouse KIAA (http://www.kazusa.or.jp/create). Anti-tubulin antibody was used for a positive control and spotted at each corner of the matrix. For the reference SPR angle changes, reference ROIs (regions of interests) were drawn in close proximity to each target and simultaneously analyzed with the target angle changes, Fig. (2B). The reference data were subtracted from each target datum, and the subtracted data were used for subsequent validation and analysis. Each curve in Fig. (2C) consists of chronologically detected SPR angle changes (Resonance change units: RCU), and each color indicates a different target. To assess the reproducibility of this SPR-based antibody microarray system, we performed regression analysis using the results from independent experiments. Fig. (2D) is a scatter plot of the RCU of the corresponding targets on differently spotted Affinity Chips. The results using brain sample showed high reproducibility between replicate experiments, with correlation coefficients of 0.94. Several technical and practical problems remain unresolved, however. A central unresolved issue in SPR-based antibody microarray concerns the detection of lowabundance proteins from small tissue samples. Clinical samples, such as patient biopsies, are often limited in the amount and concentration of the targeted proteins. Although a

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cubic centimeter of tissue contains approximately 109 cells, tissue samples from needle biopsy or cell aspiration contain less than 105 cells. It is therefore necessary to improve

Fig. (2). SPR-based antibody microarray system on which approximately 400 different antibodies are spotted. (A) Overview of the Affinity Chip. 400 spots are located within 1 cm2 grid area, and an adhesive gasket forms a flow cell with an approximate volume of 47 µl between the Affinity Chip and the gasket. (B) Immobilized antibodies were visualized through a CCD camera and defined as regions of interest (ROIs). Reference ROIs were also drawn in close proximity to each target ROI by the system software. SPR signals of these ROIs were measured by a FLEXCHIP™ Kinetic Analysis System (HTS Biosystems). (C) We prepared a sample derived from an adult mouse brain and performed an analysis using the SPR-based antibody microarray system. Each curve consists of chronologically detected RCU, and each color indicates a different target. (D) RCUs for replicated experiments are plotted against each other. The plot shows a high correlation between two independent experiments with correlation coefficients of 0.94 (brain sample).

the sensitivity for an antibody microarray system. In our SPR-based antibody microarray system, this can be satisfied by avoidance of the electrostatic non-specific adsorption. We reduced the density of the carboxyl group on the gold surface by using a mixture of HS-PEG-COOH and HS-PEG-OH, Fig. (3A, Scheme C). Fig. (3B(c)) shows that the SPR signal change on the blank spot (which indicates the non-specific adsorption was completely abolished by preparation of the mixture solution with 0.1 mM HS-PEG-



COOH and 0.9 mM HS-PEG-OH. Using clued protein samples, satisfactory sensitivity (approximately 30 ng/ml) was achieved by surface chemistries for antibody immobilization. Further improvement has been achieved by refinement of the detection instrument MultiSPRinter® (Toyobo Co., Ltd.), Fig. (3C), [31]. Especially, the refinement of the flow cell and flow pass enabled high-throughput SPR analysis with minimal sample volume (~ 200 µl).

Fig. (3). Surface chemistries and a novel instrument for improvements to the SPR-based antibody microarray system. (A) Carboxyl groups were introduced on the gold surface via three procedures. SAMs of 11-CDT (Scheme A), HS-PEG-COOH (Scheme B), a HS-PEG-COOH and HS-PEG-OH mixture (Scheme C) were formed on the UV-irradiated surface. The introduced carboxyl groups were activated by EDC and NHS, and then reacted with antibodies. (B) The unreacted NHS ester groups were blocked by H2N-PEG-OH. SPR signal changes on the antibody microarray by exposure of the crude lysate (mouse brain) using 1.5 % BSA containing HEPES buffer as the running buffer. The antibody arrays were prepared via (a) Scheme A, (b) Scheme B and (c) Scheme C. The data shown in panels ( A) and (B) are reproduced from Kyo et al., [31]. (C) An overview of the detection unit for MultiSPRinter® (Toyobo).

THE INTERPRETATION MICROARRAY RESULTS

OF

THE

PROTEIN

AND

ANTIBODY

Measurement of gene/protein-expression profiles using microarray technology is becoming increasingly popular among the biomedical research community. Although

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there has been great progress in this field, we are still confronted with a difficult question after completing their experiment: how to validate the large data sets regarding gene/protein-expression? Experimental verification of microarray results is extremely time-consuming. In this context, the browser system shown in Fig. (4) was developed to enable us to readily integrate microarray results on an intracellular pathway related to spotted targets molecules.

Fig. (4). The interpretation of protein and antibody microarray results. (A) Schematic representation of the pathway including mKIAA1027. The proteins are essentially represented by ellipses. A modification of the shape refers to a protein’s functions (e.g., an eclipsed shape represents protein kinase). The proteins are also displayed with several different colors. Dark blue indicates the targeted mKIAA protein. Light blue indicates identified mKIAA-interactors. Red indicates other components of the cellular pathway selected by Ingenuity Pathway Analysis. The interactions and regulations among the proteins are illustrated by the different lines of connection. A detailed explanation of these differences is recorded on the Search Results of InCeP (IntraCellular Pathway, based on mKIAA protein-protein interactions) database (http://www. kazusa.or.jp/create/). These data will be freely available through our InCeP database. Each component of the pathway is represented by the gene symbol (e.g., Promyelocytic leukemia, PML, etc.). (B) Confirmation of microarray results by MAKOT. Temporal gene expression data from GEO (Gene Expression Omnibus: http://www.ncbi.nlm.nih.gov/geo/) including mKIAA1027 genes were incorporated into MAKOT, and then the data were visualized on reconstructed pathway views. Each gene is represented by a circular symbol; each time a point is represented by a different diagram. The color of each circular symbol represents the expression level of an individual gene at each time point (red = upregulated, black = unchanged, green = downregulated). Gene names are also indicated at the lower right of each symbol (only the diagram at 0 hour is represented).

Since the spotted targets of our antibody microarray system are mKIAA proteins, which are large proteins with an average length of 4.6 kb and deduced gene products of 830 amino acid residues [32], we decided to generate intracellular pathways related to the mKIAA proteins. For the first step, we developed an intracellular pathway database based on mKIAA protein-protein interactions. mKIAA protein interactions were



identified by MS/MS analysis following immunoprecipitation with anti-mKIAA antibodies. Most mKIAA proteins are still functionally unresolved, although the interactions with biologically known molecules should enable us to extend the pathway using pathway-assist softwares such as Ingenuity Pathway Analysis software (Ingenuity Systems, Mountain View, CA) and PathwayAssist (Ariadne Genomics, Inc., Rockville, MD). The generated pathways are now distributed through InCeP (IntraCellular Pathway, based on the mKIAA protein-protein interactions) database (http://www. kazusa.or.jp/create). Users can freely access InCeP through the Internet and download the graphical display as well as the curated information, Fig. (4A). For the next step, we generated MAKOT (Mutualistic Associations of Knowledge obtained by Computational Comparative Technology), in which researchers are able to superimpose the information across gene/protein-expression datum on a related pathway. Temporal changes in the gene/protein-expression profile could be visualized on the pathway and subsequently validated from the detectable expression-linkage among proximate edges (represented by a circular symbol in Fig. (4B); proteins, protein complexes, and small molecules). Furthermore, the reliability between two proximate edges of the pathway could be statistically evaluated by Pearson’s correlation coefficient. The statistical evaluation on MAKOT could also consider delayed expression-linkage of downstream target proteins on the same signal cascade. We are now preparing to distribute MAKOT through our website (http://www.kazusa.or.jp/create). Fig. (4A) represents the pathway related to mKIAA1027, also known as talin 1, which plays a significant role in the cell-cell and cell-extracellular matrix adhesions through interactions with the intracellular domain of integrin β. In addition to the previously identified function of mKIAA1027/talin 1 in the transduction of the integrin signal, we have identified a novel functional aspect of mKIAA1027/talin 1 mediated by Promyelocytic leukemia (Gene Symbol in Fig. (4A) is PML), a novel binding partner for mKIAA1027/talin 1. PML was originally identified as a fusion protein with retinoic acid receptor α, but this novel interaction was experimentally verified by us. To demonstrate the usefulness of MAKOT in the validation of gene/protein-expression profiles, we superimposed temporal gene expression changes of the pathway members such as mKIAA1027/talin 1 and PML on this pathway. For instance, the application of MAKOT to this expression profile revealed statistical significance in many proximate edges such as mKIAA1027/talin 1 and PML, Fig. (4B). Therefore, superimposition of gene/proteinexpression profiles on the intracellular pathway would be helpful in validating the profiles. In this review article, we used gene-expression datum transferred from GEO (Gene Expression Omnibus: http://www.ncbi.nlm.nih.gov/geo/). MAKOT is similarly applicable to protein-expression data. ACKNOWLEDGEMENTS The authors gratefully acknowledge all staff at Kazusa DNA Research Institute. This study was supported by the CREATE Program from JST , a grant from Kazusa DNA Research Institute to H.K. and a Grant-in-Aid for Young Scientists (B) by MEXT KAKENHI (17710173) to K.U. ABBREVIATIONS NCI60

= The 60 human cancer cell lines used by National Cancer Institute to screen for new anticancer agents

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KIAA

= “KI” stands for “Kazusa DNA Research Institute” and AA are reference characters.

SAMs

= Self-assembled monolayers

11-CDT = 11-carboxy-1-decanethiol EDC

= 1-ethyl-3-[3-dimethylaminopropyl] carbodiimide hydrochloride

NHS

= N-hydroxysuccinimide

SPR

= Surface plasmon resonance

RCU

= Resonance change unit

InGaP

= Integrative Gene and Protein expression database

InCeP

= IntraCellular Pathway based on mKIAA protein-protein interactions

MAKOT = Mutualistic Associations of Knowledge obtained by Computational Comparative Technology REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]

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Nishizuka, S.; Charboneau, L.; Young, L.; Major, S.; Reinhold, W.C.; Waltham, M.; Kouros-Mehr, H.; Bussey, K.J.; Lee, J.K.; Espina, V.; Munson, P.J.; Petricoin, E. 3rd.; Liotta, L.A.; Weinstein, J.N. Proc. Natl. Acad. Sci. USA, 2003, 100, 14229-14234. Nishizuka, S.; Chen, S.T.; Gwadry, F.G.; Alexander, J.; Major, S.M.; Scherf, U.; Reinhold, W.C.; Waltham, M.; Charboneau, L.; Young, L.; Bussey, K.J.; Kim, S.; Lababidi, S.; Lee, J.K.; Pittaluga, S.; Scudiero, D.A.; Sausville, E.A.; Munson, P.J.; Petricoin, E.F. 3rd.; Liotta, L.A.; Hewitt, S.M.; Raffeld, M.; Weinstein, J.N. Cancer Res., 2003, 63, 5243-5250. Sakatani, T.; Kaneda, A.; Iacobuzio-Donahue, C.A.; Carter, M.G.; de Boom Witzel, S.; Okano, H.; Ko, M.S.; Ohlsson, R.; Longo, D.L.; Feinberg, A.P. Science, 2005, 307, 1976-1978. Zhu, H.; Snyder, M. Curr. Opin. Chem. Biol., 2003, 7, 55-63. Wang, C.C.; Huang, R.P.; Sommer, M.; Lisoukov, H.; Huang, R.; Lin, Y.; Miller, T.; Burke, J. J. Proteome Res., 2002, 1, 337-343. Angenendt, P.; Glokler; J.;Murphy, D.; Lehrach, H.; Cahill, D.J. Anal. Biochem., 2002, 309, 253-260. Koga, H.; Shimada, K.; Hara, Y.; Nagano, M.; Kohga, H.; Yokoyama, R.; Kimura, Y.; Yuasa, S.; Magae, J.; Inamoto, S.; Okazaki, N.; Ohara, O. Proteomics, 2004, 4, 1412-1416. Koga, H.; Yuasa, S.; Nagase, T.; Shimada, K.; Nagano, M.; Imai, K.; Ohara, R.; Nakajima, D.; Murakami, M.; Kawai, K.; Miki, F.; Magae, J.; Inamoto, S.; Okazaki, N.; Ohara, O. DNA Res., 2004, 11, 293-304. Kyo, M.; Usui-Aoki, K.; Koga, H. Anal. Chem., 2005, 77, 7115-7121. Okazaki, N.; Imai, K.; Kikuno, RF.; Misawa, K.; Kawai, M.; Inamoto, S.; Ohara, R.; Nagase, T.; Ohara, O.; Koga, H. DNA Res., 2005, 12, 181-189.


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The Use of Biomarkers to Detect Cervical Neoplasia and to Diagnose High-Grade Cervical Disease Douglas P. Malinowski* TriPath Oncology, 4025 Stirrup Creek Drive – Suite 400, Durham, North Carolina 27703, USA Abstract: The detection of cervical carcinoma and its malignant precursors is currently accomplished using the Pap smear in conjunction with testing for the presence of human papillomavirus (HPV). This screening approach is successful at identifying patients with cervical disease with a very high sensitivity, but with limited specificity. In order to improve the accuracy of cervical disease detection and diagnosis, a number of approaches have been employed to incorporate molecular diagnostics into the testing procedure. Two investigational approaches to identify biomarkers have been employed: (1) the use of specific biomarkers based upon known keratinocyte response to HPV infection; and (2) the use of genome-wide expression profiles to identify new genes whose expression is altered in response to HPV infection and the transformation process. Both approaches have identified biomarkers that appear suitable for the detection of the mild dysplasia precursors of disease, the malignant precursors of moderate-severe dysplasia and cervical carcinoma. Some biomarkers are suitable for the detection of HPV infected cells displaying mild dysplasia while others are more specific to moderate-severe dysplasia and carcinoma (disease-specific markers). The disease-specific markers appear to be over-expressed in high-grade cervical disease and represent aberrant entry of the infected cells into the S-phase of the cell cycle. These markers appear promising in molecular diagnostic applications to detect malignant cells in both histology and cervical cytology specimens. An emerging diagnostic paradigm will be discussed where HPV DNA analysis represents measurements of transient infection and risk for future disease; analysis of HPV oncogene transcripts distinguishes active versus transient infection; and detection of aberrant S-phase induction represents a measure of active disease.

INTRODUCTION Cancer of the uterine-cervix represents a major gynecologic cancer that affects women on a worldwide basis. Each year, there are approximately 517,000 new cervical cancer cases diagnosed on a global basis. In the US and Europe, the incidence of cervical cancer has diminished during the last five decades due to the introduction of the Pap

*Corresponding author: Tel: (919) 206-7102; E-mail: [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.

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smear for the routine detection of cervical cancer and its malignant precursors. In comparison to the global incidence of cervical cancer, there are approximately 10,370 new cases of cervical carcinoma diagnosed in the USA during 2005. Cervical neoplasia is a slowly progressing disease that is initiated through the infection of cervical keratinocytes by oncogenic strains of Human Papillomavirus (HPV). The development of cervical carcinoma is preceded by the malignant precursors of moderate-severe dysplasia, which constitute Cervical Intraepithelial Neoplasia 2, 3 (CIN2, 3) in histology specimens and High-Grade Squamous Intraepithelial Lesion (HSIL) in cytology specimens. Mild dysplasia, which can harbor underlying cervical disease, is referred to as CIN1 in histology and Low-Grade Squamous Intraepithelial Lesion (LSIL) in cytology. In this review, high-grade cervical disease is defined as moderate-severe dysplasia in a cervical biopsy specimen (CIN2+ lesion). Finally, suspicious appearing cells can be categorized as either Atypical Squamous Cells – cannot rule out HSIL (ASC-H) or Atypical Squamous Cells of Uncertain Significance (ASC-US). Underlying cervical disease can still be present within the LSIL (CIN1), ASC-H and ASC-US specimens. Appropriate clinical management guidelines exist to manage patients across this spectrum of morphological classifications of cervical abnormalities [1-3]. Current diagnostic methods used to screen for the presence of cervical neoplasia include the use of testing for the presence of HPV DNA in a cervical sample and the use of the Pap smear for the detection of morphologically abnormal cells within the cervical specimen. These current methods provide high sensitivity for the detection of cervical neoplasia, but with a low specificity for disease detection. Furthermore the reliance on morphology to detect cervical neoplasia if often confounded by the appearance of cellular abnormalities related to benign physiological conditions that often appear as malignant. This can result in a large number of false positive cases being referred to follow up examination and unnecessary procedures. As a result of these diagnostic limitations, considerable research has been conducted to identify additional molecular markers that could be used to improve the specificity for disease detection and to eliminate the subjectivity inherent in morphology-based disease classification methods. Investigations into the genomic alterations of the HPV infected cervical keratinocytes, primary human cervical lesions and known host genes affected by HPV have been investigated to identify suitable markers for use in cervical disease screening. Of particular note, alterations in cell-cycle control, DNA replication, RNA transcription and extracellular matrix interactions have been consistently identified using a number of different microarray-based transcriptional profiling approaches. In this review, we will review the current status of translational research related to the characterization of these changes and in particular, the characterization of markers suitable for the detection of HPV induced dysplasia and the specific identification of malignant transformation with subsequent diagnosis of high-grade cervical disease. Markers suitable for the detection of cervical dysplasia and the detection of high-grade cervical disease (CIN2+) will be discussed. Markers specific for high-grade cervical disease represent aberrant entry of the cervical cells into the S-phase of the cell cycle and are distinct alterations associated with cervical disease. Thus, the detection of aberrant S-phase entry appears to be a specific characteristic of high-grade cervical disease that can be used to detect malignant cells in both histology and cervical cytology specimens. An emerging diagnostic paradigm will be discussed where HPV DNA analysis represents measurements of

The Use of Biomarkers


transient infection and risk for future disease, analysis of HPV oncogene transcripts represents active infection versus transient infection and detection of aberrant S-phase induction represents a measure of active disease. MOLECULAR BIOLOGY OF CERVICAL NEOPLASIA: Human papillomavirus (HPV) is recognized as the pathogenic agent responsible for the development of cervical cancer and the presence of HPV has been associated with >99% of all cervical cancers. HPV is a circular DNA virus, consisting of approximately 8 kilobases of double-stranded DNA and encodes a small number of viral proteins. Currently, there are over 100 HPV viral subtypes identified, yet all these viruses share a tropism for infection and replication within epithelial cells. Within the HPV family of viruses, there are both non-oncogenic forms and oncogenic forms of the virus. The nononcogenic forms of the virus (including HPV types 6 and 11) are associated with common warts and condyloma. The oncogenic forms of HPV (including types 16 and 18) are associated with cervical carcinoma. The oncogenic forms of HPV can be classified into high-risk and intermediate-risk viral subtypes. The high-risk HPV viral subtypes include: HPV types 16, 18, 45, and 58; and the intermediate-risk HPV viral subtypes include: HPV types 31, 33, 35, 39, 51, 52 and 69 [4]. Of particular interest are the viral subtypes associated with cervical cancer [5, 6]. These high-risk HPV viral subtypes are DNA tumor viruses that encode two viral specific oncogene proteins (E6 and E7) responsible for the transformation of infected cervical keratinocytes [7, 8]. The pathogenesis of cervical cancer involves changes in cell cycle control, alterations in HPV structure and expression patterns and finally changes in the chromosome integrity of the infected keratinocytes. These changes are summarized in Fig. 1. Some of the molecular changes within HPV infected keratinocytes include the inactivation of the key tumor suppressor pathway functions of p53, Rb [5, 9] and TGFBeta [10]; the immortalization of cells [11]; abrogation of DNA damage response [12,13]; and the generation of centrosome abnormalities leading to genomic instability [14,15]. The HPV oncogenes E6 and E7 are known to produce alterations in the cell cycle including over-expression of cyclin dependent kinases (CDK) [9], cyclin proteins [16-18] and CDK inhibitors such as p16INK4A [19-21]. Likewise, an increase in proliferation is observed with HPV infected cells including activation of telomerase [2224]. Cervical neoplasia is also characterized by re-entry of the differentiated keratinocytes into the cell cycle. This is evidenced by detectable expression of Minichromosome Maintenance (MCM) Proteins within the stratified epithelial layers of the uterine-cervix based upon detection of MCM5 and MCM2 [25, 26]. These effects are summarized in Fig. 2 and in Table 1. HPV TESTING AND DNA DETECTION IN CERVICAL SCREENING Most HPV infections are transient in nature, with the viral infection resolving itself within a 12-month period. For those individuals who develop persistent infections with one or more oncogenic subtypes of HPV, there is a risk for the development of neoplasia in comparison to patients without an HPV infection. Given the importance of HPV in the development of cervical neoplasia, the clinical detection of HPV has become an important diagnostic tool in the identification of patients at risk for cervical neoplasia development. Both the high-risk and the intermediate-risk HPV viral subtypes are

38



Fig. (1). The molecular characteristics of cervical neoplasia. The alterations that occur within cervical neoplasia are initiated through infection of the undifferentiated cervical keratinocyte by oncogenic viral subtypes of HPV. HPV viral replication occurs concomitantly with the differentiation of the cervical keratinocyte. In order to maintain DNA replication, the HPV oncogenes E6 and E7 re-initiate entry into the cell cycle. The activities of the HPV also result in additional abnormalities that include: (i) alteration in the structure of the HPV virus from an episome to viral integration within the keratinocyte genome; (ii) genetic instability of the infected keratinocyte leading to tetraploidy, aneuploidy, and chromosomal amplification (3q+); (iii) loss of HPV E2 expression; and (iv) increased expression of the HPV oncogenes E6 and E7.

detectable through the use of the DiGene Hybrid Capture II assay (DiGene Corporation, Gaithersburg, MD.) The NCI ALTS clinical trial (ASCUS LSIL Triage Study) helped define the clinical utility of HPV testing in combination with annual Pap screening. Within the ASCUS patient population, 50-60% of these patients harbored HPV infections with 7% of patients presenting high-grade disease upon colposcopy and biopsy confirmation. The ALTS trial concluded that HPV triage of ASCUS patients was more effective for disease detection upon referral to colposcopy than repeat cytology or direct referral to colposcopy. However, HPV testing of LSIL patients was not useful as a triage for colposcopy because of the high prevalence of HPV infection in this patient population [27-31]. The use of HPV testing has been approved by the FDA in combination with Pap screening as both a reflex test within the ASCUS patient population and as a primary screening for cervical disease detection [32]. The utility for HPV testing in a primary diagnostic setting has been recommended in recent guidelines from the American College of Obstetrics and Gynecology (ACOG) [33]. In addition to the Hybrid Capture II assay, a number of PCR-based methods have been shown to



Fig. (2). Molecular pathogenesis of cervical cancer. A variety of cellular processes are altered through HPV induced cervical neoplasia and collectively contributes to neoplastic transformation and the onset of cervical cancer.

support HPV detection and viral genotyping using liquid-based cervical cytology specimens [34-38]. The presence of HPV positive patients has been shown to predict the future occurrence of CIN3+ disease [39]. This study monitored patients with a normal history of Pap smears and followed the natural history of disease development over a 10year period in relation to the presence or absence of HPV. The presence of HPV was shown to correlate with a higher incidence of CIN3+ disease over the 10 year period than HPV negative patients as shown in Fig. 3. A similar study evaluated disease development in women with a normal Pap history over a 10 year period. This study demonstrated that the presence of HPV types 16 and 18 correlated with a higher likelihood of disease recurrence than the other high-risk HPV types within the HPV positive, cytology normal patients [40]. The clinical utility of HPV-based screening for cervical disease is in its negative predictive value. An HPV negative result in combination with a history of normal Pap smears is an excellent indicator of a disease-free condition and a low risk of cervical neoplasia development during the subsequent 1-3 years. However a positive HPV result is not diagnostic of cervical disease; rather it is an indication of infection. Although the majority of HPV infections is transient and will spontaneously clear within a 12-month period, a persistent infection with a high-risk HPV viral subtype indicates a higher risk for the development of cervical neoplasia. To supplement HPV testing, a number of

40



molecular markers associated with cervical neoplasia have been evaluated in order to improve the clinical specificity for cervical disease diagnosis. Table 1.

Human Papillomavirus Open Reading Frame Genes

HPV Gene LCR

Viral Function

Keratinocyte Interactions

Regulatory control of transcription, replication and host interactions

None reported

L1

Major capsid protein

None reported

L2

Minor capsid protein

None reported

E1

Viral replication Maintenance of viral episome

E2

Transcriptional regulation Cofactor for DNA replication

Repression of telomerase expression

E4

Keratin interactions Viral shedding

Actin-cytoskeleton interactions

E5

Growth factor receptor interactions and signal transduction

EGFR and MAPK signaling pathways

Prolongs division phase of the cell-cycle to promote replication. Responsible for malignant transformation of the cervical keratinocyte following infection with oncogenic subtypes of HPV

- MAPK Signaling pathway activation

E6 and E7

- p53 and Rb inactivation - Abrogation of G1/S and G2/M cell-cycle checkpoints - Activation of E2F transcription pathways - Activation of centrosome duplication - TGF B pathway inativation - c-myc interaction - Telomerase activation

HPV E6 AND E7 mRNA DETECTION AND ACTIVE HPV INFECTIONS The ability to detect HPV E6 and E7 mRNA has been reported using the NASBA nucleic acid amplification technology for potential clinical applications. The detection of the E6 and E7 mRNA from cervical samples infected with high-risk viral subtypes has been reported in several publications [41-46]. The utility of detecting mRNA for E6 and E7 in comparison to the detection of HPV DNA, based upon the L1 major capsid protein gene, was compared for the ability to detect disease within the ASCUS and LSIL patient population in a 2-year follow-up study. The results of this study showed that the detection of E6 and E7 mRNA was more specific for the detection of patient with a high likelihood of future cervical disease when compared to the detection of HPV L1 DNA [44]. These results suggest that mRNA detection reflects active viral transcription



Fig. (3). Predictive value of HPV testing in cervical disease screening. The presence of HPV positive specimens is associated with an increase in the development of CIN3+ disease. The absence of HPV is associated with a significantly lower incidence of cervical disease over the same time period.

associated with ongoing disease whereas the detect of HPV DNA is more likely to detect transient infections in patients who will clear the viral infection within a 12-24 month period and thus will not progress on to cervical disease. These results are summarized in Table 2. Table 2.

Prediction of CIN2+ Cervical Disease During 2-Year Follow-up of ASCUS and LSIL Patients: Comparison of Detection Methods for High-Risk HPV E6/E7 mRNA and L1 DNA*

Measurement

NASBA Detection of E6/E7 mRNA

PCR Detection of L1 DNA

HPV Subtypes Detected

16,18,31,33,45

13 HPV Viral subtypes

HPV Prevalence

23.4%

54.6%

Sensitivity

85.7%

85.7%

Specificity

84.9%

50.0%

PPV

37.5%

15.4%

NPV

98.3%

97.1%

Odds Ratio

69.8

5.7

* Data summarized from reference [44].

42


BIOMARKERS NEOPLASIA

ASSOCIATED

WITH


HPV

INDUCED

CERVICAL

Microarray Analysis of Cervical Carcinoma and HPV Cell Line Models The application of DNA microarray transcriptional profiling of primary cervical carcinoma lesions, cultured primary cervical carcinoma cell lines and HPV infected cell line model systems has yielded information related to both the over-expression and the reduction of gene expression associated with HPV-induced cervical neoplasia [47-59]. Among these different experimental approaches, a common set of genes with altered expression patterns have been detected including: (i) activation of a proliferation signature (including cell cycle regulation, signal transduction, DNA replication, and cellular proliferation); and (ii) increase in gene expression associated with extracellular matrix (ECM) interactions and proteolysis (ECM remodeling, tissue invasion and metastasis) [60-72]. The common over-expressed genes that have been identified in cervical neoplasia using these transcriptional profiling approaches are shown in Table 3. A number of these genes have been examined for expression patterns of the encoded proteins within cervical neoplasia. The most interesting biomarkers have also been characterized for potential utility in molecular diagnostic applications. A number of genes associated with regulation of the cell cycle and DNA replication have been shown to undergo an increase in expression level in cervical neoplasia by microarray analysis. These include various cyclins, the cyclin dependent kinase inhibitor, p16INK4A and proteins associated with DNA replication including PCNA, CDC6, members of the MCM family of DNA licensing factors (MCM2-7) and Topoisomerase II alpha (TOP2A) [51,53,58,59]. Cell Cycle Regulatory Proteins and Cervical Neoplasia Cyclins and Altered Expression in Cervical Neoplasia Results from microarray analyses have consistently identified cyclins as overexpressed genes in cervical neoplasia and HPV induced cellular transformation. Cyclin E, A and B have all been shown to be over-expressed in squamous cell cervical neoplasia as well as in LSIL and HSIL lesions [9,15-18, 60-62]. Likewise, Cyclins A and B have been shown to be over-expressed in cervical adenocarcinoma and its malignant precursors [18]. Over-expression of these various cyclins are detected in HPV-induced neoplasia and are summarized in Table 4. Cyclin Dependent Kinase Inhibitors p16INK4A (p16) is an inhibitor of cyclin dependent kinases (CDK 4 and CDK6) and functions in the progression from G1 to S phase of the cell cycle. In response to infection by high-risk HPV infectious, p16 is over-expressed in cervical neoplasia including HSIL lesions and carcinoma [63]. Over-expression of p16 has been shown to correlate with HPV type 16 and 18 infections and can be detected in both squamous cell carcinoma and adenocarcinoma [9, 63-66]. The specificity of p16 over-expression has been examined and is associated with carcinoma, biopsy confirmed CIN 2+ lesions and a significant number of LSIL – CIN 1 lesions [67-70]. The p16 protein can be detected in both histology specimens as well as liquid-based cytology specimens [9, 64, 71, 72]. Immunohistochemical detection of p16 has been shown in both the cytoplasm and the



nucleus of mild-severe dysplasia (CIN1-CIN3), squamous cell carcinoma and adenocarcinoma [73]. Table 3.

Microarray Analysis of Cervical Neoplasia and HPV Infected Cell Lines. Identification of Common Gene Categories and Specific Over-Expressed Genes* Extracellular Matrix Interactions

Investigational Signal Approach Transduction

Cell cycle regulation

DNA replication and transcription

Cellular proliferation

Primary genes identified by transcriptional profiling of human cervical carcinoma

IGFBP3

Cyclin A Cyclin B1 Cyclin B2 Cyclin E CDK2

TOP2A MCM2 MCM4 MCM6 B-Myb FOSL2 BTEB1 PCNA

BST2 IFI27 Co-actosin MDK NP25 CRIP1

Claudin 1 Claudin7 Aquaporin 5

Additional genes identified using HPV induced expression in cell line model systems

Pleitrophin

p16INK4A p21 Waf-1

MCM3 MCM5 MCM7 Telomerase TOP2B c-myc

Bcl-2 Survivin Ubiquitin E2-C

ITGA9

Mesothelin ITGB6 ITGA3 Laminin V MMP-2 MMP-9 UPA

Ref.

[50] [51] [54] [55] [58]

[22] [47] [48] [49] [52] [59]

CDC6

* Gene identities are listed using the currently accepted gene ID nomenclature available at the National Center for Biotechnology Information (http://www.ncbi.nlm.nig.gov).

DNA Replication and Cellular Proliferation Cell division cell protein 6 (CDC 6) and minichromosome maintenance (MCM) proteins participate in the early stages of eukaryotic DNA replication through the regulated assembly of the pre-replication complex onto DNA during the G1 phase of the cell cycle. CDC6 participates through recruiting additional proteins into the prereplication complex consisting of the origin recognition complex (ORC), CDC6, Cdt1 and the MCM hexamer (MCM2, MCM3, MCM4, MCM5, MCM6, MCM7). This multiprotein complex is responsible for the initiation of DNA replication during the S-phase of the cell cycle. The MCM proteins consist of 6 distinct protein members, MCM2-7, which form a hexamer and participate in both the formation of the pre-replication complex and function as a helicase to unwind DNA during replication [74]. CDC6 has been shown to be elevated in cervical neoplasia at both the mRNA and the protein level [73, 75]. Furthermore, CDC6 expression has been reported to show some specificity for high-grade cervical dysplasia [75]. MCM5 was the first member of the MCM family that was demonstrated as a potential biomarker in cervical neoplasia [25]. Subsequently, the proteins for MCM2 and MCM7 were also shown to be promising biomarkers in cervical

44



neoplasia; detection was consistent with re-entry of the differentiated keratinocytes into the proliferative phase of the cell cycle [26, 76]. More recently, MCM5 has been characterized in cervical neoplasia and shown to be elevated at both the mRNA and the protein levels [73, 75]. Likewise, characterization of MCM2, MCM6 and MCM7 expression in cervical neoplasia has been reported and shown to be elevated at both the mRNA and the protein levels for moderate-severe dysplasia and carcinoma [77, 78]. Table 4.

Alterations of Cyclin Expression in Cervical Neoplasia

Cyclin Protein

Function

Expression Status in Cervical Neoplasia

References

Cyclin D1 and D3

Activation of G1 Phase of Cell Cycle Regulatory subunits of CDK4 and CDK6 Links mitogenic stimuli to cell-cycle progression

- Cyclin D1 decreased in 97% of HSIL and 72% of invasive cervical cancer - Cyclin D3 decreased in 51% of squamous cell carcinoma - Increased expression within cervical carcinoma associated with poor disease-free survival

[9, 17, 61, 62]

Cyclin E

Activation of G1 Phase of the Cell Cycle CDK2 regulatory subunit

Increased in 97% of LSIL, 92% of HSIL and 82% of squamous cell carcinoma

[9, 16, 160]

Cyclin A

Activation of S and M Phases of the Cell Cycle Regulatory subunit for CDK2 (S-phase) and CDK1 (M-phase) Links anchorage dependent cell growth to cell cycle progression

Increased in 35% of squamous cell carcinoma and adenocarcinoma

[9, 18]

Cyclin B

Activation of the G2 and M Phases of the Cell Cycle CDK1 regulatory subunit

Increased in cervical neoplasia, squamous cell carcinoma and adenocarcinoma

[9, 18]

The application of these markers has been reported to detect atypical cervical cells that are consistent with aberrant S-phase entry and display a high likelihood of CIN2+ disease upon biopsy [77-79]. A summary of the expression patterns for these proliferation signature markers is shown in Table 5. The MCM genes are known to be under the control of the E2F-1 transcription factor. As a result of HPV E7 disruption of the Rb tumor suppressor pathway, the constitutive activation of the E2F-1 transcription factor results in constitutive transcription of Sphase genes and permits the aberrant entry of the transfected cells into the S-phase of the


Table 5.


Analysis of Cell Cycle Regulatory and S-Phase Genes in Cervical Neoplasia

Marker

Function

Increase in mRNA Between Cervical Carcinoma and Normal Cervical Epithelium

Protein Over Expression in Cervical Disease by IHC Analysis

Reference

MCM2

DNA Licensing Factor

5-fold

CIN2+ >> CIN1

[26, 77, 78]

MCM6


3-fold

CIN2+ >> CIN1

[77, 78]

MCM7

DNA Licensing Factors

3-fold

CIN2+ >> CIN1

[76-78]

TOP2A

DNA unwinding

4-fold

CIN3+ > CIN2 > CIN1

[77, 78]

CDK inhibitor

6-fold

CIN1+

[73, 75]

MCM5


Cancer, CIN3 >>> Normal

CIN1+

[73, 75]

CDC6

Pre-initiation complex formation

Cancer, CIN3 > Normal

CIN3+ > CIN2, CIN1

[73, 75]

INK4A

p16

cell cycle [9, 79]. The aberrant over-expression of the S-phase genes, including MCM27 and TOP2A, appears similar to the same set of genes implicated in the proliferation signature of tumorigenic cell lines [80]. This aberrant expression of the S-phase genes also appears to be a characteristic of cervical neoplasia with potential diagnostic applications [79] (Fig. 4). Extracellular Matrix Proteins and Cervical Neoplasia Several categories of genes associated with extracellular matrix proteins have been identified by DNA microarray profiling of cervical carcinoma including Claudin 1 and 7, Laminin V and various members of the integrin family. Claudins are a group of membrane proteins that form tight junctions in epithelial tissues and appear to participate in the formation of watertight barriers in tissues. Claudins have been shown to be elevated in cervical neoplasia, with tissue expression increasing with the severity of cervical disease [81-83]. Similar observations have been made for several proteases including cathepsins, and matrix metalloproteinases [84], Laminin V [85-86] and integrin beta 3 [87-89]. Although useful in histology applications, none of these markers has demonstrated utility in cervical cytology applications. These results are summarized in Table 6. Signal Transduction Pathways and Biomarker Characterization MAP Kinase and ERK Investigations into the function of the HPV oncoproteins E5, E6 and E7 have revealed that these HPV encoded proteins interact with a number of key signal transduction and proliferation pathways within infected cervical keratinocytes. The E5 protein has been shown to interact with EGFR [90, 91], and both E5 and E6 have been shown to activate the MAP kinase pathway [92-94]. Both EGFR and MAP kinase link external growth signals to cellular proliferation through signal transduction pathways.

46



The activation of the MAP kinase pathway, through the phosphorylation of ERK1 and ERK2, has been shown to increase with increasing severity of cervical disease. However, there was no correlation with the phosphorylation status of ERK1 and ERK2 and the clinical outcome of cervical neoplasia or the detectable presence of high-risk HPV [94].

Fig. (4). Molecular pathogenesis of cervical carcinoma. The persistent infection of cervical keratinocytes with oncogenic subtypes of HPV ultimately results in the aberrant entry of the infected cells into the S-phase of the cell cycle. The resulting constitutive over-expression of Sphase genes occurs through abrogation of the normal cell-cycle control of S-phase entry through the combined action of E6 and E7. This aberrant S-phase induction appears to be a molecular characteristic of HPV induced neoplastic transformation.

Beta-Catenin and Signal Transduction Pathways HPV has been shown to interact with the Notch signal transduction pathway. The Notch signaling pathway is an evolutionarily conserved pathway in metazoan organisms that is responsible for the determination of cell fate based upon interaction with adjacent cells an as such, participates in the fundamental processes of embryogenesis, cell fate determination and cellular differentiation. Alterations in the Notch pathway have been described in cervical neoplasia [95-99]. The Wnt signal transduction pathway has been implicated in cervical neoplasia and one of the key components of the Wnt pathway, beta-catenin, has been demonstrated to be over-expressed in cervical neoplasia [100]. Beta-catenin is known to interact with Ecadherin in the generation of functional complexes that also participate in the linkage of



extracellular matrix interactions though the Focal Adhesion Kinase (FAK) pathway [101,102]. Activation of the Wnt signaling pathway in cervical neoplasia results in changes to beta-catenin that include increased expression levels, re-localization of the protein from the nucleus to the cytoplasm and phosphorylation. In the majority of cervical neoplasia, beta-catenin was shown to be over-expressed and localized in the cytoplasm. However, these alterations have not been observed in all carcinomas and thus represent one of the heterogeneous alterations that are observed within cervical carcinoma. These same studies have also demonstrated alterations in E-cadherin in cervical neoplasia, most notably the loss of detectable E-cadherin in cervical neoplasia. Like Beta-catenin, there are no consistent alterations in the expression levels of Ecadherin that can be demonstrated consistently in cervical neoplasias [100,101,103-106]. Table 6.

Extracellular Matrix Gene Over-expression in Cervical Neoplasia

Marker

Function

Cervical Disease Status

Reference

Claudin 1

Tight junctions in epithelial tissues

Differentially expressed in cervical neoplasia with expression increasing with severity of lesion

[81-83]

Claudin 7

Tight junctions in epithelial tissues

Differentially expressed in cervical neoplasia with expression increasing with severity of lesion

[81-83]

Cathepsin F

Protease

Increased in cervical carcinoma

[84]

MMP-9,10,11,12

Protease

Increased in cervical carcinoma

[84]

Laminin V

Extracellular matrix component

Increased in cervical carcinoma and CIN3 lesions. Cytoplasmic localization of the protein observed in cervical carcinoma

[85,86]

Integrin Beta 3

Extracellular matrix component

Increased expression in cervical carcinoma

[87-89]

Alterations in the TGF-beta signal transduction pathway have been described in cervical neoplasia that include inhibitory effects on Smad binding to DNA and the abrogation of G1/S boundary checkpoint of the cell cycle [10]. Proliferation Signatures in Cervical Neoplasia Activation of proliferation within keratinocytes includes E6 interaction with c-myc and telomerase activation [22-24] and the activation of the E2F1 transcription pathway leading to over-expression of genes related to entry into the S-phase of the cell cycle and DNA replication [9, 107]. A summary of the signal transduction pathways affected in cervical carcinoma and the malignant precursors are summarized in Table 7. The results from these investigations clearly demonstrate that alterations in key signal transduction pathways occur within cervical neoplasia and most are produced as a

48


Table 7.


Oncogenic HPV and Key Molecular Pathway Interactions

HPV Protein

Key Pathway Component

Clinical Observation

Rb Tumor Suppressor

E7

pRb

Rb protein degraded in cervical neoplasia Loss of Rb detected by IHC

E2F-1 Transcription

E7

c-myc Transcription

E6

Pathways

Diagnostic Utility

Reference

Not routinely utilized in diagnostic applications

[5, 9]

Increased expression of S-phase genes at mRNA and protein level in both dysplasia and carcinoma

[9, 79, 107]

Increased Telomerase activation telomerase activity is observed as an early event in cervical neoplasia

[22-24]

E2F-1 target genes Induction of Sphase genes including MCM proteins

Telomerase expression

No diagnostic utility EGFR Signaling

E5

EGFR

EGFR required for No consistent HPV induced alterations in EGFR hyperplasia observed in cervical neoplasia

MAPK Signaling

E5, E6

ERK1

Increased MAPK activity reported in response of E6 sequence variants

ERK2

Levels of phosphorylated Erk1 and Erk2 correlated with stage of cervical disease.

[90]

[91-94]

No correlation observed with HPV presence of clinical outcome Wnt Signaling

Beta-catenin E-Cadherin

- Increased betacatenin expression and cytoplasmic localization as an early event in cervical neoplasia - Decreased expression of Ecadherin in cervical neoplasia

Beta-catenin overexpression not observed in all cervical carcinomas Decreased expression of E-cadherin not consistent in all cervical neoplasia

[100-106]



result of interactions with oncogenic HPV viral subtypes. Based upon our current understanding of cancer biology, it is tempting to speculate that the observed alterations in these signal transduction pathways are likely key events in the early stages of cervical neoplasia. However, none of these alterations have demonstrated diagnostic utility to detect cervical neoplasia in a majority of lesions or to yield prognostic information about disease progression and clinical outcome. BIOMARKERS AND CLINICAL UTILITY The investigations summarized in this chapter have revealed a number of molecular alterations in cervical neoplasia at both the gene and protein expression level. Some of the alterations, such as those described for the MAPK and Wnt signaling pathways have demonstrable alterations in component proteins within these signal transduction pathways. However, the variability observed in many of these markers renders them insufficient for use in routine clinical settings. However, a number of other alterations have been described with consistent correlations in cervical neoplasia. These investigations have revealed that a number of biomarkers, such as p16INK4A, MCM5 and Claudin 1 show protein expression levels that increase with the severity of the cervical dysplasia. These markers do not show a preference for any particular stage of cervical disease, yet all show a strong correlation with cervical dysplasia. These markers appear suitable for use in the detection of cervical dysplasia with the ability to detect all stages of cervical dysplasia in both histology and cytology samples. As such, these markers would appear suitable to help resolve suspicious lesions and cells where the morphology is inadequate to permit a clear diagnosis of cervical dysplasia versus more benign metaplasia or reactive repair processes within the uterine-cervix. There are a second group of biomarkers that appear to demonstrate a more quantitative distinction between moat-severe dysplasia and cervical carcinoma with minimal detectable expression in mild dysplasia or normal proliferating cervical cells. Examples of the biomarkers specific for high-grade cervical disease (CIN2+) are MCM2, MCM6, MCM7 and TOP2A. It is anticipated that future investigations into these markers will permit the development of specific molecular diagnostic tests to detect high-grade cervical disease within suspicious ASCUS and ASC-H cases or within indeterminate SIL cases. Finally, a small group of biomarkers associated with the extracellular matrix have been shown to correlate with invasive cervical cancers. Markers such as Laminin V appear suitable to detect the invasive potential of cervical carcinoma. These applications have been summarized in Table 8. BIOMARKERS AND THE EMERGING DIAGNOSTIC PARADIGM FOR CERVICAL DISEASE TESTING Given the causal affect of oncogenic HPV on the development of cervical carcinoma, testing for HPV is an important component of cervical disease testing. However, as we have reviewed in this chapter, multiple investigations into HPV have shown a strong correlation with future risk for cervical disease. However, the measurement of HPV (as practiced with the HC2 test) is not specific for cervical disease, and thus is not diagnostic for patients who have current infection versus those patients with transient infections. Measurements of the E6 and E7 mRNA transcripts within cervical samples have demonstrated a higher specificity and PPV for the detection of patients with active HPV infections and are more accurate to identify patients with an active disease process

50



versus those patients with a transient HPV infection. These dilemmas in HPV testing have been recently summarized in an article by Elizabeth Unger et al. [108]: “While viral load, transcript copies and transcript pattern were statistically associated with CIN III, none of these measures effectively discriminated between HPV-16 women with disease requiring treatment and those who could be followed. Cellular proliferation and differentiation pathways affected by HPV should be investigated as biomarkers for cervical cancer screening”. Table 8.

Markers of Cervical Neoplasia

Marker Set

Marker Expression

Clinical Utility

Markers to detect cervical dysplasia INK4A

p16 Claudin 1 Claudin 7 MCM5

Biomarker protein expression linearly correlated with severity of disease

Useful to distinguish dysplasia from benign lesions and to resolve indeterminate lesions

Markers to detect high-grade cervical disease (CIN2+) CDC6 MCM2 MCM6 MCM7 TOP2A

Biomarker protein preferentially expressed in CIN2+ lesions

Promising markers to aid in the diagnosis of CIN2+ disease in cytology and suspicious histology SIL cases

Markers to detect invasive cancer Laminin V MMP-9

Biomarker expression correlated with invasive carcinoma

Promising markers to aid in the prognosis of cervical cancers

It is interesting to note that many of the proliferation biomarkers characterized in this review appear to mirror the advice of Dr. Unger and reflect a series of critical keratinocyte response to active HPV infections. More importantly, a number of these biomarkers appear to be very specific for the detection of high-grade cervical. As such, it is tempting to speculate that future diagnostic paradigm for cervical disease screening and diagnosis could employ two or three testing methodologies. HPV testing for DNA would indicate those patients at risk for future disease – applicable for general screening. The detection of HPV E6 and E7 transcripts would further identify those patients with active HPV infections that represent a higher risk for disease. The use of biomarkers would permit the identification of those patients within the HPV positive population that have active cervical disease and require immediate clinical follow-up. Thus molecular diagnostic tests to identify patients at risk and patients with disease would constitute a future diagnostic paradigm in cervical disease screening and diagnosis. This diagnostic paradigm is shown in Fig. 5. As suggested in this paradigm, the detection of oncogenic HPV DNA in a cervical specimen would identify patients at high-risk for disease. The application of molecular diagnostic tests to these HPV positive patients would identify



the subset of HPV positive patients that have active disease and require immediate referral to colposcopy and treatment. An HPV positive patient with a negative molecular diagnostic assay result could be referred to the HPV mRNA assay to identify patients with active viral infection and an increased likelihood for cervical disease development within 2 years. These high-risk patients would be monitored routinely with a molecular diagnostic test for the detection of cervical disease.

Fig. (5). Emerging diagnostic paradigm for cervical disease detection. The presence of HPV DNA in cervical specimens is a useful measurement of transient HPV infections since the majority of these viral infections are normally cleared within 12 months. HPV DNA testing is thus a measure of risk for future cervical disease development. The detection of the E6 and E7 transcripts is associated with a higher incidence of cervical disease and appears to be a useful measurement of active viral replication and a higher likelihood of disease development within 12-24 months. Finally the detection of aberrant S-phase induction is a characteristic of neoplastic transformation and is a measurement of current cervical disease.

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New Developments in the Field of Protein and Metabolism Assays Aimed at Drug Discovery Processes Kothandaraman Narasimhan, Ponnusamy Sukumar and Mahesh Choolani* Diagnostic Biomarker Discovery Laboratory, Department of Obstetrics and Gynaecology, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074 Abstract: The advent of new high-throughput proteomic and metabolic assays using mass spectrometry (MS) has significantly benefited drug discovery process. This process is accelerated with the miniaturization of detection devises (nanotechnology and biosensors) to carry out rapid and effective screening. Using proteomics (protein arrays) it is possible to globally investigate the molecular basis of disease, drug action leading to drug development. Similarly metabolic assays for nutritional status, expanded neonatal screening through tandem MS could swift through several thousand data points associated with a particular disease (either proteins/peptides or metabolites) in a short span of few minutes and come up with highly sensitive and accurate diagnosis. Nanobiotechnology raises fascinating possibilities for new analytical array based assays (receptor-ligand binding, DNA-DNA hybridization, or both) and microanalytic separations, each of which will be mentioned here with respect to their ability to affect the drug discovery processes. Molecular profiling by DNA microarray technology has made significant contributions to the understanding of molecular targets for diseases such as cancer. One of the challenges is how to efficiently utilize the accumulated research data to develop new diagnostic and/or prognostic markers and therapeutic targets. Proteomics based disease therapeutic research involves high-throughput protein structure determination (e.g. structural biology of protein tyrosine kinases (PTKs). These processes involve conventional antibody based arrays as well as targets identified using structural biology for narrowing down targets for drug delivery. Drug discovery processes aimed at generating inhibitors for the treatment of malignancies are believed to be dependent on the gain of function of specific PTKs. Current research include Src as a target for pharmaceutical intervention, JAK kinases in leukemias/lymphomas, and phosphoproteomics. The following areas mentioned above will form the key focus of this chapter.

*Corresponding author: Tel: +65-68741625; Fax: +65-68723056; E-mail: [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.

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INTRODUCTION Impact of Proteomics Technologies on Drug Discovery The early 2000 saw the advent of highly sophisticated technologies with the merger of chemistry and biology resulting in “Chembiology”. One of the major benefactors of this amalgamation is for the field of clinical diagnostics, which has seen tremendous opportunity to develop new and effective screening approaches using modern technologies such as protein array and mass spectrometry. Over the last five years we have seen advances both in instrumentation as well as introduction of new techniques, which has resulted in higher sensitivity as well as miniaturization to accommodate more reaction surface and also considerable reduction in time required to carry out metabolic assays. The major advances were observed for tagging bio-molecules such as proteins and nuclei acids on different surfaces to enhance the assay capabilities. This chapter on the “New developments in the field of protein and metabolism assays aimed at drug discovery processes” focus on the recent developments associated with metabolic assays using (i) proteins arrays (ii) mass spectrometry (iii) recent advances in miniaturization technologies and specific assays with nanobiology and nanotechnology and (iv) finally different strategies employed for designing better drugs against kinases involved in different diseases especially for cancer. This comprehensive review covers all the recent advances as well as the most recent scientific literature, which has mentioned these technologies for better development of more sophisticated metabolic assays. Advances in proteomics have resulted in a bigger role for proteomics associated interventions in clinical screening for different diseases [1]. This technology offers researchers new approaches to investigate the molecular basis of disease, drug action, and development [2]. A proteomics approach, identifying protein targets associated with various diseases, can ultimately provide a basis for early disease detection and eventually guide to a rational design for pharmacological intervention [3]. The following sections will especially explore novel applications of chromatographic, electrophoretic, immunologic, and mass spectrometric technologies to analyze proteomes for clinical benefit. Modern proteomic technologies will have an important role in drug discovery especially for molecular diagnostics and practice of medicine in the post-genomic era. The commonly used technologies are 1-D and 2-D gel electrophoresis (1D and 2D-GE) for protein separation and followed by analysis of proteins by mass spectrometry (MS). Microanalytical protein characterization with multidimensional liquid chromatography/ mass spectrometry (MD LC-MS) improves the throughput and reliability of peptide mapping [4]. Matrix-Assisted Laser Desorption Mass Spectrometry (MALDI-MS) has become a widely used method for determination of biomolecules including peptides, proteins [5]. Functional proteomics technologies include yeast two-hybrid system for studying protein- protein interactions [6-7]. Establishing a proteomics platform in the industrial setting initially requires implementation of a series of robotic systems to allow a highthroughput approach for analysis and identification of differences observed on 2-D electrophoresis gels. Protein chips are also proving to be useful for several applications associated with disease diagnosis and high-throughput assays. Proteomic technologies are now being integrated into the drug discovery process as complimentary to genomic approaches. Toxicoproteomics involves the evaluation of protein expression for

New Developments in the Field of Protein


understanding of toxic events, is an important application of proteomics in preclincial drug safety [8-9]. Use of bioinformatics is essential for analyzing the massive amount of data generated from both genomics and proteomics [10]. Proteomics is providing a better understanding of pathomechanisms of human diseases. Analysis of different levels of gene expression in healthy and diseased tissues by proteomic approaches is as important as the detection of mutations and polymorphisms at the genomic level and may be of more value in designing a rational therapy. Protein distribution / characterization in body tissues and fluids, in healthy as well as in diseased, is the basis of the use of proteomic technologies for molecular diagnostics. Proteomics will play an important role in medicine of the future which will be personalized and will combine diagnostics with therapeutics [11]. Clinical Proteomics Market Clinical proteomics market is comprised of innovators and futurists whose work will help shape the product expectations of future researchers. For suppliers hoping to dominate this technology, understanding the current experiences of today’s visionaries and monitoring how their needs change over time is critical. The effectiveness of clinical proteomics will depend on two technological components: rapid, multiplex protein detection assays and data analysis systems to assimilate vast amounts of protein expression data from healthy and diseased individuals into clinically relevant data sets. The number of companies involved in proteomics has increased remarkably during the past few years. More than 300 companies have been identified to be involved in proteomics and 202 of these are profiled in the report with 450 collaborations. The markets for proteomic technologies are difficult to estimate as they are not distinct but overlap with those of genomics, gene expression, high throughput screening, drug discovery and molecular diagnostics [12]. Collectively based on a recent estimate available in the public domain, the value of markets for proteomic technologies in the year 2005 is about $6 billion and is expected to increase to $10 billion by the year 2010 and $18 billion by the year 2015, with a projected annual growth of 30%. The largest expansion will be in bioinformatics and protein biochip technologies. Important areas of application are cancer and neurological disorders. Introduction to Protein Arrays Microarrays were first developed as a tool for high-throughput genotyping and gene expression analysis [13]. By combining small sample volumes and the ability to generate massive amounts of information in a single experiment, microarrays vastly accelerated the search for functional effects of single nucleotide polymorphisms (SNPs) and modified gene expression (increases and decreases in mRNA production) in normal and diseased physiological states. Gene expression arrays operate under the pretence that changes in mRNA levels ultimately correlate to changes in encoded protein levels; often this assumption does not hold true. Additionally, gene expression arrays provide no information on protein post-translational modifications (phosphorylation, glycosylation, etc.) that affect cell function. To examine expression at the protein level and otherwise acquire quantitative and qualitative information on proteins of interest, this has led to the development of protein microarrays.

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A protein microarray consists of antibodies, proteins, protein fragments, peptides, aptamers or carbohydrate elements that are coated or immobilized in a grid-like pattern on small surfaces. The arrayed molecules are then used to screen and assess patterns of interaction with samples containing distinct proteins or classes of proteins [14-15]. A brief overview of each type of protein array techniques is mentioned in the following sections. Protein array technology allows high-throughput screening for gene expression and molecular interactions. Protein arrays appear as new and versatile tools in functional genomics, enabling the translation of gene expression patterns of normal and diseased tissues into protein product catalogue. Protein function, such as enzyme activity, antibody specificity, and other ligand-receptor interactions and binding of nucleic acids or small molecules can be analyzed on a whole-genome level by using this new technology. Currently development in proteomics associated with diagnostics is associated with (1) detection of antigens and antibodies in blood samples; (2) profiling of sera to discover new disease markers; and (3) in environment and food monitoring. One of the chief formats is the capture array, in which ligand-binding reagents, which are usually antibodies but may also be alternative protein scaffolds, peptides or nucleic acid aptamers, are used to detect target molecules in mixtures such as plasma or tissue extracts [15]. In diagnostics, capture arrays can be used to carry out multiple immunoassays in parallel, both testing for several analytes in individual sera for example and testing many serum samples simultaneously [16]. As the array technology develops, an ever-increasing variety of formats become available (e.g. nanoplates, patterned arrays, three-dimensional pads, flat-surface spot arrays, microfluidic chips), and proteins can be arrayed onto different surfaces (e.g. membrane filters, polystyrene film, glass, silane, gold). Various techniques are being developed for producing arrays, and robot-controlled, pin-based, or ink-jet printing heads are the preferred tools for manufacturing protein arrays. CCD cameras or laser scanners are used for signal detection; atomic force microscopy and mass spectrometry are upcoming options. The emerging future array systems will be used for highthroughput functional annotation of gene products. In addition, their involvements in molecular pathways and their response to medical treatment will become the doctor's indispensable diagnostic tools [17]. Of all the applications for protein microarrays, molecular diagnostics is most clinically relevant, and would fit in with the emerging trend in individualized treatment, or personalized medicine [18]. Different proteins such as antibodies, antigens, and enzymes can be immobilized within protein microchips. Miniaturized and highly parallel immunoassays will greatly improve efficiency by increasing the amount of information acquired with single examination and will reduce cost by decreasing reagent consumption. Application of protein biochips and microarrays in molecular diagnostics has good commercial prospects. Types of Protein Arrays Antibody Arrays in Cancer Research The most common type of protein arrays are antibody arrays. Antibody arrays have valuable applications in cancer research [19]. Many different antibody array technol-



ogies have been developed, each with particular advantages, disadvantages, and optimal applications. The methods have been demonstrated on various sample types, such as serum, plasma, and other body fluids; cell culture supernatants; tissue culture lysates; and resected tumor specimens. The applications to cancer research have included profiling proteins to identify candidate biomarkers, characterizing signalling pathways, and the measurement of changes in modification or expression level of cancer-related proteins [20]. The different other format and applications of antibody arrays include arrays to measure whole cells, arrays to measure enzyme activities, reverse phase arrays, and bead-based arrays. Further innovations in the methods and experimental strategies are broadening the scope of the applications and the type of information that can be gathered. Antibody microarrays fall into one of two subtypes: those using matched antibody pairs for sandwich-type assays and those utilizing single antibodies and a sample labelling methodology. Several published manuscripts demonstrate the utility and effectiveness of the sandwich immunoassay microarray [14]. This type of microarray consists of arrayed capture antibodies and appropriate control and orientation elements. Assays are performed by adding an antigen standard or test sample, followed by a detector antibody. The detector antibody is either modified with a directly detectable label (enzyme, fluorescent molecule, isotope, etc.), or it is biotinylated for detection after subsequent probing with labelled streptavidin. Antibody-pair microarrays essentially are multiplexed ELISAs [20]. In fact, standard commercially available ELISA pairs are readily adapted for microarray use once the pairs have been screened for cross-reactivity when multiplexed. Sandwich-style antibody pair microarrays can be used for qualitative or comparative (i.e. treated versus nontreated) detection of protein analytes or for protein quantification when appropriate standards are used to assemble calibration curves. When matched antibody pairs are not available, single-antibody protein microarray protocols involving labelled samples can be used. As in sandwich antibody pair arrays, the array platform consists of arrayed antibody (or antibodies). In this assay format, however, the captured protein analytes are themselves labelled for direct detection, obviating use of a detector antibody. The method requires that protein samples be labelled beforehand (e.g. with fluorescent molecule, isotope, or biotin). The label enables detection of any proteins in the sample that interact with the microarrayed antibody and associated elements. This technique is useful for examining protein targets, such as poorly characterized cell signalling proteins, for which paired antibodies do not yet exist. The main drawback to this method is its lack of antibody redundancy, which helps to ensure specific antigen recognition. Additionally, since all sample constituents are labelled (i.e. the target as well as other proteins in the sample), non-specific background signal increases. This technique is primarily used for comparative and qualitative studies. Reverse Phase Protein Microarrays Protein arrays are described for screening of molecular markers and pathway targets in patient matched human tissue during disease progression. Reverse-phase protein microarrays represent a new technology that can generate a multiplex readout of dozens of phosphorylated events simultaneously to profile the state of a signalling pathway target even after the cell is lyzed and the contents denatured [21]. This technology may offer a new opportunity to measure and profile these signalling pathways, providing data

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on post-translational phosphorylation events not obtainable by gene microarray analysis. In contrast to previous protein arrays that immobilize the probe, reverse phase protein array immobilizes the whole repertoire of patient proteins that represent the state of individual tissue cell populations undergoing disease transitions [22]. A high degree of sensitivity, precision and linearity was achieved, making it possible to quantify the phosphorylated status of signal proteins in human tissue cell subpopulations. Using this novel protein microarray longitudinal analysis of the state of pro-survival checkpoint proteins at the microscopic transition stage from patient matched histologically normal prostate epithelium to prostate intraepithelial neoplasia (PIN) and then to invasive prostate cancer has been carried out [22]. Reverse phase protein microarrays have been widely used for the development of reference standard development for molecular network analysis of metastatic ovarian carcinoma [23]. In addition to elucidation of the molecular network within a tumor specimen, critical questions are to what extent signalling changes occur upon metastasis and are there common pathway elements that arise in the metastatic microenvironment. For individualized combinatorial therapy, ideal therapeutic selection based on proteomic mapping of phosphorylation end points may require evaluation of the patient's metastatic tissue. Extending these findings to the bedside will require the development of optimized protocols and reference standards. At present a reference standard based on a mixture of phosphorylated peptides is available to begin to address this challenge [23]. 3-Dimensional MALDI-MS Protein Array Protein profiling and characterization of protein interactions in biological samples ultimately require indicator-free methods of signal detection, which likewise offer an opportunity to distinguish specific interactions from non-specific protein binding. A more recent development in the area of protein array technology is the analysis of protein interaction and function with a 3-dimensional MALDI-MS protein array [24]. A new 3-dimensional protein microchip for detecting biomolecular interactions with MALDI-MS; the microchip comprises a high-density array of methacrylate polymer elements containing immobilized proteins as capture molecules and directly interfaces with a commercially available mass spectrometer. The chip was tested in three types of experiments by detecting antibody-antigen interactions, enzymatic activity, and enzymeinhibitor interactions. MALDI-MS biochip-based tumor necrosis factor alpha (TNFalpha) immunoassays demonstrated the feasibility of detecting antigens in complex biological samples by identifying molecular masses of bound proteins even at high nonspecific protein binding. By detecting model interactions of trypsin with trypsin inhibitors, the protein binding capacity of methacrylate polymer elements and the sensitivity of MALDI-MS detection of proteins bound to these elements surpassed that of other 2- and 3-dimensional substrates tested Immobilized trypsin retained functional (enzymatic) activity within the protein microchip and the specificity of macromolecular interactions even in complex biological samples. The underlying technology should therefore be extensible to whole-proteome protein expression profiling and interaction mapping. Protein and Peptide Arrays for Quantifying Kinase Activity in Cell Extracts High-throughput quantitation of kinase activity in cell extracts has applications in biomedical research and medical diagnostics. Kinetic analysis of signal transduction



pathways can provide insight into cellular processes such as mitosis and differentiation. In addition, many disease states are associated with upregulated levels of cellular signaling molecules. However, accurate measurements of kinase activity directly from cell extracts are complicated by the complex nature of the extracts, as they contain multiple kinases as well as phosphatases. Protein arrays have been developed to assay (to quantify) the activity of multiple tyrosine kinases in cell extracts [25]. Substrates for different kinases are spotted onto a surface and covalently linked into a polyacrylamide hydrogel. This hydrogel provides a porous three-dimensional support for the kinase substrates and maintains the substrates in a hydrated environment [26]. Cell extract is prepared and incubated over the array. Phosphorylation of the substrates can be detected using fluorescently labeled antiphosphotyrosine antibodies and scanning the array for fluorescence. Glutathione-S-Transferase Green Fluorescent Protein (GST-GFP) fusion protein arrayed on a glass microscope slide. The other option is to use peptide arrays. Peptide microarray uses protein fragments on an array [27]. An example of a peptide microarray and its uses can be found in work performed by Pellois, et al. (2002) [28] and Houseman, et al. (2002) [29], these papers demonstrate the use of peptide chips for profiling p53 and kinase activities, respectively. This diverse set of applications clearly demonstrates that the protein microarray is a powerful tool. Unfortunately, costs for equipment such as arraying robots and slide scanners limit the number of researchers able to take advantage of this technology. Future developments in this field will focus on direct methods to detect phosphorylation of the immobilized substrates and applying this assay to highthroughput analysis of signal transduction networks and to screening of chemical kinase inhibitors. Cellular Lysate Protein Arrays to Study Kinase Activity Microarrays of cellular proteins are either arrayed as complex protein mixtures, or arrayed as purified or overexpressed proteins. Complex protein mixture arrays are essentially dot blots of cellular lysates. Investigations performed by Paweletz, et al. 2001 [22] demonstrate the utility of arraying cellular lysates and probing these arrayed elements with a variety of antibodies recognizing various intracellular proteins. Creating microarrays consisting of libraries of purified or overexpressed proteins allows for screens for protein:protein interactions and kinase activities. A representation of this type of array is the yeast kinase microarray produced by Zhu, et al. (2000) [30], this array consisted of 119 of the 122 known yeast kinases. Using this novel protein chip technology high-throughput analysis of biochemical activities, and analysis of nearly all of the protein kinases from Saccharomyces cerevisiae is possible. The protein chips were made from disposable arrays of microwells in silicone elastomer sheets placed on top of microscope slides. The high density and small size of the wells allows for highthroughput batch processing and simultaneous analysis of many individual samples. Only small amounts of protein are required. Of 122 known and predicted yeast protein kinases, 119 were overexpressed and analysed using 17 different substrates and protein chips. The results of this study showed many novel activities and that a large number of protein kinases are capable of phosphorylating tyrosine. This study also showed that, tyrosine phosphorylating enzymes often share common amino acid residues that lie near the catalytic region [30].

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Surface Plasmon Resonance Imaging-Based Protein Arrays The most recent technique in the use of protein arrays is the development of surface plasmon resonance imaging-based protein arrays for high-throughput screening of protein-protein interaction inhibitors [31]. This technique was first applied for the highthroughput screening of inhibitor molecules targeting RB-E7 interaction. The E7 protein produced by high-risk human papillomavirus (HPV) induces a degradation of the retinoblastoma tumor suppressor RB through direct interaction, which suggests that an inhibitor for the interaction can be a potential anticancer drug. The glutathione Stransferase-fused E7 protein (GST-E7) was first layered onto a glutathionylated gold chip surface that had been designed to specifically bind to GST-fused proteins. Subsequently, a microarrayer was used to spot the hexa-histidine-tagged RB proteins (His(6)-RB) onto the GST-E7-layered gold chip surface, and the resulting SPR image could be analyzed. Upon increased His(6)-RB concentration in the spotting solution, the SPR signal intensity increased proportionally, indicating that His(6)-RB bound to GSTE7 in a concentration-dependent manner. The His(6)-RB/GST-E7 interaction was challenged by spotting the His(6)-RB solution in the presence of a RB binding peptide (PepC) derived from a motif on E7. The SPR imaging data showed that PepC inhibited the His(6)-RB/GST-E7 interaction in a concentration-dependent manner. This report show that the SPR imaging-based protein array chip can be applied to screen small molecule inhibitors that target protein-protein interaction. Arrays for Detection of Lipid-Protein Interactions Protein domains that bind phosphoinositides specifically have emerged as major determinants in localizing proteins to their site of function. PIP Array and PIP Strips membranes were used for identifying proteins that have phosphoinositide recognition domains and for analyzing the lipid-binding specificities of proteins. PIP Array membranes provide eight different phosphoinositides arrayed in amounts from 100 to 1.6 picomoles, allowing assessment of the strength of protein binding, in addition to the lipid specificity of protein to lipids. Phosphoinositide-mediated binding of proteins to PIP Array and PIP Strips membranes is typically analyzed by protein-lipid overlay assays [32]. Proteins may be detected using standard Western blot procedures in conjunction with our high-performance, fluorescence-based alkaline phosphatase- and horseradish peroxidase (HRP)-mediated signal generation systems. Protein Arrays Based on SELDI-TOF-MS Another area of research is the development of protein arrays is the use of surface enhanced laser desorption ionisation-time of flight-mass spectrometry (SELDI-TOFMS). This technology could be used for the identification and quantification of specific protein profiles in crude biological samples as well as to characterize bioactive peptides, Fig. (1). A typical example of SELDI-TOF-MS application is in the identification of apolipoprotein variants in plasma [33]. Expression levels of apoA-I and apoA-II and their glycosylated products were carried out using 1 microL plasma samples. Strong anionic and weak cationic exchanger ProteinChips (SAX2 and WCX2 chip surfaces) and WCX2 chip was found to be selective for specific apolipoproteins. Using the WCX2 chip and analysis via SELDI-MS, apoA-I and apoA-II were separated as sharp peaks at 28 and 17 kD and did not overlap with other serum protein peaks. Since



these assays can be completed on a large number of clinical samples in approximately 1 h, further development of this technique will facilitate both epidemiological studies and therapeutic trials in assessing the role of the apolipoproteins and their glycosylated products in atherosclerosis [33]. Similarly a further modification of SELDI-TOF-MS by coupling the chip surface with antibodies could facilitate to carry out immunoassays. In one such study microheterogeneity of transthyretin (TTR) was identified in plasma and urine using SELDI-TOF-MS immunoassay [34]. TTR was extracted from plasma or urine onto an antibody-coated (via protein A) affinity chip surface (PS20) using the SELDI technique. Subsequently samples were subjected to SELDI analysis. In healthy individuals, TTR in plasma occurred rather consistently in two variants of 13732 +/- 12 and 13851 +/- 9 Da for the native and S-cysteinylated forms and at a smaller signal of 14043 +/- 17 Da for the S-glutathionylated form. In urine of pregnant women, various signals were observed with a dominant signal at 13736 +/- 10 Da and a varying number of smaller immunoreactive fragments. These fragments are possibly the consequence of metabolism in plasma or kidney.

Fig. (1). Identification and characterisation of bioactive peptides using a combination of combinatorial approach coupled with SELDI-TOF-MS approach.

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Other novel applications were also developed for protein arrays based on binding antigens to array surface. In one such study employing protein arrays, with 266 target antigens spotted on a protein chip investigators were able to predict if the present autoantibody repertoire be consulted to predict resistance or susceptibility to the future development of an autoimmune disease [35]. A selected panel of 27 different antigens (10% of the array) revealed a pattern of IgG antibody reactivity in the pre-CAD sera that discriminated between the mice resistant or susceptible to CAD with 100% sensitivity and 82% specificity (P = 0.017). Surprisingly, the set of IgG antibodies that was informative before CAD induction did not separate the resistant and susceptible groups after the onset of CAD; new antigens became critical for post-CAD repertoire discrimination. Thus, at least for a model disease, present antibody repertoires can predict future disease, predictive and diagnostic repertoires can differ, and decisive information about immune system behaviour can be mined by bioinformatic technology [35]. Metabolomics of Small Molecules Aiding Biomarker Discovery Metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind" - specifically, the study of their small-molecule metabolite profiles [36]. The metabolome represents the collection of all metabolites in a biological organism, which are the end products of its gene expression. Gene expression data based on mRNA turnover and proteome analyses do not tell the whole story of what might be happening in a cell, while metabolic profiling can give an instant 'snapshot' of the physiology of that cell. Metabolomics as a Tool for Biomarker Discovery Metabolomics is a new field of research were high-throughput techniques for metabolic profiling is carried out using a combination of different chromatography and MS techniques. Techniques such as gas chromatography (GC), high-pressure liquid chromatography (HPLC), nuclear magnetic resonance spectroscopy (NMR) and capillary electrophoresis (CE) are used to separate metabolites according to their various chemical and physical properties. The molecules are then identified using methods such as MS. The benefit of metabolomics analysis is the ability to monitor immediate biochemical consequences of consequences of mutations, changes in the environment and the effect of treatment with drugs as well as monitor drug metabolism in the body and their interaction with proteins. The outcome of this new application will help in the development of new and better drugs for specific type of diseases. In the following sections a combination of advanced analytical techniques for global metabolic profiling and chemometrics will be described to illustrate the use of metabonomics for biomarker detection and identification in different types of metabolic diseases as well as cancer. Tandem Mass Spectrometry (MS/MS) an Ideal Tool for Newborn Screening Tandem mass spectrometry (MS/MS) was introduced in the 1990s for populationbased newborn screening has enabled health-care providers to detect an increased number of metabolic disorders in a single process by using dried blood-spot specimens routinely collected from newborns [37-38]. One of the initial applications were MS was



used is in the development of high-throughput screening approaches to detect inherent metabolic errors in new born babies. The advantages of using tandem mass spectrometry (MS/MS) for newborn screening substantially increases the number of metabolic disorders that can be detected from dried blood-spot specimens. Metabolic assays based on this technique could also be used to monitor the nutritional status of newborn child for inborn metabolic disorders. Currently during each year, approximately 4 million babies in the United States have dried blood spots analyzed through newborn screening programs. This screening is intended to detect inborn disorders that can result in early mortality or for life threatening diseases later in life. Detectable metabolic disorders include e.g. (a) phenylketonuria (PKU), (b) endocrinopathies e.g. congenital hypothyroidism and (c) hematologic disorders e.g. sickle cell disease. These three groups of disorders account for approximately 3,000 new cases of potentially fatal or debilitating disease each year for which outcomes are improved with early identification and treatment through newborn screening systems. At present electrospray tandem mass spectrometry (ESI- tandem-MS/MS) is used as an alternative automated high throughput and broad-spectrum approach to screen for relatively large number of disorders using minimal amount of patient blood samples in a very short period of time and was also found to be cost effective. This highly sensitive technique could result in the compilation of metabolic screening methods to detect amino acid disorders such PKU, maple syrup urine disease, and homocystinuria among newborns, and does so with a low false-positive rate [39]. By using specific scan functions, a large number of amino acids and acylcarnitines in blood spots are quantified in 2 minutes analytical time. This approach is successfully used to quantify and screen for argininosuccinic acid in blood spots, which is a key metabolite in the diagnosis of argininosuccinase deficiency [39]. Tandem Mass Spectrometry enables health-care providers to detect an increased number of metabolic disorders in a single process by using dried blood spot specimens routinely collected for newborn screening. MS/MS allows screening of 31 metabolic disorders using a single analytical run and can detect these disorders within 1 to 2 minutes (Table 1). The advantages of this new platform is to incorporate a number of target candidate markers for several diseases in one single panel i.e. the number of disorders that can be detected, by incorporating an acylcarnitine profile, which enables detection of fatty acid oxidation disorders (e.g. medium-chain acyl-CoA dehydrogenase [MCAD] deficiency) [40-41] and other organic acid disorders. Frontiers in Metabolomics for Cancer Research The term ‘tumour metabolome’ was coined by Mazurek and Eigenbrodt (2003) [42] to indicate the metabolic characteristics of tumour cells. The tumor metabolome primarily constitute high levels of glycolytic and glutaminolytic capacities, high phosphometabolite levels, and a high channelling of glucose carbons to synthetic processes. Among the different enzymes associate with the tumor metabolism, tumor M2-pyruvate kinase (M2-PK) has been investigated in detail [43]. Tumor M2-PK is a key regulator of the tumor metabolome. The Tumor M2-PK test is unique in the sense it measures the tumor metabolic activity, which in turn acts as a measure of tumor aggressiveness. Consequently, tumor M2-PK can also be very effective for monitoring

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the effects of therapy, detecting possible relapses or metastases, and providing useful supportive information in the diagnosis and detection of various new secondary tumours. Table 1.

Common metabolic disorders studied in newborns using tandem MS. For a more comprehensive description please refer to reference [49]. The disorders have been detected from analyses of dried blood-spot specimens collected during the newborn period. Certain disorders require complex metabolic profiles and intermetabolic relation to detect disease with low false-positive and no falsenegative rates. The list of primary metabolic indicators serves only as a guideline

Disorder

Primary metabolic indicator

Amino Acids Phenylketonuria Maple syrup urine disease Homocystinuria (cystathione synthase deficiency) Hypermethioninemia Citrullinemia Argininosuccinic aciduria Tyrosinemia, type I

Phe Leu/Ile, Val Met Met Cit Cit Tyr

Fatty Acids Medium-chain acyl-CoA dehydrogenase deficiency Very-long–chain acyl-CoA dehydrogenase deficiency Short-chain acyl-CoA dehydrogenase deficiency Multiple acyl-CoA dehydrogenase deficiency Carnitine palmitoyl transferase deficiency Carnitine/acylcarnitine translocase defect Long-chain hydroxy acyl-CoA dehydrogenase deficiency Trifunctional protein deficiency

C8, C10, C10:1, C6 C14:1, C14, C16 C4 C4, C5, C8:1, C8, C12, C14, C16, C5DC C16, C18:1, C18 C16, C18:1, C18 C16OH, C18:1OH, C18OH C16OH, C18:1OH, C18OH

Organic Acids Glutaric acidemia, type I Propionic acidemia Methylmalonic acidemia Isovaleric acidemia 3-hydroxy-3-methylglutaryl CoA lyase deficiency 3-methylcrotonyl CoA carboxylase deficiency

C5DC C3 C3 C5 C5OH C5OH

Similarly using metabolomics approach it is possible to monitor the dynamics of glucose metabolism in cancer tissues and body fluids. Another area of research is the lipidomic profiling changes in carcinogenesis. All the above approaches could lead to a next wave of metabolomics preceding proteomics techniques to understand the metabolic phenotype of the tumor. This could facilitate in the deciphering the metabolic phenotype changes in response to different types of therapy during a particular type of cancer.



Lipidomics in Cancer Research Lipids like other metabolites play an important role in cell, tissue and organ physiology. This fact is demonstrated by a large number of genetic studies as well as human diseases that involve the disruption of lipid metabolic enzymes and pathways. Examples of such diseases include cancer, diabetes, as well as neurodegenerative and infectious diseases. The field of lipids is still in its early stages, and is largely due to the complexity of lipids and the lack of powerful tools for their analysis [44]. With advances in analytical techniques such as LC and MS for systems-level analysis of lipids and their interacting partners, lipidomics could become a promising area of clinical research, and drug discovery. One of the established studies using metabolomics based biomarker discovery was in the field of ovarian cancer research. The phospholipid, lysophosphatidic acid (LPA) was identified as potential diagnostic and prognostic biomarker for ovarian and other gynaecologic cancers [45]. Initial studies to detect LPA were dependent on using GSMS approaches [46]. To further improve the accuracy and potentially increase the sensitivity and specificity of the assay, ESI-MS-based method to analyze LPA and related lysophospholipids [44, 47]. LPA, lysophosphatidylinositol (LPI), lysophosphatidylserine (LPS), and lysophosphatidylcholine (LPC) could be detected with high sensitivity (in low pmol range) using this method. LPA and closely related lysophospholipids isolated from thin-layer chromatography (TLC) plates were analyzed directly by ESI-MS. This ESI-MS-based assay allows simultaneous detection and quantitation of all different species of LPAs and LPIs in a sample over a range of at least 5-300 pmol. Moreover, this test was at least 50 times more sensitive when a multiple reaction-monitoring (MRM) mode was used. Using these protocols in a limited set of analysis, both LPA and LPI were found to be much elevated in patients with ovarian cancer. Other key issue metabolome could address are those were proteomics and genomics has failed to address. Obviously the focus of this technology would be to carry out metabolic profiling, identify individuals who will respond from treatments with either drugs or dietary components? Answers might be associated with a much higher platform with integration of the data with systems biology approaches. Some of the key questions to be addressed for metabolomics would be to understand the role of metabolites in understanding the tumor behaviour. The identification of key metabolites, development of metabolic biomarkers and type of tissue and body fluids to be looked into will the future of metabolomics in tumor biology. Similarly one the challenges would be to use this technology to differentiate metabolic biomarkers that are distinct in tumor cell environment or in pre-cancerous tissues. Cost-Benefit Analysis of Universal Tandem MS for Newborn Screening A study was carried out to estimate potential costs and benefits of routinely using tandem mass spectrometry (MS/MS) to screen newborns for inborn errors of metabolism [48]. The analysis of costs and benefits resulting from use of MS/MS in screening of 32, 000 newborn infants using data from the Kaiser Permanente Medical Care Program of Northern California in addition to other published data showed in the base scenario, the cost per quality-adjusted life year saved by MS/MS screening was $5827; in the least favourable scenario, this cost was $11,419, and in the most favourable scenario, $736.

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This clearly indicate, the costs per quality-adjusted life year saved by MS/MS screening for inborn errors of metabolism compare favourably with other mass screening programs, including those for breast and prostate cancer [48]. The Role of Metabonomics in Understanding Global Systems Biology and Disease Processes Metabolic profiles give deeper insights into interactions between the gene and surrounding environment. This could give a better knowledge about the development of many diseases. Metabonomics and metabolomic approaches provide important phenotypic benchmarks in systems biology studies and novel chemometric methods allow direct integration of multi-omics data with real-world metabolic and physiological end-points. With developments in both hardware and software fronts this new technology could be successfully applied to problems in clinical scenarios, toxicology, functional genomics, personalized healthcare, patient stratification scenarios and for large scale molecular epidemiology studies in human populations and drug administration. Nanoscience and Nanotechnology: an Emerging Opportunity in Biomedical Science Nanotechnology is an enabling technology, with high potential impact on industrial, health-related, biomedical, environmental, economy. This is an emerging technology with bright future in a clinical perspective. This technology has been termed as “BioNanoMedicine” and has been envisaged as a platform for the 21st century [50]. The prospects include tailor made therapy for different diseases, diagnosis and drug targeting. The following sections describe the latest developments and possible impact of this new technology on the biomedical field. Nanobiotechnology Nanotechnology is currently evolving in two ways, one in the development of nanodevises to screen different diseases conditions [51], drug discovery [52] and the other in the delivery of specific types of drugs to specific organs affected with specific diseases [53-54]. The advantages are many as this technology is very flexible and requires very little amount of target molecules to be coated on the microspheres required for the disease detection. The ability to study processes at the single-cell level promises to provide a host of information with benefits in the area of therapeutics and drug discovery. The terms “microchip”, “microdevice” and “lab-on-a-chip” all refer to minute, devices that are engineered for biomedical applications. They are rapidly being developed for use on a single-test basis and show promise as tools for clinical research and at home self-testing. Areas of intense research in nanotechnology currently focus on (a) cellular analysis in microdevices (b) development and fabrication of innovative devices (c) novel biofluid separators; (d) advances in microanalytical systems; (e) electrokinetics; dielectrophoresis; (f) advances in chemical, electrochemical, and optical in-line sensor technology; and (g) novel low concentration detection in capillary electrophoresis systems. Microfluidic technology to probe chemical and biochemical responses at the cellular and sub-cellular levels. Subsequent techniques which require novel technology associated with nanotechnology is in simulation and modelling studies, materials



modification to improve system performance, novel sample preparation protocols, and analytical techniques. Nanobiotechnology raises fascinating possibilities for new analytical assays in various fields such as bioelectronic assembly, biomechanics and sampling techniques, as well as in chips or micromachined devices. Recently, nanotechnology has greatly impacted biotechnological research with its potential applications in smart devices that can operate at the level of molecular manipulation. Micro total analysis system (microTAS) offers the potential for highly efficient, simultaneous analysis of a large number of biologically important molecules in genomic, proteomic and metabolic studies. This review aims to describe the present state-of-the-art of microsystems for use in biotechnological research, medicine and diagnostics. This technology has been envisioned as an innovative strategy for identifying biological markers for cancer detection. Development of Flow-thru Chip Proteomics [55] as well as chemical amplification using continuous–Flow PCR on Chip are significant advances, which has taken place in the field of nanotechnology and microfluidics, which could significant effects on the future drug delivery, patient screening and therapeutics. Similarly development in the field of micro analytical chemistry has opened up a macro-opportunity for clinical nutrition. Chemotherapy on its own or in combination with other treatments is very effective for anticancer therapy. Introduced in the middle of the last century, chemotherapy today still faces the problem of determining which specific agent or agents are able to yield the desired clinical therapeutical effect for a particular tumor and patient. Numerous tests in vitro have been developed to detect chemosensitivity and chemoresistance and also for screening new drugs. Three groups of tests can be defined: (i) cell viability tests (ii) measurements of cell metabolism and (iii) clonogenic assays. Test time, tissue preparation, complexity of test performance, and correlation with the clinical progress of the disease are criteria used to judge how successful the tests are. The introduction of Sisensor chips [56], which are able to detect metabolic changes in living cells, has opened up new possibilities in this field. Basically two sensor principles or types can be considered: (i) the light-addressed potentiometric sensor (LAPS) and (ii) the multisensor array (MSA). Whereas LAPS measures one, MSA registers online many parameters (for instance, impedance, pH, O2, temperature). Targeted Drug Delivery Achieved with Nanoparticle-Aptamer Bioconjugates For the first time that targeted drug delivery is possible using nanoparticle-apatamer conjugates is becoming a reality [57]. Nucleic acid ligands (referred to as aptamers) are short DNA or RNA fragments that can bind to target antigens with high specificity and affinity; analogous to monoclonal antibodies. In the field of cancer nanotechnology, aptamers have the potential to act as targeting molecules – directing the delivery of nanoparticles to tumour-antigens, present on the surface of cancer cells. In general terms, therapeutic nanoparticles (~50 – ~250 nanometer) are specially designed delivery vehicles that can encapsulate a drug within them and release the drug in a predetermined and regulated manner which can vary from a sudden release to a slow release over a period of several years. Using prostate cancer as a model disease, proof of concept nanoscale targeted drug delivery vehicles were developed (1 nanometer = 0.000000001 meter), which can target

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prostate cancer cells with high specificity and efficiency [57]. Once bound to prostate cancer cells, the nanoparticle/aptamer bioconjugates were internalised making it possible for their cytotoxic payload to get released directly inside the cancer cells. The combination of targeted delivery and controlled release of drugs at the site of cancer will likely result in "smart therapeutics" that are more effective, yet safer than what is available today. It as been successfully shown that bioconjugates can efficiently target and get taken up by the prostate LNCaP epithelial cells, which express the prostatespecific membrane antigen protein (77-fold increase in binding versus control, n = 150 cells per group). In contrast to LNCaP cells, the uptake of these particles is not enhanced in cells that do not express the prostate-specific membrane antigen protein [57]. As the initial step, researchers synthesised nanoparticles for controlled drug release made from a biocompatible and biodegradable PLA polymer system and encapsulated a fluorescently labelled model drug within them, in order to visualise nanoparticle uptake into target cells. The nanoparticles in question were designed for attachment to aptamers so that the binding properties of aptamers for targeting could be preserved. Additional design criteria consisted of the development of nanoparticles that demonstrated a long circulating half-life (meaning that they are not readily cleared by the body's immune system) and nanoparticles that exhibited a strong preferential binding to targeted cancer cells. In another study using prostate cancer targeting was modelled using a microfluidic device and shown to occur under physiological fluid flow conditions that are present in systemic microvasculature, making their use after intravenous administration therapeutically relevant [58]. Experimental results show that these bioconjugates successfully and selectively adhered to PSMA-positive prostate cancer cells, while PSMA-negative cells were not targeted. The investigators also used high magnification microscopy and 3-D image reconstruction to study the localisation of the bioconjugates after incubation with the prostate cancer cells and confirmed that the particles were rapidly internalised into the targeted cells – an important fact since the payload of nanoparticles may be released inside the cancer cells in a regulated manner over an extended period of time. Further studies are going in different laboratories were chemotherapeutic agent such as docetaxel are encapsulated within the nanoparticles and the preliminary results were found to be promising. Bioconjugates represent an exciting prospect in the advancing field of cancer nanotechnology and hold significant promise for future cancer treatment. With further modification of the controlled-release polymer system or tweaks to the aptamer targeting group it may be possible to produce a diverse range of specific and selective bioconjugates. Drug delivery 'vehicles' can be made to target a many of important human cancers. Nanoscale drug delivery vehicles are getting closer to clinical realization." Application of Nanotechnology Based Assays: Nanotechnology Enables New Approaches for Detecting Proteins and DNA Controlling the architecture of materials at the scale of 1 to 100 nanometers is a grand challenge for chemists and engineers and will take another fifty years or more to accomplish. However the results from this accomplishments will change the way we diagnose disease and, perhaps more importantly, to give researchers the ability to ask entirely new questions about the etiology of cancer and other diseases. Most approaches



use (1) antibody-labeled magnetic microparticle (2) DNA barcodes (3) conjugating a second antibody to the DNA-conjugated gold nanoparticle (4) ELISA-type assay for detecting proteins and all these techniques often achieve PCR-like sensitivity. In one example this approach was used to detect a potential marker of Alzheimer’s disease present in attomolar concentrations in spinal fluid [59]. Doctors currently don’t have any means for diagnosing Alzheimer's disease in their patients. The disease can only be confirmed after death, by studying brain tissue. Nanotechnology-based technique could lead to a test for diagnosing the early signs of Alzheimer’s disease [60]. The BioBarcode-Assay can recognize ADDL, a protein that accumulates in the brains of sufferers [61]. The first set of experiments that quantify the number of ADDLs in cerebrospinal fluid was reported in early 2005 [59]. It is several times more sensitive than conventional tests and could revolutionize disease detection. In future, it might form the basis not only of a test for Alzheimer’s but also for types of cancer, the human form of mad cow disease and HIV. The next exciting step would be to move to blood. Detection of these diseases in blood opens up a vast sea of new opportunities in clinical diagnostics based on nanotechnology. To perform a Bio-Barcode-Assay, researchers select antibodies on the basis of the biomarker they need to detect in a solution. Some antibodies are fixed to magnetic particles while others are attached to spherical gold particles just 30 nanometres in diameter. Strands of DNA are fixed to the gold nanoparticle. When antibodies bind to a target biomarker, it becomes sandwiched between a magnetic particle on one side, and a gold particle and its strands of DNA on the other. Applying a magnetic field brings this entire "complex" out of solution. Researchers then release the DNA strands and use a DNA detection device to recognise their signature sequences. Attempts were also pursued to determine if a protein known as inhibin can be used to detect ovarian cancer in its earliest stages using the same technology. Future Avenues for Nanobiotechnology Research Utilizing nanoscale atomic force microscope tips of various shapes, dip-pen lithography is capable of creating gene and protein chips containing millions of spots. In just a short time, this technology has become the standard method for creating materials one atomic layer at a time, making it an important tool for developing new nanoscale materials and characterizing their unique chemical and physical properties. The private sector is already commercializing dip-pen lithography equipment and working to develop additional applications. Development of new probe techniques such as single molecule FRET (fluorescence energy transfer) at the tip-cell/macromolecule interface, near-IR scanning near-field optical microscopy using microfabricated devices and magnetic property imaging could be used for probing electron transport in cells. Large-scale sensor arrays for diagnostics of biological fluids. The arrays in question can consist of a wide variety of devices, ranging from micromechanical cantilevers, each with a slightly different chemical binding property, to microcalorimeters to measure energy released upon chemical reactions with different molecules resident within each calorimeter. Electronic Properties and Exploitation of Biomolecules Though the electrical characteristics of biomolecules may not generally be relevant to their biological function, they do represent a fascinating class of new materials for

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possible use in electronics. Typical examples for these include use of scanning tunnelling microscopy (STM) as well as special-purpose substrates prepared in the nanofabrication facility to measure and eventually to make use of the electrical characteristics of biomolecules. Disease Specific Assays Miniaturized Assays in Biotechnology Molecular profiling by DNA microarray technology has made significant contributions to the understanding of cancer. One of the challenges is how to efficiently utilize the accumulated research data to develop new standards for cancer classification and to develop new diagnostic and/or prognostic markers and therapeutic targets. The ultimate value of these molecular markers will be determined by testing in large-scale, multi-centre clinical trials that require an integrated, easy-to-use and robust assay system. The rationale for miniaturization is several-fold: (i) reduction of reagent and—more importantly—(ii) sample consumption, (iii) improving analytical speed by shortening analysis time or by running several analyses in parallel. Microarray Technology The introduction of DNA microarray and SAGE techniques has had dramatic implications on clinical research especially for cancer research, Fig. (2), allowing researchers to analyze expression of multiple genes in concert and relate the findings to clinical parameters [62]. The main discoveries in breast cancer, as well as in other malignancies, have so far been with respect to two key issues [63]. First, individual tumours arising from the same organ may be grouped into distinct classes based on their gene expression profiles, independent of stage and grade. Second, the biologic relevance of such classification is corroborated by significant prognostic impact. Microarray technologies can provide unique possibilities to explore the mechanisms of tumor behaviour in vivo that will allow evaluation of prognosis and, potentially, drug resistance. These techniques have the capabilities to be implemented for routine clinical use, whether to define prognostic factors or to predict sensitivity to therapy. A typical example is the use of microarray for breast cancer prognosis [64]. Microarray technology has been widely used for studying the prognosis predictor for stage II and III colon cancer patients [65] as well as to discriminate low malignant potential and invasive epithelial ovarian tumours [66]. Molecular profiling showed estimated accuracies of 78 and 83%, respectively for prognosis prediction [65]. The other use of microarray technology is to study the cancer outcome and subtype classification by gene expression as carried out for bladder cancer [67]. Using a total of 10,368 human gene elements, genes driving the separation between tumor subsets were shown to be important biomarkers for bladder cancer development and progression and eventually candidates for therapeutic targeting [67]. A more recent use of this high-throughput technology is to profile cytochrome P450 profile of ovarian cancer and identify novel therapeutic targets and establish the prognostic significance of expression of individual cytochrome P450s in this type of cancer [68-69]. The cytochromes P450 are a multigene family of enzymes with a central role in the oxidative metabolism of a wide range of xenobiotics, including anticancer



drugs and biologically active endogenous compounds. The study showed several P450s show increased expression in ovarian cancer and this provides the basis for developing P450-based therapeutics in ovarian cancer.

Fig. (2). Flowchart representing the use of SAGE and microarray based approach for drug discovery in cancer research.

Enzymatic Assays in a Microsphere Environment The study of enzymatic reactivity is an important part of current proteomics research. Enzyme assays include identification and localization of enzymes within the cellular network, screening of drug candidates, development of diagnostic assays as well as optimization of enzymatic function through gain or loss of functions. Since many of these applications require high-throughput analysis with minimal consumption of the precious samples, the transfer of such assays to microarrays is desirable. Several investigations regarding the transfer of enzymatic assays to nanowells and microarrays have been reported. Majority of the reported studies are related to the enzymatic phosphorylation of peptides and proteins by kinases [70-71]. Similarly for drug

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discovery process, cell-free expression and analysis of green fluorescent protein and bgalactosidase (b-gal) in nanowells has been performed [70]. One of the major challenges is to retain the unbound reaction products on the spot of reaction. This could be performed by chemical compounds in nanoliter droplets on flat surfaces and enzyme solution on top by aerosol deposition [72]. Similarly for signal amplification in microarray a new technique named Rolling Circle Amplification was introduced recently [73]. This technique relies on the enzymatic extension of a primer-antibody conjugate followed by hybridization of labeled probes to the generated DNA strand. Cemiluminescence for the sensitive detection of multiple cytokines [74] has also bee used as to an alternative to fluorescent detection. Using ‘‘classical’’ methods, a common measurement principle can be employed, both for measuring enzyme levels as well as for measuring metabolite levels, i.e. methods that are based on the formation or consumption of NAD(P)H. This allows the use of fluorescence as a common detection method, which can be adapted for a wide range of analyses. Currently the technology evolving at a rapid pace and more challenges includes the use of multiplex screening of analytes against enzymes on single platform. To perform this more sophisticated liquid handling, surface modifications and additional equipment will be required in the near future. Targeting Kinase for Cancer Therapy Overview of Protein Tyrosine Kinases Protein tyrosine kinases (PTKs) are enzymes which catalyze the phosphorylation of tyrosine residues. It is expected that the total number of PTKs does not exceed 1000, based on the genome project analysis. There are two main classes of PTKs: receptor PTKs and cellular, or non-receptor, PTKs. Of the 91 protein tyrosine kinases identified thus far, 59 are receptor tyrosine kinases and 32 are non-receptor tyrosine kinases. These enzymes are involved in cellular signaling pathways and regulate key cell functions such as proliferation, differentiation, anti-apoptotic signaling and neurite outgrowth. Unregulated activation of these enzymes, through mechanisms such as point mutations or over-expression, can lead to various forms of cancer as well as benign proliferative conditions. Indeed, more than 70% of the known oncogenes and proto-oncogenes involved in cancer code for PTKs. The importance of PTKs in health and disease is further underscored by the existence of aberrations in PTK signalling occurring in inflammatory diseases and diabetes. Increasing knowledge of the structure and activation mechanism of RTKs and the signalling pathways controlled by tyrosine kinases provided the possibility for the development of target-specific drugs and new anti-cancer therapies [75]. Approaches towards the prevention or interception of deregulated RTK signalling include the development of selective components that target either the extracellular ligand-binding domain or the intracellular tyrosine kinase or substrate-binding region. Receptor PTKs possess an extracellular ligand binding domain, a transmembrane domain and an intracellular catalytic domain. The transmembrane domain anchors the receptor in the plasma membrane, while the extracellular domains bind growth factors. Characteristically, the extracellular domains are comprised of one or more identifiable structural motifs, including cysteine-rich regions, fibronectin III-like domains, immunoglobulin-like domains, EGF-like domains, cadherin-like domains, kringle-like



domains, Factor VIII-like domains, glycine-rich regions, leucine-rich regions, acidic regions and discoidin-like domains. The intracellular kinase domains of receptor PTKs can be divided into two classes: those containing a stretch of amino acids separating the kinase domain and those in which the kinase domain is continuous. Activation of the kinase is achieved by ligand binding to the extracellular domain, which induces dimerization of the receptors. Receptors thus activated are able to autophosphorylate tyrosine residues outside the catalytic domain via cross-phosphorylation. The results of this auto-phosphorylation are stabilization of the active receptor conformation and the creation of phosphotyrosine docking sites for proteins, which transduce signals within the cell. Signaling proteins, which bind to the intracellular domain of receptor tyrosine kinases in a phosphotyrosinedependent manner include RasGAP, PI3-kinase, phospholipase C, phosphotyrosine phosphatase SHP and adaptor proteins such as Shc, Grb2 and Crk. In contrast to receptor PTKs, cellular PTKs are located in the cytoplasm, nucleus or anchored to the inner leaflet of the plasma membrane. They are grouped into eight families: SRC, JAK, ABL, FAK, FPS, CSK, SYK and BTK. Each family consists of several members. With the exception of homologous kinase domains (Src Homology 1, or SH1 domains), and some protein- protein interaction domains (SH2 and SH3 domains), they have little in common, structurally. Of those cellular PTKs whose functions are known, many, such as SRC, are involved in cell growth. In contrast, FPS PTKs are involved in differentiation, ABL PTKs are involved in growth inhibition, and FAK activity is associated with cell adhesion. Some members of the cytokine receptor pathway interact with JAKs, which phosphorylate the transcription factors, STATs. Still other PTKs activate pathways whose components and functions remain to be determined. Tyrosine kinases are important mediators of the signalling cascade, determining key roles in diverse biological processes like growth, differentiation, metabolism and apoptosis in response to external and internal stimuli. Recent advances have implicated the role of tyrosine kinases in the pathophysiology of cancer. Though their activity is tightly regulated in normal cells, they may acquire transforming functions due to mutation(s), overexpression and autocrine paracrine stimulation, leading to malignancy. Constitutive oncogenic activation in cancer cells can be blocked by selective tyrosine kinase inhibitors and thus considered as a promising approach for innovative genome based therapeutics. The modes of oncogenic activation and the different approaches for tyrosine kinase inhibition, like small molecule inhibitors, monoclonal antibodies, heat shock proteins, immunoconjugates, antisense and peptide drugs are reviewed in light of the important molecules. As angiogenesis is a major event in cancer growth and proliferation, tyrosine kinase inhibitors as a target for anti-angiogenesis can be aptly applied as a new mode of cancer therapy [76]. Classification of Tyrosine Kinases 1. Receptor tyrosine kinase (RTK) e.g. EGFR, PDGFR,FGFR and the IR Note: receptor tyrosine kinases are not only cell surface transmembrane receptors, but are also enzymes having kinase activity. 2. Non-receptor tyrosine kinase (NRTK) e.g. SRC, ABL, FAK and Janus kinase.

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Structural Biology of Protein Tyrosine Kinases Crystallography for Protein Kinase Drug Design Proteomics based disease therapeutic research and drug discovery involves highthroughput protein structure determination i.e., elucidation of potential targets for docking target drugs by studying the structural biology of PTKs. Strategies to produce crystallisable protein kinase constructs include (i) truncation to the catalytic domain (ii) co-crystallization with rigidifying ligands (iii) crystallization of known rigid forms and (iv) point mutation to improve homogeneity or mimic less crystallisable proteins. In an ideal case especially for protein kinases, a broad range of crystal structures should be obtained to characterize target flexibility, structure modulation via co-factor binding or posttranslational modification, ligand induced conformational changes, and off-target complex structures for selectivity optimization. PKA, the prototypical serine/threonine protein kinase, and SRC, a tyrosine kinase and the first identified oncoprotein, provide multiple examples of these various approaches to protein kinase crystallography for drug design. Protein crystallography can be used throughout the drug discovery process to obtain diverse information critical for structure based drug design [77]. Src structures of a major inactive form have shown how the protein kinase is rigidified by several interdomain interactions. Hence relatively little structural information available regarding the presumably more flexible active forms. Site and Means of Targeting Inhibiting the activity of tyrosine kinases by low molecular weight compounds capable of interfering with either ligand binding (in the case of receptor tyrosine kinases) or with protein substrate (in case of non receptor tyrosine kinase) has proved to be difficult. Although the bisubstrate inhibitor approach offered promise, but with very little practical progress. Approaches to generate non-competitive or allosteric inhibitors have also failed. The ATP competitive inhibitors appear to be the target of choice. The major strategies currently employed for tyrosine kinase inhibitions are described in, Fig. (3). The five different categories could broadly classified based on the type of receptor used (1) ligand (2) monoclonal antibody (3) immunotoxins (4) Hsp inhibitor and by the use of (5) antisense technology. The ATP Binding Site the Most Studied Target for Kinase Inhibition The characterisation of the human kinome [78-79] has resulted in the emergence of numerous kinase drug targets in a variety of therapeutic areas [80]. Through the elucidation of the sequence and structural composition of kinase active sites, coupled with the solution of numerous ATP competitive ligand complex structures, significant advances have been made in developing inhibitors that are highly selective. ATP binds within a deep cleft formed between the two lobes of the tyrosine kinase domain. Though the ATP binding site is highly conserved the architecture in the regions proximal to the ATP binding site does afford some key diversity for designing new drug and has potential application in drug discovery [81]. This has shown to be the case not only for kinases that are divergent in primary structure, but also for isoforms with highly conserved structure and ATP binding sites. Hence selection of selective inhibitors and design of highly potent and selective kinase ATP competitive ligands are key to specific kinase inhibition. These include the use of small molecules to sequester kinases in



inactive conformations, and to block phospho-transferase activity by preventing substrate docking and recruitment.

Fig. (3). Common strategies used for inhibition of tyrosine kinase activity. Broadly they could be classified into five different categories based on the type of receptor used (1) ligand (2) monoclonal antibody (3) immunotoxins (4) Hsp inhibitor and by the use of (5) antisense technology [Modified and adapted from reference 99].

Substrate recruitment sites are promising from a structure based design perspective as they contain features unique to individual protein kinases [81]. A recent report by Breitenlechner et al. (2005) [82] used three crystal structures of a dimeric active c-Src kinase domain, in an apo and two ligand complexed forms, with resolutions ranging from 2.9A to 1.95A. The structures show how the kinase domain, in the absence of the rigidifying interdomain interactions of the inactivation state, adopts a more open and flexible conformation. The ATP site inhibitor CGP77675 binds to the protein kinase with canonical hinge hydrogen bonds and also to the c-Src specific threonine 340. In contrast to purvalanol B binding in CDK2, purvalanol A binds in c-Src with a conformational change in a more open ATP pocket. A similar approach was also used to study the structural basis of Janus kinase 2 inhibition by a potent and specific pan-Janus kinase inhibitor [83]. The development of JAK2 specific inhibitors has tremendous clinical relevance. JAK2, a member of the Janus kinase (JAK) family of PTKs, is an important intracellular mediator of cytokine signalling. Mutations of the JAK2 gene are associated with hematological cancers and

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aberrant JAK activity is also associated with a number of immune diseases including rheumatoid arthritis. Accordingly, critical to the function of JAK2 is its protein tyrosine kinase (PTK) domain. A crystal structure of the active conformation of the JAK2 PTK domain in complex with a high affinity, pan-JAK inhibitor that appears to bind via an induced fit mechanism. This inhibitor, the tetracyclic pyridone 2-tert-butyl-9-fluoro-3,6dihydro-7H-benz[h]-imidaz[4,5-f]isoquinoline-7-one, was buried deep within a constricted ATP-binding site, in which extensive interactions, including residues that are unique to JAK2 and the JAK family, are made with the inhibitor [83]. Antibody Arrays and Protein Kinase Assays Profiling Receptor Tyrosine Kinase Activation by Using Ab Microarrays A protein-based microarray allows the global observation of biochemical activities, where hundreds or thousands of proteins can be concurrently screened for proteinprotein, protein-nucleic acid, and small molecule interactions. This technology holds great potential for basic molecular biology research, disease marker identification, and toxicological response profiling and pharmaceutical target screening [14]. The development of methods to analyze intracellular signaling molecules on microarrays would make Ab arrays widely useful in systems biology. Multiplex Ab arrays are ideal and sensitive to the amounts and modification states of signal transduction proteins in crude cell lysates and the integration of these arrays with 96-well microtiter plate technology to create microarrays in microplates. Ab arrays were used to monitor the activation, uptake, and signalling of ErbB receptor tyrosine kinases in human tumor cell lines Nielsen et al. (2003) [84]. Data obtained from multicolor ratiometric microarrays correlate well with data obtained by using traditional approaches, but the arrays are faster and simpler to use. The integration of microplate and microarray methods for crude cell lysates should make it possible to identify and analyze small molecule inhibitors of signal transduction processes with exceptional speed and precision. Direct scale-up to array-based screening in 96- and 384-well plates should allow small molecules to be identified with specific inhibitory profiles against a signaling network. Ab microarrays are mainly used for (i) profiling protein abundance, (ii) profiling the functional state of a signaling system, (iii) analyzing the kinetics of ligand-activated signaling, (iv) measuring the in vivo inhibitory constant of a small molecule EGFR inhibitor, and (v) array-based profiling in 96-well plates for screening [84]. Microtiter Plate Array System for Protein Kinase Assays The microtiter plate array system is well suited to the study of protein kinase substrates, antigens, binding molecules, and inhibitors since these all can be quantitatively studied at a single uniform, reproducible interface. Synthetic peptides have played a useful role in studies of protein kinase substrates and interaction domains. Fully automated synthesis of (phospho) peptide arrays in microtiter plate wells provides efficient access to protein tyrosine kinase characterization [85]. Synthesis of peptides and phosphopeptides on microtiter plate wells overcomes previous limitations and demonstrates utility in determination of the epitope of an autophosphorylation site, phospho-motif antibody and utility in substrate utilization assays of the protein tyrosine kinase, p60c-src [85]. The most recent type of assay is to use protein-acrylamide copolymer hydrogels for array-based detection of tyrosine kinase activity from cell lysates and extracts [26]. For



this purpose glutathione S-transferase-Crkl (GST-Crkl) fusion proteins are covalently immobilized into polyacrylamide gel pads via copolymerization of acrylic monomer and acrylic-functionalized GST-Crkl protein constructs on a polyacrylamide surface. The resulting hydrogels resist non-specific protein adsorption, permitting quantitative and reproducible determination of Abl tyrosine kinase activity and inhibition, even in the presence of a complex cell lysate mixture. This approach will have a direct application to the detection and treatment of cancers resulting from upregulated tyrosine kinase activity, such as chronic myeloid leukemia (CML). These findings also establish a basis for screening tyrosine kinase inhibitors and provide a framework on which proteinprotein interactions in other complex systems can be studied. Monoclonal Antibody as Inhibitors for PTKs The extracellular domain of the receptor tyrosine kinase provides an excellent target for monoclonal antibodies. With the advancement of genomics, design, selection and production of therapeutic monoclonal antibodies have become much easier. The revolution in antibody technology now allows us to produce humanized, human chimeric or bispecific antibody for targeted cancer therapy [86]. Monoclonal antibodies (mAbs) against growth factors or their receptors have been revealed to be effective therapeutic agents for solid tumours. Blocking angiogenesis is now considered to be a promising approach for anticancer therapy and the use of anti-VEGF MAb has demonstrated tumor suppression [87]. Similarly certain types of antigens are over expressed in cancers. P12 antigen over expression is associated with a wide range of cancer cell lines and tissues and antibody directed towards these antigens may serve as important contributors to cancer therapeutics as exemplified by the results of preliminary trials. Mab P12 reacts with the carbohydrate sequence present on high molecular weight glycoproteins. Oncologists are now interested in newer MAbs as promising agents for the treatment of cancer. The EGFR family of receptor tyrosine kinase comprises of four members – the EGFR/ Erb B1, HER-2/ Erb B2, HER-3/ Erb B3 and HER-4/ Erb B4. Members of the EGFR family are involved in some complex biological signal transduction network. EGFR and HER-2/ neu are amplified in tumor samples of breast, lung and colorectal tissues. Two targets in particular-the process of new blood vessel development, or angiogenesis, and the epidermal growth factor receptor and its signalling pathway-are exploited by the newest monoclonal antibodies that are available for use in colorectal cancer patients [88]. The orphan receptor tyrosine kinase HER2 gene is amplified in breast cancer and acts as a major signalling partner for other EGFR family members leading to proliferation, differentiation, antiapoptosis and tumor progression [89]. Herceptin was the first genome based targeted anticancer therapeutic, approved by FDA in 1998. Binding of Herceptin to HER receptor leads to receptor internalization, inhibition of cell cycle progression and antibody dependent cellular toxicity or eliciting the immune response. Herceptin induces normalization and regression of angiogenesis in HER-2 overexpressing breast cancer [90] and even blocks cleavage of HER2, which generates a membrane-bound constitutively active truncated receptor. Hsp 90 and Other Novel Strategies Heat shock protein 90 is a molecular chaperone whose association is required for stability and function of multiple mutated, chimeric, and over-expressed signalling

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proteins that promote cancer cell growth and/or survival. The accumulation of Hsp-s is seen in pathological conditions and tumours [91]. Heat shock proteins (Hsp-s) are ubiquitous proteins known for the maintenance of cellular homeostasis and are inducible under variety of stresses. Hsp-s are mainly involved in the proper folding of other proteins and hence referred to as molecular chaperons. Most kinases require molecular chaperons to maintain their activation competent conformation. Hsp-s interacts with and stabilizes various kinases [92]. Chaperon based inhibitors other than interacting with protein kinases, prevent the associated chaperon(s) from maintaining the activation competent conformation of the kinase. The result being the proteosomal degradation of the misfolded kinases, thus diminishing the level of many kinases. The Hsp-s has an unique ATP binding site, including a Bergerat fold characteristic of bacterial gyrase, topoisomerases and histidine kinases. Thus the ATP binding site serves as a robust antitumor target for kinase related chaperone machinery. Important examples are Geldanamycin, Cisplatin, Novobiocin, Radicol and other purine based inhibitors. Geldanamycin affects ErbB2, EGF, v-Src, Raf-1 etc. Hsp90 small molecule inhibitors, by interacting specifically with a single molecular target, thus promote the destabilization and eventual degradation of multiple cancer cell survival and growth promoting proteins, and these inhibitors have shown promising anti-tumor activity in preclinical breast cancer model systems and it has also the unique ability to inhibit multiple survival pathways utilized by cancer cells. This property of Hsp 90 has been used as a target for breast cancer [93]. Immunotoxins Based Therapeutics One of the ways to increase the efficacy of the antibodies that targets specific molecules expressed by tumor cells can be increased by attaching toxins to them. Few common immunotoxins are (i) bacterial toxins like pseudomonas exotoxin, (ii) plant exotoxin like ricin or (iii) radio-nucleotides. The toxins are chemically conjugated to a specific ligand such as the variable domain of the heavy or light chain of the monoclonal antibody. The advantage of this technology is that normal cells lacking the cancer specific antigens are not targeted by the targeted antibody. The most promising immunotoxin is the EGF fusion protein DAB389EGF [94], which is a fused EGF specific sequence and diphtheria toxin and have been found to be effective in EGFR over expressing breast tumor and non small cell lung cancer. The use of therapeutic antibody for the development of antibody-drug conjugate has been tried to improve the therapeutic potential e.g. Tositumomab by GlaxoSmithcline, Anti-Tac(Fv)-PE38(LMB2) against CD25, in B, T cell lymphoma and anti-B4-bR against CD19 in B-Non Hodgkins lymphoma. As molecular studies of cancer have started revealing an increased epitope repertoire due to great strides in genomics and proteomics, the search for more effective Antibody-drug conjugate has got an impetus recently. Antisense Strategies and Peptide Drugs Antisense are small pieces of synthetic oligonucleotides that are designed to interact with the mRNA by Watson-Crick base pairing to block the transcription and thus translation of target proteins. Antisense oligodeoxynucleotides (ODN) targeting IGF-1R induces apoptosis in malignant melanoma and is also effective in breast cancer [95]. Peptides are other potential mediators for inhibition of specific types of kinase activities. Peptide and peptidomimetics that interfere with the interaction of protein-



protein interaction has a significant impact on the cellular processes. A typical examples is provided by Grb2 which is an essential component in the Ras signaling pathway and it’s interaction with Sos is responsible for the down stream signaling process. Proline rich “Peptidimer” having two VPPVPPRRR sequence specifically recognizes Grb2 and selectively blocks Grb2-Sos interaction an important step in EGFR over expressing cancers [96]. Small peptides mAZpTyr-(_-Me)pTyr-Asn-NH2 [97] and peptidomimetics like AHNP anti erbB2 are even known to inhibit unwanted tyrosine kinase dimerisation by competing with target proteins thus the peptides or peptidomimetics acts as antagonist of RTK [98]. Mining the Kinome With the completion of Genome project, mining the kinome is an effective strategy to meet the future challenges to identify suitable target for cancer therapy. Cancer arises by clonal proliferation from a cell, which builds up a series of mutations leading to abnormal signaling were kinases play a major role during each event from cancer onset to metastasis. With over 500 kinases in the Extracellular Domain Disease relevant signaling of RTK in human kinome and as many as 200-300 protein kinases mediating a large number of pathways in a cell at a particular time, selectivity becomes very important considering the cost of drug development. Hence strategies, Fig. (4) to move kinase drug discovery in a more rapid, comprehensive and efficient manner, including tyrosine kinase target validation, selectivity and durability are key to the success of drug discovery [100]. For a more comprehensive review on methods for deciphering the kinome please refer to the review article by Johnson and Hunter, (2005) [101].

Fig. (4). A unique strategy to identify unknown kinase based on known substrate candidate or unknown substrate candidate. (Modified based on reference [100]).

Biochemical, cell based assay and screening methods for the through profiling of the kinase inhibitors using monoclonal antibody based multi-immunoblotting, fluorescent polarization assays, non-radioactive high throughput assays, 2-D NMR approaches should be exploited. Structure based drug design (SBDD) strategies depending on

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computational approaches, mathematical models of tumor and normal tissue response, high-throughput X-ray crystallography and chemogenomic approaches can be used to advance molecules through the routine drug discovery process. The use of recent advances in RNA interference (RNAi) appears to be a promising approach for silencing gene expression, thus elucidating genetics of human disease with emphasis into the biological role of the kinase signalling pathways [102]. The promising progress made by the drugs Gleevec, Iressa and Herceptin has brought to light the potential of new innovative genome based molecular therapeutics. CONCLUSION The future looks very bright for clinical diagnostics. The advent of newer technologies will revolutionize the way many diseases are currently treated. Protein arrays will evolve both in sensitivity and specificity with the discovery of more markers for various diseases. Miniaturization will have a significant impact on disease diagnosis and targeted therapy. Kinome based drug discovery will be one of the major areas of research in the years to come and will have a major role in the discovering better drugs for cancer therapy. Drug delivery will hold key for better and efficient treatment strategies. Hence a combination of nanotechnology coupled with efficient delivery process will determine the effectiveness of these new advances in the field of newer metabolic assays and disease screening as well as therapeutic applications. ACKNOWLEDGEMENTS The authors gratefully acknowledge National Medical Research Council (NMRC, Singapore) and Biomedical Research Council (BMRC, Singapore) for providing funds (Grant numbers- NMRC: R174-000-071-213 and BMRC: R174-000-091-305) for research. ABBREVIATIONS 1-D and 2-D GE

=

1 dimensional and 2 dimensional gel electrophoresis

Ab

=

Antibody

ATP

=

Adenosine tri phosphate

CE

=

Capillary electrophoresis

CML

=

Chronic myeloid leukaemia

Da

=

Dalton

DNA

=

Deoxyribonucleic Acid

EGFR

=

Epithelial growth factor receptor (another name - Erb B)

ELISA

=

Enzyme Linked ImmunoSorbent Assay

ESI- tandem-MS/MS =

Electrospray tandem mass spectrometry

FRET

=

Fluorescence energy transfer

GC

=

Gas chromatography



GST

=

Glutathione S-transferase

GFP

=

Green Fluorescent Protein

HER-2

=

Human epidermal growth factor receptor 2

HIV

=

Human Immunodeficiency virus

HPLC

=

High-pressure liquid chromatography

HPV

=

High-risk human papillomavirus

Hsp 90

=

Heat shock protein 90

LAPS

=

Light-addressed potentiometric sensor

LPA

=

Lysophosphatidic acid

mAbs

=

Monoclonal antibodies

MALDI-MS

=

Matrix-Assisted Laser Desorption Mass spectrometry

MCAD

=

Medium-chain acyl-CoA dehydrogenase

MD LC/MS

=

Multidimensional liquid chromatography/mass spectrometry

micro-TAS

=

Micro total analysis system

MRM

=

Multiple reaction-monitoring

mRNA

=

Messenger ribonucleic acid

MSA

=

Multisensor array

NMR

=

Nuclear magnetic resonance spectroscopy

NRTK

=

Non-receptor tyrosine kinase

ODN

=

Oligodeoxynucleotides

PIN

=

Prostate intraepithelial neoplasia

PKU

=

Phenylketonuria

PS20

=

protein pull down Ciphergen ProteinChip™ [www.ciphergen.com]

PTK

=

Protein Tyrosine Kinase

RNAi

=

RNA interference

RTK

=

Receptor tyrosine kinase

RTK

=

Receptor Tyrosine Kinase

SAGE

=

Serial Analysis of Gene Expression

SBDD

=

Structure based drug design

SELDI-TOF-MS

=

Surface Enhanced Laser Desorption-time of flight mass spectrometry

84


SNPs

=

Single nucleotide polymorphisms

SPR

=

Surface Plasmon Resonance

STM

=

Scanning tunnelling microscopy

TLC

=

Thin-layer chromatography

TNF-alpha

=

Tumor necrosis factor alpha

TTR

=

Transthyretin

VEGF

=

Vascular endothelial growth factor

WCX2

=

Weak Cation Exchange

Narasimhan et al.

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Proteomic Screening for Novel Therapeutic Targets in Kidney Diseases Visith Thongboonkerd* Siriraj Proteomics Facility, Medical Molecular Biology Unit, Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, 12th Fl. Adulyadej Vikrom Bldg., 2 Prannok Rd., Bangkoknoi, Bangkok 10700, Thailand Abstract: The high-throughput capability of proteomics allows simultaneous examination of numerous proteins and makes a global analysis of proteins in cell, tissue, organ or biofluid possible. This strength of proteomics has been extensively applied to examine altered proteins caused by various diseases. Some of these altered proteins may particularly be important for the disease progression and/or complications. Thus, information on these altered proteins is valuable for better understanding of the pathogenesis and pathophysiology of medical diseases. Additionally, functional analysis of such proteins may lead to identification of novel therapeutic targets and development of new drugs for improving therapeutic outcome as well as for preventing serious complications. This review focuses mainly on applications of renal and urinary proteomics to define novel therapeutic targets in kidney diseases. Several recent studies on various kidney diseases have successfully identified altered renal and urinary proteins, some of which may potentially be the novel therapeutic targets. Urinary proteome profiling has also been applied to biomarker discovery that will be useful for clinical diagnostics, prognosis, prediction of treatment response, and development of personalized medicine. Finally, potential roles of proteomics for drug design and discovery are discussed.

INTRODUCTION Traditional approach to define therapeutic targets for drug design and discovery has applied conventional biochemical methods to hypothesis-driven research focusing on a specific disease pathway and on involving protein(s) as well as metabolite(s). Although successful, it is time-consuming and the targets to be studied require a priori assumption. Currently known therapeutic targets are underestimated and, thus, there is a need of other methods to screen for a large number of potential therapeutic targets simultaneously. In the post-genomic era, especially after the Human Genome Project was completed, several biotechnologies have been developed to utilize the genomic information to examine other cellular elements (proteins, transcripts, metabolites, lipids, etc.) on the genomic scale. Since then, respective ‘omics’ fields (proteomics, transcriptomics, metabolomics, lipomics, etc.) have been defined. The successfulness of

*Corresponding author: Tel/Fax: +66-2-4184793; E-mail: [email protected] (or) [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.

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these omics fields is due mainly to advances in separation sciences (using either gelbased or gel-free methods) and mass spectrometry (MS). The great contribution of MS technology to life science has been confirmed as the 2002 Nobel Prize in Chemistry went to John B. Fenn (who developed electrospray ionization; ESI) and Koichi Tanaka (who developed matrix-assisted laser desorption/ionization; MALDI) [1]. Because the main sites of drug action mainly compose of proteins and many of the pharmacologically regulating systems operate through proteins it is most likely, therefore, that extensive analysis of proteins in disease states will lead to the successful identification of novel therapeutic targets. This chapter focuses mainly on the potential role of proteomics for the identification of novel therapeutic targets as well as for drug design and discovery, particularly in kidney diseases. RENAL AND URINARY PROTEOMICS: A BRIEF OVERVIEW The kidney is one of the major organs responsible for (i) eliminating waste products (particularly urea and metabolites) from the plasma; (ii) maintaining normal homeostasis of water, acid-base, minerals and solutes; and (iii) producing hormones (i.e. renin, erythropoietin and 1,25 dihydroxy vitamin D3) [2]. It contains several microstructures including glomerulus, proximal tubule, the loop of Henle, distal tubule, collecting duct, renal pelvis, vessels, etc. The kidney size is only 0.5% of total body mass but generates 150-180 L/day ultrafiltrate from plasma (renal plasma flow is approximately 350-400 mL/100 g of tissue per min) [2]. The kidney then selectively reabsorbs water, nutrients and necessary constituents in the ultrafiltrate until less than 1% of ultrafiltrating volume is excreted with waste products as the urine [2]. Because the kidney and urine are related to several physiological functions, data on their protein compositions during normal and disease states would be expected to provide a wealth of information to better understand normal renal physiology and pathophysiology of kidney diseases. To study proteins, two different but complementary fields need to be clarified. Protein chemistry is to examine structure, physical and chemical properties as well as function of each protein in details, whereas proteomics is to simultaneously examine a large number of proteins or the entire proteome in a complex mixture of biological samples [3]. Both of them are aimed to better understand the cellular biology and physiology, and to determine functional significance of proteins in normal and disease states. Therefore, these two fields are different but complementary. Proteomics can be divided into two broad categories: ‘expression proteomics’ and ‘functional proteomics’. Expression proteomics is to define normal proteome profile and differential protein expression among different sets of biological samples, and to determine alterations in protein expression caused by experimental interventions, physiological stimuli, or pathogenic conditions. Functional proteomics is to examine function of the protein of interest; e.g., its complexes, protein-protein interactions, and post-translational modifications [4]. Proteomics has been extensively applied to several fields of biomedical research with four main objectives: (i) to better understand normal biology and physiology of cells, microorganisms, tissues, and organs; (ii) to explore the pathogenic mechanisms and to better understand the pathophysiology of medical diseases; (iii) to identify novel biomarkers for early disease detection, prediction, and prognosis; and (iv) to identify novel therapeutic targets for drug and vaccine discovery. In renal research, proteomics has also been applied with similar objectives as those applied to other research areas.

Proteomics for Drug Design & Discovery


Kidney diseases are caused by several etiologies including congenital defects (abnormal genes), metabolic derangement, physical injury, neoplasm, infection, inflammation, autoimmunity, hemodynamic dysregulation, toxicity from drugs or chemicals, and renal involvement of systemic diseases. These various pathogenic mechanisms affect kidney microstructures differently. Expression and function of proteins in affected intrarenal microstructures are expected to be altered. Therefore, extensive analyses of the kidney proteome and/or subproteome(s) using expression and functional proteomics during disease states are crucial to better understand the complexity of the pathogenesis and pathophysiology of kidney diseases. As the urine is a body fluid excreted from the kidney, proteomic analysis of the urine also provides a wealth of information. While renal proteomics may lead to identification of novel therapeutic targets, urinary proteomics is suitable for biomarker discovery because of the availability of urine samples in almost all of patients and the simplicity of specimen collection. To date, renal and urinary proteomics have been applied to a wide variety of kidney diseases including glomerular diseases (e.g., IgA nephropathy, focal segmental glomerulosclerosis or FSGS, and diabetic nephropathy); tubulointerstitial disorders (e.g., Dent’s disease and nephrotoxicity induced by cyclosporine, gentamicin, radiocontrast medium and lead); renal vascular disease (i.e. renovascular hypertension); renal cancers; renal transplant allograft rejection; acute renal failure; and end-stage renal disease [5-7]. PROTEOMIC TECHNOLOGIES FOR ANALYSIS OF KIDNEY AND URINE Gel-Based Methods Two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) is the most commonly used method in current proteomics studies. The first dimension of 2-D PAGE separates proteins on the basis of differential pH or isoelectric point (pI), whereas the second dimensional separation resolves proteins on the basis of differential molecular size (M r) [8]. Separated proteins in 2-D gel are visualized by various stains (Coomassie Brilliant Blue, silver, fluorescence, etc.). Visualized protein spots can then be excised, in-gel digested with proteolytic enzymes (trypsin, chymotrypsin, Arg-C, Asp-N, Lys-C, PepsinA, V8-E, V8-DE, etc.), and identified by MALDI-MS followed by peptide mass fingerprinting (PMF). The most common type of mass analyzer employed in MALDI analysis is time-of-flight (TOF). MALDI-TOF MS provides a high-throughput manner of protein identification – hundreds of proteins can be identified within a day [9-11]. Consequently, MALDI-TOF MS has become an integral part of today’s modus operandi in proteome analysis. Even with lots of advantages, gel-based approach has some limitations. 2-D PAGE procedures are time-consuming, particularly when a large number of clinical samples are analyzed. Additionally, low abundant, transmembrane and highly hydrophobic proteins may not be detectable in a 2-D gel. Note that SDSPAGE or 1-D PAGE can also be applied to proteomic analysis, particularly when membrane or highly hydrophobic proteins are analyzed. Gel-Free Methods Liquid Chromatography (LC) Coupled to Tandem MS (MS/MS) High-performance (HP) LC coupled to electrospray ionization-tandem MS (ESIMS/MS) has gained a wide acceptance for gel-free proteomic analysis and become a

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method-of-choice for analysis of membrane and low abundant proteins [12-14]. ESI is the process of ionization from electrospray source, whereas tandem MS refers to the strategy of multi-step mass analyses. When compared to 2-D PAGE approach, LC-based methods are more effective for analysis of small proteins and peptides, as well as membrane or highly hydrophobic proteins. Recently, the high-throughput LC approach has been developed namely ‘multidimensional protein identification technology (MudPIT)’ or 2D-LC-MS/MS [15]. This approach involves proteolytic digestion of the total protein mixture to obtain a set of protein-derived peptides that are then separated by strong cation-exchange (SCX) chromatography (‘Bottom-Up’ approach). Peptides present in fractions from this SCX step are separated further by reversed-phase (RP) LC and then sequenced by MS/MS. Several thousands of peptides can be sequenced by this way in a relatively short time. In contrast to the Bottom-Up approach, the undigested protein mixture can also be separated by HPLC and resolved proteins in each fraction are then digested with a proteolytic enzyme prior to MS/MS sequencing (‘Top-Down’ approach) [16, 17]. Surface-Enhanced Laser Desorption/Ionization (SELDI) or Protein Chip Technology SELDI is a suitable method for proteome profiling of human body fluids. SELDITOF MS combines MALDI-TOF MS with surface retentate chromatography. Specifically, a protein sample is applied onto a chip surface carrying a functional group (e.g., normal phase, hydrophobic, cation- or anion-exchange). After incubation, proteins that do not bind to the surface are removed by a simple washing step and bound peptides/proteins are analyzed by TOF mass spectrometer. The detection of a protein by SELDI-TOF MS is critically determined by its concentration in the sample, its binding to the chromatographic surface and its ionization process within the mass spectrometer. This approach reduces the complexity of the sample being analyzed by selecting only a subset of proteins. SELDI-TOF MS offers some advantages for urinary proteome analysis. Only 5 to 10 µL of urine is needed for a single analysis and this method can be readily automated, making it particularly useful for the high-throughput study. For comparative analysis of urine samples, it is important to determine factors that may affect the composition and relative abundance of urinary proteins, but are not related to the investigated disease. Additionally, operating parameters must be strictly the same for each run to reduce the inter-assay variability. Capillary Electrophoresis Coupled to MS (CE-MS) CE-MS is another method suitable for proteome profiling of human body fluids. It offers some advantages as it is fairly robust, uses inexpensive capillaries and is compatible with essentially all buffers and analytes [18-21]. In contrast to LC, CE generally has no flow rate but requires a closed electric circuit. Various MS coupling techniques can be applied to CE [22, 23]. The predominant ionization method for CEMS is ESI, while MALDI has also been used extensively [24, 25]. The main advantages of MALDI appear to be the enhanced stability as well as an easier handling in comparison to ESI. Additionally, once the analytes are deposited on the target, they can be re-analyzed several times without the need of a new CE run. Moreover, the deposited analytes can be subsequently manipulated. However, the disadvantages of MALDI are the decreased dynamic range in comparison to ESI and the higher sensitivity towards signal suppression. For detection of the narrow CE-separated analyte zones, a fast and sensitive mass spectrometer is required. Thus, both ion trap and TOF systems appear



adequate. While ion trap MS acquires data over a suitable mass range with the rate of several spectra per second, the resolution is generally too low to resolve the single isotope peaks of >3-fold charged molecules. Consequently, assignment of charge to these spectra is hampered. Modern ESI-TOF mass analyzers record up to 20 spectra per second and provide the resolution of more than 10,000 and a mass accuracy better than 5 ppm. Thus, the most suitable MS method to date for this type of analysis is ESI-TOF MS. Mass Spectrometric Immunoassay (MSIA) This method combines immunoassays with MALDI-TOF MS [26-39]. Proteins are first captured by micro-scale affinity techniques and are subsequently examined qualitatively and quantitatively using MALDI-TOF MS [40]. This approach has a potential of greatly extending the range, utility and speed of biological research and clinical assays. In the initial stage of development, agarose beads (derivatized with an affinity ligand) were used to create a microliter-volume column inside a micropipettor tip (thus, creating an affinity pipette) [26, 28]. More recently, tailored, high-flow rate, high binding-capacity affinity micropipettes have been manufactured and used in combination with robotic platforms for the preparation of up to 96-samples in parallel [30, 34, 38]. Using this approach, the proteins of interest can be selectively retained and concentrated by repeat flowing of a biofluid through the affinity pipette. After washing to remove unspecified compounds, the retained proteins are eluted, mixed with a MLADI matrix (i.e. α-cyano-4-hydroxycinnamic acid), and targeted onto the MALDI plate. The eluted proteins are then analyzed with a mass spectrometer (TOF-MS). For quantification, a known variant of the analyte (chemically modified to shift the molecular mass without significantly altering the affinity for the immobilized ligand) is introduced into the biofluid at a constant concentration. The protein variant is then retained, eluted and analyzed simultaneously with the investigated protein. The protein of interest can then be quantified by normalizing its signal with that of the internal reference [26-39]. URINARY PROTEOME PROFILING FOR BIOMARKER DISCOVERY AND CLINICAL DIAGNOSTICS Proteomics of human urine can be performed using a ‘classical approach’ or an ‘alternative approach’ [7]. The ‘classical approach’ is to extensively and systematically examine protein expression and function to better understand the normal physiology and pathophysiology of medical diseases [7]. The analytical techniques involve in this approach include expression proteomics, bioinformatics, quantitative analysis and functional proteomics. The ‘alternative approach’ is to examine proteome profiles or patterns of overall protein expression in biological samples to differentiate types or groups of the samples (i.e. normal versus diseases; a specific disease versus others), rather than focusing on a specific protein or a particular disease pathway [7]. Analytical techniques commonly used in this approach are microarrays [41], SELDI [42], and CEMS [43]. The advantage of the latter approach is that detailed characterizations (identity, protein-protein interactions, post-translational modifications, etc.) of proteins are unnecessary. This approach is, therefore, suitable for clinical diagnostics and biomarker discovery, especially in multi-factorial diseases for which one marker may not be sufficient for effective detection or diagnosis.

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The alternative approach or proteome profiling also offers an opportunity of its use as a complementary diagnostic tool for some medical diseases, in which clinical diagnosis relies only on invasive procedures that may be limited in some occasions. For example, renal biopsy, which remains the gold standard for the diagnosis of glomerular diseases, may not be possible in patients whose indications for this invasive procedure are not fulfilled and those with bleeding tendency or flank skin infection, thereby delayed diagnosis. In these cases, urinary proteome profiling may potentially be useful as a complementary diagnostic tool. Moreover, information about dynamic changes of the urinary proteome profile during or after treatment may be useful to predict therapeutic outcome and/or prognosis of the disease. Evaluation of treatment response by various regimens of therapy, looking at urinary proteome profiles, may also lead to the development of personalized medicine for each individual. SCREENING FOR NOVEL THERAPEUTIC TARGETS DISEASES USING PROTEOMIC TECHNOLOGIES

OF

KIDNEY

Proteomics is probably the best screening tool, to date, to screen for the novel therapeutic targets that have not previously been defined by conventional methods. One of the major objectives in recent renal and urinary proteomics studies is to identify altered proteins modified by kidney diseases. Major findings from recent renal and urinary proteomics studies applied to kidney diseases are summarized in Table 1. Table 1.

Partial List of Altered Proteins Identified in Kidney Diseases Using Proteomic Technologies A. Glomerular diseases IgA nephropathy Plasma proteins - IgA1 Focal segmental glomerulosclerosis Proteases - MBL-associated serine protease Plasma proteins - Albumin - Apolipoprotein J - γ-Fibrinogen - Vitronectin Matrix proteins - Fibulin Diabetic nephropathy Cytoskeletal proteins - Myosin - Tropomyosin


Frontiers in Drug Design & Discovery, 2006, Vol. 2 93 (Table 1) contd….

- Vimentin Proteases - Contraception-associated protein - Elastase Protease inhibitors - Anti-thrombin III - Elastase inhibitor - Phosphatidylethanolamine-binding protein - T-kininogen Apoptosis-related proteins - Deoxyribonuclease I - Histone H3.2 Redox-associated proteins - Ferritin Calcium-binding proteins - Annexin V - Calbindin-D28k - Calmodulin - Crocalbin-like protein Transport regulators - Na-H exchanger regulator - Retinol-binding protein - Syntaxin 11 - Transthyretin Signaling proteins - C1q-binding protein Plasma proteins - Apolipoprotein A-IV Matrix proteins - Elastin Miscellaneous - HSCP207 B. Tubulointerstitial diseases Cyclosporin A nephrotoxicity Calcium-binding proteins - Calbindin-D28k Lead nephrotoxicity Metabolic enzymes - Aflatoxin B1 aldehyde reductase

94


(Table 1) contd….

- Aldose reductase - Argininosuccinate synthase - Glutathione S-transferase P - Sorbitol dehydrogenase - Transketolase Calcium-binding proteins - Calbindin-D28k - Calcineurin Stress regulatory proteins - Heat shock protein 70 (HSP70) - Heat shock protein 90 (HSP90) Plasma proteins - α2-Microglobulin - Transferrin Radiocontrast nephrotoxicity Plasma proteins - α2-Microglobulin Gentamicin nephrotoxicity Cytoskeletal proteins - Actin - α-Tubulin Protease inhibitors - Serine protease inhibitor (SPI1) Metabolic enzymes - Acetyl-CoA carboxylase - ATP synthase α subunit - ATP synthase β subunit - ATP-specific succinyl-CoA synthetase - cGMP-specific 3'5'cyclic phosphodiesterase - Cytosolic malate dehydrogenase - DNA polymerase α subunit IV - α-Enolase - F1-ATPase - Fructose 1,6 bisphosphatase - Glutamate decarboxylase - Glycine amidinotransferase - GTP-specific succinyl-CoA synthetase - Methylacyl-CoA racemase α - NG,NG-dimethylarginine dimethylaminohydrolase 1 - Phospholipase B




Calcium-binding proteins - Regucalcin Transport regulators - Fatty acid transport protein Signaling proteins - Moesin Stress regulatory proteins - DnaK-type molecular chaperone Plasma proteins - Albumin - α2-Microglobulin Dent's disease Cytoskeletal proteins - Profilin Protease inhibitors - Cystatin C - Cystatin M - Phosphatidylethanolamine-binding protein Metabolic enzymes - Carbonic anhydrase - Lysozyme Transport regulators - Cubilin - Retinol-binding protein - Transthyretin Signaling proteins - Epidermal growth factor precursor - Insulin-like growth factor-binding protein Plasma proteins - Angiotenogen - Apolipoprotein A-I - Apolipoprotein A-IV - Complement factor H-related protein - β2-Glycoprotein I - Hemopexin - Megalin - β2-Microglobulin - Pigment epithelium-derived factor - Uromodulin - Vitamin D-binding protein

96


(Table 1) contd….

Miscellaneous - Lipocalin C. Vascular disease Renovascular hypertension Cytoskeletal proteins - Troponin T Metabolic enzymes - 3-Mercaptopyruvate sulfurtransferase - Aldehyde dehydrogenase family 7 - Pyruvate kinase M1 isozyme - Vacuolar ATP synthase subunit B Calcium-binding proteins - Nucleolin-related protein Signaling proteins - Phosphotyrosyl protein phosphatase - Serine/threonine protein kinase 10 Matrix proteins - Lamin A Miscellaneous - 3010027A04Rik - Type A/B hnRNP p40 D. Renal cancers Cytoskeletal proteins - Cytokeratin 8 - Keratin 10 - Smooth muscle protein 22-α - Stathmin - α-Tubulin - β-Tubulin - Vimentin Metabolic enzymes - Aconitase - Agmatinase - Aldehyde dehydrogenase 1 - Aldolase A - Aldose reductase - Aminoacylase-I - Carbonic anhydrase I




- α-Enolase - γ-Enolase - Enoyl-CoA hydratase - FK506-binding protein 4 - Glutathione peroxidase - Glutathione-S-transferase P - Glyceraldehyde-3-phosphate dehydrogenase - α-Glycerol-3-phosphate dehydrogenase - Inorganic pyrophosphatase - Lactate dehydrogenase - Mn-superoxide dismutase - N-acetylglucosamine phosphate mutase - NADH-ubiquinone oxidoreductase complex I - Nicotinamide N-methyl transferase - Phosphoglucomutase - Phosphoglycerate kinase 1 - Phosphoglycerate mutase - Pyruvate kinase M1 isozyme - Pyruvate kinase M2 isozyme - Triosephosphate isomerase - Ubiquinol cytochrome C reductase - Ubiquitin carboxyl terminal hydrolase 1 - Ubiquitin-activating enzyme E1 - Valosin-containing protein Redox-associated proteins - Thioredoxin Calcium-binding proteins - Annexin I - Annexin II - Annexin IV - Annexin V Transporters - CLIC-4 Transport regulators - α-S1 Casein Signaling proteins - Cofilin 1 - G protein - Moesin - Platelet-derived endothelial cell growth factor

98


(Table 1) contd….

Stress regulatory proteins - Glucose regulatory protein (GRP78) - HSP27 - HSP90 - Protein disulfide isomerase Plasma proteins - β-Fibrinogen Matrix proteins - Lamin B1 Miscellneous - Elongation factor 2 - Major vault protein E. Renal transplant allograft rejection Plasma proteins - β2-Microglobulin F. Acute renal failure Proteases - MBL-associated serine protease Transport regulators - Retinol-binding protein - Transthyretin Plasma proteins - Albumin - Apolioprotein A-IV - β2-Microglobulin - Transferrin - Vitamin D-binding protein - Zn α-2 glycoprotein G. End-stage renal disease Protease inhibitors - α1-Antitrypsin - Cystatin C Transport regulators - Retinol-binding protein Plasma proteins - Albumin - Complement factor D




- α-Fibrinogen - β2-Microglobulin - Salivary proline-rich protein

The altered proteins that have been identified in each disease from different studies are integrated and classified into various functional groups, based on their major functions appeared in the Swiss-Prot/TrEMBL database and/or literatures. Some of the listed proteins have multiple functions (e.g., annexin, phosphatidylethanolamine-binding protein, and aldose reductase) but are designated only in one functional class in the table. Note that these altered proteins can be one of the following possibilities: (a)

Altered proteins that are the causes of kidney diseases and definitely are the therapeutic targets.

(b) Altered proteins that are not the direct causes but play important roles in the pathogenic mechanisms of kidney diseases (i.e. those involve in the cascade of disease pathways) and may be the novel therapeutic targets. (c)

Proteins that are altered as a result of kidney diseases and their changes are to compensate for the disease mechanisms or to maintain the normal homeostasis.

(d) Proteins that are altered as a result of kidney diseases but their changes have no functional significance or have only minor effects on renal physiology. (e)

Proteins that are not actually altered but their changes are observed in a proteomic analysis as a spurious result of quantitative analysis (as equal amount of total protein is normally used for quantitative analysis, especially in gelbased methods).

(f)

Proteins that are altered only in a specific kidney disease and may be the novel biomarkers (regardless of their functional significance in the disease mechanisms).

Although some of the altered proteins showed in Table 1 may potentially be the novel therapeutic targets of kidney diseases, none of them has been verified. Extensive functional evaluation and bioinformatic analysis of these altered proteins, which have been identified by expression proteomics, are required to define the validated therapeutic targets. PHARMACOPROTEOMICS FOR DRUG DESIGN AND DISCOVERY After defining the validated novel therapeutic targets for kidney diseases, the next step is to discover novel drug compounds by designing their molecular structures to fit into the functional parts of protein molecules that are the therapeutic targets. Bioinformatics plays a critical role for such design. Proteomics applied to pharmaceutical purposes or ‘pharmacoproteomics’ involves almost all of basic methodologies described above, particularly MS techniques. Indeed, various mass spectrometric methods; including electrospray and nanospray ionization, atmospheric pressure chemical ionization, photoionization, and their interface with LC have been

100 Frontiers in Drug Design & Discovery, 2006, Vol. 2


utilized to measure levels of drugs and their metabolites in the plasma and urine for quite some times [44]. Recent advances in HPLC coupled to tandem MS (HPLC-MS/MS) make the identification of drug compounds more sensitive with a better resolution. A high-throughput capability of HPLC-MS/MS, with or without stable isotope labeling, facilitates the studies of in vitro and in vivo drug metabolisms, examination of metabolite activities, identification and characterization of impurities in the pharmaceuticals, analysis of chiral impurities in drug substances, and drug discovery [45-48]. Pharmacoproteomics can also be applied for prediction of therapeutic response to a specific drug. However, response to a particular drug may not be predicted easily because of the inter-individual variability [49], which is partly due to genetic factors [50]. Therefore, combination of pharmacoproteomics and pharmacogenomics is important for prediction of therapeutic outcome as well as for evaluation of genetically and biochemically dynamic processes during medications [51]. Thus, proteomic technologies are not used alone for drug design and discovery or for other pharmaceutical purposes, but rather they are integrated with genomic and chemical approaches. INTEGRATIVE ‘OMICS’ FOR PERSONALIZED MEDICINE Currently, the suffix ‘-omics’ is used frequently for the nomenclature of several fields in biomedical research (i.e., genomics, transcriptomics, proteomics, lipomics, metabolomics, interactomics, and several others). Most of recent studies have applied each of these omics sciences separately for individual study project. It is unlikely that the complexity of disease mechanisms will be completely understood by a single omics approach. Integrating all of them is required for future biomedical research. With the capability of bioinformatics to link all these omics fields together, each omics field is complementary to the others and serves as an individual piece of the jigsaw to fulfill the dynamic images of the pathogenesis and pathophysiology of medical diseases, as well as their complications. Recently, the concept of ‘systems biology’ has been emerging for the global evaluation of biological systems and has included ‘integrative omics’ as one of the analytical processes [52, 53]. Systems biology has been defined by Weston and Hood [54] as “the analysis of the relationships among the elements in a system in response to genetic or environmental perturbations, with the goal of understanding the system or the emergent properties of the system”. A system may be a few protein molecules carrying out a particular task such as galactose metabolism (termed a biomodule), a complex set of proteins and other molecules working together as a molecular machine such as the ribosome, a network of proteins operating together to carry out an important cellular function such as giving the cell shape (protein network), or a cell or group of cells carrying out particular phenotypic functions. Thus, a biological system may encompass molecules, cells, organs, individuals, or even ecosystems [54]. The major keys that make systems biology being the ideal approach for future biomedical research are: (i) the successfulness of the Genome Projects and the availability of databases; (ii) the emergence of cross-disciplinary biology that allows biologists, chemists, physiologists, computer scientists, engineers, mathematicians, statisticians, physicians and healthcare professionals work closely together; (iii) the availability of bioinformatics that serves as a link for a cross-talk among different fields; and (iv) advances in the high-throughput platforms of biotechnologies that permit simultaneous study of a large complement of genes, transcripts, proteins, lipids or other



elements. Systems biology or integrative omics is, therefore, the ideal approach for future biomedical research and should result to: (i) better understanding of the pathogenesis and pathophysiology of medical diseases and their complications; (ii) identification of new therapeutic targets; (iii) discovery of novel drugs and biomarkers; (iv) better therapeutic outcomes; and (v) successful prevention of the diseases and their complications. Because of the wide spectrum of data to be obtained from integrative omics or systems biology, this ideal approach can also be utilized to develop and optimize personalized medicine. CONCLUSIONS Proteomics has lots of advantages and strengths to be used in renal research. The data obtained from recent renal and urinary proteomics studies have demonstrated the potential of using proteomics as a tool to screen for novel therapeutic targets of kidney diseases. Additionally, pharmacoproteomics in combination with pharmacogenomics, chemoproteomics, and bioinformatics is very important for drug design and discovery in the post-genomic era. Finally, integrative omics or systems biology holds the greatest promise for future biomedical research. This ideal approach may lead to the ultimate goals of improved therapeutic outcome and successful prevention of diseases, as well as development of personalized medicine to gain the best therapeutic response for individual patients. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]

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Aptamer-Based Technologies as New Tools for Proteomics in Diagnosis and Therapy Vittorio de Franciscis* and Laura Cerchia* Istituto di Endocrinologia ed Oncologia Sperimentale del CNR “G. Salvatore”, via S. Pansini 5, 80131 Naples, Italy Abstract: Proteomics has provided a tool to define protein profile of a specific cell or tissue and to associate protein expression levels and post-translational modifications with disease states therefore developing innovative technologies for measurement of protein levels has become a major challenge of the last few years. Specific nucleic acid-based compounds, named aptamers, have been shown as high-affinity ligands and potential antagonists of disease-associated proteins. Aptamers, isolated from combinatorial libraries by an iterative in vitro selection process, discriminate between closely related targets thus representing a valid alternative to antibodies or other bio-mimetic receptors, for the development of biosensors and other bio-analytical methods. Moreover they can be easily stabilized by chemical modifications for in vivo applications and numerous examples have shown that stabilized aptamers against extracellular targets such as growth factors, receptors, hormones or coagulation factors are very effective inhibitors of the corresponding protein function. By integrating the aptamer-based biosensor development with the maturing technology for in vitro selection of anti-protein aptamers results in the highthroughput production of proteome chips. Furthermore, aptamer arrays and biosensors will reveal the most effective tools for the detection of biomolecular interactions and the identification of protein targets, particularly with regard to those not detectable by known receptors like enzymes or antibodies. We will review here the main and innovative methods based on the use of aptamers as biosensors for protein detection that, in alternative or combined to the classical proteomic approaches, could reveal suitable for both diagnostic and therapeutic purposes.

INTRODUCTION The deciphering of the human genome and the enormous amount of data collected on disease mechanisms has lead to the discovery of an increasing number of potential specific targets for new therapeutics or diagnostics. Indeed, through the use of different technologies, including mass spectrometry and bioinformatics, proteomic has recently provided the possibility to define the protein expression profile for a given tissue or *Corresponding authors: Tel: +390817462036; Fax: +390817701016; E-mails: [email protected] and [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.


de Franciscis and Cerchia

organ and its alterations caused by a disease or infection [1]. The detailed knowledge of potential disease-associated targets is an extremely useful tool for the rational design of novel therapeutic molecules or diagnostics. Therefore, the selective targeting of these signatures has become a main goal to develop patient-oriented new therapeutic agents especially for devastating disease as cancer and neurodegenerative diseases. In cancer an increasing number of proteins involved in cell growth, including growth factors, receptors, intracellular mediators and transcription factors have been found to be altered through multiple mechanisms of activation. These include the altered expression/activity of cell surface proteins as receptor-type tyrosine kinases, including the epidermal growth factor receptor (EGFR) that is frequently found over-expressed in non-small cell lung carcinoma, bladder, cervical, ovarian, kidney and pancreatic carcinoma, and the HER-2/neu receptor that is over-expressed in various types of cancers, including breast (were it occurs in 30% of early stage cases), ovarian, gastric, lung, bladder, and kidney carcinomas [2,3]. Likewise, the altered activation or expression of intracellular proteins as the serine/threonine kinases Akt and Erk or nuclear effectors as p53 and pRB is needed to uncontrolled cell survival and proliferation. Therefore, these proteins are suggested as possible therapeutic or prognostic biomarkers, however, no single biomarker was able to identify those patients with the best (or worst) prognosis or those which would be responsive to a given therapy. On the other hand, the anomalous protein misfolding and aggregation, with an accompanying "toxic gain of function" occurs in several neurodegenerative conditions including Prion diseases, Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease and is central to their pathogenesis. For example, in AD, misfolded amyloid beta peptide 1-42 (Abeta), a proteolytic product of amyloid precursor protein metabolism, accumulates in the neuronal endoplasmic reticulum and extracellularly as plaques. In contrast, in PD cases there is abnormal accumulation of alpha-synuclein in neuronal cell bodies, axons, and synapses [4]. Hence, finding specific ligands capable to detect and measure the altered pattern of gene expression is a strategic and plausible objective for the diagnosis and therapy of important diseases. To be effective as tools to detect disease-associated biomarkers these ligands should have the following characteristics: (i) ability to discriminate between different conformations of the same target proteins; (ii) capability to quantify the level of expression of the altered versus the physiologic forms; (iii) ideally, be usable both for in vitro and in vivo purposes; and finally, for therapeutic applications they should (iv) have the power to interfere with the altered product. To this goal, different types of molecules have been shown to be of potential utility for diagnosis and therapy, including small chemical compounds, peptides, antibodies and nucleic acid ligands. Here we will discuss the properties of nucleic acid-based compounds (named aptamers) isolated from combinatorial libraries by a selection procedure, the Systematic Evolution of Ligands by EXponential enrichment (SELEX) technology [5-7], which, in the last few years, has yielded several high-affinity ligands and potential antagonists of disease-associated proteins [8-15]. Aptamers are single-stranded nucleic acids that unlike ribozymes and antisense oligonucleotides, assume three-dimensional shapes that dictates high-affinity binding to a variety of targets. In particular, in this review we will

Aptamer-Based Technologies


focus on the main methods based on the use of aptamers as biosensors for protein detection. OVERVIEW OF THE SELEX TECHNOLOGY The SELEX technology is an evolutionary, in vitro combinatorial chemistry process used to identify aptamers, as specific ligands of a given target, from large pools of diverse oligonucleotides, Fig. (1). The starting point for the generation of an aptamer is the synthesis of a nucleic acid library (RNA, DNA or modified RNA) of large sequence complexity followed by the selection for oligonucleotides able to bind with high affinity

Fig. (1). Schematic representation of the SELEX process. The single-stranded (ss) DNA library is amplified by Polimerase Chain Reaction (PCR) in order to generate the double-stranded DNA pool that will be transcribed by T7 RNA Polymerase. The pool of RNA molecules with different conformations will be used for the selection process (see text for details).



and specificity to a target molecule. Randomisation of a synthetic sequence stretch from 22 up to 100 nucleotides in length have been used to create an enormous diversity of possible sequences (4N different molecules) which in consequence generate a vast array of different conformations with different binding properties. The SELEX method includes steps of: (i) incubating the library with the target molecule under conditions favourable for binding; (ii) partitioning: molecules that, under the conditions employed, adopt conformations that permit binding to a specific target are then partitioned from other sequences; (iii) dissociating the nucleic acid-protein complexes and (iv) amplifying of the nucleic acids pool to generate a library of reduced complexity enriched in sequences that bind to the target. This library will be then used as starting pool for the next round of selection. After reiterating these steps (the number of rounds of selection necessary is determined by both the type of library used as well as by the specific enrichment achieved per selection cycle), the resulting oligonucleotides are subjected to DNA sequencing. The sequences corresponding to the initially variable region of the library are screened for conserved sequences and structural elements indicative of potential binding sites and subsequently tested for their ability to bind specifically to the target molecule. Affinities of aptamers for the targeted proteins are typically very high, with dissociation constants ranging from low picomolar (1 x 10-12 M) to low nanomolar (1 x 10-9 M) that are better than those obtained for peptides derived from phage display selection and comparable to antibody-antigen interactions. Furthermore, due to the fact that the specific, three-dimensional arrangements of a small number of contact points of the aptamer mediates the protein-aptamer interaction, rather than a general affinity for the sugar-phosphate backbone of the nucleic acid, aptamers can achieve high target selectivity. In addition, the binding characteristics of aptamers can be influenced by the type of the experimental system used for the selection and counter-selection (depletion of aptamers that bind to non-target molecules). IN VIVO APPLICATIONS OF APTAMERS During the last few years an increasing interest has emerged for aptamers designed against cellular or viral targets of biomedical interest in vivo. Several aptamers are actually in clinical trials [16] and the Food and Drug Administration has recently approved one aptamer developed by Eyetech (Macugen™) that inhibits the human Vascular Endothelial Growth Factor 165 (VEGF165), for the treatment of age-related macular degeneration [17]. Since the first description of SELEX in 1990, aptamers have been generated toward a variety of different targets, including proteins, peptides, small molecules, organic dyes, viruses etc. that are potential targets for therapeutic or diagnostic intervention. In several cases it has been shown that the SELEX procedure permits to generate molecules that display high selectivity for the target thus allowing to easily discriminate between even very close molecules. Indeed, it has been demonstrated that anti-theophylline aptamer can discriminate between caffeine and theophylline, two molecules that differ only by a methyl group [18]. For instance, RNA aptamers with high selectivity have been generated that bind with nanomolar affinities the protein kinase C, a potential target in cancer medicine, and are capable of discriminating between the beta II from the highly related beta I isoenzymes [19]. DNA aptamers have been obtained that recognise both the native and the denatured state of ERK-2, a member of the family of mitogen-



activated protein kinases, which are central transducers of extracellular signals [20]. RNA ligands with high affinity for the Ras binding domain (RBD) of Raf-1 have been isolated and shown to inhibit either Ras binding to Raf-1 and Ras-induced Raf-1 activation, but they did not affect the interaction of Ras with B-Raf, a Raf-1 related protein [21]. Furthermore, highly specific aptamer has been generated against plateletderived growth factor (PDGF) that suppress PDGF B-chain (PDGF-BB) but not the epidermal- or fibroblast-growth-factor-2-induced proliferation [22]. Other proteins for aptamer targeting are: tenascin-C, an extracellular matrix protein that is over-expressed during tissue remodelling processes, including tumour growth [23]; human epidermal growth factor receptor-3 (ErbB3/HER3; 24) and the IFN-γ-inducible CXCL10 chemokine [25]. In the latter case, Marro et al. identified a series of nuclease-resistant RNA aptamers with high binding affinity for human and/or mouse CXCL10. CXCL10 is a chemokine involved in a variety of inflammatory diseases. Since some of the aptamers are highly selective for CXCL10, they represent a powerful tool to further elucidate the complex cross-talk between the CXCL10/CXCR3 receptor and other chemokine/ receptor system. Even though a large number of aptamers have been selected for preferential targeting of extracellular proteins or protein epitopes the use of living cells as complex target has been recently described to develop a differential whole-cell SELEX protocol to target cell surface bound proteins in their natural physiological environment. The aptamers produced bind specifically to the Ret receptor tyrosine kinase and inhibit its downstream signalling effects [26]. Despite the increasing number of aptamers isolated of potential medical importance their use in therapy is still lagging behind because of the lack of an efficient and safe delivery system to target specific cells with adequate amounts of aptamer. Indeed, to be successfully applied in therapy aptamers must possess defined molecular properties needed to cross the collagen microfibrillar network of the extracellular matrix, and reach the target tissue or cells and, most importantly, also penetrate the cell membrane. Coupling aptamers to inert large molecules, as cholesterol or polyethylene glycol, have been used to keep them in circulation anchored to liposome bilayers [27]. Furthermore, since aptamers, especially RNA-based aptamers, are rapidly degraded by nucleases in whole organisms major efforts have therefore been addressed to improve their stability by a variety of approaches [28]. RNA aptamers with 2'-fluoro (amino) pyrimidine modifications, 2'-O-alkyl nucleotides, 3'end cap and locked nucleic acids, LNA [29] modifications that significantly enhanced their stability, may survive for several hours in vivo against degradation by nucleases [30]. Hence, the development of a safe, efficient, specific, and non-pathogenic system for the delivery of therapeutic RNA is highly desirable. Protocols have been also developed that allow the targeting of intracellular proteins with inhibitory aptamers (named intramers) that are delivered into intra-cellular compartments either by direct transfection or through the use of expression systems for the aptamer sequences [31-33]. The cytoplasmic expression of intramers demonstrates the aptamers striking potential as rapidly generated intracellular inhibitors of biomolecules. Nevertheless and despite the very short time from the development of the SELEX process several aptamers are actually enrolled in phase 2 or phase 3 clinical trials as



promising therapeutics. In this respect, the application for in vivo imaging is especially promising due to the very wide range of possibilities available to introduce changes in their structure that will enhance the bioavailability and tune the pharmacokinetics properties. Indeed, apart those mentioned above, there are very few drawbacks for the use of aptamers in vivo. In fact, there is no experimental evidence so far for aptamers being immunogenic, a very useful property for reagents that need to be administered repeatedly to the same individual for therapy or diagnostic when studying disease progression. DEVELOPMENT OF BIOSENSORS RECOGNITION ELEMENT

WITH

APTAMERS

AS

BIO-

Proteins are critical for the normal functioning of the human body. They provide structure, transmit and receive information, and carry out important chemical reactions. In persons with a disease, many proteins may be affected. Compared to normal health, some proteins may become overly abundant, while others may become scarce. By measuring these proteins, associated with a clinical state of interest, such as the presence of disease or response to a therapy, it becomes possible to discover new insights into disease and health. For these reasons there is an obvious need to develop new technologies that could speed both diagnosis and therapy of diseases by enabling direct detection of the expression and modification of proteins closely correlated with disease and by accelerating the process of drug discovery and development. Proteomics is defined as the study of the structure, function, expression, cellular localisation, interacting partners, and regulation of every protein produced from a complete genome [1]. In the last few years a big effort has been devoted to design innovative approaches alternative or combined to the classic proteomic methods, represented by two-dimensional gel separation followed by mass spectrometric analysis, to define protein profile of a specific cell, tissue, or organism and to associate protein expression levels and post-translational modifications with disease states. The aim of the improvement of the proteomic technology is to overcome the evident limitations in speed and sensitivity in samples processing by allowing the quantification of even the most weakly expressed proteins. Instead of identifying proteins on the basis of their charge and size a useful approach is to identify them by specific recognition. In this way it is possible to discriminate proteins that have similar physical properties but different conformations. As described above, nucleic acid aptamers like antibodies recognize and bind a specific protein target on the basis of their three-dimensional structure. Labelled aptamers have been used in the same way as antibodies, for example in enzyme-linked immunosorbent assay (ELISA)-like assays, chromatographic and electrophoretic techniques, Western blotting analyses and more generally as biosensors, including in arrays [34,35]. Several features render aptamers a promising alternative to antibodies as tools for the specific detection of proteins. Differently from antibodies they are synthetic molecules and are generated entirely in vitro; the synthetic nature of aptamers will facilitate manufacture and arraying, while the in vitro SELEX process, has facilitated automation for high throughput probe generation [36,37].



Moreover, the minimal cross-reactivity reached by the addition of negative counterselection steps during aptamers generation and the high affinity for the target, provide the potential for producing multiplex chips. The use of the aptamers, in fact, allows to overcome the most critical obstacle to the development of antibody microarrays for protein detection which is the lack of specificity of many available antibodies that crossreact with multiple proteins [38]. In addition, while many antibodies are temperaturesensitive and can denature upon contact with surfaces, aptamers are stable to long-term storage, can be transported at room temperature, and undergo reversible denaturation. Furthermore, since the chemistry for the production and the modification of oligonucleotides is well developed, once aptamers are selected, they can be functionalised using a wide variety of fluorophores, as well as cobalt or iron paramagnetic particles, gold, radio-isotopes and biotin. These characteristics render the aptamers suitable as ligands for protein detection in a great number of different methodologies. In addition to report in detail the most innovative methods using the aptamers as biosensors in chip arrays (see “Aptamers as probes in microarray format” paragraph), we have summarised in Table 1 some of the recent applications for protein detection and quantisation that use immobilised aptamers in chromatographic, electrophoretic and Mass Spectrometry techniques. As revealed by the reported examples, the aptamers employed as ligands in separation methods, are all raised against proteins of biomedical importance. Table 1.

Different Aptamer-Based Separation Methods for Protein Detection

Aptamer-based separation method

Aptamer

Application

Ref.

Affinity Chromatography

DNA-aptamer for L-selectin

Purification of human L-selectin receptor globulin

[39]

Affinity Chromatography

Aptamer for the thyroid transcription factor 1 (TTF1)

Purification of TTF1 from bacterial lysates

[40]

Affinity capillary electrophoresis

DNA-aptamer for HIV-1 reverse transcriptase

Detection of HIV-1 reverse transcriptase

[41, 42]

Non-equilibrium capillary electrophoresis of equilibrium mixtures (NEC-EEM)

Aptamer against human thrombin

Quantitative analysis of thrombin

[43]

MALDI-Mass Spectrometry

DNA aptamer for human thrombin

Detection of thrombin from human plasma

[44]

Aptamers as Probes in Microarray Format The functional features of the aptamers allows their use as biosensors and their adaptation to chip arrays. The nature of the platform most suitable for the generation of aptamer arrays is open to question. Simply printing aptamers on polylysine-coated slides is not a good approach since the aptamers will denature upon electrostatic capture. Walt



and co-workers have adapted an anti-thrombin DNA aptamer to high-density fiber-optic arrays [45]. The aptamer was immobilised on the surface of silica microsheres and the resulted beads were loaded in microwells on the distal tip of an imaging fiber. Beads prepared by using a different oligonucleotide were included to detect the non-specific binding. The imaging fiber was coupled to a modified epifluorescence microscope system, and the distal end of the fiber was incubated with a fluorescein-labelled thrombin solution. The system showed a good selectivity towards thrombin and could be reused without any sensitivity change. It presented an apparent dissociation constant of 300 nM for thrombin binding and a detection limit of 10 nM. Another use of DNA and RNA aptamers as bio-recognition element in optical sensors is represented by a chip-based biosensor for multiplex analysis of proteins related to cancer in complex biological mixtures [46]. In this study, carried out at the company Archemix, four fluorescently labelled aptamers (RNA-based aptamers against basic fibroblast growth factor (bFGF), inosine monophosphate dehydrogenase, and VEGF, and an anti-thrombin DNA-based aptamer) were immobilized onto a glass surface within a flow cell and fluorescence polarization anisotropy was used for solidphase measurements of target protein binding. It has been demonstrated specific detection and quantification of cancer-associated proteins (inosine monophosphate dehydrogenase II, VEGF and bFGF) in the context of human serum as well as in cellular extracts. The anti-thrombin aptamer yielded a 10-fold increase in signal above background, while RNA aptamers against bFGF, inosine monophosphate dehydrogenase and VEGF, showed 2-, 1.4-, and 25-fold increases, respectively, in signal compared to background controls. Furthermore the dissociation constant values for the complex aptamer-target in the array were comparable to those from solution-phase experiments, thus suggesting that the immobilization of the aptamers did not interfere with their functionality. Furthermore, the aptamer arrays technology has been integrated with a device that could also deliver samples to perform complex assay procedures [47]. In more detail, the “electronic tongue”, a chip-based microsphere array developed for the digital analysis of complex fluid by using chemical sensors and antibodies [48], has been adapted to the use of aptamers as biosensors. The aptamer-based electronic tongue setup contained a fluid delivery system, a fluorescence microscope, a digital camera, a flow cell in which the aptamer chips were loaded, and a computer for data analysis. The aptamer chips were constituted by silicon chips with multiple wells in which were deposited the beads modified with the sensor. To this aim, streptavidin agarose beads were incubated with 5’ends-biotinylated aptamers to produce aptamer-bead sensor elements. The aptamer biosensor array was tested to functionally screen aptamers anti-lysozyme, previously selected. Furthermore, it was used to quantify labelled proteins by means of aptamers as capture reagents, and to quantify unlabelled proteins, in a sandwich assay format with antibodies. In the first case, an aptamer originally selected to bind to biothreat agent ricin was biotinylated, immobilised and probed with a solution contained Alexa-Fluor488labelled ricin, once introduced into the chip wells. In the sandwich assay, the anti-ricin aptamer bound to unlabelled ricin, while a fluorescent anti-ricin antibody served as a reporter. The limits of detection (320 ng/ml) in sandwich assay format were comparable to those observed with antibody anti-ricin assay. The biosensors were reusable by heating or by washing with a buffer containing 7 M urea.



Integrating aptamer microarray production with the maturing technology for automated in vitro selection of antiprotein aptamers result in the high-throughput production of proteome chips. Recently, high-throughput methods for generating aptamer microarrays have been described [49]. As a proof-of-principle, the microarrays have been used to screen the affinity and specificity of a pool of robotically selected anti-lysozyme RNA aptamers. 5’-biotinylated RNA aptamers were spotted on streptavidin-coated microarray slides and the resultant arrays were validated by showing the specific, dose-dependent detection of lysozyme target protein. The lower limit of detection was in the low-pg/ml range, and the protein could be detected even against a background of labelled cellular lysate. Recently, an attractive method represented by the direct site-specific biotinylation at the desired positions by transcription using unnatural base pairs, has been used to immobilise a previously selected RNA-based aptamer on sensor chips [50]. The same research group developed unnatural base pairs between 2-amino-6-(2-thienyl)purine (denoted by s) and 2-oxo(1H)-pyridine (denoted by y) or 2-amino-6-(2-thiazolyl)-purine (denoted as v) and y. These unnatural base pairs function in transcription and can be used in the direct site-specific biotinylation of RNA molecules. In fact, the position 5 of y can be modified to introduce various functional groups via the nucleoside of 5-iodo-2oxo-(1H)pyridine (5-iodo-y). In this study, the procedure for the synthesis of the substrate of biotinylated-y (Bio-yTP) and the incorporation of Bio-y into RNA by T7 RNA polymerase transcription, was used to transcribe a biotinylated anti-(Raf-1) RNA aptamer [21]. The biotinylated RNA aptamer was efficiently immobilised on streptavidin-coated sensor chips and accurately recognised its target protein. The aptamer interaction with Raf-1 was detected by 27 MHz quartz-crystal microbalance (QCM) or surface plasmon resonance (SPR) technology. The unmodified RNA aptamer, in which Bio-y was not incorporated, did not bind to either sensor chip. The protein bound on the sensor chips could be removed by urea treatment, to regenerate the sensor chip surface. Furthermore, the binding of another RBD, GST-RBD of RGL, to the sensor chip-bound aptamer was not detected, indicating that the immobilised RNA aptamer retains its specificity for Raf-1. By using the QCM technology it has been recently developed an aptamer-based biosensor to detect the protein trans-activator of transcription (Tat) of human immunodeficiency virus type 1 (HIV-1) [51]. Tat protein controls the early phase of the HIV-1 replication cycle [52]. With the aim to generate a detection method for the fulllength Tat protein, it has been successfully used the aptamer selected against Tat [53], which displays an efficient binding specificity against Tat, but not for other cellular factors. This aptamer is a very promising bio-recognition element for the detection of Tat, since it combines unique characteristics such as high affinity for Tat and possibility of altering its design to enhance sensitivity and stabilisation against nucleases, allowing monitoring of viral protein levels in vivo. For these reasons, it has been used as a model also in aptamer beacon generation (54, see “Protein detection by molecular aptamer beacons” paragraph). For the production of the biosensor, the RNA aptamer was biotinylated and immobilised on streptavidin deposited on a gold electrode of piezoelectric quartzcrystals. The interaction with Tat protein in solution has been studied following the changes in the oscillation frequency of the crystal. Furthermore, the aptamer-based biosensor has been compared with the corresponding immuno-sensor, based on the



specific monoclonal anti-Tat antibody. The antibody was immobilised on a layer of carboxylated dextran previously deposited on the gold electrode. Both receptors, aptamer and antibody, detected Tat at a minimum concentration of 0.25 ppm with a comparable reproducibility in terms of coefficient of variation. The functional properties of the developed piezoelectric aptamer-biosensor showed that the approach can be applied to detect with specificity and reproducibility other different protein targets with the advantage to regenerate and reuse the biosensor. As demonstrated by all the above studies, in addition to strengthen the basic research in proteomics such functional aptamer-based microarrays could accelerate the analysis of combinatorial libraries of already selected aptamers. Furthermore, the aptamer array technology combined to bioinformatics allows to discover disease-specific biomarkers and protein signatures and to verify drug compounds efficacy. The measurement, using an aptamer array, of the concentrations of a large number of proteins in a patient’s clinical sample could be obtained. A patient’s protein profile is likely to change in the presence of disease, and different profiles may be associated with varying responses to therapeutics or other clinically relevant parameters. PhotoSELEX and Photoaptamer-Based Chips as Highly Sensitive and Specific Capture Agents Aptamers with modified DNA bases that photo-crosslink to captured proteins (photoaptamers) have been investigated for use as arrayed probes for proteomic analysis. They are highly sensitive and specific capture agents developed using the photochemical SELEX (PhotoSELEX) process. This new in vitro selection methodology was used by Golden et al. [55] to identify ssDNA aptamers capable of photo-crosslinking the human bFGF, representing the target molecule. The bFGF photoaptamers could cross-link picomolar concentrations of target in the presence of serum with very little non-specific cross-linking. They displayed functional properties comparable with commercially available ELISA monoclonal antibodies and perfectly distinguished bFGF from consanguine proteins, VEGF and PDGF. The PhotoSELEX method is based on the incorporation of at least one bromodeoxyuridine (BrdU) (in place of a T nucleotide) in the oligonucleotide libraries that, as a consequence of the modification, can form a specific covalent crosslink with the target proteins when exposed to ultraviolet light. Because crosslinking requires both affinity-based binding and close proximity between a BrdU (at a specific location in the photoaptamer) and an amino acid (at a specific location in the target protein), photoaptamers can offer extraordinary specificity. Photoaptamers can be assembled into aptamer arrays in order to measure simultaneously large numbers of proteins, eventually thousands [56,57]. Since photoaptamers covalently bind to their target analytes before fluorescent signal detection, the photoaptamer arrays can be vigorously washed to remove background proteins, providing the potential for superior signal-to-noise ratios and lower limits of quantification in biological matrices. The sensitivity and specificity of photoaptamers, combined with the ability to automate and scale up their selection and the ability to use them on solid surfaces, indicate that they could become an important factor in the development of proteomic technology.



The application of photoaptamer technology to proteomics was investigated in microarray format by using the above bFGF photoaptamer [55] and a new photoaptamer raised against the HIV coat protein gp120MN [58]. To this aim, the photoaptamers were synthesised with a 5’ C6-amino linker and immobilised by spotting on N-hydroxysuccinimide -activated slides. The aptamers were then assayed for photocross-linking activity by dose response to their targets. The obtained results showed that the behaviour of the aptamers was the same on surface as well in solution, in fact they displayed subnanomolar sensitivity with little or no cross-reactivity. Furthermore, a mathematical model for the kinetic analysis of photoaptamer-protein photocross-linking reactions has been reported by using two photoaptamers that crosslinked human bFGF and one against HIV MN envelope glycoprotein [59]. Multiplexed photoaptamer-based arrays that allows for the simultaneous measurement of multiple proteins of interest in serum samples have been recently described [60]. Microarrays spotted with photoaptamers specific for 17 extracellular proteins were probed with target proteins, illuminated with 308-nm light to covalently crosslink the photoaptamers to their affinity-bound target proteins, subjected to harsh denaturing washes to remove non-specific binders, treated with an amine-reactive fluouorescent dye, and then scanned. The array exhibited limits of detection below 10 fM for several analytes including interleukin-16,VEGF, bFGF and endostatin and able to measure proteins in 10% serum samples. To date some photoaptamer-based chips are commercially available as highly sensitive and specific capture agents to discover disease-specific biomarkers and protein signatures (Somalogic Inc.). Furthermore, a continuous and crescent consideration is given to the challenges involved in producing multiplex aptamer chips composed of aptamers taken from disparate literature sources, and to the development of standardised methods for characterising the performance of capture reagents used in biosensors. PROTEIN DETECTION BY APTAMER-BASED PROXIMITY LIGATION ASSAYS In the field of the development of innovative specific and sensitive procedures to evaluate proteomes, worthy of note is the literature by Landegren and co-workers. They have dedicated a lot of work to the study of the ligation of oligonucleotide probes as a mechanism for protein detection [as a review see 61]. The mechanism of proximity ligation is depicted in Fig. (2). The act of DNA ligation creates sequences that can be used for amplified detection of macromolecules with excellent specificity and sensitivity. The mechanism has been used for analyses of nucleic acid, but it can also be applied to detect proteins if affinity probes are coupled to DNA sequence extensions. In fact, in the proximity ligation technique, target proteins are analysed using two or more “proximity probes” each composed of one ligand-binding component able to bind the target protein, and one attached oligonucleotide strand. Binding of the probes to the same target molecule brings the oligonucleotides into close proximity. Thereby the DNA strands can be joined through ligation, generating a sequence with elements required for amplification of the ligation product to detectable levels. Probes that fail to bind the target molecule do not come in proximity and give rise to low non-specific signal.

114



Fig. (2). The mechanism of proximity ligation.

Antibodies such as aptamers can be used as protein binders, even if DNA aptamers are ideal reagents in such assays, since the attachment of DNA sequence extensions is trivial, and since reagents can be selected, recorded as DNA sequences, and shared in the scientific community just like PCR primers. The method, based on the use of a DNA aptamer for the PDFG-BB protein [62], has been successfully applied to detect the homodimer of PDGF-BB [63]. It has been demonstrated that homogeneous proximity ligation assays can be established that exceed the detection sensitivity of standard ELISAs by a factor of a thousand. The technique can also be combined with amplification via the rolling-circle replication mechanism by which ligated probes can be induced to form circles of DNA that represent an ideal tool for localised analysis of individual protein molecules and of interactions between proteins in situ. PROTEIN DETECTION BY MOLECULAR APTAMER BEACONS Large impulse is given to the development of molecular aptamer beacons for protein recognition and quantitative analysis. The design of molecular beacons by using nucleic acid aptamers employs the high versatility of target recognition of the aptamers so it will be possible to develop beacons to detect a variety of target proteins of biomedical importance. The unique properties of the molecular beacons, which combines the signal transduction mechanism of molecular beacons and the high specificity of aptamers, will enable the development of a class of protein probes for real time protein tracing in living specimen and for efficient biomedical diagnosis in homogeneous solutions. Molecular beacons are oligonucleotide probes that assume a hairpin structure in which the single-stranded loop can pair with complementary sequences and the paired stem contains fluorescent reporters (or a fluorophore and a quencher) that interact with one another [64]. Hybridisation of a complementary target sequence leads to the formation of a long duplex region, destabilisation of the hairpin, and a spatial separation between the two dyes. Ultimately, interaction with target oligonucleotides leads to either



the loss of fluorescence resonance energy transfer (FRET) or to dequenching of a fluorophore, optical signals that can be readily detected. The utility of this kind of biosensors relies in their capability to report the presence of specific nucleic acids in homogeneous solutions and are used in real time PCR. Molecular beacons have been modified by using nucleic acid aptamers in order to detect non-nucleic acid ligands, including proteins. A lot of examples have recently been reported in which the aptamer dictates the specificity of target recognition. Similar to molecular beacons, aptamer beacons can adopt two or more conformations, one of which allows ligand binding. Some uses of molecular beacon aptamers have been reported that employ the thrombin as model ligand. In the study of Hamaguchi et al. [65], an antithrombin aptamer has been engineered by adding nucleotides to the 5'-end, which are complementary to nucleotides at the 3'-end of the aptamer. In the absence of thrombin, the aptamer beacon is forced into a stem-loop structure. In the presence of thrombin, the aptamer beacon undergoes a conformational change that in turn causes a change in the distance between a fluorophore attached to the 5'-end and a quencher attached to the 3_end. The fluorescence-quenching pair is used to report changes in conformation induced by ligand binding. An alternative strategy has been reported by Li et al. [66]. They constructed two aptamer beacons, one labelled with a fluorophores-quencher pair and the other with two fluorophores. The binding of thrombin to the beacon causes significant fluorescent signal change attributed to a significant conformational change from a loose random coil to a compact unimolecular quadruplex. The aptamer beacon recognises its target at 112 picomolar thrombin concentration in homogeneous solutions and allows protein quantisation in living specimen. A special class of signalling aptamers named "structure-switching signalling aptamers" has been reported [67,68]. Signalling aptamers refer to aptamers or modified aptamers able to generate a recordable signal. This new class of signalling aptamers are designed to function by switching structures from a pre-formed, lowly fluorescent duplex assembly to a ligand-aptamer complex having a higher level of fluorescence. In the assay having the thrombin as a model target, in the absence of the protein the fluorophore-labelled DNA aptamer forms a duplex with a complementary oligonucleotide modified with a quencher. In the presence of the target, it is favourite the formation of the complex aptamer-protein with a consequent increase in fluorescence intensity. A recent recent approach has been described [69] involving protein-induced coassociation of two aptamers recognising two distinct epitopes of the same protein. Two fluorophore-labelled “signalling” oligonucleotides are attached to both the aptamers by non-DNA linker. When the aptamers bind the protein, the signalling oligonucleotides are brought in proximity causing a change of FRET between the fluorophores. This assay has been developed by using thrombin as a model system but it could detect different disease-related proteins in biological samples. Furthermore, a novel electrochemical detection method for aptamer biosensors has been recently reported for detection of thrombin [70]. Methylene blue (MB), intercalated into the beacon sequence, represents the electrochemical marker. When the beacon aptamer, immobilised on a gold surface, binds thrombin, the hairpin forming beacon



aptamer undergoes a conformational change leading to the release of the intercalated MB. As a consequence, a decrease in electrical current intensity is registered in voltamogram. The peak signal of the MB is clearly decreased by the binding of thrombin onto the aptamer beacon. This method was able to linearly and selectively detect thrombin with a detection limit of 11nM. Moreover, even quaternary structural changes have been exploited to create aptamer biosensors [54]. Two RNA oligomers derived from an anti-Tat aptamer were constructed to analyse the Tat protein of HIV-1. One of the oligomer forms a hairpin structure that contains fluorophore and quencher at both ends of the RNA and another was a nonstructured oligomer. Specifically in the presence of Tat or its peptides, but not in the presence of other RNA binding proteins, the two oligomers undergo a conformational change to form a duplex that leads to relieving of fluorophore from the quencher, and thus to the generation of a fluorescent signal. In addition, to overcome the step of aptamer engineering to obtain a functional aptamer beacon, it has been reported the possibility to directly select aptamer beacons starting from a pool of random sequence DNA molecules [71]. This method couples in vitro selection for ligand binding to a nucleic acid conformational change that in turn leads to the production of a fluorescent signal. A very recent study [72], reports the development of an aptamer beacon as a synthetic high-affinity DNA probe that exhibits FRET in response to a specific protein biomarker, the PDGF. This approach could be applicable to protein biomarkers relevant in cancer and other important diseases. It consists in a novel assay method that is based on specific conformational features of synthetic, high-affinity DNA aptamers in the presence of the specific protein targets to which these aptamers bind. This assay can detect as little as 2.5 ng of PDGF in the presence of a nearly 1000-fold excess of serumderived proteins commonly present in cell culture media. It has been observed that, with a DNA aptamer carrying multiple binding sites for a multimeric protein target, the FRET bioassay can be accomplished by using a mixture of two individually labelled DNAs, one carrying the fluorophore and the other with the matching quencher. The incubation of the aptamers and the target protein results in FRET because of the specific closed conformation of both aptamer molecules. This finding could be useful for the future design of more selective DNA-based FRET bioassays that use more than one ligand for the same protein target. CONCLUDING REMARKS One of the most pressing problems facing those attempting to understand the regulation of gene expression and translation is the necessity to monitor protein production in a variety of metabolic states. Many of the available methods for characterising and quantitating protein biomarkers are time consuming, labour intensive and require multiple steps, such as immobilisation, repetitive incubations and washings, as well as additional reagents to amplify the signal. The use of aptamers as biorecognition element for the development of biosensors to detect protein targets offers over classical methods mainly based on antibodies, a lot of advantages, such as the possibility of easily regenerate the immobilised aptamers, their homogeneous preparation and the possibility of using different detection methods due to easy labelling. Moreover, the enormous diversity of random oligonucleotide libraries can exceed the



diversity of antibodies in the mammalian genome by several orders of magnitude. Since aptamers are nucleic acids, experience with DNA, as in the production of DNA-arrays, should be applicable to the development of aptamer-based biosensors. On the other side, the aptamer arrays can potentially expand the scope of DNA microarrays to recognise expressed proteins as well expressed mRNAs. In this regard, numerous aptamers have already been selected against a wide array of proteins, and the possibility of acquiring aptamers against proteomes has been advanced by automation of the in vitro selection procedure. These considerations, explain why now the aptamer-based technology for protein detection is in advanced stages of development as useful tools in clinical diagnosis and therapy. ACKNOWLEDGEMENTS This work was supported by the European Union FP6 EMIL LSHC-CT-2004503569. We wish to thank members of our laboratory C. L. Esposito and I. Amelio for scientific advice and fruitful discussions. ABBREVIATIONS AD

=

Alzheimer’s disease

bFGF

=

Basic fibroblast growth factor

EGFR

=

Epidermal growth factor receptor

ELISA

=

Enzyme-linked immunosorbent assay

FRET

=

Fluorescence resonance energy transfer

HIV-1

=

Human immunodeficiency virus type 1

MB

=

Methylene blue

PD

=

Parkinson's disease

PDFG-BB

=

Platelet-derived growth factor B-chain

PCR

=

Polymerase chain reaction

QCM

=

Quartz-crystal microbalance

RBD

=

Ras binding domain

SELEX

=

Systematic Evolution of Ligands by EXponential enrichment

SPR

=

Surface plasmon resonance

TAT

=

Trans-activator of transcription

VEGF

=

Vascular endothelial growth factor

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Recent Developments in Proteomics: Mass Spectroscopy and Protein Arrays Aarohi Kulkarni and Mala Rao* Division of Biochemical Sciences, National Chemical Laboratory, Pune 411008, Maharashtra, India Abstract: Proteomics has been gaining increasing attention in order to understand the relevance of the genomic information available through high throughput DNA sequencing and microarray techniques. Proteomics can be viewed as an experimental approach to explain the information contained in the genomic sequences in terms of the structure, function and control of biological processes and pathways. It thus systematically analyses the proteins expressed in the cell. It is well known that several modifications of proteins like posttranslational modifications and splicing are not visible at the DNA level but alter the functions of the proteins involved greatly. These can be envisaged at the protein level wherein the functions can be assigned. Coherent strategies and technologies are required to elucidate protein expression, interactions and functions. The most well established approach to the identification of proteins and their separation is the 2-D PAGE. It allows the resolution and identification of proteins from a wide variety of sources without understanding their functions. There are limitations to this approach, which lacks sensitivity and its inability to separate effectively hydrophobic membrane proteins. The technique however remains the leader methodology for delivery of protein expression and for recording change in protein structure. Recently mass spectrometry has become the method of choice for the identification and characterization of proteins after their purification. In the current methods of mass spectroscopy the protein is identified from fragments and as such does not yield information about posttranslational modifications. Its application is still at a developing stage and in future it will be one of the leader techniques for depicting protein structure. Proteomics has recently seen advancement in the form of microarray technique. Microarray based analysis is a high throughput technology, which can be performed at a dynamic range and also has the added advantage of performing functional proteomics. Its ease of operation and ability to control key parameters such as temperature, pH and cofactor concentrations makes it suitable to automation. This review will essentially focus on the different mass spectroscopy approaches being used to identify and characterize proteins from diverse sources and the developments in protein array technology.

*Corresponding author: Tel: +91-20-25902228; Fax: +91-20-2588 4032; E-mail: [email protected]

Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.

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INTRODUCTION Proteomics is defined as the study of the proteome, but is more commonly viewed as a collection of technologies and tools. The technology has seen enormous progress over the past few years and significant optimism for continued progress. Proteomics has recently received a lot of attention as the focus for gaining a wider and comprehensive understanding of the relevance of genomic information generated. This information is now being generated at an accelerating rate. Ideally it is possible to study the proteins at both expression and functional levels but for their diverse physicochemical properties. In addition to their physical properties, expression levels of proteins also vary widely within a cell, often showing poor correlation to mRNA levels [1]. Proteomics aims to supplement analytical techniques designed to study proteins by ‘‘one species-at-a-time’’ with methodologies that enable thousands of proteins to be studied concomitantly. Rather than being hypothesis-driven where subsequent studies are directed based on previous findings and specific results are anticipated, proteomics is largely discoverydriven where newly acquired data provide details about the system under study, mostly without inclination of predictable results. A generalized approach to analyzing protein expression having common applicability is required [2]. Recently analysis of proteinprotein interactions for pathway elucidation has been undertaken at a meaningful scale. Despite the complete sequencing of several genomes, the functions of most of the putatively expressed proteins remain to be established. A very challenging task is the development of computational methods which will be able to assign predicted function to proteins on the basis of the genetic information [3]. It is known that several modifications of proteins, not immediately apparent at the DNA level, such as posttranslational modification and differential splicing can significantly alter protein function. It will therefore only be through practical studies at the protein level that functions will be assigned. Rational strategies and technologies are required to expound protein expression, interactions and function. Despite the challenges, some studies have attempted to analyse the proteome at a near genomic scale. The subcellular localization of expressed proteins and their mapping has been made possible by the tagging experiments with yeast proteins [4], whilst protein-protein interaction data for Helicobacter pylori and other organisms is emerging [5-8]. At the functional level, 6144 yeast open reading frames (ORFs) were expressed as glutathione S-transferase (GST) fusions and screened for activity, allowing ORFs of previously unassigned function to be assigned. Mass spectrometry (MS) plays a central role in proteomics research, not only since the Nobel Prize in Chemistry has been awarded to John B. Fenn and Koichi Tanaka in 2002 [9,10], for their role in the development of electrospray ionization (ESI) and laser desorption/ionization (LDI) but because of its potential application in understanding the structure and function of proteins. In the 1980s, these so called “soft ionization” techniques have laid the groundwork for the modern MS analysis of proteins and peptides. Mass spectrometers have become more powerful, easy to use and affordable in recent years. For the analysis of proteins from complex biological mixtures, a number of limitations still remain that cannot be overcome simply by improvements of MS systems alone. A common strategy applied in performing proteomics is outlined in Fig. (1).

Recent Developments in Proteomics


Fig. (1). A common strategy to perform proteomics.

TWO DIMENSIONAL POLYACRYLAMIDE GEL ELECTROPHORESIS The need to analyze proteins at a genomic scale has led to a number of parallel approaches being developed. A well known technique for protein separation is two dimensional polyacrylamide gel electrophoresis (2-D PAGE). 2-D PAGE allows the separation and detection of proteins from a wide variety of sources without the need for any prior knowledge of function. Presently, numerous hyphenated front-end protein and peptide separation methods are utilized to increase the depth and breadth of proteomic measurements. In the earliest applications of proteomics, 2D-PAGE was utilized to resolve, visualize and compare protein abundances between two samples. The principle procedure is based on isoelectric focusing (IEF), which separates the proteins based on their isoelectric points (pI), followed by electrophoresis in the presence of sodium dodecyl sulphate (SDS) to separate the proteins based on their molecular weight [11]. These separation parameters allow for the resolution of proteins differing by a single charge, thereby allowing such in vivo modifications such as phosphorylation and certain mutations to be detected. The high resolving power of 2D-PAGE and the development of various staining procedures to visualize these protein ‘‘spots’’ have resulted in a very robust methodology for identifying protein abundance changes between two proteome samples. In conducting such studies, proteome samples from a control and treated (or


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generically different) cell (or organism or tissue) are extracted and separated on distinct gels. The proteins can be visualized by colorimetric staining and the spots resolved on both gels are aligned to enable the relative staining intensities of each protein on the distinct gels to be compared. Protein spots that show a difference in staining intensity can be excised from the gel and digested within the gel plug [12, 13]. The resulting peptides can be extracted from the gel and analyzed by either MS or tandem MS. The MS or tandem MS data are bioinformatically compared with an appropriate database to identify their protein of origin. Minor modifications in the techniques have led to increased specificity. These include the introduction of immobilized pH gradients (IPGs), which provide increased reproducibility and resolution [14-17]. As a result of such advances, 2-D PAGE is capable of profiling many thousands of proteins on a single matrix with intense resolution, separating even isoforms differing in post-translational modifications as minor as a single deamidation event. Other post-translational modifications detectable by 2-D PAGE include phosphorylation and glycosylation. In conjunction with major advances in image analysis and improved statistical tools, it is possible to define those proteins whose expression alters under defined conditions. As a result, skilled scientists are able to minimise the experimental variance and produce reliably quantitative data. Mass spectrometry (MS) has become the method of choice for protein identification and characterization following separation by 2-D PAGE. This is achieved by either protein mass fingerprinting using MALDI-TOF mass spectrometry or de novo sequencing using electrospray MS. MS is a rapidly evolving discipline and both of these methodologies together have recently seen major improvements in terms of sensitivity and automation. However, although a powerful technique, there are limitations to 2-D PAGE based approaches. A typical two dimensional gel electrophoresis pattern is shown in Fig. (2). The most important drawback of 2D-PAGE-based proteomics is its inability to analyze the entire proteome. Proteins displayed in a single 2D gel represent only a portion of all the proteins that are present in a sample. The low-abundance proteins are mostly missed out in 2-D PAGE. The limit for the detection of proteins with silver staining is approximately 1 ng, (i.e., 20 fmol for a 50 kDa protein). Proteins, which are very small and very large proteins, alkaline proteins, and hydrophobic proteins, are also not open to identification and analysis by 2D-PAGE. One particular class of proteins that is not readily amenable to 2D-PAGE is membrane proteins. 2D-PAGE is labor-intensive and has a relatively low throughput. The throughput of 2D-PAGE is adequate for many basic research studies, but it may present a serious obstacle for projects that involve screening of a large number of clinical samples, or for any other research where the analysis is critical. The narrow range of silver stain is a limiting factor in the accuracy of the method. Another troublesome aspect is that different proteins have different staining characteristics. Coomassie Blue stain is more suitable for protein quantification, but the sensitivity is significantly lower than the sensitivity of silver staining. MS analysis of proteins from silver-stained 2D gels is not routine. Silver stain detects proteins down to the fmol level and, consequently, the quantity of protein in a silver-stained spot is usually low. The amount of sample available for analysis is further reduced through



losses that occur during the preparation of peptide digests. MS data from digests of silver-stained proteins may contain only a few peptide signals, which may not be enough for unambiguous protein identification. Because of the low amounts of the analyte, analysis of proteins from silver-stained gels is also more susceptible to interferences from sample contamination [18]. One particularly bothersome class of contaminating proteins is keratins from human skin and hair that can create a serious problem for MALDI-ToF-MS. Despite these disadvantages the technique remains the major established methodology for delivery of protein separation data.

Fig. (2). A typical two-dimensional gel pattern of liver proteins as adapted from Cutler, P (Cutler, P., 2003). The liver proteins are exquisitely separated on the two dimensional electrophoresis gel yielding a pattern easy for identification of individual proteins.

SOLUTION-BASED PROTEOMICS Due to the recognized limitations of 2D-PAGE, a significant amount of effort has been focused on the development of alternative, non-gel methods of resolving proteome samples prior to MS analysis. Yates and co-workers [19-22] developed an early example showing the utility of solution-based fractionation techniques. Their separation method, called MudPit, is a hyphenated strong cation exchange/reversed-phase liquid chromatography (i.e., SCX/RPLC) separation method coupled online with MS detection. In the MudPIT experiment, the entire proteome sample is digested with trypsin and the tryptic peptides were systematically resolved based on charge in the first dimension (i.e., SCX) and hydrophobicity (i.e., RPLC) in the second. The peptide mixture is loaded onto a SCX column and discrete fractions are displaced directly onto the RPLC column, and are then separated and eluted from the RP column and directly analyzed by MS/MS to identify the eluting peptides. To maximize sample transfer from the stationary phases, the SCX and RP stationary phases are sequentially packed into a single capillary column.


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MASS SPECTROMETRY (MS): A POWERFUL TOOL IN PROTEOMICS Sequencing of an entire genome (genomics) or analysis of multitudes of gene transcripts using mRNA array technologies (transcriptomics) are efficient tools in studying the genetic make up of diverse organisms including the human genome. Like genomics and transcriptomics, the current state-of-the-art proteomic techniques relate strongly to the development of specific new methodologies and instrumentation that have come up in the past two decades. This includes mass spectrometry (MS), protein/peptide fractionation techniques, and bioinformatics to name a few. The current chapter will be focusing on mass spectrometry and its applications in understanding the structure of proteins and peptides. Mass spectrometry is an analytical technique which determines the mass-to-charge (m/z) ratio of ions [23]. It is generally used to find the composition of a physical sample by generating a spectrum representing the individual masses of the components of a sample. It has several broad applications exemplified by the following •

Identifying unknown compounds by the mass of the compound and/or fragments thereof.

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Determining the isotopic composition of one or more elements in a compound.

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Determining the structure of compounds by observing the fragmentation of the compound.

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Quantitating the amount of a compound in a sample using carefully designed methods (mass spectrometry is not inherently quantitative).

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Studying the fundamentals of gas phase ion chemistry (the chemistry of ions and neutrals in vacuum).

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Determining other physical, chemical or even biological properties of compounds with a variety of other approaches.

In the analysis of complex proteomes, intact proteins are rarely used for identification; rather, they are more typically digested by chemical or enzymatic means into their constituent peptides. These are then identified in the MS mode to produce a ‘‘peptide map’’ or a ‘‘peptide fingerprint’’. These measured masses can be used to compare with theoretical peptide maps to identify the protein. Mass spectrometers measure the mass/ charge ratio of analytes. For protein studies, this can include intact proteins and protein complexes, fragment ions produced by gas-phase activation of protein ions (top-down sequencing), peptides produced by enzymatic or chemical digestion of proteins (mass mapping), and fragment ions produced by gas-phase activation of mass-selected peptide ions (bottom-up sequencing) [24]. The application of mass spectrometry and MS/MS to proteomics takes advantage of the vast and growing array of genome and protein data stored in databases. The information produced by the mass spectrometer, lists of peak intensities and mass-to-charge (m/z) values, can be manipulated and compared with lists generated from ‘‘theoretical’’ digestion of a protein or ‘‘theoretical’’ fragmentation of a peptide. Applications to analyze piezomolar quantities of sample are driving the development of more sensitive mass spectrometers, as well as low flow, high-resolution separation technologies, to provide structural information on individual components in complex mixtures of thousands of proteins



derived from biological samples. Protein identification by mass spectrometry requires interplay between mass spectroscopy instrumentation and gas phase peptide chemistry. Mass spectroscopy instrumentation deciphers the ionization, activation and detection of molecules while gas phase peptide chemistry identifies the bonds broken and the rate of cleavage. The bond cleavage depends on factors such as peptide/protein charge state, size, composition and sequence. MASS SPECTROSCOPY Mass Spectrometers A mass spectrometer is a device that produces the mass spectrum of a sample to find its composition. The first mass spectrography technique was described in an 1899 article by English scientist J.J. Thomson. Arthur Jeffrey Dempster and F.W. Aston in 1918 and 1919 [23] respectively devised the processes that more directly gave rise to the modern versions. The schematic view of a mass spectrophotometer is outlined in Fig. (3).

Fig. (3). Schematic representation of a mass spectrophotometer; the basic instrument.

The basic principle used in a mass spectrometer is that different molecules have different masses. For example, table salt (NaCl), is vaporized (turned into gas) and broken down (ionized) into electrically charged particles, called ions, in the first part of the mass spectometer. The sodium ions and chloride ions have specific molecular weights. They also have a charge, which means that they will be moved under the influence of an electric field. These ions are then sent into an ion acceleration chamber and passed through a slit in a metal sheet. A magnetic field applied to the chamber pulls on each ion equally and deflects them onto a detector. The lighter ions deflect further than the heavy ions because the force on each ion is equal but their masses are not (this is derived from the equation F = ma which states that if the force remains the same, the mass and acceleration are inversely proportional). The detector measures exactly how far


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each ion has been deflected, and from this measurement, the ion's 'mass to charge ratio' can be worked out [25, 26]. From this information it is possible to determine with a high level of certainty what the chemical composition of the original sample was. Ion Source The ion source is the part of the mass spectrometer that ionizes the material under analysis (the analyte). Magnetic or electrical fields then transport the ions to the mass analyzer. Techniques for ionization have been instrumental in determining what types of samples can be analyzed by mass spectrometry. Electron ionization and chemical ionization are used for gases and vapors. In chemical ionization sources the analyte is ionized by chemical ion-molecule reactions during collisions in the source. Two techniques often used with liquid and solid biological samples include electrospray ionization [9, 27] and matrix-assisted laser desorption/ionization [28, 29]. Inductively coupled plasma sources are used primarily for metal analysis on a wide array of samples types. Others include fast atom bombardment (FAB), thermo spray, atmospheric pressure chemical ionization (APCI), secondary ion mass spectrometry (SIMS) and thermal ionization. Mass Analyzer Mass analyzers separate the ions according to their mass per charge (m/z). There are many types of mass analyzers usually categorized on the basis of the principles of operation. Sector MS: It uses an electric and/or magnetic field to affect the path and/or velocity of the charged particles in some way. The force exerted by electric and magnetic fields is defined by the Lorentz force law: F=q (E + v x B) where E is the electric field strength, B is the magnetic field induction, q is the charge of the particle, v is its current velocity (expressed as a vector). All mass analyzers use the Lorentz forces in some way either statically or dynamically in mass-to-charge determination. Sector instruments change the direction in which ions are flying through the mass analyzer. The ions enter a magnetic or electric field which bends the ion paths depending on their mass-to-charge ratios (m/z), deflecting the more charged and fastermoving, lighter ions more. The ions eventually reach the detector and their relative abundances are measured. The analyzer can used to select a narrow range of m/z's or to scan through a range of m/z's to catalog the ions present. TOFMS: Perhaps the easiest to understand is the Time-of-flight (TOF) analyzer. It increases the kinetic energy of ions by passage through an electric field and measures the time they take to reach the detector [30, 31]. Although the kinetic energy is the same, the velocity is different so the lighter more highly charged ion would reach the detector first. QMS: Quadrupole mass analyzers use oscillating electrical fields to selectively stabilize or destabilize ions passing through a RF quadrupole field. QIT: The quadrupole ion trap works on the same physical principles as the QMS, but the ions are trapped and sequentially ejected. Ions are created and trapped in a mainly



quadrupole RF potential and separated by m/z, non-destructively or destructively. There are many mass/charge separation and isolation methods but most commonly used is the mass instability mode. The cylindrical ion trap mass spectrometer is a derivative of the quadrupole ion trap mass spectrometer. Linear QIT: In the linear quadrupole ion trap the ions are trapped in a 2D quadrupole filed instead of the 3D quadrupole field of the QIT. FTMS: Fourier transform mass spectrometry measures mass by detecting the image current produced by ions cyclotroning in the presence of a magnetic field. Instead of measuring the deflection of ions with a detector such as an electron multiplier, the ions are injected into a Penning trap (a static electric/magnetic ion trap) where they effectively form part of a circuit. Detectors at fixed positions in space measure the electrical signal of ions that pass near them over time producing cyclical signal. Since the frequency of the ions' cycling is determined by its mass to charge ratio, performing a Fourier transform on the signal can deconvolute this effect. Each analyzer type has its strengths and weaknesses. In addition, there are many more or less common mass analyzers. Many mass spectrometers use two or more mass analyzers for tandem mass spectrometry (MS/MS). Detector The final element of the mass spectrometer is the detector. The detector records the charge induced or current produced when an ion passes by or hits a surface. In a scanning instrument the signal produced in the detector during the course of the scan versus where the instrument is in the scan (at what m/z) will produce a mass spectrum, a record of how many ions of each m/z are present. Typically, some types of electron multiplier are used, though other detectors (such as Faraday cups) have been used. Because the number of ions leaving the mass analyzer at a particular instant is typically quite small, significant amplification is often necessary to get a signal. Microchannel Plate Detectors are commonly used in modern commercial instruments. In FTMS, the detector consists of a pair of metal plates within the mass analyzer region that the ions only pass near. No DC current is produced; only a weak AC image current is produced in a circuit between the plates. Types and Applications Mass spectroscopy was earlier limited to volatile compounds and therefore was largely a technique for organic chemists. The development of methods of ionization which could be applied to non-volatile compounds were developed in the last two decades. This widened the spectrum of application of mass spectroscopy which could then be applied to biological molecules which were not studied till then due to their non volatile nature. The major development in this field was the development of ‘soft ionization techniques’. These award winning developments were the development of electrospray ionization (ESI) and of soft laser desorption (SLD) by John Fenn and Koichi Tanaka (2002, Nobel Prize in Chemistry) [9, 10]. An improved SLD method, matrix-assisted laser desorption/ionization (MALDI), was developed by Franz Hillenkamp and Michael Karas in 1988. Improved versions of these techniques have come up in the recent past which will be described further in the chapter.

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ELECTROSPRAY IONIZATION (ESI) Electrospray Ionisation (ESI) is one of the Atmospheric Pressure Ionisation (API) techniques and is adapted suitably to the analysis of polar molecules ranging from less than 100 Da to more than 1,000,000 Da in molecular weight. An ESI module is simplified in Fig. (4).

Fig. (4). Schematic representation of the principle of ESI.

Electrospray ionization (ESI) occurs during the electrostatic nebulization of a solution of charged analyte ions by a large electrostatic field gradient. The advantage of ESI is that highly charged droplets are formed at near atmospheric pressure. Although the exact mechanism awaits full experimental verification, it is understood that molecular ions are produced from liquid solution under mild conditions. ESI was originated by Dole et al. in 1968 [32]. The experiments were extended by Fenn et al. after a gap of almost 10 years. The work of Fenn outlined the fundamental aspects of ESI demonstrating its application in the analysis of biomolecules of modest molecular weights. Access to higher molecular weights by the production of ions bearing multiple charges was demonstrated later. ESI is especially useful in producing ions from macromolecules because it overcomes the propensity of these molecules to fragment when ionized. In electrospray ionization a liquid is pushed through a very small charged, usually metal, capillary. The liquid contains the substance which is to be studied, the analyte, as well as a large amount of solvent, which is usually much more volatile than the analyte. The charge contained in the capillary is transfered to the liquid and the analyte molecule becomes charged. As like charges repel, the liquid pushes itself out of the capillary and forms a mist or an aerosol of small droplets about 10µm across. A neutral carrier gas, such as nitrogen gas, is sometimes used to help nebulize the liquid and to help evaporate the neutral solvent in the small droplets. As the small droplets, now suspended in air, evaporate the charged analyte molecules are forced closer together. The proximity of the molecules becomes unstable as the similarly charged molecules come closer together



and the droplets once again explode. This is referred to as Coulombic fission because it is the repulsive Coulombic forces between charged analyte molecules that drive it. This process repeats itself until the analyte is free of solvent and is a lone ion [33]. There remains debate as to the exact mechanisms of electrospray processes particularly in the later part of the process as the lone ion is formed. The lone ion then continues along to the mass analyzer of a mass spectrometer. There are two major competing theories about the final production of lone ions, the charged residue model (CRM) and the ion evaporation model (IEM). The CRM states that Coulombic fission (explosions) continues until a lone ion is formed. The IEM, however, suggests that ions are evaporated (usually surrounded by a layer of solvent) from the surface of the small droplets produced later in the cascade of Coulombic fissions. It has been suggested that both models probably occur for different analytes/solvents and in the limit of both models they have a tendency to converge. That is to say that the distinction between a droplet containing an analyte molecule and an analyte molecule surrounded by a layer of solvent eventually disappears and columbic fission looks a lot like ion evaporation. The real question is scale and timing and the techniques to definitively determine this are not available. The use of the word "ionization" in "electrospray ionization" is criticized by some due to that many of the ions observed are thought to be preformed in solution before the electrospray process or created by simple changes in concentrations as the aerosolized droplets shrink. It is argued that electrospray is simply an interface for transferring ions from the solution phase to the gas phase. Some of the common immonium ions used as reference ions are listed in Table 1 obtained from [23]. In electrospray processes the ions observed are quasimolecular ions that are ionized by the addition of a proton (hydrogen ion) to give [M+H] (M=analyte molecule, H=hydrogen ion), or other cation such as sodium ion [M+Na], or the removal of a proton [M-H] for example. In electrospray multiply charged ions such as [M+2H] are often observed. For large macromolecules there will often be a distribution of many charge states and the charge on the ions can be great such as [M+25H]. Note that these are all even-electron species. Electrons themselves (alone) have neither been added or removed as with some other ionizations. The formation of ions in electrospray is somewhat homologous to acid-base reactions. Redox reactions do occur and a circuit with measurable current flow exists but atomic and molecular ions are the primary carriers of charge in the solution and gas phases. Recent variant, called electrosonic spray ionization (ESSI) produces ions with one or only a few charge states [34]. In ESI, higher voltages favor lower charged forms, and if a peptide is large, the lower charged forms might not be within the mass range of the mass analyzer. However, lower voltage is better for smaller analytes. Instrument and ionization parameters are a compromise; when analyzing complex samples such as peptide digests that have widely varying chemical properties. Also in ESI there is competition between analytes for charge as they are extruded from the spray droplets. If a protein digest is analyzed by infusion, with no separation of the peptides, only a small number of the most easily ionized peptides are observed. To detect more ions, peptides are separated by a chromatographic method directly coupled to the MS. Thus, only a few peptides elute at the same time, and nearly complete coverage of a protein can be achieved, although the chromatography has its limitations. For instance, the most commonly used method is reverse phase


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chromatography, where peptides bind to beads packed into a column and binding is via hydrophobic interactions with alkyl-terminating chains covalently bound to the beads. When carbon loading is high, smaller or more hydrophilic peptides are recovered in high yield, but the larger or more hydrophobic peptides are poorly recovered; when carbon loading is lower, the larger or more hydrophobic peptides give higher yield, but the smaller, hydrophilic peptides do not bind. To get around these issues, the easiest approach is to produce different types of digests, to achieve complete coverage of a protein. Although it is not yet widely used in practical proteomics studies, ion mobility, in which ions of different cross sections, charge states, and m/z are separated by Table 1.

m/z Values of Common Immonium Ions

Immonium ion (m/z)

Amino acid residue

Major (M) or minor (m) peak

60.04

S

M

70.07

R or P

M

72.08

V

M

73.00

R

m

74.06

T

M

84.08

K or Q

M

86.1

I or L

M

87.09

N or R

M

88.04

D

M

100.09

R

m

101.11

K or Q

M

102.06

E

M

104.05

M

M

110.07

H

M

112.09

R

M

120.08

F

M

126.06

P

M

129.1

K or Q

m

136.08

Y

M

138.07

H

m

159.09

W

M



collisions with a bath gas in a uniform electric field, is being explored as an additional separation that may improve the numbers of peptides that can be successfully identified from a digest of a protein mixture. ESI interface is combined in various ways with different mass analyzers. In ESI quadrupole mass analyzers resolve m/z by applying radio frequency (RF) and DC voltages, allowing only a narrow mass/charge range to reach the detector. Quadrupoles however have low resolution. Commercially available instruments usually have mass/charge limits ranging from 0 to 4000 m/z and at best are normally set to resolve the various 13C isotope peaks for a singly charged ion (which differ by one Da), although the resolution may be intentionally degraded to improve sensitivity. In ESI, multiple charging enables quadrupole mass measurement of molecules >100,000 Da, if the molecule can be charged sufficiently. Variants There exist many variations on the basic electrospray technique. Two important ones are microspray (µ-spray) and nanospray. The primary difference is in the reduced flow rate of the analyte containing liquid; however many other differences, such as the reduced internal diameter of the tubing or lack of nebulization gas, exist because of the difference in flow rate. These variants are important because they generally offer better sensitivity over traditional electrospray. The µ and nano designations refer to the flow rate of the analyte containing liquid; µLiters/minute and nanoLiters/minute respectively. Applications ESI has been used for the analysis of small proteins and large oligopeptides. They all show a distribution of multiply charged molecular ions arising by either proton or alkali ion attachment. The largest protein examined so far is the native covalent dimer of bovine serum albumin with a molecular weight of more than 130kDa. Electrospray ionization is the primary ion source used in liquid chromatography-mass spectrometry and many other advanced forms of spectrometry. This is due to being a liquid-gas interface that is capable of coupling liquid chomatography with mass spectrometry. Electrospray ionization is the method of choice in studying noncovalent gas phase interactions. The electrospray process is capable of transferring liquid-phase noncovalent complexes into the gas phase without disrupting the noncovalent interaction. This means that a cluster of two molecules can be studied in the gas phase by other mass spectrometry techniques. Although ESI is a powerful technique applied to analysis of bioorganic molecules, it has some distinct shortcomings. The flowing nature of ESI demands a large amount of sample. The continuous flow cannot be analysed by the available mass analyzers and therefore it results in sample loss. ESI is also susceptible to ion suppression ion effects. All samples thereofre have to be desalted rpior to application onto an ESI analyser. If complex mixtures are present the highter-concnetration analytes can suppress ion formation by lower concentration analytes. This shortcoming has been addresses in the development of MALDI. MATRIX ASSISTED LASER DESORPTION IONIZATION (MALDI) Koichi Tanaka received 1/4 of the 2002 Nobel Prize in Chemistry for the development of soft laser desorption (SLD), however MALDI itself was developed by

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Franz Hillenkamp and Michael Karas previously to SLD. SLD is not used currently for biomolecules analysis, meanwhile MALDI is widely used in mass spectrometry research laboratories. MALDI deals well with thermolabile, non-volatile organic compounds especially those of high molecular weight and is used successfully in biochemical areas for the analysis of proteins, peptides, glycoproteins, oligosaccharides, and oligonucleotides. It is relatively straightforward to use and reasonably tolerant to buffers and other additives. The mass accuracy depends on the type and performance of the analyser of the mass spectrometer, but most modern instruments should be capable of measuring masses to within 0.01% of the molecular weight of the sample, at least up to 40,000 Da. It is most similar in character to electrospray ionization both in relative softness and ions produced. However unlike ESI the ions in MALDI are produced in pulses, the sample is cocrystallized with a solid matrix that can absorb the wavelength of light emitted by the laser [35]. The ionization is triggered by a laser beam (normally a nitrogen-laser). A matrix is used to protect the biomolecule from being destroyed by direct laser beam. MALDI is based on the bombardment of sample molecules with a laser light to bring about sample ionisation. The sample is pre-mixed with a highly absorbing matrix compound for the most consistent and reliable results. The matrix converts the laser energy into excitation energy for the sample, which leads to sputtering of analyte and matrix ions from the surface of the mixture. In this way energy transfer is efficient and also the analyte molecules are spared excessive direct energy that may otherwise cause decomposition. Most commercially available MALDI mass spectrometers now have a pulsed nitrogen laser of wavelength 337 nm [10, 29, 36, 37]. This basic principle is outlined in Fig. (5).

Fig. (5). The basic principle of MALDI: A diagrammatic representation.

The choice of the matrix is critical in MALDI. The matrix consists of crystallized molecules, of which the three most commonly used are 3,5-dimethoxy-4-hydroxycinnamic acid (sinapinic acid), α-cyano-4-hydroxycinnamic acid (alpha-cyano or alpha-



matrix) and 2,5-dihydroxybenzoic acid (DHB). A solution of one of these molecules is made, in a mixture of highly purified water and another organic compound (normally acetonitrile (ACN) or ethanol). Normally some trifluoroacetate (TFA) is also added. The matrix-solution is then mixed with the analyte molecule (e.g. protein-sample) under investigation. The organic compound ACN allows the hydrophobic proteins in the sample to dissolve into the solution, while the water allows the water-soluble (hydrophilic) proteins to do the same. This solution is spotted onto a MALDI plate (usually a metal plate designed for this purpose). The solvents vaporize, leaving only the recrystallized matrix, but now with proteins spread throughout the crystals. The matrix and the analyte are said to be co-crystalized in a MALDI spot. Although multiply charged ions can be produced, more typically, only singly charged ions are observed in MALDI [38]. Charging can be induced by addition or loss of protons (at acidic or basic pH values for the delivering solvent, respectively), by loss or gain of electrons or by adduction of small ions. It is important to remember the limitations of these methods when interpreting results of analysis of a sample. For example, after digesting a protein with trypsin, the analysis of the digest by MALDI will produce only a limited subset of the expected peptide ions. Peptides must be able to cocrystallize efficiently with the matrix. MALDI predominantly generates singly charged molecular-related ions regardless of the molecular weight; hence the spectra are relatively easy to interpret. Fragmentation of the sample ions does not usually occur. In positive ionisation mode the protonated molecular ions (M+H+) are usually the dominant species, although they can be accompanied by salt adducts, a trace of the doubly charged molecular ion at approximately half the m/z value, and/or a trace of a dimeric species at approximately twice the m/z value. Positive ionisation is used in general for protein and peptide analyses. In negative ionisation mode the deprotonated molecular ions (M-H-) are usually the most abundant species, accompanied by some salt adducts and possibly traces of dimeric or doubly charged materials. Negative ionisation can be used for the analysis of oligonucleotides and oligosaccharides. The laser is fired, the energy arriving at the sample/matrix surface optimised, and data accumulated until an m/z spectrum of reasonable intensity is amassed. The time-offlight analyser separates ions according to their mass (m)-to-charge (z) (m/z) ratios by measuring the time it takes for ions to travel through a field free region known as the flight or drift tube. The heavier ions are slower than the lighter ones. The m/z scale of the mass spectrometer is calibrated with a known sample that can either be analysed independently (external calibration) or pre-mixed with the sample and matrix (internal calibration). The laser is fired at the crystals in the MALDI spot. The spot absorbs the laser energy and it is thought that primarily the matrix is ionized by this event. The matrix then transfers a part of its charge to the analyte (e.g. a protein), thus ionizing it. Ions observed after this process are quasimolecular ions that are ionized by the addition of a proton to [M+H]+, or other cation such as sodium ion [M+Na]+, or the removal of a proton [M-H]- for example. MALDI generally produces singly-charged ions, but doubly-charged ions such as [M+2H]2+ have been observed as well. Note that these are all even-electron species. Ion signals of radical cations can be observed eg. in case of matrix molecules and other stable molecules.

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The proteins are then ready to be extracted into a mass spectrometer. The type of a mass spectrometer most widely used with MALDI is the TOF (time-of-flight mass spectrometer), mainly due to its large mass range [39]. In Time-of-flight mass spectrometry the mass accuracy can be enhanced by increasing the length of the tube for a given detection system. Common TOF-MALDI instruments are equipped with a "reflectron" which acts as an "ion mirror", deflecting molecular ions within an electric field at the end of the tube, thus nearly doubling the traveling distance and increasing precision. MALDI spectrum and the identification scheme of a protein are outlined in Fig. (6).

Fig. (6). The MALDI spectrum of a protein leading to database search and protein identification.

Attributes MALDI has several favorable attributes. Due to the pulsed nature of most lasers, ions are formed in discrete events. If mass analysis is then synchronised with ion formation very little sample is wasted. High level of sensitivity is achieved is obtained especially when coupled to time-of-flight analysers. It can be used to provide analysis of one or more analytes. Singly charged analytes are generated and therefore analysis is rapid and high-throughput analysis can be achieved. A practical advantage is its resistance to salts and buffers. Even then MALDI has some distinct disadvantages. MALDI has problems in being coupled to some mass analysers. The presence of a matrix which facilitates ionization causes a large degree of chemical noise and the ratios below 500 daltons have a lot of background interference.



LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY (LC-MS) LC-MS is an analytical chemistry technique that combines the physical separation capabilities of various chromatographic techniques (Gas/ liquid) with the mass analysis capabilities of mass spectrometry [40]. LC-MS is a powerful technique with high sensitivity and specificity. Generally its applications are oriented towards the specific detection and potential identification of chemicals in the presence of other chemicals in a complex mixture. Various parameters like the size of the column, flow splitting, mass analyzer used and the interface have to be considered for judging the efficacy of the instrument. A major difference between traditional HPLC and the chromatography used in LC-MS is that the scale is smaller with respect to the internal diameter of the column and even more so with respect to flow rate since it scales as the square of the diameter. Initially1mm columns were standard for LC-MS (as opposed to 4.6mm for HPLC). Recently 300µm and 75µm capillary columns have become more prevalent. At the low end of these column diameters the flow rates approach 100nL/min and are generally used with nanospray sources [22, 31, 41-43].When standard bore (4.6mm) columns are used the flow is often split ~10:1. This can be beneficial by allowing the use of other techniques in tandem however at the cost of MS sensitivity. Not splitting the flow generally does not increase sensitivity since the interface is usually concentration dependent and can actually decrease sensitivity. Scaling the chromatography appropriately is the answer for increased sensitivity. Some ion sources/interfaces have been made to be more flow dependent and can handle higher flow rates. Most mass spectrometers can be used in LC-MS however quadrupole and quadrupole ion traps are most common. An interface between a liquid phase technique which continuously flows liquid and a gas phase technique that must remove all but the gas phase ions seems initially to be difficult and was for a long time. The advent of electrospray ionization changed this. The interface is most often an electrospray ion source or variant such as a nanospray source; however fast atom bombardment, thermospray and atmospheric pressure chemical ionization interfaces are also used. Applications Pharmacokinetics LC-MS is commonly used in pharmacokinetic studies (how quickly the drug clears the body) in animals and humans. It is well suited for this application because of its sensitivity and specificity in detecting and quantitating the drug in the complex matricies such as blood and urine of these applications. Proteomics LC-MS is also used in the study of proteomics where the components of a complex mixture are to be detected and identified. The bottom-up LC-MS approach to proteomics generally involves protease digestion (usually Trypsin) followed by LC-MS with peptide mass fingerprinting or LC-MS/MS (tandem MS) to derive sequence of indiviual peptides Fig. (7).

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Fig. (7). A typical peptide mass mapping using LC-MS/MS.

TANDEM MASS SPECTROMETRY (MS-MS) Tandem mass spectrometry (MS-MS) is used to produce structural information about a compound by fragmenting specific sample ions inside the mass spectrometer and identifying the resulting fragment ions. This information can then be pieced together to generate structural information regarding the intact molecule. Tandem mass spectrometry also enables specific compounds to be detected in complex mixtures on account of their specific and characteristic fragmentation patterns. A tandem mass spectrometer is a mass spectrometer that has more than one analyser, in practice usually two. The two analysers are separated by a collision cell into which an inert gas (e.g. argon, xenon) is admitted to collide with the selected sample ions and bring about their fragmentation. The analysers can be of the same or of different types, the most common combinations being: quadrupole - quadrupole magnetic sector – quadrupole magnetic sector – magnetic sector quadrupole – time-of-flight.



Fragmentation experiments can also be performed on certain single analyser mass spectrometers such as ion trap and time-of-flight instruments, the latter type using a post-source decay experiment to effect the fragmentation of sample ions. The basic modes of data acquisition for tandem mass spectrometry experiments are as follows: Product or daughter ion scanning Precursor of parent ion scanning Constant neutral loss scanning Selected/multiple reaction monitoring. Product or daughter ion scanning: the first analyser is used to select user-specified sample ions arising from a particular component; usually the molecular-related (i.e. (M+H)+ or (M-H) -) ions. These chosen ions pass into the collision cell, are bombarded by the gas molecules which cause fragment ions to be formed, and these fragment ions are analysed i.e. separated according to their mass to charge ratios, by the second analyser. All the fragment ions arise directly from the precursor ions specified in the experiment, and thus produce a fingerprint pattern specific to the compound under investigation. This type of experiment is particularly useful for providing structural information concerning small organic molecules and for generating peptide sequence information. Precursor or parent ion scanning: the first analyser allows the transmission of all sample ions, whilst the second analyser is set to monitor specific fragment ions, which are generated by bombardment of the sample ions with the collision gas in the collision cell. This type of experiment is particularly useful for monitoring groups of compounds contained within a mixture, which fragment to produce common fragment ions. Constant neutral loss scanning: this involves both analysers scanning, or collecting data, across the whole m/z range, but the two are off-set so that the second analyser allows only those ions which differ by a certain number of mass units (equivalent to a neutral fragment) from the ions transmitted through the first analyser . Selected/multiple reaction monitoring: both of the analysers are static in this case as user-selected specific ions are transmitted through the first analyser and user-selected specific fragments arising from these ions are measured by the second analyser. The compound under scrutiny must be known and have been well-characterised previously before this type of experiment is undertaken. This methodology is used to confirm unambiguously the presence of a compound in a matrix. This method combines not only high specificity but also high sensitivity [44]. Applications Peptide Sequencing by Tandem Mass Spectrometry The most common use of MS-MS in biochemical areas is the product or daughter ion scanning experiment that is particularly successful for peptide and nucleotide sequencing.


Kulkarni and Rao

Peptides fragment in a reasonably well-documented manner. The protonated molecules fragment along the peptide backbone and also show some side-chain fragmentation. There are three different types of bonds that can fragment along the amino acid backbone: the NH-CH, CH-CO, and CO-NH bonds. Each bond breakage gives rise to two species, one neutral and the other one charged, and only the charged species is monitored by the mass spectrometer. The charge can stay on either of the two fragments depending on the chemistry and relative proton affinity of the two species. Hence there are six possible fragment ions for each amino acid residue and these are labelled as in the diagram, with the a, b, and c” ions having the charge retained on the Nterminal fragment, and the x, y”, and z ions having the charge retained on the C-terminal fragment. The most common cleavage sites are at the CO-NH bonds which give rise to the b and/or the y” ions. The extent of side-chain fragmentation detected depends on the type of analysers used in the mass spectrometer. A magnetic sector – magnetic sector instrument will give rise to high-energy collisions resulting in many different types of side-chain cleavages. Quadrupole – quadrupole and quadrupole – time-of-flight mass spectrometers generate low energy fragmentations with fewer types of side-chain fragmentations. Immonium ions appear in the very low m/z range of the MS-MS spectrum. Each amino acid residue leads to a diagnostic immonium ion, with the exception of the two pairs leucine (L) and iso-leucine (I), and lysine (K) and glutamine (Q), which produce immonium ions with the same m/z ratio, i.e. m/z 86 for L, m/z 101 for K and Q and I. The table of immonium ions has already been mentioned in ESI. An example of an MS-MS daughter or product ion spectrum is illustrated below. The molecular weight of the peptide was measured using standard mass spectrometric techniques and found to be 680.4 Da, the dominant ions in the MS spectrum being the protonated molecular ions (M+H+) at m/z 681.4. These ions were selected for transmission through the first analyser, then fragmented in the collision cell and their fragments analysed by the second analyser to produce the following MS-MS spectrum. The sequence (amino acid backbone) ions have been identified, and in this example the peptide fragmented predominantly at the CO-NH bonds and gave both “b” and “y”” ions. (Often either the b series or the y” series predominates, sometimes to the exclusion of the other). The b series ions have been labelled with blue vertical lines and the y” series ions have been labelled with red vertical lines. The mass difference between adjacent members of a series can be calculated e.g. b3-b2 = 391.21 – 262.16 = 129.05 Da which is equivalent to a glutamine (E) amino acid residue; and similarly y4 – y3 = 567.37 – 420.27 = 147.10 Da which is equivalent to a phenylalanine (F) residue. In this way, using either the b series or the y” series, the amino acid sequence of the peptide can be determined and was found to be NFESEGK (n.b. the y” series reads from right to left!). The immonium ions at m/z 102 merely confirm the presence of the glutamine (E) residue in the peptide. The protein identification by the MS/MS daughter ion scanning is represented in Fig. (8). Generally a protein identification study would proceed as follows: The protein under investigation would be analysed by mass spectrometry to generate a molecular mass to within accuracy of 0.01%. The protein would then be digested with a suitable enzyme. Trypsin is useful for mass spectrometric studies because each proteolytic fragment contains a basic arginine (R) or lysine (K) amino acid residue, and



thus is eminently suitable for positive ionisation mass spectrometric analysis. The digest mixture is analysed by mass spectrometry to produce a rather complex spectrum from

Fig. (8). MS-MS Daughter or Product Ion Spectrum adapted from Johnson, R.S; Biemann, K. (1989).

which the molecular weights of all of the proteolytic fragments can be read. This spectrum, with its molecular weight information, is called a peptide map. For these experiments the Q-Tof mass spectrometer is operated in the “MS” mode. With the digest mixture still spraying into the mass spectrometer, the Q-Tof mass spectrometer is switched into “MS-MS” mode. The protonated molecular ions of each of the digest fragments can be independently selected and transmitted through the quadrupole analyser, which is now used as an analyser to transmit solely the ions of interest into the collision cell. An inert gas is introduced into the collision cell and the sample ions are bombarded by the collision gas molecules causing them to fragment. The optimum collision cell conditions vary from peptide to peptide and must be optimised for each one. The fragment (or daughter or product) ions are then analysed by the second (timeof-flight) analyser. In this way an MS-MS spectrum is produced showing all the fragment ions that arise directly from the chosen parent or precursor ions for a given peptide component. An MS-MS daughter ion spectrum is produced for each of the components identified in the proteolytic digest. Each fragmentation spectrum provides different sequence information and the spectra need to be interpreted carefully (Fig. (9)).

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Fig. (9). Q-TOF Operating in MS-MS Mode (courtesy of Micromass UK Ltd., UK).

The amount of sequence information generated will vary from one peptide to another, Some peptide sequences will be confirmed totally, other may produce a partial sequence of, say, 4 or 5 amino acid residues. Table 2 lists the mass values of amino acid residues in peptides and has been adapted from [23]. Limitations of Mass Spectroscopy The complex nature of cellular proteomes has time and again proved to be a challenge for proteomic technology development. Unlike a genome, a proteome is a highly dynamic entity. Protein expression in a biological system changes with the state of development, in response to environmental stimuli, with the progression of a disease, etc. In addition, different cells within a multi-cellular organism have different proteomes. The number of proteins in a proteome is very large. Although no precise calculations can be made, it is estimated that up to 50,000 protein species may be simultaneously present in a eukaryotic cell. Furthermore, proteins within a proteome are structurally diverse and have various physicochemical characteristics. Because of these considerations, comprehensive characterization of cellular proteomes is an enormous undertaking. 2D-PAGE in combination with MS plays a central role in proteomics. CONCLUDING REMARKS During the last two decades, mass spectrometry applications have revolutionized analysis of proteins, moving from simple studies of purified proteins, blocked N-termini,


Table 2.


Mass Values of Amino Acid Residues in Peptides

Residue Mass Amino acid

Single letter code Monoisotopic

Average

Glycine

G

57.02147

57.052

Alanine

A

71.03712

71.079

Serine

S

87.03203

87.078

Proline

P

97.05227

97.117

Valine

V

99.06842

99.133

Threonine

T

101.04768

101.105

Cysteine

C

103.00919

103.144

Isoleucine

I

113.08407

113.160

Leucine

L

113.08407

113.160

Asparagine

N

114.04293

114.104

Aspartic Acid

D

115.02695

115.089

Glutamine

Q

128.05858

128.131

Lysine

K

128.09497

128.174

Glutamic Acid

E

129.04260

129.116

Methionine

M

131.04049

131.198

Histidine

H

137.05891

137.142

Phenylalanine

F

147.06842

147.177

Arginine

R

156.10112

156.188

Tyrosine

Y

163.06333

163.17

Tryptophan

W

186.07932

186.213

Homoserine lactone

83.03712

83.090

Homoserine

101.04768

101.105

Pyroglutamic Acid

111.03203

111.100

Carbamidomethylcysteine

160.03065

160.197

Carboxymethylcysteine

161.01466

161.181

Pyridylethylcysteine

208.06703

208.284


Kulkarni and Rao

modified peptides, and analysis of peptide synthesis reactions, to the current array of new methods and instruments, as well as inspiration for the new field of systems biology. Proteomics is a significant and upcoming area of research. In the same time, we have shifted from an era where our understanding of protein and peptide gas-phase chemistry was built up slowly, to an era where large datasets can now be mined for rules that rapidly increase our understanding of the fundamental chemical processes. This will enhance identification of components of complex samples and yield significant advances in medicine and biology. The great reliance of the field on mass spectrometry for protein characterization has spurred many advances in mass spectrometry instrumentation, separations technology, and software, and data management capabilities. Yet, this amazing growth has only whetted our appetites, and we can look forward to even more powerful instrumentation and algorithms used in creative ways to yield a rich information source in the future. Protein Arrays The coming to age of miniaturized technology for the interactive studies of molecules has changed the way in which cell biology is studied. Microarray based analysis is a relatively high-throughput technology, which is suited to automation and has the potential for genome and proteome wide analysis. A key advantage of the microarray format is the use of nonporous solid surface such as glass, which permits precise deposition of capture molecules (probes) in a high density and ordered fashion. Various methods exist for creating arrays. Photolithography employs light, directed spatially via a photo mask, to specifically modify surfaces for derivatization. Piezoelectric approaches use accurate dispensing techniques to deliver reagents onto the surface at subnanolitre levels. Microspotting makes use of direct contact with the surface by solid or split pins. In addition, there are means of in situ synthesis of probes onto solid phases. Following creation of the chip, a fluidic system delivers the reagents, a laser scanner reads the chips and sophisticated software analyses and interprets the data. Microarray technology is still at a developing stage. This is mainly because the properties of proteins are changed markedly by the ambient conditions. Proteins do not behave as predictably as DNA when immobilized on solid surfaces, and certain classes may not be expected to be stable under such conditions. One must be aware of the possible changes in protein conformation and hence activity or affinity induced by common operations such as immobilization to a solid surface, derivatization with a fluorescent probe, heating or drying. However, the concept of protein arrays has the potential advantage of being scalable, flexible, ease of automation and performance [45]. Arrays may also enable the control of key parameters such as temperature; pH and cofactor concentration not easily afforded by cell based systems [46]. All proteins from higher eukaryotic genomes may not be recognized by the first generation of arrays. Bearing in mind the aforementioned need to discriminate different isoforms generated by post-translational modification, in conjunction with the need to validate the selectivity and affinity for each analyte, highlights the magnitude of the task required to produce a fully optimized array. Antibodies are relatively stable to immobilization and are attractive ligands in arrays technology. However, the key disadvantage of this technology is the inherent lack of stability of protein reagents. A number of workers have therefore investigated aptamers,



which have affinity for individual protein molecules. In addition, they offer the capability to directly detect interacting proteins using generic protein stains without interference from the probe. The sensitivity of such an approach may not match that achievable by more sophisticated detection approaches. Extensive selection procedure is essential in selecting a particular aptamer. Briefly, this includes binding of proteins to a solid phase for exposure to aptamer libraries and aptamer selection. The importance of aptamers in protein arrays has yet to be demonstrated, and the ability to select them without having the target protein readily available may prove problematic. The need for rapid parallel expression and purification of proteins has led Cahill and co-workers from the Max Planck Institute in Berlin to develop protein expression arrays of tagged proteins in Saccharomyces cerivisiae and Pichia pastoris under the control of inducible promoters. Antibody screening, protein-protein interactions, epitope mapping and protein expression profiling are some of the key areas where arrays seem to have application. Other groups have created arrays using GST fusion proteins. This tag can be used for either purification [47], immobilization [48], or in combination with another tag, for both purification and immobilization [49]. The combination of surface plasmon resonance (SPR) and MS has created a novel approach in protein investigations. SPR quantifies interactions between proteins and ligands while MS deciphers the structural features of the bound proteins. Proteins are captured by affinity tags from solutions using ligands which are covalently attached to the SPR-sensor surface. The technique is non-destructive and therefore retains the active structure of proteins which are directly analysed by MS from the SPR surface or separately after elution and recovery from the surface [48]. The technique was first demonstrated in 1996 and immediately was followed by advancements. Although offering a potential insight into ligand binding, this method cannot be seen as a test of receptor function due to the inability to demonstrate downstream signaling in response to ligand binding. Similarly, protein arrays have been formed in lipid monolayers, using specific lipids to form ordered arrays of both hydrophobic and hydrophilic proteins [51]. As shown in Fig. (10), specific protein capture on microarrays can be performed using affibodies (a), aptamers (b) or antibodies (c, d) as immobilized specific capture reagents. Detection of bound analytes is usually performed by direct labelling of the analyte molecules (a–c) or by antibody sandwich formation (d). Such assays can be used for protein identification and quantification and therefore will be useful tools in multiparametric diagnostics in the near future. The reverse screening approach uses immobilized cell lysates, which represent the whole repertoire of proteins in cells at a distinct state. Unspecific adsorption of protein mixtures can be governed by electrostatic, hydrophobic van der Waals, or metal–chelate interactions. Captured proteins can be identified using mass spectrometry (e) or specific antibodies (f). Different antibodies or patient sera can be used to screen these microarrays for the presence of absence of distinct target proteins (f). Immobilized tissue samples (g) or cells (h) can be used for reverse screening approaches with antibody detection of specific markers as well. Reversed screening assays have a high potential as tools for biomarker detection in proteome analyses. Specific interaction microarrays have been described for receptor–ligand (e.g. small molecule drug candidates, phospholipids) interactions (i), enzyme–substrate interactions (j), protein–protein interactions (k), protein-oligosaccharide interactions (l) and protein–DNA interactions (m) and can be used to identify interaction partners of proteins in a highly parallelized manner.

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Fig. (10). Types of protein interaction and capture microarrays (adapted from Stoll et al., 2004).

Expression Arrays One of the simplest formats of protein array consists of a large number of defined spots on a planar support comprised of reagents capable of recognizing individual proteins. The 96-well microtitre plate format of the standard ELISA can be used in setting up a protein microarray [1,52,53]. New developments may help in reaching the potential of moving from current standard arraying throughput of 96-wells/30s to a more appropriate scale of 5000–10 000 assays. Robotic picking and high-density gridding of phage display libraries provided filter based ELISA format assays. The dream development in protein arrays is the demonstration of methodologies capable of assaying proteins at physiologically relevant concentrations. Applications to date have mainly restricted themselves to relatively low-density arrays. Huang et al. [54,55] recently described an ECL based immunoassay array for the simultaneous assay of 24 cytokines from either cultured media or patient sera. The system was based on the standard sandwich ELISA technology but the initial capture antibodies raised to the various cytokines were transferred in an ordered format onto a membrane. Cytokines were detected at levels as low as 60 pg. Specific analytes in clinical samples have been determined fluorimetrically at physiologically relevant levels by an antibody array using a CCD camera [56]. The scope for increased levels of sensitivity may be realised by use of minaturised ligand binding assays employing affinity capture to “harvest” the analyte [57]. A recent review discussed the relevance of spot density to sensitivity and the limitations of such approaches [58]. Madoz-Gurpide and coworkers [59] have proposed a method of prefractionation prior to arraying proteins. Preferring a sandwich immunoassay format they were able to demonstrate subpicomole sensitivity in lung adrenocarcinoma lysates. Using a similar direct labeling approach, protein expression



changes have been described for colon carcinoma following exposure to ionizing radiation [60]. In an array of almost 2000 proteins probed with 146 antibodies, a limit of detection of approximately 6 pg was obtained. A number of protein expression changes were observed including proteins implicated in apoptosis, signal transduction and activation of transcription. In the suspension array probe molecules are bound to the surface of colour coded microspheres in a microfluidics based instrument using sophisticated optics. The colour coded microspheres identify the reaction. A second molecule is used as a reporter of the magnitude of the interaction. The two colour coding of the sphere and the reporter define the assay and the magnitude of the result respectively. Although currently limited to 100 analytes, recent developments in the area of quantum dots, a technology being developed independently of the Luminex system, may ultimately offer an increased capacity for multiplexing bead based assays [61]. Although microspheres are currently limited to approximately one hundred, quantum dots are nanocrystals of semiconducting material, which emit light at very precise wavelengths, dependent on the spot size. Defined beads may be prepared by the quantum dot technique. Different types of planar and bead-based arrays are shown in Fig. (11). Either planar microarrays or bead-based arrays can be employed for multiplexed ligand-binding assays. Planar microarrays can be generated with multiples of different capture spots whereas multiplexing in bead based arrays is limited to the number of distinguishable beads. In planar arrays analyte spots are easily distinguishable by their xy-coordinates in the array. Detection on planar arrays is performed using chemoluminescence, radioactivity, mass spectrometry or fluorescence. The latter is the preferred detection method for bound analytes in bead-based microarray assays. Interaction Arrays Interaction studies involving different proteins have wide reaching importance in understanding biological and pharmacological processes. Immunoprecipitation is a classical technique used in these interactive studies. This relatively simple technique has found wide applicability but can be complicated by nonspecific interactions which can confound the interpretation of data. Tandem affinity purification (TAP) has been described as a method to overcome this [62], although this is a relatively low-throughput technique. Interaction methodologies in a microarray format would be extremely advantagous. A universal protein array for quantitative detection of protein interactions with a range of proteins, nucleic acid and small molecules has been reported by Ge [63]. A key aim of the study was to produce low-density arrays of purified, native proteins as a sensitive screen for proteins drug targets in disease. Probably the first example of a protein array applied to an entire genome was demonstrated by Zhu et al. [64,65] who, overexpressed and purified 5800 ORFs from yeast, representing in excess of 90% of the yeast genome as double N-terminal GST, His6-fusion proteins in yeast. A 96-well format glutathione affinity purification was used for rapid parallel isolation. The isolated protein was immobilized via the His6- tag, on nickel coated glass slides and they were able to screen for protein and phospholipid interaction at a near genomic scale. A new interaction motif was described for binding with calmodulin. Notwithstanding the limitations of detecting protein interactions in vitro on solid surfaces, the studies were able to classify phospholipid binding in terms of both specificity and strength of binding. This approach demonstrates the possibility of not only high-density applications but the potential for genomic coverage on a functional chip.

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Fig. (11). Different types of planar and bead based microarrays.

A biosensor technology was applied for soluble recombinant insulin-like growth factor receptor domains isolated on a chip to define protein-protein interactions [66]. This enabled protein domains to be characterized in terms of binding of ligand, binding of other proteins and provided further information about the function of the nonbinding domains. Functional Arrays A key goal of proteomics is to assign function to proteins. The determination of protein function in an array format offers a number of advantages. Kodadek [67] prophesied that functional protein arrays composing of native proteins will precede the development of protein expression arrays. This is in part due to the need for the



requirement for high throughput isolation of high selectivity ligands and the requirement for sensitive methods of detection without derivatisation [68-70] discusses the use of common hosts (e.g. E. coli) and their ability to express proteins with correct folding which is highly significant in the field of protein expression arrays where various claims have been made for expressing functional arrays. Zhu and coworkers [64,65] have developed a method for expression of yeast genes and have expressed 119 of a predicted 122 yeast kinases, as GST-fusion proteins via a high copy expression vector under the control of a galactose-inducible promoter. Future Aspects Some of the challenges that will be faced by researchers attempting to implement high-throughput microarray technologies for proteomics include maintaining diverse protein activities on chips, especially membrane proteins, creating sufficiently diverse antibody or ligand libraries and finding attachment strategies that allow threedimensional access for binding. Although these hurdles may be difficult to surmount, it is likely that protein micro- and macroarrays will bear fruit, especially when complemented by other ‘‘classic’’ proteomics technologies. In summary, protein analytical applications in the future will benefit from the explosion of new DNA- based technologies in array and detection [47]. The rapid growth of the proteomics market and the climate for new technology is driving the new generation of companies and academic efforts that are developing novel protein microarray techniques for the future. ACKNOWLEDGEMENTS Aarohi Kulkarni acknowledges the Council of Scientific and Industrial Research for Research Fellowship. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]

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151

NMR Spectroscopy Based Metabonomics: Current Technology and Applications Clare A. Daykin1,* and Florian Wülfert2 1

School of Pharmacy, University of Nottingham, University Park, Nottingham, UK, NG7 2RD and 2School of Biosciences, University of Nottingham, Sutton Bonington, Loughborough, Leicestershire, UK, LE12 5RD Abstract: Metabonomics has, in the past decade demonstrated enormous potential in furthering the understanding of disease processes, toxicological processes, phenotypic outcome of gene expression and biomarker discovery. However, implementation of metabonomic methodology requires the development of generic, rapid, advanced analytical tools to comprehensively profile biosample (biofluids and tissues) metabolites. 1H NMR spectroscopy is arguably the most powerful tool for profiling of biosamples with inherent advantages over other analytical techniques e.g.: 1) lack of requirement for extensive sample preparation; 2) non-destructive; 3) non-equilibrium perturbing; 4) small (down to 1 µl) sample volumes and 5) quantitative and qualitative information are available from the same data set. Whilst conventional ‘target analysis’ approaches may apply more sensitive analytical methods than 1H NMR spectroscopy for detection of low metabolite levels in biological materials, the ‘target analysis’ approach involves consideration of critical biochemical pathways and pre-selection of the metabolites of interest. The subjective selection of a battery of biochemical methods is then required which is a necessary, but complex and time-consuming process and if an inappropriate or restricted range of biochemical methods or parameters are chosen, important metabolic disturbances may be over-looked. On the other hand, the use of 1H NMR spectroscopy to follow biochemical responses does not require such a pre-selection of metabolites and allows subsequent multicomponent analysis, without bias imposed by the experimenters’ expectations, hence it has been demonstrated in a wealth of publications that NMR spectra of biosamples are extraordinarily rich in information on endogenous biochemical processes in both good health and disease. This review will aim to discuss technical developments relevant to the field, commonly used data handling and data analysis strategies and the use of metabonomics as a tool to facilitate our understanding of health status at the metabolic level.

*Corresponding author: Tel: (+)44 115 9515052; Fax: (+)44 115 9515102; E-mail: [email protected]

Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.


Daykin and Wülfert

INTRODUCTION Systems Biology Whilst the definition of systems biology, also previously referred to as ‘bionomics’ [1] or ‘systeomics’ (a trademark name used by the Californian Separation Society) [2], is highly variable in the literature, our working definition for systems biology is ‘the study of biology as an integrated system of genetic (genomics), protein (proteomics), metabolite (metabonomics or metabolomics), cellular and pathway events that are in flux and interdependent’ [3, 4]. In more general terms, systems biology has come to mean an integrated approach to e.g. drug discovery where the effects of compounds on whole pathways and networks of pathways are considered, as opposed to their effects on only isolated endogenous molecules. To give a brief overview of each of the major *omics technologies, which together make up ‘systems biology’ in turn, genomics involves the observation of differential gene expression as a result of external influence such as toxicity, disease, xenobiotic exposure, or environmental factors. The technology involves new generations of proprietary “gene chips”, which are small, disposable, but expensive devices, encoded with an array of genes that respond to extracted cellular mRNAs produced as a result of external influence, the effect of which is the switching on or off of various genes [e.g. 57]. However, although many genes can be placed on a chip array and patterns of gene switching can be monitored rapidly, the technology is not cheap (although the technology is cheaper than it was several years ago). Furthermore, relationships between gene regulation/expression and the integrated function and control of cellular systems (so-called functional genomics) are still far from clear and are likely to remain so for many years. The main reason for this is that the vast majority of DNA is non-coding, yet genes i.e. DNA sequences which code for proteins, can not function as isolated units without the presence of neighboring genes and non-coding DNA. This lack in the understanding of the biological consequences of altered gene expression led to the development of proteomics, a term first suggested by Australians Marc Wilkins and Keith Williams in 1995 and which is defined as, “the study of proteins, how they're modified, when and where they're expressed, how they're involved in the metabolic pathways and how they interact with each other” [8, 9]. However, amongst other problems, the 2D gels used in the early days of proteomics were notoriously irreproducible and labor-intensive. Since this time, several significant advances have begun to overcome some of the formidable obstacles associated with proteomic analysis. For example, Washburn and Yates [10, 11] led the way from 2D gels to multidimensional liquid chromatography (LC) coupled with mass spectrometry (MS), which has resulted in higher throughput, greater proteome coverage and improved dynamic range and sensitivity. Wilm and Mann [12] developed nano-electrospray, which further enhanced sensitivity limits. However, despite these advances, and the fact that proteomics is potentially less expensive than genomics; it is still very slow and labor intensive at present. More importantly, although these measurements may ultimately provide new biomarkers of disease, lead to new drug discovery targets and provide insights into toxicological mechanisms, it is currently very difficult to relate genomic and proteomic findings to classical indices of toxicity. Explanations for this include limitations in terms of both speed and sensitivity of the current technology, which

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precludes the measurement of a detailed time-course of the proteomic response to disease or drug exposure and the measurement of responses is usually focused on one organ such as liver and hence effects in other important tissues such as kidney, lung or heart muscle could be overlooked. The most recent of the *omics technologies is metabo*omics. However, whilst metabonomics or metabolomics are considered relatively new technologies, with the terms and their definitions not coming into existence until late last century/ early this century [13, 14], a large number of publications reporting methods for the metabolic profiling of biological fluids date back to the 1970’s [e.g. 15-17]. Since this time, Jeremy Nicholson and his group based at Imperial College, London in the UK has carried out much of the pioneering work in NMR spectroscopy-based metabonomics whilst the group of Jan van der Greef from Leiden University in the Netherlands has lead the way in the parallel LC-MS based arena. In both cases, their published work in this field dates back to the early 1980’s [e.g. 18-23]. Now, the field is sufficiently advanced that numerous companies offer metabo*omic analysis as a contract research service. Although metabo*omics is complementary to genomics and proteomics, it also has certain advantages. In particular, the metabonome (or metabolome) is further down the line from gene to function and so reflects more closely the activities of the cell or organism at a functional level. Furthermore, metabolic fluxes are not regulated by gene expression alone, but all physiological perturbations result in disturbances in the ratios and concentrations, binding or fluxes of endogenous biochemicals, either by direct chemical reaction or by binding to key macromolecules that control metabolism. If these disturbances are of sufficient magnitude, they will affect the efficient functioning of the whole organism. In bodyfluids, metabolites are in dynamic equilibrium with those inside cells and tissues and, consequently, abnormal cellular processes in tissues of the whole organism following a toxic or metabolic insult will be reflected in altered biofluid compositions. Thus whilst genomics and proteomics characterize the potential for relevant effects, metabo*omics actually catalogues the direct effects of pathological processes. Nomenclature: Are Metabonomics and Metabolomics the Same? As the most recent addition to the ‘systems biology’ toolbox, the definition and even in fact, the name, metabo*omics, is less well defined than e.g. genomics or proteomics, as reflected by the fact that different authors use different definitions for similar terms or conversely, the same approach is given different names, e.g. metabonomics [13], metabolomics [24] and metabolic profiling [25]. Whilst some research groups use these terms interchangeably, others argue that the terms refer to philosophically different aims of analysis. For example, Nicholson et al. previously defined metabonomics as: “quantitative measurement of multiparametric response of living organisms to (patho)physiological stimuli or genetic modification” [13] and later extended this definition with: “an approach to understanding global metabolic regulation of an organism and its commensal and symbiotic partners” [26]. Nicholson argues that the name ‘metabonomics’ incorporates the analysis of products of non-enzymatic reactions, otherwise known as metabonates, which can interact with and influence metabolite formation [26]. Groups adhering to this definition use the term


Daykin and Wülfert

metabolomics to discuss only work dealing with simple cell systems and mainly intracellular metabolite concentrations. Aims of Analysis A change in environmental, genetic or chemical (e.g. xenobiotic, nutriceutical, functional food and cosmaceutical) factors to a cell, tissue or whole, complex organism will cause changes in the ratios, concentrations and fluxes of endogenous biochemicals in or through key intermediary metabolic pathways. In cases of mild change, cells will attempt to maintain homeostasis and metabolic control by varying the composition of the body fluids that either perfuse through them or are secreted by them and consequently, following metabolic perturbation there are characteristic alterations in biofluid composition, which are organ- and mechanism-specific. To investigate these complex metabolic consequences to metabolic stresses, such as disease processes, toxic reactions or genetic manipulation, non-selective, but specific “informanation-rich” analytical approaches are required. Whilst this review is limited to the recent advances in NMR spectroscopy based metabonomics, several analytical methods in addition to NMR spectroscopy can and do serve as powerful means of generating multivariate metabolic data [27, 28] including; direct injection mass spectrometry (MS) [29], GC/MS [25, 30], HPLC/MS [31], HPLC-DAD [32] and optical spectroscopic techniques [33, 34]. It is also recognized that no single analytical technique is, at the present time, capable of covering the entire metabonome. Therefore, a range of techniques and approaches will be required in order to gain as comprehensive coverage of the metabonome as possible. NMR TECHNOLOGY AND METABONOMICS Background to the Role of NMR Spectroscopy in Metabonomics A key point of metabonomics studies is to have in disposition one or more generic analytical methods for rapid biological sample profiling. NMR spectroscopy is a relatively mature technique in this field and it is well established that high resolution NMR spectroscopy can provide useful qualitative and quantitative biochemical information relating to the metabolic status of a person, animal or cell system [32]. Various studies have demonstrated the application of 1H NMR spectroscopic analysis to almost every imaginable biofluid or tissue type, including amongst others; urine [35], blood plasma [36-38] or serum [39, 40], bile [41-43], seminal fluid [44, 45], cerebrospinal fluid [46-48], kidney [49-51], liver [51] and heart [52]. Furthermore, the spectral assignment of many endogenous metabolite peaks has been made previously through the concerted use of 1- and 2-dimensional NMR methods [53-56]. Proton NMR has traditionally dominated the biofluid NMR field, due to a number of advantages, such as the experimental simplicity, the speed at which data can be generated and the large amount of data which can be generated in a relatively short timeframe. Whilst other nuclei, in particular 13C and 31P can prove useful for metabonomics, traditionally their use has been limited in large studies due to the long experiment times involved. As NMR spectroscopy becomes more sensitive and technology such as cryoprobes become more widely available it is expected that the exploitation of 13C NMR and 31P NMR will become more widespread.

NMR Spectroscopy Based Metabonomics


The features of NMR spectroscopy that prove useful in this type of study are summarized in Table 1. Table 1.

Summary of Useful Features of 1H NMR Spectroscopy in Biofluid Studies (Reproduced From [37] with Modifications)

Feature

Comment

Selectivity

There is no need for the pre-selection of analytical conditions based on the chemical properties of the analyte, or postulation of the metabolites affected by a disease or toxicological process. Thus, a wide range of low MW metabolites and macromolecules can be simultaneously monitored in a short space of time, without prior knowledge or expectation of the results.

Non-Invasive

NMR spectroscopy is non-invasive, non-destructive and non-equilibrium perturbing. It thus allows subsequent analysis of a sample with other techniques.

Speed

A typical single pulse 1H NMR biofluid spectrum is obtainable in aR but vR is very small and hence the receptors are mostly isolated from each other. If on the other hand the ligand is much larger than the receptor (and in order for the response to be measurable, vR should be large), both vL(t) and vL(∞) will depend on both vR and aL, and on vL, i.e. the fraction of occupiable sites occupied. Defining θ* = θ/θ∞

(13)

where θ∞(σ) = θJ

1 + 0.3136σ2 + 0.45σ3 1–

,

1 + 1.8285σ + 0.5075σ3 + σ7/2

(14)

one has [18, 19] φ(θ*, σ) = (1 – θ*)(1 – B1θ* – B2θ*2)

(15)

for substitution into eqn (10), where the constants are: 0.7126 + 1.404σ1/2 B1 =

(16)

1/σ + 3.4363 + 2.4653σ1/2

and 0.07362 + 0.1204σ1/2 B2 =

1/σ + 0.5443 + 0.2725σ1/2

.

(17)

A Versatile Technique For Drug Discovery


The experimentally available parameters are vR (measured at the close of receptor deposition) and vL(t), i.e. the kinetic binding curve. Eqn. (10) with the appropriate substitutions can then be fitted to the vL(t) data in order to determine as many of the unknown parameters (including ka) as the experimental resolution—usually extremely high using OWLS—permits. Dissociation is measured by flooding the system with ligand-free solution, in which case we have dvL/dt = –kd(t)vL ;

(18)

good results may often be obtained by combining eqns (10) and (18) and carrying out a global fit with a change of boundary conditions (the ligand concentration in solution goes to zero) at the initiation of flooding. Time-dependent dissociation coefficients kd(t) are discussed fully elsewhere (e.g. [17]). Conformational Changes: Several waveguide parameters are useful in obtaining indications of receptor conformation. If the dimensions of a molecule are known, and if it is significantly aspherical, the average orientation of a layer of molecules deposited on the waveguide can be directly obtained from the adlayer thickness dA. The molecular orientation can also be derived from measurements of the birefringence of the adlayer, i.e. the ordinary (no) and extraordinary (ne) refractive indices. The form factor u for a layer constituted from substances 1 and 2 of differing refractive indices n1 and n2 respectively is given by [11]. u=

nA2 nB2

nm2 - no2

no2

ne2 - no2

(19)

where nm is the mean refractive index of the layer, nm =

2no2 /3 + ne2 /3 .

(20)

If the predominant molecular orientation is perfectly columnar, i.e. perpendicular to the plane of the layer, then u = 0, and if it is perfectly lamellar, then u = ∞. Changes in these parameters, whether mean adlayer thickness, or form factor, or indeed any other parameter characteristic of the polarisability profile perpendicular to the plane of the receptor layer, upon addition of a drug may be used to deduce conformational changes. Typically an experiment will begin with the receptor in equilibrium with a drug-free solution flowing over it. The solution is then switched to one containing the drug (if the concentration is sufficiently high the cover refractive index may then also change, but the new value of nC is included in the mode equations used to determine ne and no, hence this change does not affect the final results). Release of Drugs from Novel Delivery Materials: OWLS is extremely well suited to characterising influx into and efflux from microporous materials [20]. The technique is particularly effective if the delivery material can itself become the waveguide, i.e. the F layer. A typical experiment begins with the F layer in equilibrium with drug-free liquid. The drug is then introduced into the solution, and flows into the pores. If the exact functional form of the kinetics is not of importance, a simple model suffices to relate the


Jeremy J. Ramsden

measured refractive index of the F layer to the refractive indices of the F layer matrix and the drug (respectively nf and nd), and the volume fraction θ of the nanopores [21]: nF = θnd + (1 – θ)nf .

(21)

Here, nd would be the refractive index of the drug solution inside the pores. If the concentration is cd, then nd = cd dn/dc

(22)

(this equation can also be used to ascertain whether the presence of the drug makes a significant difference to the refractive index of the cover medium). The refractive index increment dn/dc is best found by measuring the refractive indices of a range of solutions. The technique is entirely reversible: a drug-loaded nanoporous layer exposed to a drug-free solvent will release its contents into the solvent, and is directly measurable. Should it be impracticable to create the entire waveguiding layer from the drugreleasing material under investigation, the waveguide may be coated with a thin (tens of nanometres) layer of the material of interest [24]. Interactions of Drugs with Lipid Membranes: Not least due to the necessity for nearly every drug to cross at least one bilayer lipid membrane to reach its target, the precise and accurate measurement of drug-lipid interactions is of tremendous importance. As already mentioned, the traditional approach is to measure the oil/water partition coefficient. Octanol is typically used as the oil phase. A significant increase in sophistication was represented by the measurement of the uptake of drugs by lipid membrane vesicles, but the method is cumbersome, possibly unrealistic because of the high curvature of the vesicles compared with natural membranes of interest, and does not allow measurement of dissociation of the drug from the membrane, which is of equal importance to the association. Hence the introduction of OWLS for the determination of drug-lipid membrane interactions was a further very significant increase in sophistication. The first application of OWLS to this domain was for the determination of partition coefficients of drugs between the aqueous solutions and bilayer lipid membranes [22]. Owing to the extremely high sensitivity of OWLS, the drug uptake of a single lipid bilayer coating the waveguide can be accurately measured. As already mentioned, the optical waveguides can be coated with the lipid bilayer either using the LangmuirBlodgett technique [11] or from vesicles [12]. Careful measurements on these so-called supported bilayers has shown that the fluidity of both the upper and lower leaflets is comparable to that of natural cell membranes [25]. The reason for this is that the lower leaflet is actually resting on a cushion of a few molecular layers of water more or less tightly bound to the metal oxide waveguide material. Other experiments have focused on determining the partial molar volume (¯v2) of a drug in a bilayer lipid membrane [23]. The mean molar volume Vm is given by Vm = [x(RM,2 – RM,1) + RM,1](nA2 + 2)/(nA2 – 1)

(23)

where x is the mole fraction of the drug (component 2) in the membrane (component 1), and the RM are the molar refractivities, and nA is the mean membrane refractive index (eqn 20). Partial molar volumes can be easily found from a plot of mean molar volume

A Versatile Technique For Drug Discovery


versus mole fraction of the drug by means of the Gibbs-Duhem equation, for two and components we have (24) Vm = x(dVm/dx) + v–1; – the intercepts at x = 0 and 1 of tangents to this curve yield v and v respectively. 1

2

The partial molar volumes of drugs in membranes at different mole fractions, especially when compared with the molecular volume, permits a wealth of mechanistic interference concerning the drug-membrane interactions to be made [23]. A further advantage of OWLS is that the kinetics of uptake and release of the drug by the membrane can be measured in real time. Most drugs are somewhat amphiphilic, and like any detergent will therefore destroy the membrane if present at sufficient concentration. This may only happen for mole fractions in excess of about one third. For more unusual molecules such as oligopeptides however, this destruction may take place at far lower concentrations, as has been shown to happen with the oligopeptide mellitin [26]. In principle, the permeability of a membrane with respect to a drug can be calculated from the partition coefficient and the uptake and release kinetics. There is also interest in directly measuring this parameter. One possible approach is to coat a porous silicon Fabry-Perot cavity sensor [27] with the membrane. In this way, the rate of accumulation of the drug on the trans side of the membrane can be directly measured. The Response of Living Cells to Drugs: One of the most fascinating recent developments of OWLS has been its application to the quantification, in situ and in real time, of the optogeometric parameters of living cells [28]. This has been shown to work very effectively even though the cell may be one or two orders of magnitude bigger than the penetration depth of the evanescent field. The reason for the success is that the most significant and relevant changes in cell morphology following environmental stimuli tend to be at the interface with the substrate. To carry out such investigations, cells are admitted into a cuvette whose floor is the optical waveguide, upon which they become attached to the waveguide. Very weakly bound cells are removed by a gentle flow of medium, and those remaining may then spread. A single cell is sufficient for measurements to be carried out. Spreading—the transformation from a sphere to a segment—involves extremely significant redistribution of the cell matter, especially at the cell-substrate interface, engendering significant changes in the propagation constants of the guided lightmodes. Thanks to the precision and accuracy of OWLS, the cellsubstrate contact area can be determined from these propagation constants with subnanometre resolution. From the mode spectrum, the cover refractive index nC is determined by solving a mode equation (1). This refractive index depends both on that of the medium bathing the cells and that of the cells themselves, i.e. nM and nK respectively, and the precise functional form of the dependence is determined by the shape of the cells, i.e. [28] nC = s[nKcv' + nM(1/scv')]

(25)

where s is the inverse penetration length of the exponentially decaying evanescent field into the cell-containing medium, c is the number of cells per unit area, and v' is the effective volume of the cells, which is obtained by taking the Laplace transform of the


Jeremy J. Ramsden

cross-section of the cell parallel to the waveguide surface (here use is made of the exponential decay of the evanescent field). For example, for spherical cells of radius r0, the effective volume is v' = 2π(r0 - 1/s)/s2

(26)

and for a spread cell having the form of a segment of radius r and height h v' = π[h(2r - h)/s + 2(h – r)/s2 - 2/s3] .

(27)

If the volume V of the cells is known, as it will be if they were deposited as spheres from culture, then either r or h can be eliminated since the volume of a segment is πh2(r – h/3), assuming that they have neither grown nor shrunk. We have here an excellent method for the rapid non-invasive determination of cell shape and size. The area a in contact with the surface is simply given by πh(2r – h). The most interesting applications of this approach are in investigating how the attachment and spreading responses vary in the presence of drugs, which can very easily be introduced into the medium bathing the cells. Again in contrast to most other techniques, it is very easy to determine the reverse effect, i.e. the drug withdrawal, simply by switching the flow to that of medium not containing the drug, or containing some agent that complexes the drug. New developments in waveguide design (the so-called ‘reverse symmetry’ waveguides [29] with significantly greater penetration depths of the evanescent field) further add to the potential applications of this technique. 5. CONCLUSIONS OWLS is as yet a relatively unknown technique, which has tremendous potential in the field of drug discovery. Hitherto, it has been applied in the biological field mainly for the study of protein adsorption problems. Its particular strengths are the versatility of experimental set-ups possible, and the readiness with which pertinent information can be extracted from the raw data by simple and transparent means. 6. ABBREVIATION OWLS

=

Optical waveguide lightmode spectroscopy

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

Tien, P.K.; Rev. Mod. Phys., 1977, 49, 361-420. Ramsden, J.J.; J. Statist. Phys., 1993, 73, 853-877. Ramsden, J.J.; Q. Rev. Biophys., 1994, 27, 41-105. Tiefenthaler, K.; Lukosz, W.; J. Opt. Soc. Am. B, 1989, 6, 209-220. Szendo˝, I.; Proc. SPIE, 2001, 4284, 80-87. Ramsden, J.J.; J. Molec. Recog., 1997, 10, 109-120. Brosinger, F.; Freimuth, H.; Lacher, M.; Ehrfeld, W.; Gedig, E.; Katerkamp, A.; Spener, F.; Cammann, K.; Sensors Actuators B, 1997, 44, 350-355. Wiggins, P.M.; J. Biol. Phys. Chem., 2002, 2, 25-37. Cacace, M.G.; Landau, E.M.; Ramsden, J.J.; Q. Rev. Biophys., 1997, 30, 241-278. Schuck, P.; Biophys. J., 1996, 70, 1230-1249. Ramsden, J.J.; Phil. Mag. B, 1999, 79, 381-386. Csúcs, G.; Ramsden, J.J.; Biophys. Biochim. Acta, 1998, 1369, 61-70. Ball, V.; Ramsden, J.J.; Biopolymers, 1998, 46, 489-492.

A Versatile Technique For Drug Discovery [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29]


Ramsden, J.J.; Lvov, Yu.A.; Decher, G.; Thin Solid Films, 1995, 254, 246-251. Ramsden, J.J.; Bachmanova, G.I.; Archakov, A.I.; Biosensors Bioelectronics, 1996, 11, 523-528. Ramsden, J.J.; Karrasch, S.; Sensors Materials, 1996, 8, 469-476. Ramsden, J.J. In Biopolymers at Interfaces; M. Malmsten, Ed.; Dekker: New York, 1998; Ch. 10, pp. 321-361. Jin, X.; Wang, N.-H.L.; Tarjus, G.; Talbot, J.; J. Phys. Chem., 1993, 97, 4256-4258. Jin, X.; Talbot, J.; Wang, N.-H.L.; AIChE J., 1994, 40, 1685-1696. Ramsden, J.J.; Roemer, D.U.; Prenosil, J.E.; Proc. 6th European Conf. Integrated Optics, April 1993, Neuchâtel., 12-38. Ramsden, J.J.; J. Mater. Chem., 1994, 4, 1263-1265. Ramsden, J.J.; Experientia, 1993, 49, 688-692. Ramsden, J.J.; J. Phys. Chem., 1993, 97, 4479-4483. Kurrat, R.; Textor, M.; Ramsden, J.J.; Boni, P.; Spencer, N.D.; Rev. Sci. Instrum., 1997, 68, 21722176. Zhang, L.; Granick, S.; J. Chem. Phys., 2005, 123, 211104. Ramsden, J.J.; Chimia, 1999, 53, 67-71. Volk, J.; Le Grand, T.; Bársony, I.; Gombköto˝, J.; Ramsden, J.J.; J. Phys. D: Appl. Phys., 2005, 38, 1313-1317. Ramsden, J.J.; Li, S.-Y.; Heinzle, E.; Prenosil, J.E.; Cytometry, 1995, 19, 97-102. Horváth, R.; Pedersen. H.C.; Appl. Phys. Lett., 2002, 81, 2166-2168.


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Cell-Based Biosensors in Proteomic Analysis Spiridon E. Kintzios* EMBIO/Laboratory of Plant Physiology, Faculty of Biotechnology, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece Abstract: In recent years there has been a rapid increase in the number of diagnostic applications based on biosensors, including live, intact cells, tissues, organs or whole organisms. Whole cells provide multipurpose catalysts, particularly in processes that require the participation of a number of enzymes in sequence. However, the sensitivity and reliability of these sensors is often limited by the signal transduction mechanisms and by non-specific interferences, due both to analyte and environmental variations. In similar fashion to DNA and protein microarrays, which deliver multiplex detection via the high-density spatial arrangement of molecular recognition elements, arrays of cells at high-density can form the basis of cell-based sensors with extremely high-throughput capability. The expression of receptors of interest within these arrays could yield cell-based sensors with defined specificities. In addition, transfected cell microarrays composed of highdensity arrays of mammalian cells expressing defined genes, could be the basis for future high-throughput cell-based protein sensing platforms. Such cellular arrays could be used for the detection of molecular interactions in functional proteomics in vitro, to the testing of proteins in functional studies in living cells. Microarrays with ordered cell arrangements of GFP-producing or luminescent bacteria may be used as an integral part of future biosensors. Recent and representative applications in this direction include (i) the profiling of antibody specificities and protein interactions with genetically engineered human immune cells, (ii) cells containing surface antibodies, specific to antigens of different pathogens and (iii) cell proliferation/metabolism sensors dedicated to screening for drug candidates and drug kinetic analysis.

INTRODUCTION According to the definition provided by Panisko et al. [1] “Proteomics seek to identify proteins and their posttranslational modifications, elucidate protein-protein interactions, and quantify relative protein abundance on a global scale. Proteomic studies are designed to analyze hundreds or thousands of proteins in single analyses and provide a global view of changes in protein expression that occur when cells are treated. The primary advantage of studying cells at the proteome level is the amount of information that can potentially be derived from a single experiment”. Proteome analysis methods are technically very challenging due to the high complexity and diversity of proteins, *Corresponding author: Tel: +3210 5294292; Fax: +3210 5294286; E-mail: [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.


Spiridon E. Kintzios

especially those proteins present at very low concentrations in cells. Favourite methods for the characterization of proteomes include mass spectrometry [2-5], two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) [6, 7] and stable isotope labelling [8]. Cell-based sensors are a particular class of biosensors. Having made their debut more than fifteen years ago, they are likely to gain a dominant position among analytical technologies of the 21st century. Indeed, research activities in the field of cell-based sensors are rapidly increasing, with an approximate increase of 70% of the number of published reports on cell biosensors between 2002 and 2004, which represents one quarter of all biosensor-related publications and conference presentations [9]. A cell-based sensor design employs the physiological responses of whole living cells as the sensing component, such as oxygen consumption, surface chemical or electrical potential, mobility or genetic activity. Thus, whole cells provide multipurpose catalysts, particularly in processes that require the participation of a number of enzymes in sequence [10, 11]. Therefore, they are able to provide physiologically relevant data in response to an analyte and to measure the bioavailability of the analyte [12]. In other words, cell-based sensors that carry out functional assays as cells, not only possess the ability to detect the presence of an agent, but also are capable of responding in a manner that can offer insight into the physiological effect of an analyte [13, 14]. Cell-based biosensors are also likely to have improved stability, higher biocatalytic activity, adding low cost in their favour. However, very few of the constructed sensors have been commercialised. The few that have become commercial products are generally used for the detection of a range of substrates and are based on BOD measurement. Most of the biosensors reported, use bacterial cells as the sensing element. Breakthrough advances in animal cell storage capacity are expected to increase the commercial applicability of cell-based sensors for high throughput pharmaceutical and disease screening. In following, the emerging application of biosensors in microarray format is briefly reviewed, i.e. biosensors based on arrays of cells at high-density, which can form the basis of cell-based sensors with extremely high-throughput capability. Emphasis is given on cell-based protein sensing platforms used for the detection of molecular interactions in functional proteomics in vitro. Representative examples of applications include the profiling of antibody specificities, detection of pathogens and screening for drug candidates and drug kinetic analysis. CLASSIFICATION OF CELL-BASED BIOSENSORS A cell-based assay system can be classified using one or more of the following criteria: 1.

The cell type used for constructing the sensor (e.g. mammalian, bacterial, etc): Although the overwhelming majority of reports refer to the use of microorganisms such as bacteria or yeast for constructing cell-based sensors, mammalian cells are used predominantly in cellular microarrays-based proteome assays. This is due to the fact that, contrary to bacteria and plants, animal cells do not have a cell wall that would impede the direct interaction of the cell surface with a protein molecule under investigation. Especially immortalized cell lines are easily handled and propagated. A typical researchers’ favourite is the Vero (African Green Monkey Kidney) fibroblast cell line [15-17]. In addition, cells of

Cell-Based Biosensors in Proteomic Analysis


neural origin that can be extracted from primary sources and maintained in culture are available [18]. These neural cell lines can be grown into nerve cell networks on substrate-integrated, thin-film microelectrode arrays in which the spontaneous electrical activity can be monitored by a large number of electrodes for several months [19, 20]. These systems are accessible by pharmacological assays and have shown a highly sensitive and reproducible, tissue-specific response to neuroactive compounds [18-28]. Human glioblastoma and/or neuroblastoma cells have also been used for monitoring different physiological parameters (such as superoxide accumulation) after a certain chemical stimulus had been provided [29-31], although the elicitation of cellular resting potential and cell differentiation was suppressed in some cases. 2.

The method used for achieving a desired level of specific response to a particular molecule (e.g. cell selection, genetic engineering): a limitation to the generation of a cell-based sensor system is the non-availability of intact signal transduction pathways for the desired stimulus. Ideally, the cell line used for constructing a sensor must have endogenous genes (usually by means of genetic engineering) that are tightly regulated by a chosen stimulus: subsequently, these genes can be tagged by a reporter gene encoding a quantifiable protein (e.g. by means of fluorescence spectrometry or microscopy) [12, 32]. Many reporter genes have been incorporated in microbial sensors [33-39]. A reporter molecule should be highly specific and sensitive, demonstrate minimal cytotoxicity and maximum stability, as well as minimal interference with endogenous cell components other than the target analyte. In addition, it should be autofluorescent or generally not requiring the addition of a substrate for signal generation [12]. Among various types of reporters, bioluminescent proteins are widely used, such as reporter genes encoding luciferases that yield luminescence as the reporter signal [40]. Two other proteins, aequorin and green fluorescent protein (GFP) (both derived from the jellyfish Aequorea victoria) are increasingly used as reporter molecules in many applications, since their ability and spectral properties change through structural alterations of the native protein [33, 34]. In another approach, orphan receptors are matched with regulatory ligands, thus resulting to elevated Ca+2 concentrations in transfected cells that interact with receptor-specific peptides. This method is analyzed in more detail below.

3.

The assay method (optical, electrochemical, etc.): the two most common methods of transducing cellular responses are optical and electrical. The instrumentation used in order to detect visible, fluorescent, or luminescent signals from cells or tissues, includes microscopes, fiber optics, CCD cameras and other optical equipment. Due to its high sensitivity and the advantages in the measurement of high-density microtiter plates, bio- and chemiluminescence imaging is quite suitable for the development of high throughput screening (HTS) systems [41].However, quantification of the results is limited by the lack of appropriate calibration systems, the invasive nature of intracellular recording and the influence of the sample properties on the emission spectrum and intensity. On the other hand, microphysiometry is based on the principle that electrically active cells or tissues can be interfaced with microelectrodes which allow the capture of extracellular spikes or impedance changes associated with cellular or tissue responses. Various potentiometric electrodes have been used to detect



extracellular metabolites, e.g. potentiometric pH electrodes measured the acidification of the external environment caused by the production of acidic catabolites such as organic acids and CO2 [42, 43]. Ion sensitive field effect transistors (ISFETs), can also be used to detect the acidification of the extracellular environment [44-46]. A significant advance in the field of microphysiometry was contributed by Hafeman et al. [47] who developed the light-addressable potentiometric sensor (LAPS): this system, which is very similar to a pH ISFET, allows for detecting the sensor surface potential by illuminating a small spot at any desired position with a focused pulsed light-pointer. In this way, surface potential measurements are not limited by the ability to microfabricate discretely insulated gates. 4.

The degree of organization of the cell community within the sensor (e.g. single cell vs. cell cluster vs. tissue): the overwhelming majority of cell biosensors are still single-cell based systems, although in recent years a steady shift towards using cell clusters in suspension or in an immobilized state is being observed. Single cell sensors are usually combined with amperometric sensors for measuring physiological parameters, e.g. in cultured human neutrophils [48], fibroblasts [49], glioblastoma [50] and monocytic cell lines (THP-1) [51]. Clusterbased sensors reflect an attempt to reconstruct the original three-dimensional configuration of a tissue segment or an organ within the body of the biosensor. In the same context, a further development is the use of whole tissues rather than cultured cells. This alternative has evolved into the development of the Tissue Microarray technology which will be discussed in detail in following.

CELLULAR MICROARRAYS – BASIC CONCEPTS Microarray technologies represent a considerable advance in genomics and proteomics, since they satisfy the need to evaluate large numbers of molecular targets for their bioactive properties. Contrary to conventional analytical and molecular techniques, they are characterized by minimal user input, speed and requirement of low sample volumes, thereby significantly increasing both the number of tissues and the number of targets that can be evaluated [52]. For example, in a single cDNA microarray experiment, one is able to determine the expression status of 50,000 human genes. Conceptually, microarrays are formed after the local immobilization of test molecules on the surface of a substrate, by covalent or non-covalent interactions and a signal is detected when the probe molecules interact with the test molecules at a specific position of the array. DNA and protein microarrays deliver multiplex detection via the high-density spatial arrangement of molecular recognition elements [53, 54]. Microarray format applications include the detection of gene expression by cDNA and oligonucleotide arrays [55], profiling of antibody specificities and protein interactions by peptide arrays [56-58] and screening for drug candidates with arrays of small molecules [59]. Protein microarrays have been developed for the detection of antibodies [60] and cytokines [61] by spotting specific antibodies onto membranes in an array format. In similar fashion, arrays of cells at high-density can form the basis of cell-based sensors with extremely high-throughput capability. Cellular microarrays are still an emerging technology; as a result, they share the weaknesses and inadequacies of any cell-based analytical system. The most important problem is the lack of absolute



specificity against a particular target molecule. Secondly, the viability of cells incorporated into the sensor system often declines rapidly with time, thus reducing dramatically the storability of the biosensor. As it will be analysed below, considerable progress has been made in ameliorating both the specificity and the viability of cell microarrays. TISSUE MICROARRAYS AND THEIR APPLICATIONS Tissue microarrays (or TMAs) are a platform for high-throughput analysis of tissue specimens in research. In principle, TMAs are an ordered array of tissue cores on a glass slide, i.e. a simple assay system that can be constructed from archival paraffin-embedded tissue for immunohistochemical staining, in situ hybridization, and other methodologies [62]. Various techniques for the construction of TMAs have been have been described. Generally, very small (< 6mm) tissue sections are embedded in paraffin, usually with the aid of a common microscope bearing a special holing needle, a sampling needle, and a proper box to fix paraffin blocks on the microscope carrier [63]. With the precise mechanical control of the microscope, the holing procedure on the recipient paraffin blocks and sampling procedure of core tissue biopsies and observation and localization of sampling regions are performed. It is generally feasible to fabricate TMAs in a simple and relatively cost-effective way. In addition, major attempts have been made to automate the process of TMA construction and of data analysis [64]. For example, commercial instrumentation for TMA fabrication is available, such as the Tissue Arrayer series from Beecher Instruments (Silver Spring, MD). TMAs represent the currently most favourite version of cellular microarrays. They offer a compromise between in vitro systems and whole organisms. In a sense, TMAs are sensor systems that identify themselves with the samples under investigation: they are (almost exclusively) built on clinical tissue samples and therefore are inherently related to a specific genotype. The TMA is then used in order to investigate the interactions between a molecule and the tissue array; consequently, the nature of these interactions is closely dependent on the origin of the tissue samples. The number of published research studies on TMAs increased 100-fold in the last eight years (from just four publications in 1998 to more than 450 in 2005). This is due to the fact that, compared to regular tissue sections, TMA technology offers a number of distinct advantages, including high speed, and throughput, easy handling of specimens and minimal sample tissue requirement, a critical issue in clinical research. In principle, all the histochemical and molecular detection techniques that can be used with regular sections can also be used with tissue microarrays. The most common application is the detection of protein expression in clinical tissue specimens by means of immunohistochemical analysis, fluorescence in situ hybridisation (FISH), or RNA in situ hybridisation (ISH) [65]. TMAs are also used to accelerate studies seeking for associations between molecular changes and clinical endpoints, a topic that land marked the era of TMA-based assays [66]. The ability to study archival tissue specimens is an important advantage as such specimens are usually not applicable in other highthroughput genomic and proteomic surveys. Construction and analysis of TMAs can be automated, increasing the throughput even further [67]. A major obstacle to broad acceptance of microarrays is that they reduce the amount of tissue analyzed from a whole tissue section to a disk, a few mm in diameter that may not be representative of



the protein expression patterns of the entire tumor [68]. Although they have a potentially very broad range of applications, TMAs have been used so far in two distinct fields, namely cancer research and assaying inflammation-related signals. TMAs and Cancer Research Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can be subsequently tested using other techniques for use in early detection of cancers [8]. Applications of TMAs in cancer research include the biochemical profiling of different tumor types and the evaluation of potential diagnostic markers. In a standard procedure, tissue cylinders with a diameter less than one millimetre can be dissected from hundreds of different primary tumor blocks and subsequently brought into a recipient tissue microarray block. Sections from such array blocks can then be used for simultaneous in situ analysis of hundreds or thousands of primary tumors on DNA, RNA, and protein level. The possibility to miniaturize tissue analyses will substantially facilitate translational and clinical cancer research in a number of ways. In a single experiment, up to 500 to 1000 tissues can be evaluated on the same microscope slide [64]. Tens of thousands of TMA sections can be generated from one paraffin block containing 10x10 mm of tissue area with a depth of 3 m. This increases the speed of analysis of very large clinical datasets, and will also facilitate the standardization and interpretation of the results. In terms of practical application, the staining of a single TMA slide provides a much greater degree of consistency and standardization than the immunostaining of hundreds of individual slides, while the quantitation of immunostainings is markedly easier on arrayed samples than on large sections. In a number of comparative studies on tumour tissues, results derived from TMAbased assays correlated very well with investigations conducted on mount sections [69]. Torhorst et al. [52] applied a TMA-based immunohistochemical approach in analyzing prognostic markers in a series of 553 breast carcinomas. Four independent TMAs were constructed by acquiring 0.6 mm biopsies from one central and from three peripheral regions of each of the formalin-fixed paraffin embedded tumors. Immunostaining of TMA sections and conventional “large” sections were performed for two well established prognostic markers, estrogen receptor (ER) and progesterone receptor (PR), as well as for p53, another frequently examined protein for which the data on prognostic utility in breast cancer are less unequivocal. Compared with conventional large section analysis, a single sample from each tumor identified about 95% of the information for ER, 75 to 81% for PR, and 70 to 74% for p53. However, all twelve TMA analyses (three antibodies on four different arrays) yielded as significant or more significant associations with tumor-specific survival than large section analyses (p < 0.0015 for each of the 12 comparisons). A single sample from each tumor was sufficient to identify associations between molecular alterations and clinical outcome. It was concluded that, contrary to expectations, tissue heterogeneity did not negatively influence the predictive power of the TMA results. Camp et al. [68] investigated the number to disks required to adequately represent the expression of three common antigens in invasive breast carcinoma-estrogen receptor, progesterone receptor, and the Her2/neu oncogene in 38 cases of invasive breast carcinoma since 1932 by creating a breast cancer microarray and evaluating the



antigenicity of these markers and others. They demonstrated that many proteins retained their antigenicity for more than 60 years, thus validating their study on archival tissues. Using microarrays of complementary DNA, Dhanasekaran et al. [70] examined gene-expression profiles of more than 50 normal and neoplastic prostate specimens and three common prostate-cancer cell lines. Signature expression profiles of normal adjacent prostate (NAP), BPH, localized prostate cancer, and metastatic, hormonerefractory prostate cancer were determined. Many associations were established between genes and prostate cancer. They assessed two genes - hepsin, a transmembrane serine protease, and pim-1, a serine/threonine kinase at the protein level - using TMAs consisting of over 700 clinically stratified prostate-cancer specimens. Expression of hepsin and pim-1 proteins was significantly correlated with measures of clinical outcome. Thus, the integration of cDNA microarray, high-density tissue microarray, and linked clinical and pathology data was proven to be a powerful approach to molecular profiling of human cancer. Another method combinatory approach was adopted by Nishizuka et al. [71], who used cDNA microarrays, oligonucleotide chips, protein microarrays and TMAs derived from the NIH panel of sixty human cancer cell lines in order to identify molecular markers for the differential diagnosis between colon and ovarian cancer. In this way they were able to identify villin as a promising candidate marker for colon cancer cells and moesin for ovarian cancer cells. Finally, Turashvilli et al. [72] used TMAs in order to investigate the genetic heterogenity of invasive breast cancer (as reflected by the wide spectrum of histological types and differentiation grades), in particular the differential expression of eight genes in lobular and ductal cancers. Cytokine Detection/Inflammation Sensors Another major application of TMA-assisted assays is the detection of inflammatory signals, such as interleukin-1 (IL-1) [73]. Certain immortalized endothelial cell lines, including ECV304 have been used for the selective detection of pg concentrations of IL1, the cytokine tumor necrosis factor-α (TNF-α) an the phorbol ester phorbol myristate acetate (PMA) [74]. In these assays, cellular response to inflammatory signals was recorder by transfecting cells with hybrid DNA constructs between the coding sequence of β-lactamase and the promoter and regulatory regions of the gene encoding for inflammation-associated genes, such as the endothelial-leukocyte adhesion molecule (ELAM) [75] and RelB [76]. Automated fluorescence analyzers further facilitated sorting out of responding cells at throughputs near 100 million cells per hours. TRANSFECTED CELL MICROARRAYS The expression of receptors of interest within cellular arrays could yield cell-based sensors with defined specificities [77]. Transfected cell microarrays composed of highdensity arrays of mammalian cells expressing defined genes could be the basis for future high-throughput cell-based sensing platforms. The signals produced from these microarrays are dependent upon the efficiency of DNA uptake by the cells within each spot. Microarrays with ordered cell arrangements of GFP-producing or otherwise luminescent bacteria may be used as an integral part of future biosensors. For example, Andrews et al. [78] studied the movement of mobile genetic elements and chemical signals (such as the pheromones acylhomoserine lactones) between bacterial cells by



guiding bacteria cells to specific areas of a microelectrode array by dielectrophoresis, where they were immobilised on electrodes. Expression screens have been also created by incubation of the whole microarray with antigen-specific antibodies and immunofluorescence [79]. In a significant development, Ziauddin and Sabatini [80] transferred plasmid DNA incorporated into agarose gel pads soaked with plasmid DNA into cells seeded onto microarrays. For the successful development of transfected cell microarrays for use in high-throughput cellbased sensor applications, careful cconsideration must be given to the factors that influence the signals generated from the spots of transfected cells. Perhaps the most important factor affecting the resulting signal emanating from the spots is the transfection efficiency, or the percentage of cells on each spot that take up and express the deposited DNA [77]. However, investigation into the factors that influence the transfection efficiency on cellular microarrays has been limited. In yet another approach, Rider et al. [81] developed the CANARY (Cellular Analysis and Notification of Antigen Risks and Yields) cellular sensor by genetically engineering human immune cells (cultured B cells) in order for them to express antibodies, specific to antigens of different pathogens. In addition, cells are engineered to produce aequorin, which emits light after interaction with an antigen, through a calcium-mediated pathway. In a particular application, B cell-based CANARY sensors were used for the identification of Yersinia pestis. A similar technique was used by Whelan and Zare [82], who constructed a single-cell detector that combined the natural signal amplification of whole-cell biosensors with the flexibility and specificity of immunological recognition. Their system was based on an immune cell expressing receptors for the constant region of immunoglobulin G (IgG); the cell was loaded with a Ca2+-indicating dye and with antibodies directed against the protein of interest. Introduction of a multivalent protein antigen caused cross-linking of the receptors, which resulted in a detectable increase in the concentration of cytosolic Ca2+. Finally, Thach et al. [83] investigated the response of primary rat neural precursor cells (NPC) and human peripheral blood mononuclear cells (PBMC) against Sinbis virus (SV) carrying GFP, suggesting their potential use as detectors of SV in a cell-based biosensor paradigm. The use of transfected cells in a microarray format is neither a flawless process nor always applicable. For example, transient transfections result in the over-expression of individual genes, with the probability of producing a biased cellular phenotype, whereas not every cell line is amenable to transfection. Electroinsertion of enzymes, antibodies and/or receptor-like molecules in the cell membrane could become an attractive alternative to cell transformation with genes expressing membrane-bound antibodies. Using this method, Moschopoulou and Kintzios [84] recently reported the development of a new, hybrid type of ultra-sensitive electrophysiological superoxide anion (O2•_) sensor. The membrane-engineering process involved the electroinsertion of superoxide dismutase (SOD) molecules in the membranes of Vero fibroblast cells, which acted as catalytic units able to convert O2•_ to H2O2. Superoxide dismutation triggered changes to the cell membrane potential that were measured by appropriate microelectrodes, according to the principle of the Bioelectric Recognition Assay (BERA) (see below). The sensor instantly responded to picomole concentrations of O2•_ with a detection limit (S/N=3) of 100 pM. This technology was further modified by the authors’ research group for the detection of



human viruses, such as Hepatitis B, D and C viruses (HBV, HDV and HCV, respectively) in whole blood samples (unpublished results). CELL CLUSTER AND IMMOBILIZED CELL ARRAYS Although the majority of cellular microarrays are based on single cell systems, sensors including cell clusters have been frequently reported. The most basic form is a suspension of genetically engineered cells, usually incorporated into a self-contained instrument that allows various types of cell-based modules to be maintained at a preset temperature and ambient or not CO2 level [13]. A number of microfabricated cellular sensors has emerged, essentially based on assaying parameters of a cell suspension culture [85-87], predominantly extracellular acidification [88, 89] as well as electric impedance [90, 91]. Real-time detection in vivo contributes to better understanding cellular physiology by monitoring the extra- and intracellular microenvironments in a quantifiable manner. Immobilized cell biosensors represent an even more advanced approach than cell clusters, since immobilization considerably facilitates the required proximity between the biomaterial and the transducer as well as the stability of the sensor for storage and reuse. Viable cells have been immobilized by means of entrapment and adsorption techniques, using various polymers. Natural polymers used for the entrapment of the cells include alginate [92], carrageenan [93], low-melting agarose [15, 16, 94] and chitosan [95]. Biopolymers such as calcium alginate are permeable and non-toxic but have poor mechanical stability and may be degraded by some organisms [96]. Agarose and related gelling agents form rigid matrixes with variable pore diameter may inhibit diffusion of large molecules, such as viral coat proteins. On the other hand, they are appropriate for long-term (e.g. several months) preservation of cell viability [15-17]. A typical immobilized cell sensor system based on agarose or alginate is the Bioelectric Recognition Assay (BERA) [15]. It is a biosensory method based on a unique combination of a group of cells, whose immobilization in the matrix preserves their physiological functions and measures the expression of the cell interaction with viruses, through the change in electrical properties. Cells are selected to specifically interact with the virus under detection. In this way, when a positive sample is added to the probe, a characteristic, ‘signature-like’ change in electrical potential occurs upon contact between the virus and the gel matrix. BERA has been used for the detection of viruses in humans (Hepatitis B and C viruses, herpes viruses) and plants (tobacco and cucumber viruses) in a remarkably specific, rapid (1-2 minutes), reproducible and cost-efficient fashion. The sensitivity of the virus detection with BERA is equal or even better than with advanced immunological, cytological and molecular techniques, such as the reverse transcription polymerase chain reaction (RT-PCR). More recently, absolute cell specification has been achieved by means of membrane engineering (see above). For mammalian cells, collagen matrixes represent the material that is closest to the natural tissue environment. Mao and Kisaalita [97] reported on the biocompatibility properties of collagen, as a first step towards establishing a functional 3-D cell-based biosensor platform, while Yang et al. [98] used a three-dimensional model for studying lung tumour/collagen matrix interaction. Armbruster et al. [99] also described a method to attach cultures of collagen lattices on a silicon microdevice.



A subgroup of immobilized cell systems are spheroid cultures, created by various techniques, such as culturing on polymer-coated culture surfaces [100] or in a porous substratum [101, 102]. Spheroid cultures have attracted attention as three-dimensional organoid culture methods, in particular hepatocyte culture systems; isolated primary cells in this culture spontaneously form spherical multicellular aggregates and maintain their morphological and functional characteristics in vitro [103, 104]. Although the spheroid culture technique is such a useful method, several obstacles hinder their widespread use for biological application, such as the inability to immobilize spheroids at a defined location and cell necrosis occurring within the core of large and coalesced spheroids because of oxygen depletion [105, 106]. Quite recently, Fukuda et al. [107] described a novel hepatocyte culture system – in actuality a spherical organoid (spheroid) microarray culture system – using a combination of microfabrication and microcontact printing. The system consisted of a chip that had cylindrical cavities of 300 mm diameter at a density of 700 cavities/cm2. Primary hepatocytes spontaneously formed spheroids with a uniform diameter at the center of each cavity on the chip. Hepatocytes forming spheroids had a cuboidal cell shape, similar to hepatocytes in vivo, and stably maintained liver-specific phenotypes, such as liver-enriched transcriptional factors, albumin secretion, urea cycle enzymes, and intercellular adhesion molecules. PERSPECTIVES In essence, cellular and tissue microarrays are newcomers in the field of proteomics. As a logical result, there is presently no clear indication for an increasing or decreasing probability of these methods and related instrumentation to replacing or substituting more conventional HTS proteomic assays, in particular automated HPLC-MS based systems, on a mass-scale, routine analysis level. Nevertheless, there is adequate evidence for the rapid expansion of research on cell-based proteomic sensors. This is not only apparent from the increasing number of related publications; a shift of research focus to integrated sensor arrays with whole cells as sensing components is being fuelled by advances in other areas, such as: 1.

Bio- and chemiluminescence imaging techniques allowing for the simultaneous measurement of multiple analytes in the same sample or even in vivo imaging at a whole organism level [41, 108].

2.

Advances in three-dimensional microfabrication technology opening new possibilities for miniaturising cell culture and analysis devices. For example, Kintzios et al. [17] recently developed a miniaturized biosensor system by combining the electrophysiological response of immobilized cells with superoxide-sensing technology, optical and fluorescence microscopy. This system enables the correlation of seven different cell physiological parameters to each other, as well as the prediction of cell proliferation or death by comparing the relative response of the electrophysiological and superoxide sensor during a oneweek long culture period. A commercial prototype of the sensor (the EMBIO® sensor, Fig. 1) is currently being disseminated in large-scale clinical trials. Further advances are resulting by improving our understanding of the performance of cell-based assays in three-dimensional substrates as more representative of an “in vivo” environment than two-dimensional culture systems. For example, Alp et al. [109] developed a cellular microarray that mimics a TMA: skin tissue constructs were made from human fibroblast cells in a fibrin gel (at a density of 100.000



human fibroblast cells/ml). By measuring the cells dielectric properties, a linear relation between the capacitance (at 0.4 MHz) and the cell number was shown.

Fig. (1). The EMBIO® sensor.

3.

Novel approaches in microphysiometry that may boost the high throughput efficiency of existing cell monitoring systems. An example is provided by the work of Xu et al. [110], who improved LAPS chips in order to facilitate monitoring the response of single neuronal cells to a pharmacological agent (acetylcholine). This will be particularly important for measuring cellular dynamics, e.g. neuronal communication between cells at distances of 10-100 µm [111].

It is finally anticipated that cell-based systems will make a difference in proteomic analysis since they reduce the complexity of the testing environment and therefore enhance the sensitivity of the measurements [112, 113]. This progress will be further sustained by parallel advances in microfabrication technology and the miniaturization of cell culture systems. ABBREVIATIONS BERA

=

Bioelectric Recognition Assay

BOD

=

Biological Oxygen Demand

CCD

=

Charge-Coupled Device

FISH

=

Fluorescence In Situ Hybridisation

GFP

=

Green Fluorescent Protein


HTS

=

High Throughput Screening

ISFET

=

Ion Sensitive Field Effect Transistor

ISH

=

In Situ Hybridization

LAPS

=

Light-Addressable Potentiometric Sensor

ROS

=

Reactive Oxygen Species

RT-PCR =

Reverse Transcription Polymerase Chain Reaction

TMA

Tissue Microarray

=


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Current Approaches in Natural BiopolymerNanoparticle Hybrid Functional Materials: From Drug Delivery to Bio-Detection Applications Roberta Brayner* Interfaces, Traitements, Organisation et Dynamique des Systèmes (ITODYS) –UMR-CNRS 7086 and Université Paris 7 Denis Diderot, case 7090; 2 Place Jussieu 75251 Paris Cedex 05 France Abstract: This review is focused on current approaches emerging at the intersection of hybrid nanomaterials research and biotechnology. This interdisciplinary field of chemistry and biology is also associated with physical and chemical properties of organic and inorganic nanoparticles, as well as to DNA, proteins and polysaccharide studies.

INTRODUCTION Astonishing advances have been made in biological sciences after the discovery of the double helix structure of DNA. Biology has evaluated from descriptive and phenomenological discipline to a molecular science. To improve and to create new revolutionary materials, it seems crucial to combine biotechnology with materials science. Fuse these important disciplines we can generate new advanced materials to solve biological problems. These hybrid functional materials exhibit specific and strong complementary recognition interactions e.g. nucleic acid-DNA, antigen-antibody. Proteins may be genetically modified with specific anchoring groups such as thiols. This facilitates the aligned binding to nanoparticles, or the site-specific linkage of the biomaterial to surfaces. This review describes the utilization of hybrid functional materials based on natural biopolymers such as proteins and polysaccharides and nanoparticles consisting of metals (e.g. Au, Ag, Ni, Co), oxides (e.g. ZnO, Fe3O4) and quantum dots (e.g. CdS, CdSe, CdSe@ZnS…) for drug delivery and bio-detection applications. 1. Biosensors for the Recognition and Monitoring of Molecule Interactions DNA sensors and gene chips are particularly relevant for directly applying the information gathered from genome projects and polymorphism databases. Polymorphism identification, sequence recognition, pathogen identification, expression profiling and mutation detection are examples of current DNA sensors applications [1-5]. We can mention electrochemical techniques used to develop methodologies to detect DNA in human genes that are potentially involved in the modulation of individual cancer susceptibility as well as therapeutic efficacy [1]. These techniques are based on the *Corresponding author: Tel: 33 1 44 27 95 41; Fax: 33 1 44 27 61 37; E-mail: [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.

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utilization of DNA-modified gold electrodes. In this case, the intensities and peak potentials change according to the modification of the gold electrode surface by anchoring of thiol-terminated double-stranded oligonucleotide. Surface-modified colloidal gold and silver can be employed as biosensors [6-7] and dielectric nanoparticles enclosed with gold shell have been applied also in tumor therapy utilizing absorption of near-infrared (NIR) light [8]. Although many biomedical applications of Au nanoparticles their toxicological effects have been ignored. Recent studies have indicated that colloidal Au does not cause acute cytotoxicity, it was demonstrated that these nanoparticles could be rapidly modified by surrounding cellular environment [9]. Matrix-assisted laser desorption-ionization (MALDI) mass spectroscopy has become a very powerful tool for biochemical analysis [10-11]. In this case, negatively charged Au nanoparticles were employed as selective probes to trap oppositely charged proteins from aqueous solutions [12]. To recover these probes from the solution, Au nanoparticles were bound covalently to the surface of Fe3O4 magnetic nanoparticles to generate Au@magnetic materials [12]. In this work, cytochrome C and myoglobin were used as target species. The trapping capacity of Au@magnetic particles, as a function of pH, was determinated by MALDI analysis (Fig. 1).

Fig. (1). MALDI mass spectra obtained from a mixture (0.1 mL) of cytochrome C (10- 6M) and myoglobin (10 -6M) when using Au@magnetic particles (1 mg) as probes to trap the target species for 1 h from buffer solutions of differing pH: (a) pH 6, (b) pH 8 and (c) pH 12. SA was used as MALDI matrix (Reprinted from ref. [12] with permission. Copyright 2004 American Chemical Society).

The most interesting and practical application is to employ this technique in the analysis of enzymatic digest product of proteins. Another example of Au nanoparticles

Drug Delivery to Bio-Detection Applications


used, as biological nanoprobes is a colorimetric DNA detection based on the sequencespecific hybridization properties of DNA [13]. In this case, Au nanoparticles smaller than 60 nm have extremely high extinction coefficients at ~520 nm [13a]. Moreover, different Au agglomeration states can result in distinctive color changes that make these nanoparticles an ideal color-reporting group form signaling molecular recognition events and render nanomolar concentration detection available [14]. An example of this detection assay is based on the use of concanavalin (conA) and mannopyranosideencapsulated Au nanoparticles (man-GNPs) to identify the binding partners for conA [15]. Here, the binding constants are determined from wavelength shifts. A detection scheme based on Chen at al work [15] is illustrated in Fig. 2.

Fig. (2). Schematic illustration for the colorimetric detection of protein-protein interactions based on ref [15].

A protein named A binds the ligands protruding from the GNP surface and promotes particle agglomerations via multivalent ligand-protein interactions giving rise to a blue colored solution. However, the addition of a putative protein, named B, capable of interacting with protein A, could influence the binding A and Au nanoparticles allowing to Au nanoparticles re-dispersion. Consequently, the solution color can change from blue to original red color. It was also studied the possibility of using the performed manGNPs/conA complex for a competitive colorimetric assay. Among ten proteins considered herein, four proteins, i.e. thyroglobulin, BS-I, SBA and MAL were found to have very drastic effects on the absorption spectrum of man-GNPs/conA. For these proteins, the wavelength was blue-shifted and the absorption intensity increased. The color changed from blue to red indicating that these proteins were able to compete with man-GNPs/conA complex and disrupt particle agglomeration. The new technology developed in this work provides not only qualitative but also quantitative evaluation of protein-protein interactions by using Au nanoparticles-based competitive assays. This method may also be straightforward to screen molecular libraries containing potential drugs to disrupt cell-cell adhesion mediated by lectins. Recently, hybrid structures of microorganisms with inorganic nanoscale moieties have received great interest owing to their potential in fabricating electronic systems. The electronic properties of metal nanoparticles, as a result of the single electron transport of current [17] make them ideal materials for nanodevices. Concomitantly, the nanostructure of microorganisms such as bacteria [18] and viruses [19-20] are attractive scaffolds for the templating of metal nanoparticles through the interactions of the former

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with surface charges and the affinity of certain metals for specific biological molecules [18-23]. Berry and Saraf [24] present a simple method to build hybrid devices that use the biological response of a microorganism to control the electrical properties of the system. In our design, a monolayer of gold nanoparticles is deposited on the peptidoglycan membrane of a live Gram-positive Bacillus cereus bacterium. The hydrophilic peptidoglycan membrane is then actuated by humidity to modulate the width of the electron-tunneling barrier between metal nanoparticles. In this work, Bacillus cereus was deposited on a silicon substrate with a layer of 500 nm of thermally grown silica and gold electrode lines spaced 7 ± 0.2 microns apart and coated with poly-Llysine. The bacteria-deposited ship was then immersed in a solution of poly-L-lysine coated gold nanoparticles (diameter d = 30 nm) [18]. The deposition is highly selective, with formation of a monolayer only on the negatively charged bacteria surface because Au nanoparticles and the substrate are both positively charged (Fig. 3). The insets of Fig. 4 show a typical bacterial bridge, coated with a monolayer of Au nanoparticles connected to Au electrodes.

Fig. (3). Scanning electron microscopy (SEM) images reveal the highly controlled and selective deposition on bacteria of poly(L-lysine)-coated-30 nm Au nanoparticles from a solution at pH 7 over (a) 30 min; (b) 1 h; (c) 2 h; (d) 4 h; (e) 8 h; (f) Positively charged Au nanoparticles are deposited on a negatively charged PSS-coated lysine/SiO2/Si substrate over 16 h. (Reprinted from ref [24] with permission. Copyright 2005 Angewandte Chemie International Edition).

Here, one bridge constitutes a device. The electrical properties of Au nanoparticle monolayer are controlled by actuating the bacterium peptidoglycan membrane. An actuation of less than 8% in the peptidoglycan membrane, induced by change in humidity from 20 to 0%, leads to more than 40-fold increase in the tunneling current. These results open up new routes to obtain active coupling between microorganisms and electrical, optical and/or magnetic devices. Another example of hybrid structures based on microorganisms with inorganic nanomaterials is the utilization of cyanobacteria as bioreactors to synthesize metallic nanoparticles [25a]. In this work, we showed that the common Anabaena, Calothrix and Leptolyngbya cyanobacteria were able to form gold,



Fig. (4). Typical device current (I, normalized per bridge) as a function of relative humidity (Hrel) for “up” (i.e. decreasing humidity; ∆) and “down” cycles (i.e. increasing humidity; ≤) at a bias voltage of 10 V. The inset shows SEM images of two typical bacteria bridges which span the electrodes. The peripheral strip is a (percolating) monolayer of deposited gold nanoparticles. (Reproduced from ref [24] with permission. Copyright 2005 Angewandte Chemie International Edition).

silver, platinum and palladium metallic nanoparticles with well-controlled size. These nanoparticles were synthesized intracellulary (Fig. 5) and naturally released in the culture medium where they are stabilized by the algal-polisaccharides, allowing their easy recovery. In addition, the size of recovered particles as well as the synthesis yield was shown to depend on the cyanobacteria genus, demonstrating the flexibility of this approach [25a]. The advantages of this new process are: (i) the nanoparticles show a narrow size distribution that depend on cyanobacteria genus employed as bioreactor (6 to 10 nm) in contrast to similar reports in the literature; (ii) these particules are naturally released and stabilized in the culture medium. This effect, associated to the proliferation capacity of these algae could open the route to natural bioreactors for nanoparticle production in a continuous way; (iii) this process is fully green both from reagents (without organic solvents or reducing agents) and cost/energy (atmospheric pressure, ambient temperature and water medium); (iv) a lot of seasoning cyanobacterial blooms was observed in the world that open new horizons to improve this new original and low cost process. In summary, Au and Ag nanoparticles have many attractive properties. They are nanometer in size and may have various functional ligands on the surface. These special properties provide many possible modes of interaction with biological cells, such as specific binding to the cell membrane and penetration through the membrane to interact with

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biological molecules inside a cell or a virus. For example, carbohydrate-functionalized Au nanoparticles have been developed to explore their interactions with cell membrane surfaces, such as lectins [25b]. These studies could have potential applications in lectin detection, control of cell fertilization, proliferation, viral infection and also inflammatory response.

Fig. (5). (a) TEM micrograph of Calothrix cyanobacteria thin section (presence of gold metallic nanoparticles); (b) TEM mimicrograph of Anabaena cyanobacteria thin section (presence of silver metallic nanoparticles).

The optical and electrical properties of colloidal gold nanoparticles are know to be dramatically affected by their size, shape and surrounding surface environments [26]. Since size-dependent photoluminescence of nanosized semiconductor materials reported by Mooradian [27a], the visible photoluminescence of small gold nanoclusters (< 25 nm) has also been observed [27b]. The unique properties of nanoscale colloidal particles are studied for their potential in various biosensor developments [28]. The optical properties of 3D aggregations of gold nanoparticles have been used to detect hybridization of specific DNA sequences in solution and or surfaces [13c, 29] as an alternative to fluorescent labeling of DNA. It was demonstrated that fluorescent labels used in DNA microarray analysis, such as cy3 and cy5 are very expensive, photosensive and special care must be taken to avoid their exposure to light during labeling [30]. On the other hand, gold nanorods are not photobleached and can interact with thiol-DNA targets through S-Au bonds. Consequently, gold nanorods may be considered as an alternative fluorescent label for heterogeneous DNA analysis. Sequence-specific DNA detection techniques have been developed which rely upon target hybridization with radioactive, fluorescent, chemiluminescent and other types of labeled probes [30]. Since the first reports by Alivisatos and Mirkin [13c, 29a-b], gold nanoparticle labeling of DNA molecules has been considered as an alternative marker for DNA hybridization monitoring events and even for the detection of a single base mismatch in the oligonucleotide sequence. Following the design concept of DNA-nanoparticles, Luong et al. [31] developed a fluorescence-based method for the determination of DNA sequences and monitoring reversible DNA hybridization events. This method was attempted by using



DNA functionalized gold nanorods [31]. These authors discovered that sufficiently long gold nanorods (aspect ratio > 13) exhibit novel optical properties by means of relatively intense fluorescence emission at 743 nm and one weaker band at 793 nm. After functionalization by DNA probes, the DNA hybridization event could be effectively monitored by measuring the fluorescence intensity. The results suggest that the unique fluorescent properties of gold nanorods could potentially be exploited as a sensitive probe in fluorescence-based microarray assay and optical biosensor development. Work is in progress using DNA sequences of interest towards the detection of important pathogenic bacteria. Another example of biomarker is silver-dendrimer nanocomposites [32]. Poly(amidoamine) (PAMAM) dendrimers hold great promise as templates for metal composite nanoparticles because of their low toxicity and highly regular, branched 3D structure allowing them to host inorganic nanoclusters and form stable dendrimer complexes and nanocomposites [33-36]. Dendrimer nanocomposites (DNC) are nanometer-size inorganic/organic hybrid composite particles containing topologically trapped guest atoms/molecules/nanodomains immobilized by dendritic polymer hosts of well-defined size, charge and terminal functionality [33-34]. Fabrication of metaldendrimer nanocomposites by reactive encapsulation requires two steps: (i) binding of metal ions to template dendrimer molecule to form complexes and (ii) immobilization of the preorganized metal ions to form nanoclusters with dendrimer templates. It is important to know that the composition and morphology of dendrimer nanocomposites depend on many factors such as chemical structure, uniformity and concentration of the template molecules, metal/template molar ratio, pH and temperature [37-38]. Balogh et al. [32] have synthesized water-soluble, biocompatible, fluorescent and stable silverdendrimer nanocomposites that exhibit a potential for in vivo cell labeling. Amino-, hydroxyl- and carbonyl-terminated ethylenediamine core generation 5poly(amidoamine) dendrimers were used to prepare aqueous silver (I)-dendrimer complexes at the biologic pH of 7.4. These hybrid nanocomposites are fluorescent and their surface charge, cellular internalization, toxicity, and cell labeling capacities were determined by surface functionalities of dendrimer templates. These materials exhibit potential applications as cell biomarkers. It is important to note that while significant advances in biological labeling have been made, few therapeutic applications of metal nanoparticles have been reported in the literature. A great example is the anti-microbial properties of silver nanoparticles, which have been used for wound healing [39]. Silver nanoparticles fabricated in Hepes buffer exhibit potent cytoprotective and post-infected anti-HIV-1 activities toward Hut/CCR5 cells [40]. These nanoparticles inhibited HIV-1 replication via an unknown mechanism [40]. In summary, it was demonstrated that gold nanoparticles are biocompatible, nontoxic [41], bind compatible with a large range of biomolecules such as amino acids [42], proteins [43] and DNA [13c, 44], and expose large surface areas for biomolecule immobilization. These nanoparticles could also serve as excellent delivery vehicles for a variety of molecules such as drugs and proteins. We present here some examples of molecule delivery based on gold nanoparticles as carriers. Sastry et al. [45] studied the binding of the hormone insulin to gold nanoparticles and its application in transmucosal delivery for the therapeutic treatment of diabetes mellitus. In this work, insulin was loaded onto bare gold nanoparticles and aspartic acid-capped gold nanoparticles and delivered in diabetic Wistar rats by both oral and intranasal (transmucosal) routes. It was observed a significant reduction of blood glucose levels (postprandial hyperglycemia) when insulin is delivered using gold nanoparticles as carriers by the transmucosal route in diabetic rats. Gu et al. [46] have


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shown that gold nanoparticles in toluene react with bis(vancomycine)cystamide in water to form vancomycin-capped gold nanoparticle. This hybrid nanomaterial enhanced antibacterial activity against E. coli strains. More recently, SiO2@Au nanoparticles, designed to absorb in the near infrared, have been used in cancer hyperthermia [47]. 2. Plasmon Resonance Measurements, Surface-Enhanced Resonance Raman Scattering (SERRS) and Resonance Light Scattering (RLS) of Biopolymer Onto Au and Ag Nanoparticles The adsorption of biopolymers onto metallic nanocolloids has industrial relevance in such areas as pharmaceuticals, wastewater treatment as well as in various biological systems. Optical, electronic, magnetic and catalytic properties of these metallic nanocolloids strongly depend on their shapes as much as their sizes [48a]. This is particularly important for Au and Ag, which have strong surface plasmon resonance oscillations. Controlling the shape of these nanoparticles is crucial in scheming their surface-related properties such as absorption and emission [48b], SERRS [48c] and other nonlinear optical properties [49]. A shift in plasmon resonance surface was measured for Au colloidal solutions upon adsorption of polyampholyte (gelatin) [50-51]. It was demonstrated that the shift in wavelength of the absorption maximum can quantitatively yield measurements of the adsorbed amount as well as information about the structure of the adsorbed biopolymer layer [51]. SERRS applications in the biophysical, biochemical and biomedical domains in the last decade are summarized in several review papers [5256]. These reviews present SERRS experiments performed on amino acids and peptides, purine and pyrimidine bases and also on macromolecules such as proteins, DNA and RNA. Other more medical applications include SERRS detection of stimulating drugs [57] and selective analysis of anti-tumor drug interactions with DNA [58-62]. Detection, identification and quantification of neurotransmitters in brain fluid are developing in neurochemistry. For example, Fig. 6 shows SERRS spectra of dopamine and norepinephrine anchoring on silver nanoparticle surfaces in aqueous solution. SERRS spectra of these two neurotransmitters were measured at concentrations between 10 -6 and 10-9M with accumulation times as short as 0.025s [63]. In these experiments, albumin was added as a protein component to silver colloidal solutions to make them close to a real biological environment. The low concentrations and fast data acquisition are on the order of physiologically relevant concentrations and very close to the time scale of neuronal events, respectively. The use of nanoparticlesbiopolymers systems for in situ detection of specific reactions has significant potential to probe living cells and tissues. For example, the detection in a cell of DNA or protein/receptor interactions will provide information of specific functionality. One form of Raman spectroscopy, the SERRS, has much important properties for labeling nanoparticles. Raman and SERRS techniques have similar sensitivities [64] but with SERRS many more codes can be written onto the surface of the nanoparticles. SERRS can be used for labeling both fluorophores and non fluorophores. To test the sensitivity of the essay, 3,5-dimethoxy-4-(6’-azobenzotriazolyl)-phenylamine anchoring on Ag nanoparticle surface was employed to discover the minimum amount that could be detected by this method (about 10-10M) [65]. A surprising feature of this experiment, which is not fully understood, is that the chromophore on the hybrid DNA-Ag nanoparticles is at least one hundred times more sensitive than that of the other dyes



Fig. (6). SERRS spectra of neurotransmitters in silver colloidal solution. Spectra were collected in 50 ms using 100 mV NIR excitation. (Reproduced from ref [63b]. Copyright 2002 Institut of Physics Publishing).

[65]. Ag nanoparticles have considerable potential for biochemical analysis and Au colloids could be similarly developed. The advantage of Ag nanoparticles is that the range of dyes which remain effective in biological media is much more extensive. On the other hand, in some biological systems such as cell suspensions, silver can react positively with the cell and it is well known as a bactericide. Natural polysaccharides such as chitosan, alginate and carragheenan have some important properties such as mucoadhesivity, biocompatibility and nontoxicity, which render them interesting biomaterials. From a physicochemical point of view, these polysaccharides have the special quality of gelling upon contact with cations (alginate and carragheenan) and anions (chitosan) under very mild conditions [66-67]. Nevertheless, due to their large size (1-3 mm), these beads are only appropriate for drugs delivery in the gastric cavity, for bio-labelling [68] and heat-triggered drug release [69]. For example, polysaccharidic alginate biopolymers have been used as templates for the controlled growth of gold metallic nanoparticles [70]. Au3+ was used to form alginate gels as spherical capsules.

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Roberta Brayner

After reduction in mild conditions, Au metallic nanoparticles were obtained inside alginate microcapsules (Fig. 7). These microcapsules were then immersed in a methylene blue (MB) aqueous solution (used as a probe molecule) at a concentration of 10-5M. It was observed a wavelength plasmon band shift probably due to an electronic coupling between Au nanoparticles and the MB molecule inside alginate network (Fig. 7). It was also observed an important SERRS effect for MB molecule adsorbed on these Au nanoparticles compared to the same molecule adsorbed on a lithographically designed 2D Au arrays film [71] (Fig. 7).

Fig. (7). (a) UV-visible spectrum and alginate microcapsules of Au3+; (b) UV-visible spectrum of Au metallic nanoparticles (Au plasmon resonance) and Au metallic nanoparticles inside alginate microcapsules; (c) UV-visible spectra of MB-Au-alginate compared with Au-alginate (red shift observed in the presence of MB); (d) Raman spectra of methylene blue excited under Au nanoparticles (SERRS effect) [70].

This polyfunctional hybrid material may be applied in drug delivery (because alginates have a great protein loading capacity), magnetic targeting or magnetic resonance imaging. A new method for the determination of proteins in aqueous solutions has been developed based on RLS of Ag nanoparticles in the presence of proteins [72]. It was showed that interaction between Ag and proteins such as bovine serum albumin (BSA), human serum albumin (HSA) and human immunoglobulins (γ-IgG) results in strong enhancement of the RLS intensity. This method has been applied to the synthetic samples and also to the analysis of human serum samples and the results obtained were



very close with those reported by the hospital, indicating that the RLS method can be used for practical applications [72]. 3. Biopolymer-functionalized Magnetic nanoparticles Magnetic nanoparticles are widely studied and applied in various fields of biology and medicine such as magnetic targeting, magnetic resonance imaging, diagnostics, DNA purification, etc [73]. Generally, we have advanced hybrid functional materials based on biological species such as cells, nucleic acids and proteins connected to the magnetic nanoparticles. Several synthetic approaches have been applied to anchoring biomolecules to magnetic nanoparticles, which were then used in various bioanalytical systems. For example, antibody biomolecules were adsorbed on Fe3O4 magnetic nanoparticles to use for specific binding to cells and posteriori separation of these cells in an external magnetic field [74-75]. Another very interesting example is the natural fabrication of iron oxide by some bacteria [76-83]. The unexpected and unusual features of these biogenic magnetite crystals are not only a narrow size distribution, but also a diameter range of 40-120 nm, which thus allocates them the highest magnetic moment (Fig. 8).

Fig. (8). Crystal morphologies and intracellular organization of magnetosomes from magnetotatic bacteria: (a) cubooctahedral; (b) bullet-shaped; (c-d) pseudohexagonal. The magnetosomes are arranged in one (c) or more (d) chains. The lengths of the bars represents 100 nm). (Reprinted from ref. [78a]. Copyright 2003 Angewandte Chemie International Edition).

Magnetospirillum sp. MGT-1 [76], Magnetospirillum sp. AMB-1 [77] and Magnetospirillum gryphiswaldense [78] produce natural Fe3O4 magnetic nanoparticles aligned in chains and enveloped by a lipid membrane [79] (Fig. 9).

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Fig. (9). TEM image of (A) Magnetospirillum gryphiwaldense with a chain of cubooctahedral magnetite crystals and one flagellum at each pole (the length of the bar represents 500 nm) and (B) it is isolated and purified magnetosomes enveloped by a membrane (the length of the bar represents 20 nm). (Reprinted from ref. [78a]. Copyright 2003 Angewandte Chemie International Edition).

These natural hybrid materials can be isolated from the parent bacteria [80] and modified with biomolecules using bifunctional coupling reagents such as glutaric dialdehyde. They have been applied for fluoroimmunoassay [81], chemiluminescence immunoassay [82] and DNA carriers [83]. Another approach is the fabrication of polyfunctional hybrid materials. The development of hydrophilic nanoparticles as drug carriers has represented over the last few years an important challenge. For example, polysaccharidic alginate biopolymers have been used as templates for the controlled growth of magnetic nanoparticles [84]. Ni2+ and Co 2+ were used to form alginate gels as spherical capsules (Fig. 10). After reduction under flowing H2/N2 at 350°C, SQUID measurements indicated that Ni presents a superparamagnetic behavior with a blocking temperature TB = 290 K [84]. Contrast agents (CAs) play an important role in magnetic resonance imaging (MRI) in medicine [85]. MRI CAs are primarily used to improve disease detection by increasing sensitivity and diagnostic confidence. There are several types of MR contrast agents being used in clinical practice today. The lanthanide ion Gd3+ is usually chosen for MRI CAs because it has a very large magnetic moment (µ = 63 µB) and a symmetric



Fig. (10). (a) alginate 50%Co50%Ni (after reduction); (b) TEM micrograph of 50%Co50%Ni metallic nanoparticles inside alginate matrix.

electronic ground state

8   S 7  . The Gd 3+ ion is toxic and in order to reduce its toxicity,  2

it is always sequestered by chelation [86] or encapsulation [87-88]. Wilson et al. [89] have employed ultra-short nanotubes or US-tubes (20-100 nm) [90-91] as “nanocapsules” for MRI-active Gd3+ ions. US-tubes are probably best suited for cellular uptake biocompatibility and eventual elimination from the body. In this work, the authors presented the internal loading of US-tubes with aqueous GdCl3 to form Gdn3@US-tube species. This hybrid material is formed by superparamagnetic metal-ion clusters that present proton relaxation centers with relaxivities 40 to 90 times larger than current clinical agents [89]. Several iron oxide-based magnetic labeling systems have been developed for monitoring stem cell migration [92-95] and tracking lymphocytes [96-97]. However, probes based on these materials have some difficulties for the successful MR cellular imaging due to their relatively low cell transport efficiencies or the use of macromolecules transport facilitating agents which can often cause unwanted side effects such as nanocrystal aggregation and cytotoxicity at a high dose level [98]. Recently, Fe3O4 nanocrystals were also employed as a probe for efficient intracellular labeling and their MRI applications [99]. In this case, by simple modulation of nanocrystal surface charge properties, the authors were able to prepare magnetic nanocrystals that efficiently label a variety of cell types. Since cell membranes are know to be weakly negatively charged [100], it is expected that only cationic water soluble iron oxide (WSIO) nanocrystals easily anchor to cell membranes through electrostatic interactions and are internalized into cells by way of a charge-mediated endocytosis process. The excellent labeling capacity of these cationic WSIO has led to a system for a preliminary but successful MRI monitoring of neural stem cells in vivo in rat spinal cord. Weissleber et al. [93] have developed a cell labeling approach using short HIV-Tat peptides to derivatize superparamagnetic nanoparticles. The nanoparticles are efficiently internalized into hematopoietic and neural progenitor cells in quantities up to 10-30 pg


Roberta Brayner

of superparamagnetic iron per cell. Iron incorporation did not affect cell viability, differentiation or proliferation of CD34+ cells. It was observed after intravenous injection that 4% of magnetically CD34 + cells homed to bone marrow per gram of tissue and single cells could be detected by MRI in tissues samples. Localization and retrieval of cell populations in vivo enable detailed analysis of specific stem cell and organ interactions critical for advancing the therapeutic use of stem cells. 4. Quantum Dots: Fluorescent Biosensing and Fluorescent Encoding Colloidal semiconductor quantum dots (QDs) are extensively used in various practical medical applications. For example, they have been used as immunofluorescent probes to detect the Her2 breast cancer marker [101], as signal transduction components in immunoassays for microbial toxins [102] and as labels for dynamic studies of cancer cell motility and correlation of metastatic potential [103]. The QDs have several advantages over conventional fluorescent dyes. Their fluorescence absorption and emission are conveniently tunable by their size and material composition, and the emission peaks have a narrow spectral linewidth. Typically, emission widths are 20-30 nm, which is only one third of the emission linewidth of a conventional organic dye [104]. The high quantum yields often range from 35-50% for CdSe/ZnS nanoparticles [104]. Moreover, QDs are about 100 times as stable against photobleaching as organic dyes and they often present a long fluorescence lifetime of several hundred nanoseconds. This allows time-delayed fluorescence measurements, which can be used to eliminate the autofluorescence of biological matrices. Another QDs application is the development of multifunctional nanoparticle probes based on QDs for cancer targeting and imaging in living animals [105]. The preparation of these hybrid materials involves encapsulating luminescent QDs with a triblock copolymer and linking this amphiphilic polymer to tumor-targeting ligands and drug-delivery functionalities. Gao et al. [105] present in this work, in vivo imaging results from three different QD surface modification: COOH groups, PEG groups, and PEG plus prostate-specific membrane antigen ab (PSMA). No tumors signals were detected with COOH probe, only weak tumor signals were observed with PEG probe (passive targeting) and intense signals were detected in the PEG-PSMA ab conjugated probe (active targeting). These results provides further evidence for the conclusion that active targeting by using tumor-specific ligand is much faster and more efficient than passive targeting based on tumor permeation, uptake and retention [105]. These new multifunctional QDs might be use in diagnosis and treatment of cancer, cardiovascular plaques and neurodegenerative disease. ABBREVIATIONS MALDI

=

Matrix-assisted laser desorption-ionization

ConA

=

Concanavalin

Man-GNPs =

Mannopyranoside-encapsulated Au nanoparticles

GNP

=

Gold nanoparticles

PAMAM

=

Poly(amidoamine)

DNC

=

Dendrimer nanocomposites



SERRS

=

Surface-enhanced resonance Raman scattering

RLS

=

Resonance light scattering

BSA

=

Bovine serum albumin

HSA

=

Human serum albumin

CAs

=

Contrast agents

MRI

=

Magnetic resonance imaging

WSIO

=

Water soluble iron oxide

QDs

=

Quantum dots

PSMA

=

Prostate-specific membrane antigen

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Drug Delivery to Bio-Detection Applications [65] [66] [67] [68] [69] [70] [71] [72] [73] [74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] [86] [87] [88] [89] [90] [91] [92] [93] [94] [95] [96] [97] [98] [99] [100]


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Spectroscopic Analysis of Cell Physiology and Function Mark Riley1,*, Iram Mondaca Fernandez1, and Pierre Lucas2 1

Agricultural and Biosystems Engineering, 2Materials Science and Engineering, The University of Arizona, 1177 E. 4th Street, Shantz Building, Room 403, Tucson, Arizona, 85721, USA Abstract: Spectroscopic methods including infrared and Raman techniques have tremendous promise for providing rapid, non-invasive information on the impact of pharmaceuticals and toxicants on cells and tissues. Spectroscopy is not a new field; however these methods have only recently been applied to study the physiology and function of cells and tissue. The infrared spectrum (2,500-25,000 nm) of a cell or tissue can supply information about the fundamental vibrational modes of functional groups existing in biological molecules thus permitting quantification of material composition and in some cases can be connected to cell function. Spectroscopy permits observation of changes within the cell occurring at the molecular level and is a rapid, nondestructive, and reagent-free measurement. Infrared and Raman spectroscopy have been applied to evaluate the biochemical composition of mammalian cells or to discriminate between cancerous and healthy cells. While discriminations can be made between stages of the cell cycle due to alterations in spectral signatures of nucleic acids, only very small differences are apparent between distinct subcellular structures. Analyses of separate cell fractions, isolated by sucrose density gradient centrifugation, present the most significant spectral differences in the C-H (carbon and hydrogen) stretching region (2800–3000 cm-1), at the ester carbonyl stretching band (1737 cm-1), and in the PO2- stretching region (1089 and 1242 cm-1). This manuscript provides a critical review of these methods, including their potential use and pitfalls for drug screening applications. Significant innovations have been made over recent years in equipment, experimental approaches, and in analysis methods.

INTRODUCTION Spectroscopic methods including infrared and Raman techniques have promise for providing rapid, non-invasive information for rapid screening of the impact of pharmaceuticals and toxicants on cells and tissues. Measurements require only minutes and can be performed without damaging the biological sample. Typically, no reagents *Corresponding author: Tel: (520) 626-9120; Fax: (520) 621-3963; E-mail: [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.


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are required and so a native sampling process can be performed repeatedly on the same cell or tissue sample. Infrared and Raman spectra yield molecular information about the chemistry of a sample. This information is related not only to the chemical composition, but also to the environment (pH, redox state, interaction between compounds, etc.). This can be used to identify and quantify compounds in volumes of femtoliters. Recent research in this area has shown that such methods can discriminate between cell types (including between bacterial species and between cancerous and non-cancerous cells), can provide a means to evaluate cell function (response to stimulating and inhibiting agents, induction of apoptosis), and can present information on alterations in cellular physiology (cell adhesion, cytoarchitecture, etc.). This review provides a background on the spectroscopic methods, their prior applications, and future use in drug discovery. APPLICATIONS OF SPECTROSCOPIC METHODS Much of the early work in applying spectroscopic methods to biological samples involved measurements of bacteria. Naumann and coworkers [1] provide one of the first uses of infrared spectroscopy to cellular systems and this was used to analyze pathogenic bacteria. They were successful at classifying bacteria using standard microbiological methods and in correlating this classification to certain features of the infrared spectra. Dehydrated samples were employed and this might explain a comparatively small degree of differentiation between species. IR measurements are highly sensitive to water content and so much of the literature in this area focuses on reducing the impact of water or on dehydrating samples prior to analyses. New sampling tools (utilizing fibers or crystals that employ an evanescent wave that samples only a small depth) can minimize the effect of water, but not eliminate it. Irudayaraj and coworkers [2] have used photoacoustic infrared spectroscopy to characterize microorganisms. The photoacoustic method is ideal for characterizing bacteria on surfaces. More recent bacterial studies have demonstrated very sensitive measurements of nucleic acids of bacteria using microcalorimetric spectroscopy with detection limits of 1 ng obtained [3]. Filip and Hermann [4] used infrared spectroscopy for discriminating Pseudomonas species (aeruginosa, fluorescens, putida, and stutzeri). They found distinct features for many cellular components, but the species could not be differentiated. The authors deduced and presented a result which has significance for all cellular spectroscopy. They determined that alterations in the composition of the nutrient broth used to grow the cells resulted in changes of the absorption ratios of multiple spectral features between fatty acids: proteins and between proteins: peptidoglycans / polysaccharides. Differences were observed between freshly harvested and starved cell biomass. It appears that richer broths lead to a greater proportion of peptidoglycan to protein than do less rich broths. Better spectra were obtained using the attenuated total reflectance (ATR) method than with transmission measurements. The difficulties presented in spectroscopic measurements of bacteria actually suggest several directions for non-invasive monitoring of cells and tissue of mammalian origin. Since cells (bacteria and otherwise) grown in a nutrient rich media have different cell spectra than do cells grown in a nutrient poor environment, this suggests that the method is highly sensitive to small variations in cellular physiology and perhaps in cell function.

Spectroscopic Analysis


Infrared spectroscopy has been applied to evaluate the biochemical composition of mammalian cells or to discriminate between cancerous and healthy cells [5-7]. Many of the difficulties encountered with bacterial measurements have been circumvented with mammalian cell work. While discriminations can be made between stages of the cell cycle due to alterations in spectral signatures of nucleic acids [8], only very small differences are apparent between distinct sub-cellular structures. Analyses of separate cell fractions presented the most significant spectral differences in the lipids, carbonyls and phosphates [5]. To best explain these results, we shall next discuss the fundamentals of these methods. FOUNDATIONS OF SPECTROSCOPIC METHODS Cell physiology and activity have been characterized using the methods of Fourier Transform infrared (FTIR) spectroscopy which is as a non-destructive technique [9-10]. A beam of infrared light is introduced to the sample and either the transmitted or reflected light is collected using a highly sensitive detector. Light absorbance at specific wavelengths usually follows a Beer’s law behavior in which light absorbance is proportional to optical pathlength, absorbtivity, and concentration: Absorbance = εbC

(1)

Light in the infrared (750 nm – 1000 µm in wavelength) is low energy and so does not disrupt proteins or nucleic acids as do high energy photons in the ultraviolet. Molecules have a set of resonance vibrations produced by thermal energy. When a molecule is exposed to radiation from a thermal source, the radiation is absorbed only at frequencies that correspond to the molecular modes of vibration in the infrared region (IR) of the electromagnetic spectrum. Absorbance features that arise from fundamental molecular vibrations (rather than the complex combinations and overtones observed in the visible and ultraviolet) can be used to identify and quantify a molecule (a protein, for example) and can also provide information on that molecule’s environment. For example, phosphates of nucleic acids absorb photons in the wavelength region of 9174 – 9216 nm. Throughout this text we will use the wavenumber (cm-1) convention. To convert from wavenumbers to wavelength, apply the following. Wavelength = 107 / Wavenumber

(2)

Spectral features from the fingerprint region between 4,000-400 cm-1 (2,500-25,000 nm) can supply information about the fundamental vibrational modes of functional groups existing in biological molecules thus permitting quantification of material composition. Fig. 1 shows an infrared spectrum of E. coli bacteria grown under nutrient rich conditions. Table 1 provides a summary of the location of notable absorbance features of biological molecules. Within this spectral region, there arise a number of features specific for biological compounds. CH 2 (that is, one carbon bonded to two hydrogens) is found predominantly in long chain hydrocarbons, such as in the lipids that comprise the cell membrane. Additional features include the so-called amide (I, II, and III) vibrations which are indicative of the vibrations of the peptide bond of proteins. Not only do the amide features characterize the amount of protein, but each feature is comprised of multiple

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smaller features, detailed in Table 1. For example, portions of the amide I usually are ascribed to α−helical protein structures, β-sheet structures, and random coils. This information is particularly useful when characterizing the response of a cell to various stresses which may alter protein production or ultimately lead to protein unfolding. For example, protein unfolding due to elevated temperatures often leads to a loss of α-helical content with an increase in β-sheet and random coils. Protein alterations are apparent in the spectra of bacteria exposed to elevated temperatures (Fig. 2). This Fourier selfdeconvolution (FSD) analysis permits identification of peak locations to simplify classification of the biochemical source of absorbance. In this case, a number of protein features are altered due to the application of heat. Specific peak locations can move several cm-1 based on hydration and temperature of the sample.

Fig. (1). Infrared absorbance spectrum of a healthy E. coli culture.

RAMAN SPECTROSCOPY Raman spectroscopy is a vibrational technique which is complementary to infrared spectroscopy. Raman spectroscopy involves the measurement of the wavelength and intensity of inelastically scattered light from molecules. Raman features are significantly less intense than are infrared absorbances as only approximately 1 out of every 106 photons is inelastically scattered. Raman spectroscopy has a number of similarities to infrared spectroscopy; however, Raman measurements are not as greatly impacted by the presence of water as are



infrared methods. Raman spectroscopy has potential for use as a means to monitor for compounds or organisms in aqueous materials particularly as water does not present a substantial Raman signal. The application of Raman spectroscopy to monitor biological systems has received less attention previously, for a number of reasons, the primary one being the low signal intensities and comparatively high limits of detection compared with traditional biochemical analyses. There are a number of means to improve this signal intensity substantially. Table 1.

Infrared Peak Assignments for Common Biochemicals Within Cells and Tissue

Assignment

Wavenumber (cm-1)

Reference

Asymmetric CH2 band (lipids)

2924

[11]

Symmetric CH2 band (lipids)

2854

[11]

C=O of Nucleic acids

1716

[12]

Amide I of β sheet protein structure

1695-1660

[13]

Amide I of α helical protein structure

1660-1648

[13]

Amide I of random coil structure

1637

[13]

Amide II of protein structure

1541

[13]

Tyrosine

1514

[13]

Methyl deformation in proteins

1458

[13]

Sugars

1419

[14]

Amide III

1350-1180

[13]

ADP

1230

[15]

ATP

1211

[15]

Carbohydrates

1150

[5]

ATP

1118

[15]

ADP

1112

[15]

Glucose

1103

[14]

P=O and P-O-P of nucleic acids and phospholipids

1090-1085

[16]

Glucose

1080

[17]

CO-O-C symmetric stretching of sugars

1058-1060

[17]

Sucrose, trehalose

1047

[14]

Osidic C-O stretching of glucose

1030

[17]

DNA, phospholipids

964

[5]

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Raman microspectroscopy combines molecular specificity with diffraction-limited resolution [18]. Biological samples can be investigated under physiological conditions and without extensive sample preparation. Several years ago Schrader [19] provided a critical review of Raman methods for application in medicine. Raman spectra have been published for biological tissue [20], nucleic acids [18]; myeloperoxidase and eosinophil peroxidase [18] and for specific locations within cells [18,22]. The approach has been used to determine the oxidation state of myeloperoxidase within activated granulocytes using resonance Raman spectroscopy [23]. Raman spectroscopy has been used to characterize and compare related types of cells. Tumorigenic and non-tumorigenic cells can be differentiated based on variations in protein and nucleic acid content [24-25].

Fig. (2). IR spectra of E. coli cells exposed to elevated temperature (57.5oC) for the prescribed times. Solid arrow highlights the decrease in the 1631 cm-1 while the dotted arrow highlights the increase in the 1619 cm-1 feature.

Raman spectroscopy has previously been used to identify microbial species of medical interest [26-27]. Freeze dried cells were found to yield good spectra, but with a comparatively low information content compared with wet cells particularly on nucleic acid content. Maquelin and coworkers [26] applied a 6 hour incubation period to increase cell numbers but had to employ a mathematical treatment to remove conflicting information from the underlying and changing culture media. They did observe features that could be used to classify bacterial species.



In biological samples, Raman signals are generally weak and often differences in peak intensities are difficult to quantify, especially in single cells or when a single molecular species is of interest, such as specific proteins, organelles, or components of the cytoskeleton or DNA. In addition, the excitation intensity must be kept low and the exposure time short in order to avoid having the light affect the cell under measurement (photo-induced damage). Photodestruction can be reduced by selecting longer excitation wavelengths, such as in the near IR (which also reduces the fluorescence background issue). Puppels [28] noted variations in sample degradation with the wavelength of excitation light. A wavelength of 525 nm caused chromosomal damage whereas 660 nm did not. Notingher and coworkers [29-31], observed that human epithelial cells are quite transparent in the range 785-800 nm. Exposure up to 20 minutes can be conducted at 785 nm with 20 mW without observable cell damage. Excitation with longer wavelength light generates weaker signals proportionally to the frequency to the 4th power. While differences in Raman spectra of healthy and non-viable cells can be subtle, changes in spectral features may be enhanced through methods to increase the Raman signal intensity. When using near infrared (NIR) excitation, the best possibility of increasing the intensity of the Raman signal is to use the Surface Enhanced Raman Scattering (SERS) effect. The SERS effect can increase Raman signals by as much as 12 orders of magnitude. The first applications of SERS were reported for the adsorption of species on electrochemically roughened silver, gold, and copper electrode surfaces and with gold and copper colloids [32]. The SERS enhancement is attributed to a metal electron mediated resonance Raman effect via a charge transfer intermediate state. Colloidal silver and gold clusters used in SERS can provide extremely high enhancement factors. This permits quantification of molecular concentrations down to 10-12 M [33] and can provide single molecule detection in femtoliter volumes [34]. Krafft et al. [35] applied Raman microspectroscopy to evaluate single cells. They found that freeze dried cells gave good spectra, but the freeze drying process apparently caused changes in the DNA conformation. Their measurements were performed with an integration time of 1 min with a power of 25 mW using 785 nm excitation. This approach has significant promise particularly due to the low effect of water on signal intensities. With a small degree of development, and interaction with other means to reduce the number of cells to be quantified at any one time, this approach has substantial promise for providing a rapid and near-continual measurement of cell physiology and function. Fig. 3 below shows Raman spectra of E. coli cells before or after application of an elevated temperature procedure designed to induce protein unfolding. These spectra were collected using a confocal micro Raman spectrometer. There are substantial alterations in spectral features particularly at 1450 and 1220 cm -1, features assigned to CH deformations predominantly of proteins. DATA ANALYSIS AND SPECTRAL INTERPRETATION For the analysis of data, mathematical approaches applied to the chemical analysis, called chemometric techniques are applied. Methods for the analysis of spectra are divided in two types, unsupervised and supervised methods [26]. The unsupervised methods are used without previous knowledge of the microorganisms used. Each spectrum is compared with others in order to make homogeneous groups or hierarchical

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clusters. These methods are used to show similarity between spectra representing organisms: same genus, species, and susceptibility to antibiotics. In supervised methods, each spectrum is formerly assigned to a definite class a relationship is sought with calibration data that provides information on the quantity of the organisms.

Fig. (3). Raman spectra of E. coli – healthy cells (top spectrum) and after application of an elevated temperature procedure (bottom spectrum).

Using supervised and unsupervised techniques of analysis it is possible to determine if the “unknown” spectra are included in the spectral database. This requires standardization of culturing conditions (such as medium composition, temperature and time of culture) and instrument parameters for the creation of reference libraries of microorganism spectra. These libraries are the basis of microorganism identification algorithms and permit the identification of an unknown microorganism on the basis of its mass, FTIR or Raman spectrum [26]. METHODS FOR PROTEIN AND METABOLIC PROFILING We have recently applied a unique fiber-based spectroscopic measurement scheme to investigate alterations in the physiology and function of lung epithelial cells exposed to inhalation hazards [36-40]. The principle of measurement is based on the concept of fiber evanescent wave spectroscopy (FEWS) [10, 41-42, 34]. When a light wave is guided through an optical fiber, part of its E field extends outside the surface and constitutes the evanescent wave. The detection principle of these optical fiber sensors is based on the absorption of the evanescent wave by substances in contact with the fiber surface. This technique yields IR spectral information very similar to that obtained with ATR spectroscopy. A challenge for biological monitoring is avoiding the effects of high



water content in samples. This may be reduced by employing a pre-screening step such as provided by an immunopurification or other means to increase cell numbers while decreasing sample water content. Utilization of evanescent wave spectroscopy reduces the water impact. Anchorage dependent cells, such as epithelial type II pneumocytes (A549 cells) can be grown attached to the surface of the fibers. Baseline spectra are collected with healthy cultures. The cells are then exposed to various chemical hazards and their response tracked through monitoring the spectral absorptions located between 3000-900 cm-1 due to hydrocarbon vibrations of methyl and methylene groups in membrane lipids, due to proteins (amide I and amide II vibrations), and other components. The cells are maintained at near to complete confluence (full coverage of their attachment surface) which inhibits cell replication thus reducing variations from cell to cell in their stage of the growth cycle. Absorption bands in the regions of 2800-3000 cm-1 (corresponding to membrane lipid features) and within 1700-1400 cm-1 (corresponding primarily to protein features) change rapidly upon exposure of the cells to each toxin. These alterations provide mechanistic information on the cell damage and response which correlates with information from standard biochemical analyses [34]. Some compounds cause initial damage to the cell membrane which is displayed in rapid alterations to the lipid features, followed by later changes in proteins. Compounds that damage nucleic acids alter the features corresponding to phosphates of nucleic acids. Similarly, membrane damaging agents primarily alter the CH2 features of lipids. A comparison of the peak intensities of A549 cells exposed to differing concentrations of inhalation hazards demonstrates a dose response with specificity in the cellular component affected [38]. For example, the alkylating agent methylmethanesulfonate (MMS) damages the cell membrane and hence lipid specific features at 2854 and 2924 cm-1, decrease by 0.7 and 0.8 absorbance units, respectively for cells exposed to 5 mM MMS; these features decrease by 0.5 and 0.6 absorbance units, respectively for cells exposed to 2 mM MMS. These changes correspond to the biochemical mechanisms of MMS toxicity. Exposure to the fungal metabolite gliotoxin leads to a complex absorbance profile over time at a number of features (Fig. 4). 5 µM gliotoxin produces a small decline in A549 cell absorbance features for lipids at 2852 cm-1 and 2922 cm-1. These changes occur consistently over 20 hours of continual exposure to gliotoxin. α-helical protein content and sugars show an increase for the first 4 hours of exposure, followed by a decline from 12 - 20 hours. Many biochemical features are fairly constant for the first 4 hours, have a step change decrease at 12 hours, and then remain constant thereafter. Some, such as ADP and glucose show a slow increase over time. Fig. 4 below shows alterations in sugar features, specifically the C-O stretch, C-O-H bend, of carbohydrates (1150cm-1). This spectral analysis provides information on both mechanisms by which cells are damaged by toxins and the approaches by which the cell responds. For example, apoptosis inducing compounds, which lead to condensation of DNA, produce significant changes in the spectral features of nucleic acids. Alterations in protein features reasonably follow the time course of induction of a number of cell response genes.

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Fig. (4). Alterations in sugar composition within A549 cells after application of 10 µM gliotoxin, a fungal metabolite.

The infrared spectroscopic measurement approach has several advantages over more traditional methods of evaluating cellular response to toxic or pharmacologically active compounds. The measurement can be performed very quickly requiring only minutes whereas cell responses can begin from minutes to hours after exposure to active compounds. The cells are sensitive to a wide range of environmental toxins and their response through initiation of a number of pathways to recover from the stressor can be identified through spectral analysis. A limitation of this spectroscopic analysis lies in the difficulty of identifying the specific identity of a compound which has caused an alteration in spectral features. Most environmental monitoring methods focus on quantifying a limited number of potential toxins such as metals, pesticides, or polyaromatic hydrocarbons. As is typical of cellbased sensing methods, the target for quantification is not specific identification of the agent but rather biological activity. Hence specific concentrations of a toxin cannot be determined, although a dose response relationship can be demonstrated. The mechanism of action can also be identified through analysis of cell components which display damage. Additional cell types can be employed in parallel so as to modulate sensitivity to certain toxins. The spectroscopic technique has the potential to be applied outside of laboratory settings and permit continual monitoring of the potential impact of toxins on human health. In a regular infrared spectroscopy experiment, the sample would have to be extracted and placed across the beam path inside the analytical instrument. This requires extremely thin samples and/or applied on an infrared transparent substrate. Similarly, an ATR experiment would require that the sample be extracted and introduced into the instrument. However the development of high quality infrared optical fibers now allows



for in situ measurement where the signal is brought to the sample and collected back to the detector using a single optical fiber. This type of remote analysis has many applications in medicine and drug discovery. It has been applied to the detection and monitoring of a wide range of biomolecules [34]. This technique is especially suited for in vivo analysis such as monitoring of biological processes [43], and biomedical sensing including non-invasive glucose measurements [44] and cancer diagnosis [45]. Additionally, fiber analysis permits one to collect signals remotely from cell culture held in controlled conditions in an environmental chamber. Remote spectroscopic sensing can only be performed with fibers that have large optical windows in the infrared, low optical losses and sufficient chemical durability. Chalcogenide fibers are based on S, Se and Te containing glasses combined with nearby elements on the periodic table, particularly, As, Ge, Sb. Owing to higher atomic mass, these glasses exhibit low phonon energies and possess a wide transparency window in the infrared region, from visible up to 18 µm depending on the composition. This optical window encompasses the vibrational modes of virtually all organic molecules and is therefore ideal for biochemical sensing. In this fiber sensing technique, the evanescent wave that interacts with the sample decreases in intensity exponentially with increasing distance from the fiber surface. The technique therefore only collects signal from within about 0.5 µm of the fiber surface where the evanescent wave is most intense. It then allows very localized probing that could prove useful for bacterial strain identification (or for analysis of membrane acting compounds) since many structural features providing the possibility of differentiation are presented on the cell surface [46]. It is also beneficial to collect strong signals from the cell itself while minimizing signal from the surrounding fluid. Another benefit of utilizing a chalcogenide fiber is the possibility of shaping the fiber into a reduced-diameter sensing zone. The fiber diameter is typically reduced from 400 µm to 100 µm along a distance of a few centimeters or less. This geometry considerably increases the evanescent wave intensity along the reduced diameter zone. This provides two major advantages to the technique. First, the sensitivity is greatly increased in comparison to the equivalent ATR due to the much larger number of internal reflections that result in many absorption events at the interface between the glass and the sample. Second, the signal is entirely collected along the reduced diameter zone and therefore prevents collecting noise signal from the surrounding environment. Finally, the chalcogenide fiber surface exhibits a hydrophobic behavior that is beneficial in minimizing interference of water during signal collection. Water has a strong infrared signal that overlaps with many organic molecules. Since many biochemical processes take place in an aqueous environment it is therefore beneficial to minimize this interference. The hydrophobic nature of the glass surface results in selective interaction and detection of organic species at the fiber surface relative to water molecules. Hydrophobic organic species with a low dielectric constant can therefore be detected more efficiently in aqueous media using chalcogenide fibers. There are several significant challenges which need to be addressed before this approach reaches field use capabilities. Cell adhesion to the fiber surface can be inconsistent thus changing the number and mass of cellular matter in contact with the evanescent wave. This leads to the requirement of performing relative measurements of cell composition before and after exposure. The poor adhesion is potentially due to


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compositional effects of the TAS material. Some arsenic does leach out of the fibers; however a washing step appears to remove this pool of mobile arsenic and so direct arsenic toxicity is likely not a significant factor. During the measurement process variations in water content may impact measurements and could distort the trends observed here. This can potentially be addressed through increased consistency in the length and thickness of the drawn sensing zone of the fiber. Interpretation of the complex spectral changes resulting from cell exposure to toxic compounds presents a significant challenge at least in part due to the interference of water vapor signals in the 900-1700cm-1 region of the cell spectrum. While the water signal overlap complicates the interpretation, some consistent and reproducible changes can be observed in some specific spectral features of the cellular material exposed to toxicants. Higher level analysis techniques such as partial least squares regression and principle component analysis could be used to extract patterns in the evolving spectral features so as to increase the predictive capability of the technique. For instance, the pattern of changing spectral features indicative of cell response to gliotoxin is different from that of MMS. Development of a substantial database of cell spectra along with such complex analysis tools could assist in discrimination of a cell response pattern indicative of gliotoxin exposure rather than that due to a genotoxin. Such a database will require spectra of cell responses to each toxin at varying concentrations. SUMMARY Spectroscopic methods including use of infrared and Raman techniques can provide information on cellular physiology and function. Alterations in cellular lipids, proteins, carbohydrates, and nucleic acids are the most readily identified. These approaches have significant potential for high throughput screening of pharmaceuticals, for testing toxicity, and for quantification of environmental hazards. ACKNOWLEDGEMENTS This work was supported by DARPA contract # N66001-C-8041, by the NIEHS sponsored Southwest Environmental Health Sciences Center # P30 ES06694, and by the Arizona Board of Regents Technology and Research Initiative Fund. ABBREVIATIONS AND SYMBOLS ATR

=

Attenuated total reflectance

B

=

Optical pathlength

C

=

Concentration of an analyte

FEWS

=

Fiber evanescent wave spectroscopy

FSD

=

Fourier self-deconvolution

FTIR

=

Fourier transform infrared spectroscopy

IR

=

Infrared



MMS

=

Methylmethanesulfonate

SERS

=

Surface enhanced Raman scattering

TAS

=

Telurium arsenic sulfide

ε

=

Molar absorbtivity

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273

Encapsulated Biomolecules Using Sol-Gel Reaction for High-Throughput Screening Kumiko Sakai-Kato †, Masaru Kato*,‡,§, Naoko Utsunomiya-Tate† and Toshimasa Toyo'oka‡ †

Research Institute of Pharmaceutical Sciences, Musashino University, 1-1-20 Shinmachi, Nishitokyo-Shi, Tokyo, 202-8585, Japan; ‡School of Pharmaceutical Sciences and COE Program in the 21st Century, University of Shizuoka, 52-1 Yada Suruga-ku, Shizuoka, 422-8526, Japan and §PRESTO, Japan Science and Technology Agency (JST), Saitama, Japan Abstract: Recently, the sol-gel encapsulation method has attracted much attention for the development of desirable protein-doped matrices as biosensors. Proteins are entrapped into a porous, silica matrix that is formed via a low-temperature sol-gel reaction. The encapsulated proteins can retain their structure and biological activity for a prolonged period. This sol-gel encapsulation method allowed the reuse of expensive protein reagents multiple times. Furthermore, the encapsulation method often improved the stability of the immobilized proteins. Based on these reasons, this technology has been used in various fields, and is expected to contribute to the effectiveness of analytical systems and the application to high-throughput screening systems. In this review, we introduce various studies in which biomolecules were immobilized on capillary-, microchip-, and microarray-based analytical systems using the sol-gel reaction. The interactions of the immobilized biomolecules and analytes were detected using UV, fluorescence, mass spectrometry, or electrochemical detection. On the other hand, many researchers are studying the sol-gel processing to improve the biocompatibility of the sol-gel derived materials using new biocompatible silane precursors and processing methods. The microstructure of the silica matrix was also investigated using various analytical systems. We also review some reports that described the fundamental aspects of the sol-gel reaction.

1. INTRODUCTION Recently, it became possible that many groups of compounds can be synthesized at the same time due to the progress of combinatorial chemistry. Furthermore, due to the development of bioinformatics, a large quantity of information can be accumulated which is indispensable to research for drug discovery [1,2]. These situations calls for flexible, fast and cost-effective strategies to meet the demands of evaluating the high content lead series with improved aspects for clinical success [3,4]. Biomaterials

*Corresponding author: Tel: +81-54-264-5654; Fax: +81-54-264-5654; E-mail: [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.


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including proteins have attracted many researchers' interest due to their highly specific recognition ability and effective catalytic ability. It was very natural that a bioreactor was produced that made use of these biomaterials outside the living body for the food or medical industries [5]. This was used in a variety of fields, and as a consequence, new technologies for the immobilization of these biomaterials have been developed for their more efficient use [6]. Various immobilization methods are reported as this way of fixation as shown in Table 1, when the enzyme is being most widely used as a biomaterial. These include the covalent attachment to supports [7-9], adsorption onto solid supports [10], cross-linking with bifunctional reagents [11,12], entrapment in gels, encapsulation in membranes with microcapsules, liposomes, hollow fibers, or dialysis membranes [13, 14]. However, such techniques are not generic, and in most cases, can be used only for a limited range of biomolecules or applications. Table 1. Enzyme-Immobilization Technique

Method

Characteristics

Advantage

Disadantage

Ref.

Covalent Attachment to supports

The most popular supports are porous ceramics.

Producing a very rigid bond.

The potential for inactivation of the active site during the immobilization process.

[7-9]

Adsorption on solid supports

Physically adsorption on various inorganic or organic supports.

The simplest and cheapest approach.

The adsorbed product is not very stable.

[10]

Cross-linking with bifunctional reagents

To form agglomerations of enzymes or incorporate enzymes into a polymer.

Producing very stable system.

The attachment can destroy catalytic activity.

[11,12]

Entrapment in gels

The enzymes were entrapped in the gel during the polymerization.

Inexpensive, performed under mild conditions.

The reagents may not always reach enzymes.

[13]

Encapsulation in membranes

With microcapsules, liposomes, hollow fibers, or dialysis membranes.

Working well. Best suited to medical applications.

Expensive.

[14]

Recently, the sol-gel encapsulation method has attracted much attention for the development of desirable protein-doped matrices for biosensors. The silicate matrix is formed by the hydrolysis of alkoxide precursors followed by condensation to yield polymeric oxo-bridged SiO2 networks. Conventional sol-gel methods involve the use of a high concentration of methanol as a co-solvent for the precursors, often with high acidity [15]. However, Elleby et al. developed a modified version of the process that removes the need for the addition of methanol. This version does not expose the biomolecules to the damaging effects of low pH [16]. These improvements enabled encapsulated proteins to retain their structure [16] and biological activity for a prolonged

Encapsulated Biomolecules


period [17] and to enhance its utility as a new type of matrix for biomolecules. Silica has physical and chemical properties which are quite attractive from the viewpoint of joining inorganic materials with biomolecules and living cells; the thermodynamic stability of the Si-O bond, 452kJmol-1, indicates a very strong inertness that excludes any interference with enzymes and functions typical of differentiated cells. The entrapment of proteins and other biological species into a wide range of sol-gel derived nanocomposite materials, and their use as biosensors and biocatalysis alpplications, has been reviewed by some researchers [18-22]. In addition to the general overview of the sol-gel encapsulation techniques for biomaterials, this review focused on the utility of biocomposite materials in numerous applications to high-throughput screening systems primarily for drug discovery, including the stationary phases for affinity chromatography, capillary electrochromatography, microchip electrophoresis, and microarrays. Finally, the additional possibility of sol-gel biocomposites and future subjects are addressed. 2. THE SOL-GEL REACTION The sol-gel reaction can be divided into the following steps, Fig. (1): (1) the hydrolysis of an alkoxysilane; (2) the condensation of hydrated silica to form siloxane bonding (≡ Si-O-Si ≡); and (3) the polycondensation that involves the linking of an additional silanol group to form cyclic oligomers.

Fig. (1).Typical sol-gel reaction and monomer compounds.

The properties of each individual sol-gel matrix can be altered by a number of factors. These include pH, temperature, reagent concentrations, reaction time, the rate of hydrolysis and condensation, and the nature of the catalyst. The most common starting

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reagents of the reaction are tetramethoxysilane (TMOS) or tetraethoxysilane (TEOS) because they can be readily hydrolyzed and condensed under mild conditions. Many alkoxysilanes and their derivatives are now commercially available. The hydrolysis is mainly performed with a catalyst: acid or base catalysis. It is generally agreed that, under acid catalysis, it is easy to form a linear network, because the velocity of the hydrolysis reaction is faster than that of the condensation reaction. Whereas, under base catalysis, entangled or randomly branched chains are formed, because the velocity of the condensation reaction is faster than the hydrolysis reaction [15]. 3. STRUCTURE OF THE MONOLITH 3.1. Porous Monolith Many silane compounds are used as precursors of the silica monolith. These include tetraalkoxysilanes, mono-, di- or tri-alkyl alkoxysilanes; many comprise functional groups ranging from alkenyl, aryl, amino, carboxyl, thiol or other groups. The precursor is hydrolyzed by water, either spontaneously or under acidic or base catalysis, to form hydroxy derivatives (silicic acids, hydroxysilanes, etc.). A cascade of condensation reactions gives rise to soluble, colloidal and ultimately phase-separated polymers (polysilicates, polysiloxanes, etc.), which produce the final matrices (called monoliths). The monolith has nano-sized pores, which are used for the encapsulation of biomolecules. The initial gels are typically brittle or semiflexible, and contain 50-80% interstitial water, with pore volumes of 0.4-3.4 mL/g, pore distributions of 4-200 nm, and surface areas of 600-2100 m2/g [19, 20,23-25], Fig. (2).

Fig. (2). Schematic representation of the sol-gel process.

Aging of the wet silica network over a period of days to weeks promotes further condensation and strengthens the network. Most of the monolith can been dried by evaporation of the solvent. If only simple silica alkoxides are used as precursors, the wet gels mostly carry Si-OH side groups which are hydrophilic and induce a considerable capillary force (typically 70%) in the entire structure. That is to say, the walls of the nano-sized pores shrink, and a monolith called an “xerogel” is produced.



There is another type of monolith which is termed an “aerogel”. The aerogel is made by ambient pressure drying or by prevention of solvent evaporation during drying. A supercritical drying is used for ambient pressure drying [26]. If the monolith is used for the encapsulation of some molecules, the monolith must have a pore size that is sufficiently small to prevent leakage of the molecules, but large enough to allow smaller analytes to enter the monolith with ease. These monoliths form nano-capsules for the encapsulation of molecules or function as a glue for the immobilization of the molecules. Examples of their applications are shown in the latter part of this review. Although these types of monoliths can be prepared by a simple procedure, they do not have µm-sized pores, which are termed “through-pores”, therefore, they cannot be used for the analysis of large molecules that cannot enter the monolith. To overcome the above-mentioned defect, another type of monolith, which has throughpores, was developed. 3.2. Bimodal Porous Monolith A bimodal porous monolith has two different pore sizes, which are µm-sized through-pores and nm-sized mesopores, in the matrix. A porogen is often used for creating a bimodal porous monolithic bed. Porogens play dual roles: they serve (a) as a through-pore template, and (b) as a solubilizer of a silane reagent. A porogen is used to create desired morphologies with intended permeabilities and surface areas for the construction of a monolith. A water-soluble organic polymer, poly(ethylene oxide) (PEO) or polyethylene glycol (PEG) has been used as a porogen by many researchers. Toluene has also been used as a suitable porogen for a photopolymerized sol-gel (PSG) monolith [27,28]. Using the proper porogen, spinodal decomposition occurs and a porous monolith is produced, Fig. (3). Spinodal decomposition is a clustering reaction in a homogeneous solution. The phase separation was solidified by gel formation, resulting in a silica rod with a bimodal porous structure consisting of µm-sized through-pores and meso- or nano-porous silica skeletons. The decomposition mechanism of the polycondensation of an alkoxysilane in the presence of PEO was extensively studied by Nakanishi et al. [29-33].

Fig. (3). Schematics of spinodal decomposition.

Silica rods with a bimodal porous structure typically possess 0.3-5 µm silica skeletons, 0.5-8 µm through-pores, and 2-20 nm mesopores in the skeleton [34]. The sizes of the skeletons and through-pores were independently controllable by changing the preparation conditions. A porogen has often been used for controlling the size, Fig. (4). Alkali treatment of the monolith has been reported to form mesopores on the

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surface, and the strength of the used alkali, treatment time, and temperature affect the size of these meso-pores [35]. These monoliths were used for separation columns, bioreactors, and so on. Examples of this application are shown below.

Fig. (4). Effect of porogen content on the bimodal porous structure.

4. SILANE PRECURSOR AND SOL-GEL PROCESSING FOR IMMOBILIZATION OF BIOMOLECULES The sol-gel reaction involves the formation of a colloidal sol solution by hydrolysis of a precursor, such as TEOS or TMOS. A buffered solution containing the biomolecule of interest is then added to the sol to initiate rapid polycondensation of the silane. Following polycondensation, a hydrated gel is produced that immobilizes the biological element without the need for a covalent tether. Although the hydrolysis of the alkoxysilane, such as TMOS or TEOS, are widely used for the immobilization of biomolecules due to the simple procedures, during the hydrolysis of silane precursors, TMOS or TEOS, methanol or ethanol is produced. These byproducts will readily dissolve or destabilize existing proteins or the bilayer membrane structure. Therefore, another route using aqueous sodium silicate as a starting material was developed in order to avoid the production of an alcohol [36,37]. In this route, SiO2 networks are produced by acidifying aqueous sodium silicate. By using colloidal silica together, silicates condense on the surface of the colloidal particles that behave as nucleation sites for condensation. Aqueous sol-gel processes were produced to reduce the effect of the alcohol. This allowed the immobilization of the bacteria. Sakai-Kato et al. also used this technique and showed the immobilization of cytochrome P-450 [38]. By using this method, the activity of the oxidation of testosterone drastically improved, compared with that using TMOS-derived silica matrices. Ferrer et al. developed other aqueous routes. The methods used vacuum elimination of the alcohol by a rotary evaporation method. Biomolecues were added to the sol after removal of the alcohol and then the condensation reaction occurred [39, 40]. To achieve entrapment of active biomolecules in the sol-gel derived silica, it is necessary to maintain the active conformation of the biomolecules within the matrix, and for the enzyme, ensure that the entrapped enzyme is accessible to substrates. A number of sol-gel derived materials have been designed with the purpose of making the matrix



more biocompatible with the entrapped biological molecules. For example, new biocompatible silane precursors and processing methods have recently been reported based on glycerated silanes [20,41]. Brennan et al. developed the use of a silane precursor bearing a covalently attached sugar for the formation of protein-doped sol-gel derived silica. Silica materials containing covalently bound glucosamide moieties provide a biocompatible environment for entrapped Src kinase, and a sufficient porosity to allow polypeptide substrates to enter the glass and interact with the entrapped Src kinase [42]. Another approach involves the use of protein-stabilizing additives to increase the protein stability, including ligand-based stabilizers [43,44], organosilanes [45,46], poly (ethylene glycol)[47], graft copolymers, such as polyvinylimidazole and polyvinylpyridine [48-50], and charged polymers, such as poly(vinylimidazole) and poly(ethyleneimine) [51]. Polymers such as polyvinyl alcohol, alginate, gelatin, and chitosan were used as additives, directly mixed in the silica sol before gelation [52,53]. Entrapped proteins have also been shown to be stabilized by the addition of small molecules such as sugars and amino acids (osmolytes) during sol-gel processing due to changes in the excluded volume and protein hydration [54,55]. However, such species could be easily removed from the matrix by washing, resulting in a significant loss in protein stability and poor reusability of the entrapped protein [55]. As a further improvement, it is possible to encapsulate biomolecules together with an additive that is beneficial for the stability or bioactivity of the biomolecules. These additives can be hydrophobic moieties brought about by alkoxysilane precursors, carrying, for instance, alkyl R groups. In this regard, an important contribution was made by Reetz et al. [56] who showed that such hydrophobic moieties could improve the activity of lipase beyond that of the free enzymes. 5. INVESTIGATION OF SOL-GEL STRUCTURE 5.1. Structural Evolution It is important to discuss the structural evolution during the process of hydrolysis or condensation of a silica solution. Because the structural evolution determines the final gel structures, it affects the mode of the biomolecules entrapment. The methods of choice for determining structures are nonintrusive in situ methods such as nuclear magnetic resonance (NMR) spectrometry, Raman and infrared spectroscopies, and Xray, neutron, and light scattering. 1

H and 29 Si NMR have been extensively employed to elucidate the extent and kinetics of the hydrolysis and condensation reactions accompanying gelation, and the speciation of silicate solutions during the early stage of the polymerization of TMOS, TEOS and several oligomeric species, Si2O(OR)6, Si3(OR)8, and Si8O12(OR)12 [57-59]. In addition, 17O and 13C NMR have been used to monitor the water and solvent content in several systems [60]. Raman and Infrared spectroscopies have been used both in combination with NMR to identify specific oligomeric species in solution and to follow the evolution of inorganic frameworks by comparison with model compounds of known structure [61]. Balfe et al. combined Raman and Fourier transform infrared (FT-IR) spectroscopies to determine the hydrolysis behavior of model linear and cyclesilicate compounds. Both methods provide information about the Si-O-Si bond strain with different vibration


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modes [62-64]. Attenuated total reflectance (ATR)-FT-IR has many advantages over the conventional method of using a potassium bromide disk. ATR enables one to measure silanol groups without drying the gel. Furthermore, it allows a continuous measurement during the time-course of gelation [65]. Small-angle-scattering investigations utilizing neutron (SANS), X-rays (SAXS), or visible light have been employed to investigate the growth and topology of macromolecular networks that precede gelation, the aggregation of colloids, and the structures of porous gels and aerogels. Martin and Hurd [66], Schaefer [67], and Schaefer and Keefer [68,69], have published excellent reviews of this topic. During the process of gelation, the polycondensation of alkoxysilane monomers leads to the formation and growth of soluble particles and then colloids. These clusters coalesce and raise the solution viscosity for the sol-gel transition, at which bulk gelation occurs (gelation point) [15]. Because dynamic light scattering (DLS) method is often used for measuring the hydrodynamic radius of a macromolecule or colloid, this technique was employed to evaluate the changes in the cluster diameter during the gelation process and investigate how proteins influence the gelation [65, 70]. 5.2. Gel Structure The characteristics of the resulting gel were investigated using various methods. The pore diameter of the xerogel is measured using nitrogen adsorption isotherms, and specific surface areas were calculated from the Brunauer-Emmett-Teller (BET) analysis. The microstructures of the xerogels were observed using scanning electron microscopy (SEM) [71]. Compared with the xerogel, the structural investigation of the hydrogel is more difficult due to its high water content. The pore size distribution of hydrogels was measured using calorimetry [71, 72]. The porous materials had dynamic and thermodynamic relationships applied for the freezing and melting curves of water in gels filled with water [71, 73]. The microstructure of the hydrogel was observed using transmission electron microscopy (TEM) [38]. 5.3. The Characteristics of Immobilized Biomolecules The structure of the gel is very important, because it regulates the activity or conformation of the entrapped biomolecules. Because the porous glasses prepared by the sol-gel technique are optically clear, it is possible to examine the spectroscopic characteristics of the entrapped biomolecules for clarifying the physical environment of the immobilized protein or the mechanism of entrapment [74]. The steady-state and time-resolved fluorescence spectroscopy of the intrinsic Trp is used to probe the internal microenvironment of the sol-gel-derived materials [75]. The nature of the interaction between the sol-gel derived silica matrix and electron transfer proteins were explored using the optical absorption spectra for the proteins for determining the synthesis conditions of the materials [76]. Another approach is that the fluorescence probes are used for investigation of the local structure and dynamics of encapsulated biomolecules in the sol-gel materials. The small organic probes and fluorescent biomolecules provide information about the pore-solvent composition and polarity, internal solvent and dopant dynamics, environmental heterogeneity and phase segregation, and surface chemistry, as determined by the molecule-matrix interaction [77,78]. Resonance Raman spectrometry is also used for the protein structure in the sol-gel encapsulated state of the myoglobin [79]. IR also provides information about the



proteins entrapped in the gel [80,81]. Two representative bands that originated from BSA were observed in the region between 1800 cm-1 and 1400 cm -1, Fig. (5). The signal around 1657 cm-1 is the amide I band, which mainly consists of υC=O stretching vibrations. The signal at 1549 cm-1 is the amide II band, which mainly consists of υN-H bending vibrations. These two bands change the position depending on the structural

Fig. (5). IR spectra of BSA in gel and in free solution.

changes as well as the degree of hydrogen bonding [81]. It is also reported that the amide I/ amide II intensity and area ratios may be associated with conformational changes in the protein [81]. Table 2 shows a comparison of the peak positions and amide I/ amide II area ratios obtained from BSA in the TMOS-derived gel and in free solution. The peak positions of each peak show no differences in both formats. Furthermore, the peak area ratios are very close to each other. These strongly suggest that BSA maintains its conformation after encapsulation in the gel [65]. Table 2.

Comparison of the Amide I and II Bands for BSA Encapsulated in Gel and in Free Solution

Position

Area ratio

amide I (cm-1)

amide II (cm-1)

(amide I/amide II)

encapsulated in gel

1657

1549

1.56

in solution

1657

1549

1.43

The image of the immobilized biomolecules were directly observed using the TEM [38], Fig. (6).

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Fig. (6). TEM image of immobilized microsome. Bar: 1 µm.

6. APPLICATION Protein-doped silicate materials have been applied in a variety of forms. It is one of the most important advantages of the sol-gel reaction. Therefore, we can optimize the configuration of the sol-gel derived biomaterials to provide the utmost performance for a given application. Typically, silicate biocomposite materials can be prepared as monolithic blocks, powders, thin films, microarrays, or fibers, as shown in Fig. (7). The choice of a particular configuration relies on factors such as required protein loading, desired response time, sensitivity and detection limits, and the ability to interface the material to common analytical devices.

Fig. (7). Configurations used to interface sol-gel derived materials.

6.1. Study for Protein Charactersitics Bulk glasses (monoliths), which are prepared by adding the protein-doped sol to a mold, such as a optical cuvette, and allowing gelation to occur, provide relatively high total protein loadings and long pathlengths, making monoliths useful for the fundamental studies of protein behavior in a glass (thermodynamic stability, dynamics, accessibility,



binding constants ) [75,77,82-85], and is also useful for biophysical studies, such as the kinetics of the allosteric transition of proteins by restricting and slowing down protein structural changes [86,87]. 6.2. Sensor The bulk gel entrapping proteins have been successfully used as platforms for optical biosensors[16,43, 88]. The disadvantage of the bulk glass is the long response times for sensing applications, due to the slow diffusion of reagents within the monolith [89]. One of the most technologically important aspects of the sol-gel process is that, prior to gelation, the fluid sol or solution is ideal for preparing thin films by such common processes as dipping, spinning, or spraying. Sol-gel film formation requires considerably less equipment and is potentially less expensive. Sol-gel film was formed on one optical face of a 1-cm pathlength polystyrene cuvette. This metalloprotein–entrapped gel was used for nitrogen monoxide or carbon monoxide sensing [90,91]. Optical methods can be used to quantify reactions that generate a color change, whereas electrochemical detection is suitable for redox reactions. Biomaterials that can be used in electrochemical biosensors include enzymes, antibodies, antigens, oligonucleotides and DNA fragments. Biomaterials can either be immobilized on the surface of electrodes or trapped within conductive materials. Composite ceramic carbon electrodes in which an enzyme-loaded carbon powder is mixed in the sol-gel solution have been extensively developed [92,93]. These electrodes can be prepared in virtually any desired shape; as thick supported films, as discs or rods, and even in the form of microchips [94]. Sandwich configurations in which enzymes are deposited between two sol-gel silica layers of controlled porosity have also been developed. They offer rapid diffusion of the substrate through the upper porous layer and a shorter enzymatic reaction along with high enzyme loading [95]. Glucose oxidase (GOD) is one of the most widely used enzymes for the biosensing of glucose, due to its significance in clinical analysis for the diagnosis and treatment of diabetes, and in biotechnology and the food industry. GOD catalyzes the oxidation of glucose by molecular oxygen into hydrogen peroxide and gluconic acid. Oxygen depletion can be electrochemically determined by an oxygen-sensitive electrode [96,97]. 6.3. Chromatographic Column Although the bulk form provides relatively high total protein loadings and long pathlengths, bulk glasses tend to have a long response time due to slow diffusion of the reagents within the monolith. This is especially problematic when it comes to enzymes, because its causes a decrease in the reaction efficiency within the monolith compared with that in solution. To overcome this problem, the integration of the bio-doped gel into a flow-through system is quite ideal. The on-line method is also expected to facilitate the development of the high-throughput analytical system of multiple analytes for drug discovery research. 6.3.1. Packed Column A packed column was prepared by loading the ground glass containing proteins into a column. An organic or non-organic molecule-doped sol-gel was prepared as a bulk gel. The following typical preparatory procedures are as follows: after drying, gels are


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crushed using a mortar and pestle, then sieved. The bioaffinity columns are fabricated by loading a slurry containing the particles into a column. This format leads to good flow rates (due to interparticle spacings) along with a high surface area due to the nanometer scale pores in the material. Such materials also allow for high protein loadings. Alkaline phosphatase, and a number of other enzymes were effectively entrapped in the sol-gel glass with a retention of approximately 30% of their enzymatic activities [17]. A protein A-trapped column was prepared for the purification of sheep immunoglobulin G [98]. The sol-gel was derivatized by γ-aminopropyltriethoxysilane to provide a matrix that eliminates the nonspecific absorption of proteins. On the other hand, immunoaffinity chromatography columns can be prepared by the encapsulation of antibodies in sol-gel glass networks for the removal of carcinogenic pyrene. Pyrene could be isolated and enriched by a factor of 125 by this column. No change in the specific retention properties could be observed after 10 absorption/desorption cycles. The column was used for the analysis of PAHs [53,99,100]. Other groups also encapsulated monoclonal anti-atrazine antibodies in sol-gel matrices [101]. Zusman’s group has developed columns using glass fibers covered with antibody-doped sol-gel glass and used them for the affinity separation of tumor-associated antigens from blood [102]. Although the entrapped materials were not biomolecules, a biogel column was also prepared using this method. Narang et al. prepared a synthetic ribonuclease inhibitor-doped sol-gel column for the removal of ribonuclease from solution [103]. 6.3.2. Monolithic Column Kato et al. prepared biomolecule-encapsulated columns using the sol-gel method, Fig. (8) A, in a single step for capillary electrophoresis (CE) and capillary electrochromatography(CEC) [52, 104-110]. In their method, grinding the silica matrices before packing was not required because the polymerization reaction was performed within the separation column. The sol-gel reaction proceeds within a capped capillary, which can minimize drying of the gel. The resultant hydrogel immobilizes various biomaterials with wide range of sizes, from proteins to microsomes. The electroosomotic flow (EOF) is generated as a flow when the voltage is applied at both ends of a capillary and can carry the analyte to the encapsulated biomolecules within gel matrix. The water content of this hydrogel calculated from the weight reduction after drying is around 70 %, which coincides with the water content in living organisms. This is expected to retain the encapsulated biomolecule function for a long period. The hydrogel was prepared using TMOS as the starting material, then hydrolyzed by HCl so that the hydrolysis proceeded form a fully or partially hydrolyzed silane, SiOH4-n (OMe)n. A buffered solution containing proteins was added to the hydrolyzed silica solution. The mixture was put into a capillary, which had previously been filled with running buffer. Both ends of the capillary were sealed and placed at 4 ºC for more than 4 days. Bovine serum albumin (BSA) was encapsulated within the hydrogel, and the column was used to separate tryptophan enantiomers. A mixed solution of TMOS and methyltrimethoxysilane (MTMS) was used as a precursor for an ovomucoid-encapsulated column, and the column was used to separate benzoin and basic drug enantiomers [104]. The effects of various factors, such as pH and concentration of the eluent, on enantiomeric separation were examined. The BSA-encapsulated column was also used for the evaluation of the binding factors using the retention times of the analytes. The binding factors were calculated as 181 M-1 for D-Trp and 371 M-1 for L-Trp. Although the binding constants were smaller than those obtained by other techniques using BSA as a chiral selector, the



ratio of the binding constants of D- and L-Trp to BSA was similar. This shows that this protein-encapsulated column can be used for the measurement of the ratio of the binding constants to the analytes. The advantages of this protein-encapsulated column are that the size of the analyte and the required proteins in this system decreased a few orders of magnitude from conventional schemes, as well as the simplicity of the preparatory procedures.

Fig. (8). Schematics of separation and enzyme reaction by the biomolecule-encapsulated column (A) and FAC/MS system (B).

This biomolecule-encapsulated column could also be used as an enzymatic bioreactor. At the inlet of the capillary, trypsin is encapsulated in the gel matrix. The substrates are electrokinetically introduced from the inlet of the capillary, Fig. (8). A a). The substrates are converted into products by the encapsulated enzyme, Fig. (8). A b). Finally, the unreacted substrates and products are separated by electrophoresis, Fig. (8). A c). Sakai-Kato et al. prepared trypsin- and the microsomes-encapsulated columns. The trypsin-encapsulated column digested substrates (amino acid derivative and peptides) and separated the substrates and products within a single capillary [107]. They encapsulated microsomes which contained drug-metabolizing enzymes. The encapsulated column was used for the production of metabolites and their separation from the substrates [109]. It could also be used for metabolic inhibition screening by injection of a mixed sample into the column [110]. In both cases, the resultant monolithic reactor


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showed an excellent enzymatic activity with prolonged stability, and the separation of the unreacted substrates and products in the same capillary also showed a high selectivity. The sample size in this system decreased 3 orders of magnitude from conventional reaction schemes. Because the analytical procedures, i.e., reaction, separation, and detection, were integrated into a simple system, the systematic automation and the reduction of the analytical times and required solvents were realized. Although the preparation of the biomolecule-encapsulated column using a hydrogel is easy and simple, the EOF was a major driving force for pumping the eluent, as hydrodynamic flow does not work well in the column due to a lack of micro-sized pores (throughpores) [111]. Therefore, this column was used only for CE or CEC. Another shortcoming of this column was that large molecules could not be analyzed by the column, because these molecules could not penetrate the nanopores of the hydrogel network. To overcome this problem, the developmet of biocomposite stationary phases with micro-sized pores for the penetration of the pressurized flow and the large molecules are required. Until now, two techniques were reported for the solutions. Brennan et al. developed the protein-doped monolithic silica columns. The spinodal decomposition of the PEO-doped sol into two distinct phases prior to the gelation of the silica results in a bimodal pore distribution that produces large macropores to allow the flow of the eluent [112-114]. Kato et al. prepared a bio-doped column by coating a protein-containing gel on a photopolymerized porous silica monolith [111]. They coated a macroporous PSG monolith with a protein-containing film using the sol-gel reaction. Although Brennan’s technique prepared the column in one step, the existence of biomolecules during the spinodal decomposition limits the preparatory procedure resulting in a restriction in the size of the skeleton or through-pore of the resulting monolith. In fact, the through-pore of the resulting monolith is about 0.1um, while Kato’s techniques of the latter method enabled the fabrication of the monolith with the size of more than 1 µm, although their technique required a two-step reaction. Brennan et al. entrapped the clinically relevant enzyme, dihydrofolate reductase, within bioaffinity columns which were used to screen mixtures of small molecules using frontal affinity chromatography [112], Fig. (8). B. The protein-doped monolith had a bimodal pore distribution that produced large macropores (>0.1 µm) with minimal back pressure and mesopores (~3-5-nm diameter) that retain a significant fraction of the entrapped protein. Inhibitors present in compound mixtures were retained via bioaffinity interactions, with retention times being dependent on both the ligand concentration and the affinity of the ligand for the proteins. The results suggest that this column can provide a useful platform for the high-throughput screening of lead compounds. Furthermore, by using mass spectrometric detection, the number of compounds that can be simultaneously analyzed was significantly increased [113]. Kato et al. prepared a macroporous monolithic column using the spinodal decomposition of the sol, followed by covering the monolith with the protein-containing silicate film. The large through-pores enabled the good pressurized flow of eluents, and the analysis of large molecules. A porous monolith was used as a supporting matrix for the enzyme immobilization. This technique is advantageous for the preparation of biomolecule-immobilized reactors because (1) its micron-sized pores allow for the penetration of large molecules, and (2) its large surface area allows for an increase in the number of immobilized biomolecules. They prepared a pepsin-coated column [111]. The sol-gel reaction was optimized so that the sol solution containing pepsin forms a thin



film on the photopolymerized sol-gel (PSG) monolith that was initially fabricated at the inlet of the capillary. Pepsin was encapsulated into the gel matrix without losing its activity. The large surface area of the PSG monolith enabled the immobilized pepsin to achieve a high catalytic turnover rate, and the porous nature of the PSG promotes penetration of large molecular proteins into the column. The immobilized pepsin digested peptides and proteins, and the resulting mixture of peptide fragments could be directly separated in the portion of the capillary where no PSG monolith existed. By combination of in-capillary digestion, separation and mass spectrometry analysis, they could identify proteins [111]. The on-line digestion of insulin chain β and lysozyme provides identification of the proteolytic peptides. Therefore, this column is very promising for use in proteomic research. Dulay et al. used the PSG for the trypsinimmobilized column. Trypsin is covalently linked to a photopolymerized sol-gel monolith modified by incorporating poly(ethylene glycol) (PSG-PEG) for the on-column digestion of the Nα-benzoyl-L-arginine ethyl ester (BAEE) and two peptides, neurotensin and insulin chain β. The coupling of the enzyme to the monolith is via room-temperature Schiff chemistry in which an alkoxysilane reagent (linker) with an aldehyde functional group links to an inactive amine on trypsin to form an imine bond [115]. 6.4. Biomolecule-Encapsulated Microfluidic and Microarray Chips, and Microtiter Plates The area of integrated chemistry is rapidly growing, and many analytical systems have now been integrated into microfabricated devices. These integrated analytical systems have been used in many fields because the integration provided small volumes, faster responses, highly parallel analyses, and minimal cross contamination. The technique was thought to be suitable for high-throughput screening. The sol-gel reaction was used to immobilize biomolecules on microfluidic and microarray chips, and microtiter plates. Clark et al. developed a microchip with an immobilized IgG antibody and trypsin. They used a mixed solution of TMOS and MTMS for the immobilization of these materials on a polydimethylsiloxane (PDMS) microfluidic chip [116]. To minimize cracks in the gel microstructure, polyvinylalcohol was added in the sample solution. They also developed arrays of sol-gel encapsulated enzymes, comprising 147 microwells, suitable for the repeated assays of bioactivity or enzyme inhibition, Fig. (9). A. Liposome, glucose oxidase, and horseradish peroxidase (HRP) were encapsulated. A hydrolase array was also prepared containing twenty different lipases and proteases [117]. They recently developed a P450s-immobilized microchip for a toxicology assay (they called it a “MetaChip”) [118], Fig. (9). B. The MetaChip used two arrayed plates for the assessment of ADME/Tox (absorption, distribution, metabolism, excretion/ toxicology). P450s were microarrayed on the chip, and a prodrug was spotted on the P450-immobilized spots. An MCF7 breast cancer cell was cultured as a monolayer on another plate, and the effects of drugs and their metabolites on the cultured cell were evaluated by a combination of these two microplates. Sakai-Kato et al. used a trypsin-encapsulated hydrogel, which was previously mentioned [107], for immobilization of trypsin on a microfluidic chip. By using the microfluidic chip, large molecules, such as proteins, were hydrolyzed, and peptide fragments were separated on the microchip [108], Fig. (9). C. They also prepared an enzyme-doped bioreactor for a 96-well microtiter plate. Two approaches were evaluated;

288


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1) a silica thin film containing enzymes was fabricated on the bottom of the microtiter plate, and 2) a miniaturized enzyme reactor was prepared by coating a enzymecontaining gel on a porous silica monolith which was fabricated so as to fit into a 96well microtiter plate well, and could then be easily removed. The first approach was

Fig. (9). Schematics of microarray chip (A), MetaChip platform cited from [118] (B), and Images of a microchip used in [108] (C).

applied to the development of the P450 array [38]. The microsomes containing expressed human P450 enzymes were immobilized on the microassay plate using sol-gel chemistry. A thin-film hydrogel containing microsomes was fabricated using aqueous silicate as the starting material. The different P450 isozymes were immobilized on the microassay plate, and the metabolites by each isozyme were visualized as fluorescent images, which creates opportunity for the inhibitor assays. Because this methodology enabled the development of an assay system using P450 that is unstable and involves other enzymes for its function, it can be applicable to various screening assays that require complicated reactions involving many biological components, and paved the way for the immobilization methods of protein-chips. The second method was applied to the trypsin reactor in the microassay plate. The trypsin-containing gel was used for coating the surface of a bimodal porous silica monolith, which was fabricated using TMOS and MTMS along with PEG as a porogen [119]. The through-pores were important for fast mass transfer and the nanopores were important for increasing the quantity of the immobilized trypsin. The large surface area of the monolith enabled the immobilized trypsin to achieve a high catalytic turnover rate. Furthermore, the kinetic parameter of the immobilized trypsin indicates the absence of diffusional limitations. The immobilized trypsin effectively hydrolyzed an amino acid derivative (Nα-benzoylarginine-p-nitroanilide) and a protein (BODIPY-casein) [119]. Brennan et al. immobilized biomolecules on the bottom surface of a 96-well microtiter plate. They compared the catalytic constant, Michaelis constant, and inhibition



constant of Factor X, dihydrofolate reductase, cyclooxygenase-2, and γ-glutamyl transpeptidase before and after immobilization [120]. These biomolecules were encapsulated by diglyceryl silane (DGS). They also encapsulated two membraneassociated proteins, a nicotinic acetylcholine receptor, a ligand-gated ion channel, and a dopamine D 2Short receptor, a G-protein coupled receptor, using the sol-gel method. The protein-doped monolith was fabricated on the surface of the 96-well microtiter plate. The two receptors showed 40-80% solution activity over periods of at least 1 month [121]. They also used sugar-modified silica N-(3-triethoxysilylpropyl) gluconamide (GLTES) for encapsulation of an Src protein trypsine kinase, a cellular membrane protein which catalyzes the transfer of phosphate from ATP to a tyrosine residue within a protein [122]. Using this immobilized technique, an unstable protein, firefly luciferase, was immobilized and used for ultrasensitive ATP detection (1 pM) [123]. The ATP sensor could be repeatedly used. They also immobilized Factor Xa and urease using a mixture of GLTES and DGS. Luo et al. immobilized liposome containing two transmembrane proteins, bacteriorhodopsin and F 0F1-ATP synthase, in a TMOS-derived gel containing PEG on a glass slide [40]. Liposome provided membrane structure and protein orientation to two proteins. This transmembrane-doped gel could couple the photo-induced proton gradient to the production of ATP. Sol-gel encapsulation technique is also used for the enzymatic reaction and detection system for the determination of uric acid in human serum in microchip-based analysis. HRP and luminol was co-immobilized at the detection area, and urinase was immobilized in an enzymatic reactor. The uric acid was monitored by a chemiluminescence reaction between the hydrogen peroxide produced from the enzymatic reactor and luminol under the catalysis of HRP in the microreactor. The linear range of the uric acid concentration was 1 to 100 mg/L and the detection limit was 0.1 mg/L [124]. Tsai and Doong developed an array-based optical biosensor. Urease and acetylcholinesterase were used as model enzymes and were co-entrapped with the sensing probe, FITC-dextran, in the sol-gel matrix to measure pH, urea, acetylcholine and heavy metals (enzyme inhibitors) [125]. The biosensor exhibited a high specificity in identifying multiple analytes. No obvious cross-interference was observed when a 50spot array biosensor was used for the simultaneous analysis of multiple samples in the presence of multiple analytes. 6.5. Whole Cells in Sol-Gel Glasses 6.5.1. Chemical Applications Many intracellular microbial enzymes are produced in quantities large enough to be used in industry processes. However, the cost for their isolation and purification can be quite high. Therefore, it would be of interest to be able to encapsulate whole cells, such as yeast or bacteria, in order to avoid tedious separation and purification procedures. The first paper was published in the late 1980’s by Carturan et al. reporting the immobilization of yeast spores into thin SiO2 layers [126]. Encapsulated yeast has been used for the conversion of sugar and carbohydrates into ethyl alcohol and CO2, or environmental protection and metal recovery [127]. The sol-gel encapsulation of the bacterium esherichia coli was reported by Livage et al. Using the aqueous precursors of


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the sol-gel reaction, the cellular organization and the enzymatic activity was improved and 50% of the bacteria was still able to form metabolites after one month of aging [36,128,129]. 6.5.2. Medical Application An emerging approach to treating disease makes use of living cells encapsulated within a porous matrix that shield the cells from immune attack. Recent studies have shown that sol-gel silica could be used for cell plantation. The first experiments were made with the pancreatic islets of Langerhans, which are known to produce insulin in response of glucose. The silica spheres containing the inlets allowed the passage insulin and cytokines but not the passage of antibodies. In addition to the in vitro experiments, in vivo experiments have been performed via the transplantation of encapsulated islets into a diabetic mouse [130,131]. Another process, called ‘biosil’ was developed for the encapsulation of swine hepatocytes and rat liver. In this process, living cells are deposited onto a substrate and then partially covered by a silica film via the gas phase [24]. These cell encapsulation techniques were very promising not only for in vivo applications for cell transplantation, but also for the development of an assay system using cells for more precisely evaluating the drug candidate. 7. CONCLUSIONS As described above, the sol-gel derived biocomposites provide significant advantages for the immobilization methods used in the development of analytical instruments. Sol-gel derived silica materials can be produced using a wide variety of compositions and can immobilize a wide variety of biomolecules with a wide range of sizes, from small chemical compounds or proteins to cells. This is ascribed to the fact that the gel formation proceeds using the encapsulated biomolecules as templates. Another advantage is that because the gel reaction was started from the liquid sol, the resultant gel enabled a wide range of configurations, such as bulk, thin film, fibers, and microarray. While sol-gel derived biocomposites have been shown to be useful in many analytical applications, some problems remain to be solved. For, example, some properties of silica materials still need to be improved to reduce shrinkage, cracking, pore collapse and phase separation. The changes in the material’s properties by the gel aging should be also controlled for improving the reproducibility of the analytical systems. Another disadvantage is that the entrapped biomolecules are leaked more or less from the gel matirix during the repeated use. Non-specific interaction of the analyte and residual silanol is also problematic in evaluating the affinity between analytes and entrapped biomolecules. These would be resolved by thoroughly investigations of the new biocompatible silane precursors. For that purpose, the analytical methods of the structure and characteristics of the sol-gel matrials and entrapped biomolecules would be also required. Based on these studies, useful encapsulated biomolecules for drug discovery will be developed. Recent improvement in the sol-gel technology for the immobilization of biomaterials is very drastic. This shows that many researchers recognize that this technology is perhaps the most facile and generic immobilization technology available today. This recognition facilitates the application of the sol-gel technology to the medical or food



industries. For example, the analytical format using the sol-gel technology has been shifted to the bulk form to the high-throughput format, such as high flow- rate chromatography columns, microassay plates, or microfabricated devices. The type of encapsulated biomaterials has been also extended from single molecules to the protein complex or cells. Based on the basic studies mentioned above, useful assay systems using encapsulated biomolecules will be further developed for drug design and drug discovery. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38]

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Modeling of Environmentally Sensitive Hydrogels for Drug Delivery: An Overview and Recent Developments Hua Li*, Rongmo Luo and K.Y. Lam Institute of High Performance Computing, National University of Singapore, 1 Science Park Road, #01-01 The Capricorn, Singapore Science Park II, Singapore 117528 Abstract: A critical review of mathematical modeling for simulation of environmentally sensitive hydrogels is presented for application of drug delivery. The review demonstrates that there have been large numbers of published studies on the model development, although the majority of investigations in the research area of the drug delivery are experimental-based. Therefore, a systematical review of mathematical modeling of environmentally sensitive hydrogels is necessarily provided through comprehensive assessment on several critical developments of mathematical models to simulate the hydrogels for mechanisms of drug release. The present review classifies the developed models into the fundamental and empirical/semi-empirical groups and also discusses the properties and performance of the hydrogels as drug delivery system in kinetics and equilibrium.

INTRODUCTION Hydrogel is generally defined as a hydrophilic mixture being a form of the materials that possess both the properties of solid and liquid [1, 2]. Its structure is formed from the networks of randomly cross-linked macromolecules and it includes three phases, polymeric-network matrix solid phase, interstitial fluid phase and ionic phase. The solid phase of the hydrogels is a network of cross-linked polymeric chains where their threedimensional polymeric structure is usually described as a mesh, with the interstitial space filled up with fluid. The network meshes hold the fluid in place and also create rubber-like elastic force for expansion or contraction of the hydrogels, thus providing the solidity to the hydrogels [3]. The cross-linked network can be formed physicochemically, for instance, by hydrogen bonding, van der Waals interactions between chains, covalent bond, crystalline, electrostatic interactions or physical entanglements [4]. The fluid phase of the hydrogels fills in the interstitial pores of the network and gives the hydrogel wet and soft properties, which resembles, in some respects, to biological tissues [5]. The ionic phase of the hydrogels is generally composed of the ionizable groups bound to the polymer chains and the mobile ions (counter ions and coions) due to the presence of electrolytic solvent. The ionizable groups dissociate in *Corresponding author: Tel: +65-64191249; Fax: +65-64191280; E-mail: [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.


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solution completely for strong electrolyte or partially for weak polyelectrolyte groups, while the network is left with same charged groups fixed to the chains. The fixed charge groups produce an electrostatic repulsion force each other and play a role in the swelling of the hydrogel. In fact, there are various natural or synthetic materials being examples of the hydrogels. The natural ones include the guar gum and collagens that are modified to produce hydrogels, and the synthetic those poly(acrylic acid)− PAA, poly(hydroxyethyl methacrylate)−PHEMA, poly(acrylamide)−PAM, poly(vinyl alcohol) poly(acrylic acid)−PVA/PAA, poly(acrylonitrile)−PAN, poly(acrylonitrile) poly(pyrrole)−PAN/PPY, poly(N-isopropylacrylamide)− PNIPA, etc. Depending on the physical and chemical characteristics of the polymeric networks and fixed charge groups, the hydrogels may be categorized in other ways. For example, the hydrogels can be synthesized to be either neutral or ionic, determined by the characteristic of the pendant groups fixed onto the polymeric matrix. One of the important reasons that the responsive hydrogels attract considerable research interest is their unique property of undergoing discrete or continuous volume phase transition in response to the very small change of surrounding environment conditions, such as solution pH [6-9], solvent composition [10], salt concentration [11], temperature [12-14], light/photon [15, 16], electric field [17], electromagnetic field [18] and so on. In fact, there are some environmental variables found in the body, such as low pH and elevated temperature. Thus the environmentally sensitive hydrogels have enormous potential in various applications of drug delivery. For example, either pHsensitive and/or temperature-sensitive hydrogels can be used for site-specific controlled drug delivery. The hydrogels responsive to specific molecules such as glucose or antigens can be used as biosensors and drug delivery systems. Light-, pressure- and electro-sensitive hydrogels also have the potential in applications of drug delivery and bioseparation. The significant features make the stimuli-responsive hydrogel better known as smart/intelligent materials for wide range of applications, as they are able to sense and eventually respond to the environmental changes without need of external power source. Sensors and/or actuators [19], artificial muscle [20, 21], microfluidic control [22], ultrafiltration [23], separation [24-26], and chromatographic packing [27] are some examples of successful applications of the hydrogels. A more exceptional promise of the hydrogels is their biocompatibility and biostability potential [28] for possible substitution of the human body tissues and biomimetic applications, such as articular cartilage [29], wound dressing [30], corneal replacement [31] and tissue engineering [32, 33], especially in the medicine and pharmaceutical applications such as the drug delivery system [34-39]. The drug delivery system is such a research area that people from almost every scientific discipline can make a significant contribution. Due to the highly interdisciplinary nature of the area, the researchers play a unique role in new strategy development for drug delivery system, who emerge from the training programs that integrate the biological science such as quantitative physiology and pathophysiology, cell and molecular biology, the engineering technology such as polymer engineering and microfabrication technology, and the mathematical modeling such as pharmaco-kinetics and transport phenomena.

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In addition, the sciences of mathematical modeling and numerical simulation are generally accepted as the third mode of scientific discovery, with the other two modes being experiment and analysis, which makes this field an integral component of cutting edge scientific and industrial research in most domains. This is especially so for understanding and manipulating the fate of drug releases in humans as a classical engineering endeavor, where basic science and mathematical analysis can be used to achieve an important practical end, and the multi-physics and multi-phases are common requirements. Extensive search of literature has thus far revealed that there are numerous researchers making efforts to develop the mathematical models for simulation of environmentally sensitive hydrogels for drug delivery applications. A systematical review is necessarily presented here through comprehensive assessment on the several critical developments of mathematical models to simulate the hydrogels for applications of the drug release. FUNDAMENTAL MATHEMATICAL MODELS Fick’s Law Usually the drug delivery system may be modeled simply as one-dimensional transient mass transfer in a hydrogel disk. The disk has thickness L and an initial drug concentration profile in contact with a solvent maintained at sink conditions. The common case is low drug concentration, which doesn’t have significant effect on the drug diffusivity. In addition, the drug diffusion is often assumed to be the ratecontrolling step rather than swelling or drug dissolution. This is suitable assumption if the kinetics of swelling can be neglected for the hydrogel, for example, the systems with little swelling, or when the diffusive release occurs from a pre-swollen matrix. With the assumptions mentioned, Fick’s second law is often employed for simulation of drug diffusion, and generally expressed by

∂c ∂ ∂c = (D ) ∂t ∂x ∂x

(1)

with the boundary conditions

∂c =0 ∂x

at

x = 0, t > 0

(2)

c(t , L) = 0

at

x = L, t > 0

(3)

at

t = 0, 0 < x < L

(4)

and the initial condition

c(0, x) = v( x)

where c = c(t , x) is the drug concentration, t is the release time, v(x) is the initial drug concentration profile, x is the position normal to the effective area of diffusion in one-dimension diffusion, and D is the drug diffusivity.


Li et al.

When the drug diffusivity D depends on spatial position in the hydrogel matrix, Eq. (1) becomes the partial differential equation with variable coefficient, for which no analytical solution is available, and numerical techniques such as the Crank-Nicholson method are then employed to solve the problem [40]. The time-dependent drug flux and cumulative fractional release may be obtained by solving a set of linear algebraic equations at each time step. If the polymeric network of the hydrogels is homogeneous and the drug diffusivity D is independent of drug concentration, D can be assumed to be a constant. Then Eq. (1) is simplified to

∂c ∂ 2c =D 2 ∂t ∂x

(5)

This is a partial differential equation with a constant coefficient D, and can be solved analytically with the method of separation of variables. Varshosaz et al. [41] suggested that the diffusion coefficient D may be calculated according to Fick’s first law. This is the diffusion law, and in one dimension it describes the flux of particles through a point x by

(Jn )x = −D

∂c( x, t ) ∂x

(6)

Varshosaz et al. [41] then derived

dQ ADK d (c0 − c) = dt h

(7)

dQ / dt is the rate of mass transfer, A is the surface area of the hydrogel film, is the partition coefficient, h is the hydrogel thickness. c0 and c are the drug

where

Kd

concentrations in both the sides of the hydrogel, respectively. For the mass transfer in a three-dimensional cylindrical domain, Fu et al. [42] gave an analytical solution of Fick’s second law as,

Mt 8 = 1− 2 2 M∞ h r

∞

∑α m =1

−2 m

∞

exp(− Dα m2 t ) × ∑ β n− 2 exp(− Dβ n2 t )

(8)

n =1

with

J 0 ( rα ) = 0 where

and

βn =

(2n + 1)π 2h

(9)

M t and M ∞ are the amounts of drug released at time t and infinite time,

respectively. h denotes the half-length and r the cylinder radius. D is a constant diffusion


coefficient. α and β are defined by Eq. (9) with a zero-order Bessel function J 0 , m and n are integers. This model is applicable to tablets that range from the shape of a flat disk with the radius much larger than the thickness to that of a cylindrical rod with the radius much less than the thickness, where the drug is homogeneously distributed within the system [43], but the model doesn’t consider the volume expansion of the hydrogels. For the drug release from micro-hydrogel particle with consideration of drug dissolution and diffusion in the continuous matrices of microgels as well as the limited solubility of drug in the release medium, the present authors [44, 45] and HombreiroPerez et al. [46] presented a mathematical model for simulation of the kinetics of drug release from the microgel particles. In general, the initially loading drug concentration c0 in spherical microgels is greater than the drug saturation concentration cs . This may be achieved either by preparation of a solution and total evaporation of the solvent, or by partial evaporation or phase inversion. When the polymeric microgels are put into a well stirred release medium, the following four steps of mass transfer take place consequently: (a) drug dissolution within the microgels; (b) drug diffusion within the matrices of microgels; (c) drug diffusion through the unstirred liquid boundary layers on the surfaces of the microgels; and (d) drug diffusion and convection within the release medium. Since the convective transport within the medium is usually much faster when compared with that of the diffusive mass, the convective transport can be neglected when the overall rate of drug release from the polymeric microgels is calculated. Therefore, it is reasonably assumed here that the drug dissolution and diffusion in the continuous matrices of spherical non-swellable microgels control the drug release in a well-stirred release medium. The kinetics of drug release from the microgels with radius R can be simulated mathematically for the drug release process in a well stirred release medium, by the following partial differential governing equation,

∂c(r , t ) ∂ 2c(r , t ) 2 ∂c(r , t ) = D( + ) + k (εcs − c(r , t )) ∂t ∂r 2 r ∂r

(10)

and the following initial and boundary conditions,

c(r , t ) = εcs

at

t = 0,

0 0,

r=R

(13)

c(r , t ) (g/cm3) is the drug concentration at the radial position r (cm) of the microgel system at the release time t (s). D (cm2/s) is the drug diffusion coefficient, k (s-1) is the first-order rate constant of drug dissolution, ε is a parameter for the where

polymeric network meshes of the microgels and it is directly related to the cross-linking density of the polymeric microspheres. If cs (g/cm3) is defined as the drug saturation


Li et al.

εcs (g/cm3) refers to the equivalent drug saturation concentration in microgels with the network mesh parameter ε .

concentration in the system,

The first term of the right-hand side in Eq. (10) is the well-known Fick’s second law of diffusion for a spherical system, which describes the diffusive drug release process in the microgels due to the continuous dissolution of the drug. The second term of the right-hand side in the equation corresponds to the potential rate-limiting drug dissolution process. It is observed that, when the initially loading drug concentration c0 is smaller than the drug saturation concentration

cs , Eq. (10) is reduced to the classic Fick’s

diffusion equation. Although the drug diffusion coefficient D in the polymeric microgels may be solvent-concentration dependent, usually it is reasonably assumed that D is approximately a constant for simplicity. It is also assumed that the drug is uniformly distributed throughout the microgels with equivalent drug saturation concentration εcs initially. Under perfect sink conditions, the release medium may be considered to be well stirred, thus the drug concentration outside of the microgels is further assumed to be constant and equal to zero. In addition, Gao et al. [47, 48] developed a model to predict the relative change in drug release rate as a function of formulating composition for HPMC tablets of adinazolam mesylate and alprazolam. This model is based on the steady-state approximation of Fick’s law for the drugs release from solid matrices and it is modified by Lapidus and Lordi [49, 50] as follows:

Mt = M0 in which

S D' t 0.5 ( ) V π

(14)

M t denotes the amount of drug released at time t, M 0 is the initial amount of

drug within the tablet, S represents the surface area and V the volume available for release, D ' is the effective diffusion coefficient and defined as:

D' =

D τ

(15)

where D is the true self-diffusion coefficient of the drug in the pure release medium and τ is the tortuosity of the diffusing matrix. However, the swelling of the system is not taken into account and the mathematical analysis may be reduced to one-dimension problem. Coiviello et al. [51] considered the drug release from non-eroding/swelling cylindrical gel through the different experimental setups that may be designed to calculate the diffusion coefficient [52]. By assuming a constant drug diffusion coefficient D and neglecting the gel density variation due to diffusion, Fick’s second law in two-dimensional cylindrical coordinates system is written as:


∂c D ∂ ∂c ∂ 2c = (R ) + D 2 (16) ∂t R ∂R ∂R ∂Z where t is time, c is the drug concentration in the cylinder. R and Z are the radial and longitudinal coordinates, respectively. The corresponding initial and boundary conditions are given as:

c( Z , R) = c0 , − Z c ≤ Z ≤ Z c , 0 ≤ R ≤ Rc crel = 0

at

at

t=0

t=0

(18)

c( Z , Rc , t ) = c(± Z c , R, t ) = k p crel (t ) Vrel crel (t ) = πRc2 2 Z c c0 − ∫

Zc

∫

Rc

−Zc 0

(17)

(19)

c( Z , R, t )2πRdRdZ

(20)

2 Z c and Rc are the cylinder height and the radius respectively. c0 is the initial drug concentration in the cylinder, crel is the drug concentration in the release medium, where

Vrel is the volume of the releasemedium and k p is the drug partition coefficient between the cylindrical gel and the environmental release medium. The set of the equations (16)-(20) can be numerically solved by the control volume method [53]. Several studies reveal that the kinetics of the controlled release from scleroglucan hydrogel matrices exhibit evidently the non-Fickian macroscopic features, because of the instantaneous formation of a stagnant layer of thickness h between the gel matrix and reservoir fluid. Such phenomenon is attributed to both the limited erosion of the gel which is caused by the mechanical agitation of the fluid reservoir and the presence of the gel sustaining net. In the conditions mentioned, the kinetic profile of relevant release may be simulated well by solving the second Fick law in both gel and stagnant layer, by assuming an equal diffusion coefficient for the theophylline in the two phases. It is also assumed here that the distribution of the initial drug concentration within the gel follows a step profile, in which it is equal to its nominal value c0 for 0 < x < L − h and it is zero for L − h < x < L , where L is the sum of the thicknesses of the gel and stagnant layer. Accordingly, the experimental release is described by solving the one-dimensional second Fick law [54]:

∂c ∂ 2c = Dg 2 ∂t ∂x

(21)

with the initial and boundary conditions as

c=0

L−h< x< L

c = c0

0< x< L−h

at

t=0 at

t=0

(22) (23)


Li et al.

∂c =0 ∂x

x=0

at

t>0

(24)

c=0

x=L

at

t>0

(25)

Fick’s second law can also be used for simulation of diffusion of solute in PEG for a planar sheet as

∂c ∂ 2c =D 2 ∂t ∂z with the initial condition of uniform concentration ( c

(26)

= c0 , − l < z < l at t = 0 ), and

the boundary condition of surface concentrations equal to zero by assuming sink conditions ( c = 0 , at z = −1 and z = l for t > 0 ). The corresponding cumulative fractional release or the amount of solute released relatively to total solute loaded in a disk is obtained by [55]: 2 ∞ Mt 8 2 π t = 1− ∑ exp{ − D ( 2 n + 1 ) } 2 2 M∞ 4l 2 n = 0 ( 2n + 1) π

(27)

in which the Fickian profiles are validated to fit the experimentally measured concentration profiles to estimate the diffusion coefficients at each time point. Gu et al. [56] used Fick’s second law for investigation of the diffusion of water in the hydrogel film for the drying process. They believed that the water vapor pressure in contact with the polymer film may be considered as a constant since there is a large water vapor source, i.e. large amount of saturated aqueous salt solutions, in the sealed container where the hydrogel film is kept for drying. The rate of water exchange between the water vapor and the hydrogel film depends on the relative humidity of the air and the water concentration at the film surface. It is assumed here that the rate of water concentration change on the film surface is directly proportional at anytime to the difference between the actual surface concentration Cs and the equilibrium surface concentration

−D

C ∞ with the constant water vapor pressure in the air, namely

∂C = α (C ∞ − C s ) ∂x

(28)

where C is the concentration of water in the hydrogel (g/m3), x is the water diffusion direction. D is the diffusion coefficient of water in the film, which is generally a function of the water concentration. In the system with internal order and/or anisotropy, the diffusion coefficient should be a function of the microstructure. α is a constant of proportionality and it is related to the coefficient of mass transfer between the film surface and the adjacent air. Eq. (28) may be used as a boundary condition to solve Fick’s second equation. It is noted that Eq. (28) describes diffusion in one-dimensional geometry. From the point of experimental view, this means that the finite diameter of the


cylindrical film specimens is neglected and the lateral homogeneity is assumed in the film. This assumption is relatively reasonable for the small ratio of film thickness to film diameter. Kikkinides et al . [57] considered the combination of transport mechanisms that are responsible for the sustained release of the drug encapsulated within the micro-domains of the porous matrix, which is in analogy with the work of Varelas et al. [58, 59]. The present hydrogel is initially impregnated with a drug or another solute, thus the drug partitions preferentially into the micro-domains. By exposing the hydrogel into the drugfree surrounding environment, the drug exits the gel from the bulk phase in accordance with Fick’s law.

(1 − φ )

∂cb = (1 − φ ) D b ∇ 2 cb + J s ∂t

(29)

with the following boundary and initial conditions,

∇cb (0, t ) = 0 ,

c b ( Rb , t ) = 0

(30)

c b (r ,0) = c*

(31)

cb = cb (r , t ) is the concentration of drug in bulk phase, φ is the volume fraction of micro-domains, r is the radial direction in the bulk phase and Rb is the radius of the polymer matrix that is here assumed a cylindrical shape. Db is the where

diffusion coefficient of the solute in the polymer matrix, micro-domains, and

J s is the flux term through the

c* is the solute saturation concentration in the bulk phase.

In general, Fick’s law of diffusion for one-dimensional swelling of films assumes that the diffusion coefficient D of the penetrating agent (solvent or solution) and film thickness remain constant during the entire swelling process. However, Vazquez et al. [60] considered that the film thickness obviously does not remain constant for extensive swelling. They proposed the solutions of the differential forms of Fick’s law for thin sheets, by neglecting diffusion through the edges,

Mt 8 = 1− 2 M∞ π

1 (2n + 1) 2 π 2 Dt exp[ ] ∑ 2 4l n = 0 ( 2n + 1) ∞

(32)

which may be approximately simplified to

Mt Dt = 2[ 2 ]1 / 2 M∞ πl in which

(33)

M t and M ∞ are the masses of penetrant at time t and infinity, respectively. l

is the thickness of the sheet. D is the diffusion coefficient and usually is independent of


Li et al.

copolymer composition for all pHs, and the lowest D for swelling is found experimentally in pure water [60]. Higuchi Equation One of the most renowned mathematical equations for simulation of the release rate of drugs from matrix system is the Higuchi model [49], and its basic formulation is written as,

Mt = D(2c0 − c s )c s t A where

for

c0 > c s

(34)

M t is the cumulative absolute amount of drug released at time t. A is the

surface area of the controlled release device, such as the hydrogel, immersed in the release medium. D is the drug diffusivity in the polymer carrier. c 0 and c s are the initial drug concentration and the solubility of the drug in the polymer, respectively. Obviously Eq. (34) can be simplified to:

Mt =K t M∞

(35)

where M ∞ is the absolute cumulative amount of drug release at infinite time, which should be equal to the absolute amount of drug incorporated within the system at time t=0. K is a constant reflecting the design variables of the system. Therefore, the Higuchi model briefly reveals that the fraction of drug release is proportional to the square root of time. Higuchi initially examined the model only for planar system. The model was later modified and extended to consider different geometries and matrix characteristics including hydrogels. The classical Higuchi equation was developed by the pseudo-steady state assumptions. Thus it is difficult to directly simulate the really controlled release systems. Several assumptions of the Higuchi derivation should carefully be kept in mind [43]: (a) the initial drug concentration in the release system is much higher than the solubility of the drug. This assumption provides a platform for justification of applied pseudo-steady state approach; (b) the model is for one-dimensional diffusion and thus the edge effects are neglected; (c) the suspended drug is in a fine state such that the particles are much smaller in diameter than the thickness of the release system; (d) swelling or dissolution of the polymeric carrier is negligible; (e) the diffusivity of the drug is constant; and (f) perfect sink conditions are maintained. It is apparent that the assumptions mentioned above are far away from most really controlled drug delivery systems. As result, the Higuchi equation is often used to examine the experimental data of drug release to obtain a rough idea of the underlying release mechanism. Siepmann et al. [43] derived a proportionality between the fractional amount of the released drug and the square root of the time, by an exact solution of Fick’s second law


of diffusion for thin films with thickness δ , and based on the assumptions of the perfect sink conditions, constant diffusivity and uniform initial drug concentration with c0 < c s , i.e., monolithic solutions. The derived proportionality is given in the form as ∞ Mt Dt nδ = 4( 2 )1 / 2 {π −1 / 2 + 2∑ (−1) n ierfc } M∞ δ 2 Dt n =1

(36)

It should be noted that the second term in the second bracket will vanish after short time. However, a sufficiently accurate approximation of Eq. (36) for M t / M ∞ < 0.6 can be written as follows:

Mt Dt = 4( 2 )1 / 2 = k ' t M∞ πδ

(37)

where k ' is a constant. Therefore, the proportionality between the fraction of drug release and the square root of time may also be based on the physical phenomena which are totally different from those studied by Higuchi for his classical equation of monolithic solutions versus monolithic dispersions. Anyway, the diffusion always is dominant mechanism and thus the proportionality is commonly regarded as an indicator for diffusion-controlled drug release. Despite the approximation of the model, the Higuchi treatment for a rigid matrix under sink conditions still is a widely used model for prediction of release phenomena. It is also approximately available for swellable system [61]. Conaghey et al. [62] further simplified the Higuchi model of matrix diffusion control, which is validated after less than 30% of the drug is released. This model is able to simulate the kinetics, in which after release from the resin particles, the drug ions have to pass through the hydrogel before they diffuse across the membrane. In the simplified Higuchi model of matrix diffusion control, the quantity of drug M t released at time t is given by

M t = 2c0 ( Dt / π )

1 2

in which c0 is the initial concentration of the drug in the reservoir, and coefficient through the matrix.

(38)

D the diffusion

When a biodegradable polymer matrix is considered, the situation becomes more complicated. However, Heller et al. [63] still assumed the first-order kinetics of permeability coefficient and employed the modified Higuchi equation as [64]:

dM t A 2c0 P0 (exp(kt )) 1 / 2 = [ ] dt 2 t

(39)


Li et al.

A is the surface area of the hydrogel system, c0 is the initial concentration of the drug, P0 is the permeability of the drug in the absence of degradation of the polymer, and k is the first-order rate constant of permeability coefficient.

where

In order to investigate the approximation of the Higuchi model, Kasasulu et al. [65] took the dissolution rates of the theophylline as an example. The dissolution rates are determined by the linear regression of the Higuchi square root of time. The theophylline 1/ 2

profiles are linear for the time period 4-22 min , but thereafter demonstrate positive deviations. It is understood that the positive deviations from the Higuchi equation may result from the air entrapped in the matrix. For hydrophilic matrices the deviations may result from the erosion of gel layer. Moreover, the work of Cobby et al. [66] demonstrates that the differences in rates of release are also related to the shape factors. Power Law Relatively compared with the Higuchi equation, a much simpler semi-empirical equation is the power law for simulation of drug release from polymeric systems [67, 68]. The power law is written as

Mt = kt n M∞ in which

(40)

M t and M ∞ are the absolute cumulative amounts of drug released at time t

and infinite time, respectively. k is a constant incorporating the structural and geometric characteristics of the drug device. n is defined as the release exponent indicating the kinetics mechanism of drug release. Obviously, it is seen that the Higuchi equations (34) and (35) and the short-time approximation (37) of the exact solution of Fick’s second law for thin films are the special case of the power law at the release exponent n = 0.5 . When the release exponent n = 1 , it is known from Eq. (40) that the drug release rate is independent of time, which corresponds to the zero-order release kinetics. For the case of slabs, the mechanism creating the zero-release is known the case-II transport. In fact, one can consider the power law as a generalization of the drug release, in which the superposition of two apparently independent mechanisms of drug transport, a Fickian diffusion and a case-II transport, predicts the most general case of kinetic swelling of drug system and drug release from glassy polymers, regardless of the form of the constitutive equation and the type of coupling of relaxation and diffusion [43]. Therefore, Eq. (40) has two distinct physical meanings in the two special cases,

n = 0.5 for the Fickian diffusion-controlled drug release and n = 1.0 for the constant zero-order/case-II swelling-controlled drug release. When 0.5 < n < 1.0 , it is generally

regarded as an non-Fickian transport indicator through the superposition of both phenomena for anomalous transport. For example, in the work done by Bajpai et al. [67], the release exponent n of all the samples is larger than 0.5. This means that all the


samples demonstrate the non-Fickian swelling behavior, which may be attributed to the fact that in water the carboxylic groups presenting along the macromolecular chains undergo the ionization to yield –COO - groups. This results in the relaxation of polymeric segments due to repulsion among similar charges, and then makes the swelling process chain relaxation controlled [67]. In addition, it should noted that the two special values mentioned, n = 0.5 and 1.0, are validated only for slab geometry. The release exponent n will be different values for other geometries such as spheres and cylinders [69]. Furthermore, Peppas and Sahlin [70] developed another interesting model,

Mt = k1 t m + k 2 t 2 m M∞

(41)

k1 , k 2 and m are constants. The first term k1t m on the right-hand side represents 2m the Fickian diffusion contribution F, and the second term k 2t the case-II relaxation where

contribution R. The ratio of the two contributions can be calculated by [43],

R k 2t m = F k1

(42)

Darcy’s Law As well known, in a homogeneous matrix the pressure drop ∆P and the induced fluid flow are related linearly through Darcy’s law, namely the mean velocity V of the fluid flowing through the membrane can be written as,

V =

k ∆P µL

(43)

where k is the Darcy permeability of the membrane, µ is the viscosity of the solution, and L is the membrane thickness. The Darcy permeability k is measured in Darcy (1 Darcy= 1µm ) and it is a quantitative measure of the ability of the fluid to convect through the membrane and it depends only on the microstructure of the matrix. 2

Coluccio et al. [71] considered an enzymatically controlled system. An assumption made is that the decrease of the driving force for drug diffusion is compensated by increasing the Darcy permeability and the porosity of the polymeric matrix, due to enzymatic erosion. For examination of their Darcy measurement, they measured the mass flux J c of a solute diffusing and convecting through the membrane. This means

J c is driven by the difference of solute concentration ∆c = c s and by the pressure drop ∆P , which are imposed at the two sides of the membrane. Thus, that the flux


J c = cV − D

dc dz

Li et al.

(44)

where z is the distance from the membrane surface, c = c(z ) is the local solute concentration, and D is the effective diffusivity. V is the uniform mean velocity of the carrier fluid and is driven by the pressure drop ∆P according to Eq. (43). In order to study the motion of the hydrogel, Wolgemuth et al. [72] constructed a force balance on each phase, with the constraint that the viscous drags between the solid and fluid phases are equal and opposite. A Stokes-type of the equation for the fluid is thus written as,

ζ (V − ut ) = µ∇ x ⋅ (

∇ xV + ∇ xV T ) − ∇xP 2

(45)

ζ is the drag coefficient between the polymer and the fluid, V is the fluid velocity, u t = ∂u / ∂t is the polymer velocity, µ is the fluid viscosity, ∇ x is the gradient operator with respect to the position x , and P is the fluid pressure. This where

equation presents the balance and the drag force acting between the fluid and polymer. The first term on the right-hand side is the fluid shear, and the second term is the hydrostatic pressure which is related to the osmotic pressure of the hydrogel. When the fluid shear is negligible, Eq. (45) is equivalent to Darcy’s law. Based on the biphasic theory, Netti et al. [73] developed a model which is applicable to both macroscopically porous gels and highly entangled polymer solutions or hydrogel. In the former case, the fluid phase is present within the macroscopically porous gels, such as fibrilar gels or tissues, as a distinct phase within the porosity [74]. In these systems, external mechanical stimuli may lead to a convective fluid transport due to a non-balanced hydrostatic pressure gradient. On the other hand, if an external stress field is applied to an entangled polymer solution or hydrogel, an osmotic pressure gradient will arise to balance the solvation and the elastic forces of the polymeric network. Therefore, there will be convective fluid flow associated with osmotic pressure gradients in a similar profile to those occurring in macroscopically porous gels related to hydrostatic pressure gradients. In both cases, the mathematical formulation of the coupling of mechanics and transport can be given by a generalized Darcy’s Law. Langmuir Theory − Absorption Isotherm Model When the drug diffusion is considered in the microphase and in the bulk phase, the solute diffuses from the interior of the microdomain to its surface where it rapidly exchanges. This process may be simulated by a Langmuir absorption isotherm model [57], in which each bonding site can only absorb one molecule,

M +S← → MS ;

K=

[ MS ] [ M ][ S ]

(46)


where M represents the molecule of loading drug, and S and MS are the unoccupied and absorbed sites, respectively. The absorption equilibrium constant K depends on the functional groups in the hydrogels. Several researchers used the Langmuir absorption isotherm theory for simulation of the drug release from the hydrogels. For example, Chern et al. [75] studied the absorption isotherm of caffeine in the hydrogels at 25 oC aqueous solution by the batch tests. Their experimental results agree well with those predicted by the derived Langmuir isotherm model as,

q=

KQc 1 + Kc

(47)

where K is the absorption equilibrium constant, Q is the matrix absorption capacity, and q and c are the equilibrium caffeine concentrations in the gel and solution phases, respectively. In addition, Lorenzo et al. [76] investigated the 2,6-naphthalenedisulfonic acid (NS2) loaded into the gel, based on the Langmuir absorption model. The NS-2 loaded is higher at acidic pH with the low ionic strength of the medium and hydrogels in the collapsed state. The absorption isotherms may be examined in terms of the Langmuir equation:

A=

SKceq 1 + Kceq

where A is the amount of NS-2 loaded per unit volume of gel,

(48)

ceq is the final

equilibrium concentration in the solvent, S is the number of absorbing sites per unit volume of gel or the amount of NS-2 that can be maximally bound per volume of gel, and K is the affinity of one absorption site for a NS-2 molecule. EMPIRICAL AND SEMI-EMPIRICAL MODELS Peppas Model Peppas and his co-workers are a noted group in the field of drug release. As mentioned before, the classical power law expressed by Eq. (40) is valid for the slab geometry only, and it is rewritten here as

Mt = kt n M∞ where

(49)

M t / M ∞ is the fractional release, k is a kinetic constant, and n is the release

exponent. When the drug systems have other geometries such as spheres and cylinders, the release exponent n should be identified individually.


Li et al.

For a drug device with cylindrically geometrical shape, a simple semi-empirical formula is proposed by Peppas et al. for prediction of the release mechanism in cylinders. That is, n = 0.45 for the Fickian diffusion, 0.45 < n < 0.89 for nonFickian anomalous transport, and n = 0.89 for the case-II transport, respectively [7779]. Furthermore, Peppas and Sahlin [71] proposed a model for simulation of drug release driven by both the Fickian and polymer relaxation:

Mt = k1 t 1 / 2 + k 2 t M∞

(50)

where the first term on the right-hand side represents the Fickian contribution, and the second term the case-II relaxational contribution. k1 and k 2 correspond to the release rates of the case-I and case-II mechanisms respectively. Based on the equation (50), Varshosaz et al. [80] gave a semi-empirical model for ephedrine HCl, a water-soluble model drug, release from a hydrogel disk,

Mt = k1t 0.78 + k2t 0.2 M∞

(51)

This is a biexponential pattern with two different mechanisms of the drug release. In the first stage, there is an anomalous mechanism up to the first hour of drug release when the cylinder radius r = 0.991, in which the swelling is the rate limiting step for drug release. After the swelling, there is a Fickian release behavior up to 7hr when r = 0.996, in which the drug Fickian diffusion is the rate controlling step. However, when pH=1.2, the drug in the hydrogel demonstrates the case-II diffusion or a swelling-controlled mechanism. The reason may be the acidic pHs restrict the water uptake of the hydrogel and the swelling becomes much slower than diffusion rate. Zero-Order Release In general, a hydrogel device providing the zero-order drug release is necessarily required to be designed for overcoming Fick’s law. So far many creative systems have been developed for the zero-order drug release. Usually the drug release from swollen hydrogels follows Fickian diffusion, in which the rate of diffusion is proportional to the square root of time. However, the zero-order release is an often desired property of a controlled release device. The zero-order drug release from the swollen hydrogels may be achieved by the phasing-separated hydrogels or rate-controlling barriers [81]. Fell and Rowe [82, 83] discussed the zero-order kinetics of drug release from the spheroids, which could be attributed to the high viscosity and swelling index of the polysaccharide. The present high viscosity means the formation of a three-dimensional gel-like network that may retard the drug release. This is supported by highly swelling index of the polysaccharide, which is an indication of absorption of a large quantity of water and swelling of the polysaccharide [84]. In addition, Lu et al. [85] proposed an


initially non-uniform drug loading to overcome the disadvantage of the first-order release associated with conventional diffusion-controlled hydrogel matrix devices, through a photolaminating method to control intimately the initially non-uniform drug loading. The drug release pattern with a conventional uniform drug loading usually shows a typical first-order release behavior, in which an initially high release rate with the burst effect is followed by a rapidly declining the rate of drug release. Actually it is desirable to eliminate the burst effect because it may cause negative side effects. The concept of photolamination is to provide the initially non-uniform concentration profiles to control the release pattern. The experimental results also demonstrate that, with increasing the gradient of the drug distribution, the corresponding release profiles approach more closely to the desired zero-order release, especially in the early release period, and the burst effect is nearly eliminated. Varelas et al. [59] studied a biphasic hydrogel as efficacious platform for zero-order drug release. For the controlled release from the biphasic hydrogels, they developed two mathematical models of mass transfer through dispersed-phase networks to identify the mechanism of mass transfer within the hydrogels and to correlate the characteristics of the release profiles with structural properties of the network. The two models are based on different assumptions of the mechanism of solute release into the bulk. The first model consists of two continuity equations, one for each phase of the network, as follows:

(1 − φ ) φ

∂cb ∂c = (1 − φ ) Deff ∇ 2 cb − φ m ∂t ∂t

∂c m = f (c m , c b ) ∂t

(52)

(53)

with the boundary conditions

cb (r , t ) r = R = 0 ;

∂cb (r , t ) =0 ∂r r = 0

(54)

cm (r , t ) t = 0 = c * / α

(55)

and the initial conditions

cb (r , t ) t = 0 = c * ;

φ is the volume fraction in microdomains (cm3 microdomains/cm 3 bulk), cb is the drug concentration in the bulk phase (g/cm3), t is time (s), Deff is the effective where

diffusivity through the gel (cm2/s),

c m is the drug concentration in the microdomains (g/cm ), r is radial position within a cylindrical hydrogel device (cm), c * is the concentration in the bulk phase when saturated (g/cm3), and α is the equilibrium 3

partition coefficient (cm3 microdomains/cm3 bulk). The second term on the right-hand side of Eq. (52) is the source term giving the rate of introducing the drug into the bulk phase from the microdomains. Eq. (53) generally expresses the source term. The first


Li et al.

model consisting of Eqs. (52) and (53) is able to simulate the mechanism of the zeroorder drug release by different forms of the source terms in Eq. (53), depending on the mechanism of interfacial mass transfer. In the first model, it is assumed that the drug is encapsulated in the dispersed phase and the microdomain-bulk interface behaves as a barrier of constant mass transfer resistance through which the drug must pass. It is also assumed that diffusion through the interior of the domains is very rapid and thus the domains are well-stirred. Then the source term in Eqs. (52) and (53) becomes

φ

∂c m hA = − i (αc m − cb ) ∂t V

(56)

where h is the mass transfer coefficient across the interface between the microdomain and bulk (cm/s), Ai is the total interfacial area (cm2), and V is the total gel volume (cm3). Typical release profiles predicted by the first model mentioned above demonstrate the mechanism that produces a slow but steady decrease over time in the flux of drug to the surrounding environment due to the fact that the driving force for interfacial mass transfer, (αc m − cb ) , varies with time. However, the present model does not predict a plateau followed by a sharp drop-off in the flux, for example, as observed in the experiments with tryptophan and theophylline. The second model Varelas et al. [59] developed is for the case where the microdomains act as a perfect source, namely their composition remains unchanged for some period of operation. It is similar to a salt crystal that dissolves without any accompanying change in its surface area. The solute available for interfacial mass transfer resides in a saturated phase with constant concentration c s at the microdomain surface. The nature of the surface phase remains saturated for some period of time because it is replenished rapidly from the source. At the end of this period all the microdomains are depleted and the interfacial mass transfer ceases. Therefore, the second model is a variation of the first model, has only different forms of the source term as the second continuity equation by

φ where

 hA ∂cm − i (cs − cb ) = V ∂t  0 

cm > cmf cm = cmf

(57)

c mf is the final concentration of solute in the microdomains, corresponding to

any solute which is bound and thus unavailable for transport to the bulk. In brief, the second model consists of Eqs. (52) and (57), with the same boundary and initial conditions as Eqs. (54) and (55). Varelas et al. [59] employed the model to predict the profile with a true zero-order plateau for the biphasic hydrogels, which is qualitatively in agreement with the experiments.


Bezemer et al. [86] conducted another interesting study on the effect of polymer degradation on the diffusion of lysozyme in the polymer matrix. A relation is thus required between the diffusion coefficient and the molecular weight of the polymers. The degraded polymers are subsequently used as matrix for protein loaded films. It is observed that the release rate increases with decreasing molecular weight for the drug release from 1000PEG70PBT30 matrices with a molecular weight of 35 000 g/mol. It is also found that the drug release less follows the first-order kinetics, whereas near zeroorder release is found for the same polymer with a higher molecular weight. The diffusion coefficients obtained from the first part of the release curves are given as function of the molecular weight of the matrices. The data can be fitted by the following linear relationship between

D and M n−2 :

k2 + k3 (M n ) 2

D= the units of

(58)

k2 and k3 are cm 2g 2s −1mol−2 and cm 2s −1 , respectively. The decrease of

the molecular weight of the polymer due to hydrolytic degradation during incubation in Phosphate-buffered saline may be described by

1 1 = + k1t Mn Mn where

(59)

M n (g/mol) is the number average molecular weight at time t, M n (g/mol) is the

initial number average molecular weight. constant.

k1 (g −1s −1mol) is a degradation rate

Since the polymer is degraded hydrolytically during release, M n is a function of time. The diffusion coefficient can thus be written as a function of time by combining Eqs. (58) and (59) and substituting D = Dinitial at t=0:

D(t ) = Dinitial (1 + at + bt 2 )

(60)

This is an empirical equation for a time-dependent diffusion coefficient. For diffusion of the lysozyne from PEG/PBT matrices, for example, Eq. (60) needs to be incorporated into the well known equations for diffusion. By integration of Eq. (60), one has t

T = ∫ D(t )dt = Dinitial (t + 0

1 2 1 3 at + bt ) 2 3

(61)

Thus the lysozyne release from films with the thickness l is described by [87, 88]


Li et al.

Mt 8 π 2T = 1 − 2 exp(− 2 ) M∞ π l

for

M t / M ∞ > 0.4

(62)

Mt T =4 M∞ πl 2

for

M t / M ∞ < 0.6

(63)

and the lysozyne release from microspheres with average radius r is given by

Mt 6 π 2T = 1 − 2 exp(− 2 ) M∞ π r

for

M t / M ∞ > 0.4

(64)

Mt T 3T =6 − 2 2 M∞ πr r

for

M t / M ∞ < 0.6

(65)

The experimental release profile of the lysozyne from the polymer matrices strongly depends on the ratio of the degradation rate and diffusion one. For the highly swollen matrices where the diffusion is fast as relatively compared with the degradation, no effect of polymer degradation on the release is expected and a first-order profile is observed. For less swollen matrices, the decline in release rate caused by the reduced drug concentration in the matrix may be compensated by increasing the diffusion coefficient due to polymer degradation for an almost constant release rate. The same conclusion is valid for the effect of the molecular weight on the release profile. For the polymeric matrix with a low molecular weight, diffusion is fast as compared with degradation, resulting in a first-order release. For the release from high molecular weight polymers near zero-order kinetics may be found. Maxwell Model Usually one can use the Maxwell-type models [89] for analysis of hydrogels relaxation behavior and understanding of the mechanical properties of hydrogels and polymeric network characteristics. Michailova et al. [90] used the two types of Maxwell models to study the mechanical relaxation spectra of the mixed HPPMC/NaCMC gels obtained by the experimentally small deformation oscillatory. The two Maxwell models are the generalized Maxwell model and the adapted Maxwell model, relating the discrete spectrum of relaxation times with the material viscoelastic functions. The generalized Maxwell model is written as

G ' (ω ) = ∑ Gi ω 2τ i2 /(1 + ω 2τ i2 )

(66)

G ' ' (ω ) = ∑ Gi ωτ i /(1 + ω 2τ i2 )

(67)

n

n


η ' (ω ) = ∑ Giτ i /(1 + ω 2τ i2 )

(68)

n

The adapted Maxwell model is given as

G ' (ω ) = G '0 + ∑ Giω 2τ i2 /(1 + ω 2τ i2 )

(69)

G ' ' (ω ) = G ' '0 + ∑ Giωτ i /(1 + ω 2τ i2 )

(70)

n

n

where the material viscoelastic functions G ' and G ' ' are the storage and the loss moduli, respectively, and they are functions of the angular frequency ω . As the

Gi and τ i are the modulus and relaxation time of the ith-member of n-Maxwell elements. G '0 and G ' '0 are the second plateau moduli at the frequencies of the G ' and G ' ' curves. Based on the discrete characteristics of the relaxation spectrum, the zero-relaxation time τ 0 and the mean relaxation time θ are parameters of the relaxation spectrum,

computed by the following equations:

τ 0 = limη i / Gi = limτ i

(71)

θ = ∑ Giτ i2 / ∑ Giτ i

(72)

ω →0

n

ω →0

n

Usually the generalized Maxwell model is suitable for the homogeneous gel systems where the thermodynamic compatibility of the solvent and polymer is observed. The model can be extrapolated towards very low frequencies, showing the flow curve in the terminal region, based on which one can compute the coefficients characterizing the entangled gel network. They include the zero shear viscosity and zero-relaxation time

η 0 , plateau modulus G N0

τ 0 . The comparatively longer zero-relaxation time observed in

both media suggests a decreased mobility of the hydrated macromolecules, which is related to the low quantity of the solvent in the swollen matrix system. The type of system may demonstrate slower rates of both water penetration and hydrogel erosion/dissolution. The generalized Maxwell model is not suitable for the inhomogeneous gel systems such as the mixed HPMC/NaCMC gels. They may be simulated by the adapted Maxwell model. Usually the hydrophilic matrices are highly inhomogeneous during the swelling due to the presence of particles with different degrees of hydration. The mixing of polymers with different chemical compositions and viscoelastic behaviors increases the gel heterogeneity. With highly swelling and quickly relaxing cellulose derivatives, probably an interpenetrating network forms as a third gelphase with its own rheological properties. This kind of hydrogels demonstrates a second plateau in the loss modulus curves G ' ' , inhomogeneity in the process of polymer chain oscillation, low coefficient of relaxation and residual stress.


Li et al.

Flory−Rehner and Flory−Huggins Models For the most commonly cross-linked gels that are cross-linked randomly along the backbone of a polymer and formed by a vulcanization process, the model developed by Flory and Rehner is a suitable tool for numerical simulation. In the Flory-Rehner model, it is assumed that the molecular weight is uniform between cross-links and the crosslinking occurs in the solid state, in which state the polymer chains are in the most probable conformation and elastic stress only develops upon addition of solvent to the network. The swelling of a polymeric network is thus referenced to the volume of polymer in the solid state. Ebert et al. [91] employed the Flory-Rehner model for study of the gels formed by the condensation of terminally modified and multi-armed polymers, termed the condensation gels. Then the number v e of moles of elastically active chains in the network may be calculated by the volume fraction

v 2, s of polymer in the gel,

(V0 / V1 )[ln(1 − v2, s ) + v2, s + χ1v22, s ] ve = − (v12,/s3 − 2v2, s / f )

(73)

χ is the Flory interaction parameter, V1 is the molar volume of solvent, V0 is the volume of polymer in the absence of solvent, and f is the functionality of the crosswhere

linking. Actually the swelling characteristics and drug release behavior of a polymeric network hydrogel depend principally upon the extent of cross-linking. One of the important structural parameters is the average molecular weight

M e between two

consecutive entanglements, which is generally calculated by the Flory-Rehner model as

(v / V1 )[ln(1 − v2, s ) + v2, s + χ1v22, s ] 1 2 = − (v12,/s3 − v2, s / 2) Me Mn

(74)

M n is the number average molecular weight of the cellulose ether tested, v is the corresponding volume. V1 is the molar volume of water, v 2, s is the polymer fraction where

of the swollen hydrogel in equilibrium, and interaction.

χ 1 is the Flory parameter of polymer-water

This model is able to describe the volume swelling ratio of crosslinked polymers in equilibrium state, which is based on the assumption that the elastic retractive forces of the polymer chains balance with the thermodynamic compatibility of the polymer with the solvent molecules during swelling [92]. This assumption is validated when the hydrogels are neutral, the swelling is isotropic, there are tetra-functional crosslinks at zero volume, four chains are connected at one point, and the polymer chains with Gaussian distribution are crosslinked in the solid state.


Another important structural parameter characterizing the cross-linked polymer is the average molar mass M c between cross-links, which is directly related to the cross-link density. According to the data of experimentally swelling, the average molar mass

Mc

between the cross links of hydrogel can be computed by the Flory-Rehner model in the simplified form as follows [67],

M c = −d pVsφ 1 / 3 [ln(1 − φ ) + φ + χφ 2 ] −1 where

(75)

d p is the density of polymer, Vs is the molar volume of solvent, and χ is the

Flory-Huggins interaction parameter between the solvent and polymer. The osmotic pressure in equilibrium state approaches to zero with complex dependence on the properties of hydrogel and solution. In the Flory-Rehner model, the osmotic pressure results generally from three terms for the charged polymeric network [93]:

π = π mixing + π elastic + π ion

(76)

where the first term on the right-hand side accounts for the polymer-solvent mixing, the second term is the elastic contribution due to the polymeric network deformation, and the third term describes the electrostatic and ion-solvent interaction. The Flory-Huggins theory provides a pattern for the mixing contribution as [94]

π mixing = −

RT (ln(1 − φ ) + φ + χφ 2 ) V1

(77)

where R is the gas constant, T is the temperature, V1 is the solvent volume, volume fraction of polymer in the gel and χ is the Flory parameter. One can use the Flory-Huggins theory to describe the chemical potential

φ is the

µ mix due to

the mixing by,

µ mix = kT (ln(1 − φ ) + φ + χφ 2 )

(78)

in which φ is defined as the polymer volume fraction in the hydrogel and χ is the Flory interaction parameter. In the classic Flory theory, for a given polymer composition at constant temperature, χ is treated as a constant. However, for the anionic hydrogels with the pH-induced condensation, the parameter χ cannot be treated as a constant. The balance of the hydrogel matrix between the hydrophilicity and hydrophobicity changes with pH when protons bind to the polymeric backbone. Thus the hydrogel changes from one polymer matrix for which water is a good solvent ( χ 0.5). As a result, the hydrogel matrix at low pH collapses and squeezes out water [95].


Li et al.

For modeling of phase transitions and swelling behaviors of polymeric hydrogels, Bae et al. [96] proposed a thermodynamic model by combining the extended FloryHugghins model in the free energy due to the mixing contribution with the modified Flory-Rehner theory for elastic networks. In the model, the change in Gibbs free energy of the network, ∆G net , due to isotropic swelling deformation with free of ions may be written as,

∆Gnet = ∆Gel + ∆Gmix where

(79)

∆Gmix and ∆Gel are the changes of the Gibbs free energy due to the mixing and

elasticity contributions, respectively. The latter is written as

∆Gel = NRT [(

3Φ 02 / 3 A 1 / 3 B )(φ − φ ) + ( )φ ln φ ] 2mc mc

(80)

Φ 0 and φ are the polymer volume fractions in the network formation and swollen network states, respectively. mc is the number of lattice sites where R is the gas constant.

occupied by an average network chain, and N is the number of moles of lattice sites. The factors A and B are given as,

A= where

f − 2 2φ + f f

and

B=

2φ f

(81)

f is the functionality of the crosslinks for a perfect polymer network.

It is suggested here that the interaction parameter

χ is computed by,

1 ∆Gmix φ1 φ = ln φ1 + 2 ln φ 2 + φ 2 ∫ χ (T , φ )dφ φ2 NRT r1 r2

(82)

φ1 , φ 2 , r1 and r2 are the volume fractions and relative molar volumes of the components 1 and 2, respectively. The interaction parameter χ (T , φ ) is defined as the where

product of two functions, depending on concentration and temperature, respectively,

χ (T , φ ) = D(T ) B(φ ) D(T ) = d 0 + where

d1 T

(83) and

B(φ ) = (1 − bφ ) −1

(84)

d 0 , d1 and b are adjustable parameters. When the combinational term of the

crosslink hydrogels is negligible and the relative molar volume of solvent is approximately unity, Eq. (82) may be rewritten as [97],

Modeling of Environmentally Sensitive HydrogelsFrontiers in Drug Design & Discovery, 2006, Vol. 2 319 1 ∆Gmix = φ 0 ln φ 0 + φ ∫ χ (T , φ )dφ φ NRT

Where

(85)

φ 0 and φ are the volume fractions of the solvent and polymer in the swollen

hydrogels, respectively. Camera-Roda-Sarti Model For chemically crosslinked swellable polymeric hydrogel in kinetic state, the drug release process is strongly influenced by the diffusion of the water solvent inside the matrix which, in turn, undergoes substantial structural or morphological modifications. The kinetics of the concentrations of the species is also influenced by the kinetics of the solvent sorption-desorption in hydrogels. The kinetics of these physical changes may be modulated by the characteristic times of the corresponding molecular or internal stress rearrangements. The model proposed by Camera-Roda and Sarti [98] may be applicable for simulation of the diffusion process from and within a swollen hydrogel polymeric matrix and prediction of the drug release during the swelling or deswelling phenomena induced by temperature modification [94]. Despite the complexity of solvent penetration in the hydrogel polymeric matrix due to the coupling of stress and concentration as well as chemical potential fields, the formulation of the Camera-Roda-Sarti model may be developed by the solvent conservation law and a viscoelastic constitutive equation for the diffusive flux with concentration-dependent relaxation time and diffusivity. The constitutive equation for the diffusive flux is composed of two terms, the Fickian term J f and the relaxing term J r with a finite relaxation time τ , namely

J = J f + J r = − D f gradc − ( Dr gradc + τ

∂J r ) ∂t

(86)

which should be combined with the conservation law,

∂c = −divJ ∂t

(87)

where c is drug concentration in the hydrogel polymeric matrix, and the convective contribution to J is neglected. In order to accommodate a relaxation from an initial Fickian behavior to a final one with different diffusivities, the expressions of

D f and

Dr are considered as, D f = Din

and

Dr = D∞ (c) − Din

(88)

where Din is the diffusivity at the beginning of diffusion or when a change occurs from a steady-state situation, and it is assumed to be equal to diffusion coefficient of the unpenetrated polymer. The dependences of D∞ and τ on c are assumed in the following exponential forms,


Li et al.

D∞ (c) = Deq exp( g (c − ceq ))

(89)

τ (c) = τ eq exp( K (ceq − c))

(90)

where

Deq and τ eq are the diffusivity and relaxation time when c = ceq , and here ceq

is the penetrant concentration at equilibrium with pure penetrant in external environment. The exponential equation (89) for D∞ (c) is consistent with free volume theory. The above formulation can be rewritten in nondimensional terms as follows:

D∞+ (c + ) = Rd exp( gceq (c + − 1))

(91)

τ + (c + ) = De(exp( K (1 − c + ))) / Rd

(92)

where

c + = c / ceq , De = τ eq Deq / δ 2 is the Deborah number, and Rd = Deq / D i in

Di is the diffusivity in the unpenetrated polymer. g and K are given according to the assumption made by Camera-Roda and Sarti [98], De and Rd are adjustable parameters. τ eq is the characteristic time for comparison between the experimentally which

measured and theoretically computed data, and

t = t +τ eq Rd / De , where t and t + are

the dimensional and nondimensional time, respectively. Tanaka-Fillmore Model Thickness of the hydrogel disk is one of important parameters to control the rate of the drug release from a cylindrical disk hydrogel device. Tanaka and Fillmore proposed a very simple model to estimate the effect of geometrical size on the drug release behavior [99], that is

t=

R2 D

(93)

where t, R and D are the characteristic time, the hydrogel size, and the co-operative diffusion coefficient, respectively. The experiments indicate that, as the diameter of the hydrogel disk decreases, the amount of the drug release at various time-intervals increases [100]. This may simply be attributed to the fact that, with increasing the diameter of the hydrogel disk, the surface area available per gram of polymer decreases. As the flux is directly proportional to the surface area for given values of other dependent variables, the rate of drug release also decreases. Therefore, it may be concluded that the desired release rate can be achieved by making the hydrogel device with a suitable geometrical size, such as the diameter of cylindrical disk hydrogel device.


Hopfenberg Model As mentioned above, the geometric effects play an important role in altering the dissolution rate. Hopfenberg [101] developed a model for theoretical simulation of the drug release from surface-eroding devices with various geometries including slabs, spheres and infinite cylinders. In the Hopfenberg model for the slab, spherical and cylindrical matrices displaying heterogeneous erosion, the governing equation is given as,

Mt kt = 1 − [1 − 0 ]n M∞ c0 a0

(94)

M t is the amount of drug release from the device in time t, M ∞ is total amount of drug release when the device is exhausted. k 0 is the erosion-rate constant, c0 is the initially uniform concentration of drug in the matrix, a 0 is the initial radius of a sphere or cylinder, or the half-thickness of a slab. n is the geometric shape factor, where n = 1 for a slab, n = 2 for a cylinder and n = 3 for a sphere. In the Hopfenberg model, it is where

assumed that the release kinetics is not affected by time-dependent diffusional resistances internally or externally acting on the eroding matrix, namely the actual erosion process is the rate-limiting step. The contribution of the secondary surface area is also neglected in release process. Hopfenberg and Katzhendler et al. [101, 102] also developed another model to describe the drug release from an erodible tablet matrix, in which the matrix swelling is assumed to be slower, relatively compared with the erosion process. The matrix swelling thus occurs prior to the release of drug from the matrix. The model may be applicable for the erosion rates of the tablets with different erosion rates in the radial and axial directions. For kinetics of drug release from the erodible tablets matrix, the model with two coordinates, a in radial direction and b in axial one, is given as,

Mt kt 2k t = 1 − (1 − a ) 2 (1 − b ) M∞ c0 a0 c0b0

(95)

k a is the radial erosion-rate constant, k b is the axial erosion-rate constant, and a 0 and b0 are the initial radius and thickness of the tablets, respectively. This model is

where

able to provide the fractional amount of drug release from the surface erodible tablets [103]. In the special case when k a ≅ kb = k0 , Eq. (95) can be rewritten as

Mt kt 2k t = 1 − (1 − 0 ) 2 (1 − 0 ) M∞ c0 a0 c0b0

(96)

Kim et al. [104] investigated the kinetic swelling of pH-sensitive anionic hydrogels for oral protein delivery. It is assumed here that the penetrant sorption for long periods is


Li et al.

mainly controlled by relaxation of the polymeric network, and that the sorption process by polymeric relaxation is first-order. The model for the relaxation may be written as,

dM t = k 2 (M ∞ − M t ) dt where

(97)

k 2 is the relaxation rate constant. By integrating Eq. (97) one has

Mt = 1 − A exp(−k 2 t ) M∞

(98)

k 2 are obtained from the slopes and intercepts of the experimental plot of ln(1 − M t / M ∞ ) versus time t at time later than those at a given M t / M ∞ . where A is a constant. Usually the constants A and

The model developed by Hopfenberg and Frisch [105], including both the effects of Fickian diffusion and polymeric relaxation, may predict the solvent uptake in swellingcontrolled release systems, that is,

∂c ∂ ∂c = ( D − vc) ∂t ∂x ∂x

(99)

where c is the concentration of water within the polymeric network, D is the diffusion coefficient of water through the hydrogel, x is distance, t is time, and v is the velocity of the glassy/rubbery front during the swelling [106]. Lugo et al. [107] employed the above governing equation to analyze the transport behavior of the salmon calcitonin in the hydrogels, by defining c as the concentration of solute and v is the velocity of the swelling front. They used the analytical solution of Eq. (99), known as the BerensHopfenberg model, ∞ Mt 8 − D(2n + 1) 2 t = φ F [1 − ∑ exp( )] + φ R (1 − exp(−kt )) (100) 2 M∞ 4l 2 n =1 ( 2n + 1)π

where k is the first-order relaxation constant, D is the diffusion coefficient, φ F and φ R are the fractions of sorption contributed by Fickian diffusion and the chain relaxation, respectively. l is the half-thickness of the slab. This model can estimate the overall release in terms of Fickian and non-Fickian contributions. The analysis results in the determination of both the diffusion coefficient and a characteristic relaxation time τ defined as the reciprocal of the term k . Crank [87] demonstrated a model similar to the above Berens-Hopfenberg model for a plane film, that is


 Mt 8 ∞ exp(−(2n + 1) 2 k F t  = f F 1 − 2 ∑  + ∑ f R ,i [1 − exp(−k R ,it )] M∞ (2n + 1) 2  π n=0 

(101)

where

kF = where

4π 2 D τ 02

and

f F = 1 − ∑ f R ,i

(102)

i

f F and f R ,i are the fractions of contributions from the Fickian diffusion and

relaxation processes, and

k R ,i is the respective relaxation rate constant. The semi-

empirical analytical equation (101) is applicable for the swelling of coupled film [108]. This model in fact makes the assumption that the water sorption into glassy polymer is a linear superposition of Fickian diffusion and first-order relaxation. Scott’s Second-Order Diffusion Model Scott et al. [109] developed a second-order diffusion model for the kinetics of swelling of the hydrogels as follows,

dH = K (H ∞ − H ) 2 dt

(103)

where H is the maximum or equilibrium water uptake at time t. K is the rate constant. If

K = 1 / AH 2 , and replacing H ∞ by 1 / B and integrating Eq. (103), one can have t = A + Bt H

(104)

where A and B are the coefficients with the physical meanings [110]: after a long treatment time Bt >> A and by Eq. (104), B = 1 / H ∞ , i.e. it is the reciprocal of the maximum or equilibrium water uptake; at a very short treatment time the limit, Eq. (104) becomes to:

lim( t →0

dH 1 )= dt A

A >> Bt and in (105)

The intercept A thus is the reciprocal of initial swelling rate. By rearranging and differentiating Eq. (104), one can have

dH A = dt ( A + Bt ) 2

(106)

Actually the above equation (106) is identical with Eq. (103), which indicates the swelling rate being a function of the treatment time.


Li et al.

Nernst-Planck Equations For simulation of ion transport within the hydrogels, the total flux of the ions may be modeled by the Nernst-Planck equations with consideration of the fluxes due to the concentration gradient, electrical migration and convection [111, 112],

Γk = φ[− Dk

∂ck ∂ψ − µ k zk ck ] + ckU ∂x ∂x

(107)

∂ 2ck ∂ck ∂ψ ∂ 2ψ Dk 2 + µ k zk + µ k zk ck 2 = 0 ∂x ∂x ∂x ∂x

(k=1, 2, …, N)

(108)

Γk , Dk , c k , µ k and z k are the flux, effective diffusivity, concentration, effective ionic mobility and valence of the kth ion species within the hydrogel. φ is the gel porosity, ψ is the electric potential, U is the area-averaged fluid velocity relative where

to the polymeric network, x is coordinate associated with the deformed hydrogel and N is total number of ionic species. The electrostatic potential ψ is determined by

∂ 2ψ F N = − (∑ z k c k ) εε 0 k =1 ∂x 2 where

(109)

ε 0 is the dielectric constant of vacuum, and ε is the relative dielectric constant

of the solvent. Arrhenius Model In the work of Kwak et al. [113], the self-diffusion is regarded as a result of Brownian motions of the solute, consequently it is sensitive to temperature. An increasing diffusion is observed as the temperature increases for the solute in curdlan gels. Thus the temperature dependence scales in fact as an Arrhenius-type equation,

D = A exp(−

Ea ) RT

(110)

where D is the diffusion coefficient, A is a pre-exponential factor, E a is the activation energy, R is the gas constant, and T is the absolute temperature. The present activation energy E a associated with the diffusion is defined as the energy barrier that should be overcome when the solute moves from one surrounding environment to the other. From the slope of the linear least-square fits, E a of the self-diffusion may be computed for different gel concentrations. The

E a values increase as a function of the

molecular size of the diffusion species. The solute-solvent interactions should thus be regarded as the major factor for the energy barrier of the diffusion, and the polymer chains are considered as inert obstacles to the diffusion.


Noyes-Whitney Model The rate of drug release from a polymeric matrix such as a tablet may also be controlled by the solubility of the drug in water. Taking a hydrogel-coated polymeric stent as example, the drug release from the stent requires the diffusion through a nonerodable hydrogel layer to become available in the bulk solution. The rate of the drug release through an unstirred liquid film in steady-state can be described by the NoyesWhitney model as follows [114],

dm DA(cs − c) = dt h

(111)

dm / dt is the rate of drug release, D is the diffusion coefficient, A is the total surface area of drug in the bulk solution, cs is the drug solubility, c is the concentration of drug in bulk solution, and h is the thickness of the diffusion layer. where

Due to high degree of swelling in most hydrogel coating, the diffusion layer consists primarily of water. The diffusion constant D for a given drug through the hydrogel layer may be assumed essentially to be equivalent to its diffusion constant in water. It is also reasonably assumed that the hydrogel coating swells quickly in water and it retains the geometric dimension after a steady state is attained. Therefore, both A and h should remain fairly constant for a given hydrogel coating. Based on the assumptions mentioned, the Noyes-Whitney equation can be simplified into the following form for the release of a sparingly-water-soluble drug from a hydrogel-coated polymeric matrix,

dm = K (c H − c ) dt

(112)

where K is a characteristic constant of a specified drug in the system. For a waterinsoluble drug, it is known that the concentration of the dissolved drug in the diffusion layer may not reach the saturated solubility of the drug. Then the cs in Eq. (111) should be replaced by cH , which is the actual concentration of the drug in the diffusion layer. In a practical environment such as the human body, usually the concentration c in the bulk solution is much smaller than the drug concentration cH in the diffusion layer. Thus c in Eq. (112) may be ignored reasonably. Then Eq. (112) can be further simplified into the form as

dm = KcH dt

(113)

For a given drug uniformly distributed in the coating matrix, it may be assumed that the concentration of the dissolved drug in the diffusion layer is directly proportional to the loading of the drug in the matrix, which should be a reasonable assumption when cH < cs . Then Eq. (113) may be rewritten as follows,


Li et al.

dm = K ' cL dt

(114)

where K ' is a characteristic constant of the drug and cL is the loading of the drug in the hydrogel-coated polymeric matrix. In the Noyes-Whitney model therefore, the rate of the sparingly-water-soluble drug release from the polymeric matrix is proportional to the drug loading in the polymeric matrix. By Eq. (114), the rate of drug release from the hydrogel-coated polymeric matrix is proportional to the drug loading in the polymeric matrix [115]. Beltzmann Superposition Model Bell et al. [116] developed a model for analysis of the kinetic response of hydrogel to the pH changes of surrounding environment, including the expanding and contracting phenomena. In the model, the strain ε of isotropic swelling of the hydrogel in a solution is defined as

ε=

l − l 0 ∆l = l0 l0

(115)

where l and l 0 are the deformed and original lengths, respectively. The hydrogels subjected to various pH conditions over time exhibit the following characteristics: (i) the strain response is additive to the input of pH changes, and (ii) the response is independent of the specific time at which the input is imposed. Then the Boltzmann superposition principle is applicable and one can have t

ε (t ) = ∫ L(t − τ ) 0

∂[ H + ] dτ ∂t

(116)

ε (t ) is time-dependent strain, [ H + ] is the ionic concentration of hydrogen, τ is a dummy variable. L(t − τ ) is the mechano-chemical compliance and it describes the where

mechanical response to chemical stimulus or the conversion of chemical energy to mechanical work. The model can be extended to the isotropic swelling of the hydrogel in three-dimension domain by,

Q(t ) =

Vs (t ) l 3 (l 0 + ∆l ) 3 = 3 = = [1 + ε (t )]3 Vd l0 l 03

in which Q (t ) is the degree of volume swelling. in the swollen and dry states, respectively.

(117)

Vs and V d are the polymer volumes

Guggenheim-Anderson-deBoer and Young-Nelson models In order to understand the mechanisms of water uptake and the effect of the hydrophobic component and the drying method used in the water-copolymer interaction,


Osuna et al. [117] used Guggenheim-Anderson-deBoer (GAB) and Young-Nelson models, which are broadly applied in the characterization of water-amorphous products systems. The GAB model is given as,

Y=

ηkawYm (1 − kaw )[1 + (η − 1)kaw ]

(118)

where Y is the moisture content of the solid on a dry basis,

Ym is the moisture content of

the monolayer, and a w is the water activity. η and k are energetic constants related to the heat of sorption. The parameters of the GAB model may be estimated by multi-linear regression of Eq. (118). It is noted that the European Project Group COST 90 on physical properties of foods has recommended the present GAB model as the fundamental equation for characterization of water sorption by food materials. In addition, the Young-Nelson model fits the experimental data of sorption and desorption to equations in the following form

M s = A( β + θ ) + BθRH

(119)

M d = A( β + θ ) + BθRH max

(120)

where

M s and M d are the amounts of water sorbed and desorbed at each relative

humidity, respectively, and they are expressed as a fraction of the dry mass of the polymer. A and B are characteristic constants of each material,

A=

ρ wVol M Wm

and

B=

ρ wVol A Wm

(121)

ρ w is the water density, and W m is the weight of dry material. Vol M and Vol A are the adsorbed and absorbed water volumes, respectively. θ is the fraction of the material surface covered by at least one layer of water molecules, and Aθ is the where

mass of water in a complete adsorbed monolayer, expressed as a fraction of the dry mass of the polymer. CONCLUSIONS A comprehensive and systematical review has been presented on the recent development of theoretically modeling for numerical simulation of environmentally sensitive hydrogels for various applications of drug delivery. The review approximately categorizes the developed models into two groups, the fundamental models and the empirical/semi-empirical models, respectively. The models coming from the former group are generally derived through conventionally fundamental theories or laws, and the models from the latter one sometimes include the coefficients that are essentially determined by experiment. The review reveals that the model development of the


Li et al.

responsive hydrogels for applications of drug delivery still is in preliminary stages. Most of the developed models are continuous- and/or empirical-based. Actually they difficultly provide precisely physical and chemical bases for description of drug delivery phenomenon. As well known, the sciences of mathematical modeling and numerical simulation have been accepted generally as the third mode of scientific discovery, and other two modes are experiment and analysis, respectively. Therefore, the present reviewers believe that the more accurately mathematical models and numerical techniques, such as molecular simulating techniques in micro- and nano-scales, are critically required and they emerge soon for simulation of advanced drug, protein and gene delivery. ACKNOWLEDGEMENTS The authors gratefully acknowledge the financial support from the Agency for Science, Technology and Research (A*STAR) of Singapore through A*STAR SERC Grant – SRP on MEMS Phase II under the project number: 022 107 0009. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]

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Polyelectrolyte Nanocapsules – Promising Progress in Development of New Drugs and Therapies Silke Krol*, Alessandra Gliozzi, Alberto Diaspro CNR-INFM, Department of Physics, University of Genoa, via Dodecaneso 33, 16146 Genoa, Italy Abstract: The most promising tool for future applications in the field of science as well as in medicine is the use of nanobiotechnologies. Especially self-assembly systems with tailored properties on a nanometer level fulfill the requirements to nano-organized systems in a satisfactorily manner. Hence the development of so-called nanocapsules prepared by means of Layer-by-Layer technique was a great progress on the way to individual drug delivery systems or nano-sized bioreactors. The preparation of hollow shells for drug delivery use requires polyelectrolytes as well as a charged core that are not cytotoxic. According to this purpose CaCO3 crystals with different shapes were introduced as removable template for capsules with changeable permeability as a result of pH variations. Due to the low toxic potential of the core it could be valuable for applications in human body. Furthermore the nano-organized shells are suitable as coating of living cells or artificial tissue. With this “second” cell wall it is possible to target the encapsulated material to predefined organs, and to prevent immune response. Moreover one can choose between the breakage of the coverage using the capsule only as targeted carrier or the production of proteins inside the remaining shell. The requirements for this application are polyelectrolytes that are not toxic to the tissue of the transplantation site as well as to the coated cells.

INTRODUCTION Since long it is known that the cause for many grave diseases can be found in the partly or complete failure of organs as well as glands producing necessary hormones or proteins. Still then it was a dream of researchers to replace them by healthy tissue. But as easy as the idea sounds the conversion was hindered by a lot of nearly insolvable problems. The idea to cure diseases by substituting the damaged organ with donor tissue that should adopt its function has arisen in the year 1883 when for the first time thyroid tissue was transplanted. In the early 20th Century first experiments with the transplantation of kidneys started and the surgeons tried to use of organs of foreign origins like pigs, the so-called xenotransplants (donor and recipient are of different *Corresponding author: Tel: +39-(0)10-3536309; Fax: +39-(0)10-311066; E-mail: [email protected] Garry W. Caldwell / Atta-ur-Rahman / Michael R. D'Andrea / M. Iqbal Choudhary (Eds.) All rights reserved – © 2006 Bentham Science Publishers.


Krol et al.

species). But within a short time period graft rejection occurred leading to inflammation and destruction of the transplant. As a crucial problem in the replacement of organs or tissue, the immune response of the recipient organism against the donor tissue was determined. However, only autotransplantation (donor and recipient are the same person) or in haploid twins succeeded while allotransplantation (donor and recipient are human but different persons) mostly failed. Due to these grave problems and the need for a permanent immunosuppressive drug therapy in order to avoid rejection, a broader clinical use of organ replacement as cure is hampered. One of the most serious problems in organ replacement is the mismatch between available donor tissues and patients who urgently need an organ. Until now worldwide 470.000 kidneys, 74.000 livers and 54.000 hearts were replaced. But the major problem in the transplantation of organs or tissue is the mismatch between needed and available organs, for example in the case of hearts in 1998 in Germany around 1000 hearts are needed but only 542 can be transplanted [1]. This is due to the fact that the crucial requirement to the donor tissue is that it must fit to most of the recipient’s tissue parameters [2, 3]. Hence, in many cases only relatives are eligible as donors. Microencapsulation of tissues from different sources (allo- or xenografts) shall help to overcome these problems by creating an immunoprotected transplant. The idea to protect cells by means of a shell of biocompatible material has arisen in the early 70th of the 20th Century [4]. Coated Cells or Artificial Tissue – Hope for the Treatment of Grave Diseases In the following three main applications for polymer capsule, microencapsulated living cells or tissue, empty multilayer capsules as drug delivery system and the application of the multilayer as immune protection for living cells were discussed. First of all, whole or partial organs are coated to replace the damaged or restrictedly working tissue without immune suppression. In this case the capsule should remain permanently or at least for a long time on the enveloped artificial tissue to separate it from the recipient’s immune system. The second task is to use coated cells as an alternative approach to somatic gene therapy. For this, implanted recombinant cells with a protective coating deliver therapeutic substances, e.g. hormones, messengers or proteins directly to the target tissue. Also here only a stabile and permanent coating is useful. A third trail leads to capsules as a delivery system for specific materials, e.g. stem cells, DNA or drugs. For this application, it is absolutely necessary that the trapped material be released from the capsule. 1. Alginate Microencapsulation Since the concept of semipermeable microcapsules for transplantation without immune suppression was introduced [4] this principle has been actively investigated as therapy for different serious diseases. Encapsulated artificial tissues have been studied for the treatment of diabetes, liver, or kidney failure. Moreover genetically engineered cells producing proteins or factors raise hope as targeted drug delivery system or more important as good alternative for viral induced gene therapy. Artificial cells containing enzymes have been developed for example as a possible cure in hereditary enzyme deficiency diseases. Besides, these types of cells are also useful in biotechnology, and chemical engineering. Even modified hemoglobin as blood substitutes are now under investigation and are already in Phase III clinical trials in patients [5].

Polyelectrolyte Nanocapsules


In 2003 Yoshioka [6] called this strategy of implanted and immunoprotected living cells as an in vivo drug delivery system ‘‘cytomedicine’’. In the past, the successful transplantation of different types of living cells or tissues such as islets of Langerhans [7-12], hepatocytes [13, 14], parathyroid cells [15-17], pituitary cells [18], and thymic epithelial cells [19] with alginate microencapsulation or more advanced techniques was reported. But apart from technical problems with encapsulation of artificial tissue or cells also ethic arguments especially in the case of xenotransplants have to be taken into account. Due to the explosive development in this field, in the following, the introduced applications for the method highlights only exemplarily some interesting progresses. The most prominent example for microencapsulated artificial tissue is the pancreatic islet or β-cells as possible therapy for insulin-dependent diabetes. The aim of this therapy is a transplantation of the islets with a minimum or no immune suppression. Due to the fact that there are several good reviews [5-8] summarizing the research in this field, briefly the most important obstacles and advantages in the use of the different coatings should be stated. But also for numerous other cell types or artificial organs the alginate microencapsulation was used in the past (Table. 1). Table 1.

Overview of Cell Types Used with Alginate Microencapsulation

Cells

Therapy against

References

Encapsulated Artificial Tissue Langerhans’ islets

diabetes

[5-8, 20-25]

insulin-secreting beta-cell line (BRIN-BD11)

diabetes

[26]

Liver

Liver replacement

[13, 14, 27]

parathyroid tissue

Thyroid replacement

[15-17, 28]

mesenchymal stem cells

Cartilage replacement

[29, 30]

Encapsulated Producer Cells as Bioreactor Endostatin releasing producer cells

Brain cancer

[31-35]

NO producing cells

cancer

[36]

Interleukin-2

cancer

[37]

Monoclonal antibodies

Malignant brain cancer

[38]

glial cell line-derived neurotrophic factor (GDNF)

Parkinson’ disease

[39, 40]

alpha-iduronidase producer cells

mucopolysaccharidosis VII (MPS)

[41]

iduronate-2-sulphatase producer cells

Hunter syndrome (MPS II)

[42]

336


Krol et al.

But also for other serious diseases the microencapsulation in alginate beads was used with satisfying success in some documented studies. A new cancer therapy basing on the implantation of so-called producer cells is under investigation. As can be seen from Tabble (1) the list of products from these cells span from anti-angionesis factors to antibodies or transmitters. In all case neat but ultra-pure alginate beads or coated alginate particles were implanted and the functionality of the enveloped cells was followed for up to 12 month [32]. But for the therapeutic use of coated pancreatic islets there is still the problem with the shortage of donor organs. In the last few years, significant progress was made for xenotransplants from pigs [22]. Especially after the most serious constriction, a possible infection with porcine endogenous retroviruses, was proved to be without cause [43, 44]. Another strategy is to transplant insulin-secreting beta-cell lines as substitute for the native pancreas. But with immortal genetically engineered cell line there remain the risk of cancer from the transplant [25]. Talking about microencapsulation in this context means, entrapment of cells or cell clusters in high-viscose alginate, a marine polysaccharide, droplets stabilized with divalent positively charged ions like e.g. barium or calcium are used (Fig. 1). An exception is the work of Storrs et al. [45] using Langerhans’ islet sheets as a thin planar bioartificial endocrine pancreas.

Fig. (1). Concept for microencapsulation of cells or cells clusters. 1 indicates the major compound of the microbead, usually alginate; 2 is an additional coating e.g. to increase the tolerance of the recipient or induce angiogenesis; 3 sketch the cells; and 4 one of the major problem e.g. incomplete coating.

Analyzing the existing literature about alginate beads for microencapsulation, several problems can be identified. The material must always be investigated under consideration of biocompatibility of the building blocks. An important parameter for biocompatibility is the fibrotic overgrowth of the implanted material. Overgrowth of the



capsule is on one side linked to attraction and adhesion of fibroblast by the polymer. On the other side the release of factors like e.g. cytokines, nitroxide (NO) or antibodies by the enveloped cells of allo- or xenografts can attract macrophages, lead to antibodymediated cytotoxicity or again fibrosis. Fibrosis can lead to necrosis of the enveloped cells due to malnutrition or hypoxia. Apart from direct response of the immune system another factor for long-term survival of the incorporated tissue is the angiogenesis of the capsule surface which allows a good connection to the blood circuit. Only recently, e.g. it was shown for a glucose sensor that the functionality is increased significantly if revascularization occurs [46]. For alginate the researchers identified as major problem the ratio between the both building blocks of alginate, L-guluronic (G) and D-mannuronic (M) acid. So the new generation of capsules was usually prepared of alginates with an intermediate (G) and a high (M) content because in this way biocompatibility could be gained [25]. Besides neat alginate beads polyelectrolyte coatings were applied on top of the beads to improve there biocompatibility, e.g. alginate/L-lysine capsules were constructed as a scaffold for hepatocytes [27] or polyacrylic acid (PAA)/polyethylenimine (PEI) multilayers on barium-alginate beads with parathyroid tissue or single parathyroid cells [28]. In case of enveloped single cells a thinner fibrotic capsule was observable in case of the synthetic polyion (PAA) as outermost layer. In order to reduce the necrosis due to hypoxia and/or malnutrition, microcapsules of barium cross-linked alginate with a high D-mannuronic acid to L-guluronic ratio (reduced fibrosis) with incorporated perfluorocarbon (material with high oxygen storage capacity, prevent hypoxia) were developed [25]. These hybrid capsules showed a good functionality of the enveloped cells over a period of more than two years. Anyhow, some important drawback for the alginate droplets must be mentioned. The unfavorable ratio between encapsulated cell volume and overall capsule volume that allow for a limited possible transplantation site like the peritoneal cavity. As well as that the random trapping of the islets sometimes can lead to incomplete coverage, undefined number of cells and prolonged response times to external stimulation. 2. Other Encapsulation Systems Apart from the alginate microbeads other encapsulation systems or even complete devices were investigated for their utility as barrier against the immune system. The microbeads are a direct entrapment of the tissue but also hollow microporous fibers or entrapment in synthetic polymer aggregates show big advantages. But in some specific cases the requirements to the material are more demanding (Fig. 2). One approach was the use of a TheraCyte® device plus a single dose of anti-CD4 antibodies to prevent rejection of different types of encapsulated xenogeneic cells [47]. They tested the protection ability in immunodeficient mice, normal animals and in culture. Furthermore the foreign body reaction of the empty device in normal mice. But the results were poor. The device elicited an immune response and only in immunodeficient mice a survival of the implanted cells was observed. The device system seems to be not flexible enough to solve the problem with infiltrating cells and induced cytotoxicity of antibodies. In case of cartilage reconstruction a multilayer photopolymerized hydrogel was doped with chondrocytes taken from three different zones of native cartilage order to

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take into consideration the complex structure of articular cartilage [48]. Another work describes the encapsulation of human septal cartilage with polyelectrolyte complexes

Fig. (2). A. Concept for microencapsulation of cells or cells clusters in a device e.g. TheraCyte®. B. Concept for the more demanding cartilage replacement approach. Here, the physical properties of the material play a crucial role for the functionality.

consisting of sodium cellulose sulfate and poly-(diallyldimethylammoniumchloride) (PDADMAC) [49]. Comparison of neat cartilage with a polyelectrolyte-protected one showed that the coated tissue does not degrade and the level of chronic inflammation is significantly lower. Encapsulation procedures basing only on the use of synthetic polyelectrolytes were also tested in a new Parkinson’ disease therapy. Here cells were genetically engineered for a continuous production of the neurotransmitter L-dopamine. As a good example the investigations of Vallbacka et al. [50] can be mentioned. They implanted hydroxyethyl methacrylate–methyl methacrylate (HEMA–MMA) coated PC12 cells in rats while Yoshida et al. [51] used the same system in Japanese monkeys as preclinical study. They checked for graft rejection as well as for continuous release of dopamine and levodopa. The results proved a release even 8 weeks after implantation and only a weak immune response. Polymer fibers with incorporated producer cells for in vivo expression of neurotrophic factor like ciliary neurotrophic factor (CNTF) or glial cell line-derived neurotrophic factor (GDNF) were implanted. The experiments described by Zurn et al. [52] in her review based on BHK cells encapsulated in hollow polymer fibers. These cells are designed to release GDNF. They stated a satisfactorily protection of nigral dopaminergic neurons against lesion-induced cell death in rodent as well as monkey models of Parkinson’s disease by the released GDNF. But a comparative study by Bensadoun et al. [53] revealed that 4 weeks post-surgery the GDNF level, released in the striatum of rats, decreased significantly with polymer rods, whereas they remained stable with encapsulated cells or lentiviral vectors as protective shell. Tao et al. [54] implantred



the same cell/fiber hybrid system into the eye of a canine model for retinitis pigmentosa. The experiments indicate that the surgically transplanted, cell containing capsules were well tolerated, and the cells inside remained viable for at least 7 weeks. The obtained protection by the secreted CNTF was proved in all animals. How widespread the possible impact in medical therapy for the hollow microporous fiber/cell tool is, show studies of Schwenter et al. [55] or Boison and Huber et al . [56, 57]. In their approaches genetically engineered encapsulated cells were transplanted to secrete the hormone human erythropoietin as a treatment of Epo-responsive anemia or fibroblasts engineered to release adenosine by inactivating the activity of the adenosinemetabolizing enzymes adenosine deaminase and adenosine kinase as epilepsy treatment. After optimization of the infection conditions they found that in vivo erythropoietin secretion leads to an increase in the hematocrit during the first 2 weeks and elevated levels over a 6-week period. In case of epilepsy the implantation of the coated cells leads to a complete protection from clonic seizures, and a nearly complete one from focal seizures for at least 2 weeks. Also as protection against tissue damage in case of cerebral infarcts encapsulated grafts of basic fibroblast growth factor producer cells were tested [58]. In comparison with non-transfected BHK cells and rats without coated cells implanted the producer cells enveloped in polymer capsules with a semipermeable membrane revealed a reduction of the infarct volume by approximately 30% and a significantly decreased number of apoptotic cells were observed. Direct interaction between polymer and DNA are well-described as new transfection system in the gene therapy. But also encapsulated producer cells have an impact in gene therapy. This became clear from the investigations by Saller et al. [59]. They trapped in polymerized cellulose sulfate retroviral vector packaging amphotropic cells and tested their in vitro function and in vivo release of virions in mice after implantation. For at least 6 weeks survival of the coated cells was proved in culture as well as in two animal models and also a gene transfer into lymphoid cells was achieved. Summarizing the results for the different microencapsulation methods in which the polymers not only cover the cells or cell clusters but also serves as a scaffold it became clear that the problems are multifaceted. Especially if we memorize the requirements to a cartilage-replacing scaffold like elasticity, low friction, good nutrition of the embedded cells etc. These reflections together with the problems discussed in the first part of the article lead to the conclusion that perhaps a single or double component system could be too limited to fulfill all the demands to the polyelectrolyte matrix. 3. Polymer Capsule as Delivery System In the first two parts of this review an overview was given of the impact of microencapsulated living cells as “cytomedicine” as possible therapy for severe disease. However, there are several drawbacks mainly related to the fact the used polymer system is too simple. Basically the polymer capsules around the cells consist of only one or two components. In the following part of the article polymers were introduced as transporter and drug delivering unit. In this case, the crucial requirement for the polymer is biodegradability and non-toxicity neither of the polymer itself nor of the breakdown products, its metabolites. Furthermore, an ordered multilayer polyelectrolyte system will be discussed which have the potential to overcome some of the drawbacks highlighted

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earlier. Especially in the latter case only a very small section of the field could be included in this review because the number of inventions is nearly exploded in the last 20 years. 3.1. Polymer-Based Drug Delivery Systems Due to the importance of gene therapy as a promising strategy for the treatment of many inheritable or acquired diseases that are currently considered incurable the research focus on the delivery of DNA to damaged cells. There are two mayor routes, which are followed: one is the transfection using a virus injection system as nano syringe. The other one is a non-viral transfection of the cells by uptake through endocytosis. One of the most interesting applications in this context is a polymer-based DNA delivery exploits the interaction between a polycation and DNA as polyanion. In this way the risk of infection or allergic reactions to the virus material can be excluded. Two main tracks can be distinguished: First co-encapsulation of DNA and target cells in a polymer matrix like showed by e.g. Quick and Anseth [60]; second, the condensation of the DNA in a polycation matrix and uptake of the nanoparticles by the cells (Fig. 3).

Fig. (3). Concept for polymer condensation of DNA and gene delivery to the nucleus of the target cell.

In the following the focus lays on the DNA/polymer system because here numerous recently published works indicate the importance of the technique for a possible gene therapy. In this context it becomes fast clear why polyelectrolytes are the perfect transport for drugs into the human body. Apart from the polymer-based delivery also cationic liposomes were developed as carrier for the DNA. A large variety of polycations commercially available as well as exclusively designed for the specific purpose are used to transfect cells with alien DNA. Furthermore one can distinguish applications by means of more natural polymers like poly-amino acids like poly-L-lysine (PLL) from synthetic one like poly-(ethylenimine)



(PEI) or poly-(methylacrylate) (PMA). Important for the entrance of the nanoparticles in the cells is a slightly positive net charge. Widely used in order to condense DNA into deliverable units is the polymer PEI like e.g. described in the review article of by Lungwitz et al. [61]. In addition to linear PEI also branched or better dendrimeric PEI serves as carrier for DNA They analyzed and identified the intercellular obstacles preventing a successful transfection with the DNA of the target tissue as well as potential disadvantages in the polyamine as low transfection efficiency and cytotoxicity of the material. They suggested extending the delivery system to endosomolytic agents or nuclear localization signals. Moreover they refer to the requirement of strategies to prevent unspecific transfection to cells other than the target tissue. In terms of a more complex polymer system to overcome obstacles the work of Kim et al. [62] shall be mentioned. They investigated a bi-componental polymer matrix composed of poly-(L-lactic acid) or the block copolymer poly-(D,L-lactide-coglycolide) mixed with PEI. In their preparation the plasmid DNA is attached on the surface of polymer nanoparticles instead of a co-condensation incorporating the DNA in the beads. Changing the amount of PEI hence increasing the positive net charge of the beads can vary the amount of bound DNA. Unfortunately, the transfection efficiency of bi-component beads is significantly lower in comparison to pure PEI beads. But the cytotoxicity is diminished for bi-component beads. Both results support the conclusion that first PEI is cytotoxic and second a positive charge is necessary to facilitate the entrance of the particles into the cell. In order to solve the problems with the quite high cytotoxicity another approach deals with the design of co-polymers instead of constructing beads of two or more components. Unfortunately in the past, modifications of PEI with dextran sulfate [63] or human serum albumin [64] always led to a decrease in the transfection efficiency. But the conjugation of a non-ionic hydrophilic polymer such as poly-(ethylene glycol) (PEG) to PEI have showed a potential to overcome that drawback [65]. They build nanoparticles with the co-polymer and DNA degrading at 37°C, non-cytotoxic and depending on the molecular weight of PEG, the transfection rate for some of the investigated cell types was augmented with respect to pure PEI/DNA polyplexes. Furthermore, PEG can enhance the half-life of the particles in bloodstream by reducing interactions with plasma proteins or other circulating cells [66-69]. But also chemical modifications of the PEI can lead to reduced toxicity under preservation of DNA condensing features like proved by Kim et al . [70] with their acid-labile PEI. The idea that smaller breakdown-products of bigger PEI polymers are an efficient DNA carrier but non-toxic was supported also the study of cross-linked PEI of different but low molecular weights by Thomas et al. [71]. The results of their study were more than promising because they found a higher DNA binding capacity; significantly lower cytotoxicity and increased transfection rate by linkage of two PEIs. The mechanism for the strong dependence of the toxicity on the molecular weight is clarified by Moghimi et al. [72]. They have defined a two-step mechanism to describe the cell-polyion interaction. In the first phase they observed necrotic-like changes resulting from compromised membrane integrity, assessed by considerable lactate dehydrogenase release and phosphatidylserine translocation from the inner plasma membrane to the outer cell surface. At the second stage the polymer activated presumably a “mitochondrially mediated apoptotic program”, due channel formation in the outer mitochondrial membrane. This led to the release of proapoptotic cytochrome C,


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subsequent activation of caspase 3, and alteration in mitochondrial membrane potential as a result of caspase translocation into the mitochondria. Another step in the direction of a tailored polymer fulfilling a couple of requirements is the recently published work of Wang and Hsiue [73]. They combine the gene-loading capability and high transfection efficiency provided by the polycation PEI with the hydrophobicity of poly-(L-lactide) to enhance the ability of the particles to interact with cells and better permeation of the tissue and folic acid in order to increase the selectivity for tumors. The resulting block-co-polymer binds efficiently DNA, allows for transfection of the cells with good yield and expresses a comparably low cytotoxicity. A promising application for PEI-condensed DNA in conjunction with a second polymer is the work of Huang et al. [74, 75]. They followed the bone regeneration in rats with cranial defects after implantation of a scaffold of the co-polymer poly-(lactic-coglycolic acid) with embedded PEI-condensed DNA. The bone morphogenetic protein-4, encoded by the plasmid, induced a significant increase in osteoid and mineralized tissue. The example of PEI showed impressively the large diversity of tools to change and improve the properties of a polyion or polymer with the potential to transport drugs but some unfavorable features. Only for DNA delivery the list of studied polymers span from natural glycoaminoglycans like hyaluronic acid [76], to synthetic block copolymers, e.g. connecting poly-(2-dimethylaminoethyl)methacrylate, poly(ethylene oxide), and poly(propylene oxide) [77] or permanently charged poly(amidoammonium) salts [78]. In this context it must be mentioned the work of Cho et al. [79] because it presences the first step towards multicomponent system combining the suitable functionality of the single compound to a tailored construct able to fulfill the requirements in vivo. They condensed plasmid DNA with PEI to nanoparticles to which they apply a block-copolymer of folate-poly-(ethylene glycol)-poly (L-lysine). This conjugate should allow the folate receptor mediated uptake of the particles. The properties of PEG to prevent cell binding with blood components were formerly mentioned. With this core-shell construct they found an increased uptake in folate-receptor overexpressing cells in comparison to deficient ones and a better cell viability and gene expression even in comparison to PEI/DNA polyplexes. From this part of the review we can summarize that only one or two component systems can fulfill the demands of model cell systems but if the delivery or installation of polymer-coated cells shall take place in a complex organism like the body the system must be more flexible and consist of several compound with its own function. This is the starting point of a new generation of coatings, more ordered and more tunable. 3.2. Polyelectrolyte Multilayer-Based Drug Delivery Since the invention of the layer-by-layer (lbl) deposition of oppositely charged polyelectrolytes [80, 81] as strategy to coat surfaces the field exploded. Apart from developing the shells, empty or filled, as drug delivery system it was also used to tailor the surface properties in order to serve a specific purpose. A good overview gives the publication of Ai et al. [82]. Here, it was depicted how broad the spectrum of biomedical applications for multilayered structures is.



Even a multilayered DNA delivery system was described [83]. Jewells and his coworkers constructed layers of plasmid DNA that serves as polyanion and a hydrolytically degradable synthetic polycation to transfect cells growing on the treated surface as a possible transfection system from implants. But mainly the drug delivery was carried out by colloidal systems, means hollow capsules afterwards filled with the drug and sealed or capsules retarding the delivery of crystalline drugs used as core for the layer self-assembly. The idea for ordered encapsulation of particles or entrapment in multilayer shell is to protect valuable proteins, enzymes, DNA or hormones against degradation. Moreover, with the shell it is possible to gain access to the delivery of hydrophobic drugs without chemical modifications of the molecule. Usually the chemical modification in order to increase the solubility of hydrophobic drugs leads to decrease in activity. Furthermore encapsulation of drugs allows having a triggered or sustained release [84]. This capability was investigated by Ai et al. for furosemide [85] or by Qui et al . for ibuprofen [86]. Either for natural polyelectrolytes like polysaccharides [86] or for synthetic polyions like PSS and PDADMAC (poly-(diallydimethylammonium chloride)) in addition to gelatin [85] a sustained drug release was found in dependence of the number of layers. Notable in this context is the fact that furosemide is practically insoluble in water. But also the encapsulation of living cells (Fig. 4) is possible as shown by Diaspro, Gliozzi and Krol et al. [87-90]. They were able to prove that encapsulation of yeast cells as a model for living cells was firstly possible and second the cells maintain their functionality and are even able to duplicate after the coating procedure. This hybrid

Fig. (4). Scheme of cell encapsulation by the Layer-by-Layer technique [81].


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system of polyelectrolyte multilayer capsules with a specific property allows also for non-invasive attachment of the cells to surfaces [91]. Furthermore they assume a protection capability in aggressive environments. In order to study this feature in more detail a model system was established. For this purpose yeast cells were encapsulated in a multilayer under specific conditions under which the survival of the cells was guaranteed [92]. In the first preliminary experiments it was shown that the system is suitable as model system but the protection could only be gained under different ionic strength or variations in the number of layers. That the prevision of a multilayer capsule able to protect or even have a tuned permeability based on experiments performed on empty capsules. The characterization of the biophysical properties is mainly investigated by the group of Moehwald and Sukhorukov. In their studies it could impressively be shown that the nanometer-sized multilayer on colloids can be influenced by environmental factors like ionic strength, nature of the polyion, number of layers or temperature [86, 93,94]. That the multilayers can be functionalized to serve a specific purpose was shown by Diaspro et al. [95]. They incorporated a pH-measuring unit in the capsule wall and were able to observe local pH changes induced by an uncaging of protons under two-photon excitation. Also calcium carbonate proved to be a good core because of its low toxicity and in some cases its amorphous appearance allows for the controlled delivery and release of attached drugs [96]. Due to the fact that ionic particles can be included in the capsule walls [97] they can have a storage function for the embedded cells. Moreover, experiments with uncoated cells onto polyelectrolyte multilayer surfaces in order to trigger cells adhesion or repel them are very successful [98, 99]. The attention was focused on the characterization of flat layers and the parameters leading to cell adhesion or inhibition of the settlement. But only a few works until now dealing with the tailoring of best-fitting multilayer capsules for living single cells or cell clusters. Future Vision In this review we tried to figure out the general importance of native or genetically engineered cells for medical therapies. The idea is that as long as physicians are not able to repair damaged cells directly in vivo in a kind of nanosurgery the transplantation of donor cells accomplishing the same function could be of help. But even now, the donated organs do not satisfy the need and the prevision for the future is a rise in patients with the urgent need of fitting tissue. Unfortunately, the use of tissue-engineered material as well as organs from animals is problematic for several reasons. The new form of encapsulated and in this way immune-protected cells are a useful and handy tool against most of the severe diseases of our century like cancer, Alzheimer’s and diabetes. Experiments in the past have showed that the microcapsules in use are too simple to fulfil the numerous requirements in a complex system like the body. But in parallel in the last 20 years new nanometer-sized multilayered capsules were developed. This new generation of shells are actually investigated for a diversity of applications, e.g. drug delivery, protection capability of proteins or enzymes with maintenance of their activity, and immobilization of yeast cells without disturbance of the cell functionality. The system seems to be more suitable for the requirements in the body. Due to the fact that



uncountable natural and synthetic polyelectrolytes are known the palette of instruments to tailor shells best fitting for durability or degradation, biocompatibility or cell repletion, incorporated drug release to enhance wound healing or suppress inflammation. Imaginable is also that the outermost layer serves to transport coated cells to a target tissue, then will be peeled off, giving way for the next layer which reduces the fibrosis in that site for some weeks by degrading in subunits interacting with the macrophages or cytokines and so on, so every layer has its specific function. Another advantage is the small thickness of the shell because with that it is possible to reduce the volume of implanted material (Fig. 5).

Fig. (5). Scheme of a multilayer polyelectrolyte capsule as tool to fulfill several functions to allow long-term transplantation of immune protected cells or artificial tissue.

The diversity of materials and the possibility to arrange them in multilayers where every layer can have its own feature rise hope that the shell can be as diverse as the complex body demands for. ACKNOWLEDGEMENT S. Krol is grateful to the EU for the financial support by the EC contracts: BARP+: NMP3-CT-2003-505614 and HPRN-CT- 2000-00159. Further the authors thank Prof. K. Ulrichs for stimulating discussions and the careful correction of the manuscript. REFERENCES [1] [2]

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Contributors


Contributors Gary W. Caldwell

Johnson & Johnson Pharmaceutical Research and Development, LLC, Welsh and McKean Roads, P.O. Box 776, Spring House, PA 19477-0776, USA.

Stanley M. Belkowski


Roberta Brayner

Interfaces, Traitements, Organisation et Dynamique des Systèmes (ITODYS) –UMR-CNRS 7086, Université Paris 7 Denis Diderot, case 7090 ; 2 Place Jussieu 75251 Paris Cedex 05 FRANCE.

Laura Cerchia

Istituto di Endocrinologia ed Oncologia Sperimentale del CNR “G. Salvatore”, via S. Pansini 5, 80131 Naples, ITALY.

Mahesh Choolani.

Diagnostic Biomarker Discovery Laboratory, Department of Obstetrics and Gynaecology, National University Hospital, 5 Lower Kent Ridge Road, 119074, SINGAPORE.

Clare A. Daykin

School of Pharmacy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.

Michael R. D’Andrea


Alberto Diaspro

CNR-INFM, Department of Physics, University of Genoa, via Dodecaneso 33, 16146 Genoa, ITALY.

Vittorio De Franciscis

Istituto di Endocrinologia ed Oncologia Sperimentale del CNR “G. Salvatore”, via S. Pansini 5, 80131 Naples, ITALY.

James M. Dixon


Martijn van Doorn

Centre for Human Drug Research, Zernikedreef 10, 2333 CL Leiden, THE NETHERLANDS.

Iram Mondaca Fernandez

Agricultural and Biosystems Engineering, The University of Arizona, 1177 E. 4th Street, Shantz Building, Room 403, Tucson, Arizona, 85721, USA.


Contributors

Alessandra Gliozzi


Brenda Hertzog


Ewoud J. van Hoogdalem

Johnson & Johnson Pharmaceutical Research and Development, LLC, Turnhoutseweg 30, B-2340 Beerse, BELGIUM.

Dan Horowitz


Sergey I. Ilyin


Williams Jones


Masaru Kato

School of Pharmaceutical Sciences and COE Program in the 21st Century, University of Shizuoka, 52-1 Yada Suruga-ku, Shizuoka, 422-8526, Japan. PRESTO, JAPAN Science and Technology Agency (JST), Saitama, JAPAN.


EMBIO/Laboratory of Plant Physiology, Faculty of Biotechnology, Agricultural University of Athens, Iera Odos 75, 11855 Athens, GREECE.

Silke Krol


Aarohi Kulkarni

Division of Biochemical Sciences, National Chemical Laboratory, Pune, 411008, Maharashtra, INDIA.

Wensheng Lang


K. Y. Lam

Institute of High Performance Computing, National University of Singapore, 1 Science Park Road, #0101 The Capricorn Singapore Science Park II, 117528 SINGAPORE.

Contributors


Danielle Lawrence


Gregory C. Leo


Hua Li


Xing Jian Lou


Pierre Lucas

Materials Science and Engineering, The University of Arizona, 1177 E. 4th Street, Shantz Building, Room 403, Tucson, Arizona, 85721, USA.

Rongmo Luo


Andrew Mahan


Douglas.P. Malinowski

TriPath Oncology, 4025 Stirrup Creek Drive, Suite 400, Durham, North Carolina, 27702, USA.

John A Masucci


Kothandaraman Narasimhan


Debbie Polkovitch


Jeremy J. Ramsden

School of Industrial and Manufacturing Science, Cranfield University, MK43 0AL, UK.


Contributors

Mala Rao

Division of Biochemical Sciences, National Chemical Laboratory, Pune, 411008, Maharashtra, INDIA.

Mark Riley

Agricultural and Biosystems Engineering, The University of Arizona, 1177 E. 4th Street, Shantz Building, Room 403, Tucson, Arizona, 85721, USA.

Kumiko Sakai-Kato

Research Institute of Pharmaceutical Sciences, Musashino University, 1-1-20 Shinmachi, Nishitokyo-shi, Tokyo, 202-8585, JAPAN.

Ponnusamy Sukumar



Siriraj Proteomics Facility, Medical Molecular Biology Unit, Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, 12th Fl. Adulyadej Vikrom Bldg., 2 Prannok Rd., Bangkoknoi, Bangkok 10700, THAILAND.

Meghan Towers


Toshimasa Toyo'oka

School of Pharmaceutical Sciences and COE Program in the 21st Century, University of Shizuoka, 52-1 Yada Suruga-ku, Shizuoka, 422-8526, JAPAN.

Naoko Utsunomiya-Tate

Research Institute of Pharmaceutical Sciences, Musashino University, 1-1-20 Shinmachi, Nishitokyo-shi, Tokyo, 202-8585, JAPAN.

Florian Wülfert

School of Biosciences, University of Nottingham, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK.

Subject Index


SUBJECT INDEX TO VOLUME 2 Absorption isotherm model 308 Acetonitrile 135 Activated partial thromboplastin time (aPTT) 13 Adenocarcinoma 42 Adenosine deaminase 339 in epilepsy treatment 339 Adenosine kinase 339 in epilepsy treatment 339 Age-related macular degeneration 106 treatment of 106 Alginate microencapsulation 334 Alkaline phophatases 25 Alpha-naphthylisothiocyanate (ANIT) 167 Alzheimer’s disease 10 heterogeneity of 10 Aminophenol (PAP) 4-167 Anchorage dependent cells 267 Antibody 58,144 in cancer research 58 types of 58 Antibody arrays 78 profiling receptor tyrosine kinase activation by 78 Antibody-labeled magnetic microparticle 71 Antibody microarray 26 as high-throughput technology 26 Antibody-pair microarrays 59 Antigens 217 Anti-PDGF-RTK activity 11 Anti-ricin assay 110 Antisense oligodeoxynucleotides (ODN) 80 targeting IGF-1R induced apoptosis in 80 Antisense oligonucleotides 104

Aptamer 104, 106, 108,109 affinities of 106 as probes in microarray format 109 based separation methods for protein deterection 109 in enzyme-linked immunosorbent assay (ELISA)-like assays 108 in vivo applications of 106 three-dimensional shapes of 104 Aptamer-based technologies 103 as tools for proteomics 103 Arrhenius model 324 Artificial neural networks (ANNs) 164 for prediction of metabolite concentration 164 Atherosclerosis 10 heterogeneity of 10 Atmospheric pressure chemical ionization (APC) 202 ATP binding site 76 as target for kinase inhibition 76 Atypical squamous cell 36 Automated capillary electrophoresis (CE) 195 Beltzmann superposition model 326 Beta I isoenzymes 106 Bimodal porous monolith 277 Bioconjugates 70 in cancer nanotechnology 70 Bioelectric recognition assays (BERA) 232 Biofluid 159 for multivariate analysis (MVA) 159 Biomarker 16,23,35 associated with HPV induced cervical neoplasia 42 clinical utility of 49


diagnose high-grade cervical disease 35 discovery of 23 for cervical disease testing 49 high-throughput methodologies to 23 pharmacogenomic type 16 protein/antibody microarrays in pharmacogenetic type 23 to detect cervical neoplasia 35 validation of 16 Biomarker discovery 5 for drug development 5 strategies of 5 Biomolecule 71,287 electronic properties of 71 exploitation of 71 sol-gel processing for immobilization of 278 silane precursor of 278 Biopolymer 248,251 plasmon resonance measurements of 248 resonance light scattering (RLS) of 248 surface-enhanced resonance Raman scattering (SERRS) of 248 Biosensors 108,241 for recognition/monitoring of molecule interactions 241 with aptamers as biorecognition element 108 Biotechnology 72 miniaturiazed assays in 72 molecular profiling by 72 Biotin 25 Bisphosphonates 11 anti-angiogenic effect of 11 Bovine serum albumin 250,284 Breast cencer 231 Bromoethylamine (BEA) 2-167 Bronomics 152

Subject Index

Brunauer-Emmett-Teller (BET) 280 by immunohistochemistry 9 Camera-Roda Sarti model 319 Cancer 10,35,104 heterogeneity of 10 Cancer therapy 74 Capillary electrochromatography (CEC) 284 Capillary electrophoresis (CE) 195,284 Carbon tetrachloride CCl4 167 Cation exchange/reversed-phase liquid chromatography 125 CE/MS based 205 Cell adhesion 269 to fiber surface 269 Cell based assay system 226 Cell based biosensors 225 classification of 226 in proteomic analysis 225 Cell physiology function 259 spectroscopic analysis of 259 Cell proliferation 11 Cell surface protein 104 expression/activity of 104 Cellular lysate protein arrays 61 to study kinase activity 61 Cellular microarrays 228 Cellular proliferation 42 Central nervous system (CNS) receptor 15 Cervical carcinoma 42,45 claudin 1/7 in 45 DNA microarray profiling of 45 laminin V in 45 microarray analysis of 42 Cervical neoplasia 36 alterations of cyclin expression in 44 altered expression in 42

Subject Index

analysis of cell cycle regulatory genes in 45 beta-catenin in 46 by oncogenic stains of human papillomavirus (HPV) 38 cell cycle regulatory proteins in 42 cellular proliferation in 43 cyclins in 42 DNA replication in 43 ERK in 45 extracellular matrix proteins in 45 focal adhesion kinase (FAK) pathway in 47 MAP kinase in 45 markers of 50 microarray analysis of 43 molecular biology of 37 origin recognition complex (ORC) in 43 proliferation signatures in 47 signal transduction pathways in 45,46 S-phase genes in 45 WNT signal transduction pathway in 46 Charged residue model (CRM) 131 Chalcogenide fiber 269 Chemiluminesence immunoassay 252 Chemotherapy 69 Chromatography mass spectrometry 193 based metabonomic analytical methods 193 Ciliary neurotrophic factor (CNTF) 338 glial cell line-derived 338 in vivo expression of 338 Ciprofibrate 175 chemometric analysis of 179 PLS-DA models for 179 Collagenase-3 9 Collagens 296 Condensation 275


Congenital hypothyroidism 65 Cosmaceutical 154 Coumarin 13 hepatic toxicity of 13 C-reactive protein (CRP) 9 as disease-related biomarker 9 Cross-linked macromolecules 295 Cryogenic probes 157 CXCL10/CXCR3 receptor 107 Cyclins 42 in HPV-induced neoplasia 42 Cyclin dependent kinases (CDK) 37 over-expression of 37 CDK 4 as 42 CDK 6 as 42 Cyclin dependent kinase inhibitors 42 Cyclooxygenase-2 289 Cytochrome P450 72 and ovarian cancer 72 Cytokines 337 Cytomedicine 339 Darcy’s law 307 Death 11 Delivery system 339 polymer capsule as 339 Dendrimer nanocomposites (DNC) 247 Deoxyribonucleic acids (DNA) 194 Developed matrix-assisted laser desorption/ionization (MALDI) 88 Differentiation 11 Dihydrofolate reductase 286 Dihydroxybenzoic acid (DHB) 2,5-135 Dihyrofolate reductase 289 Disease specific assays 72 Disease-specific expression patterns 7 proteomic/metabolomic approaches for 7


D-mannuronic (M) 337 DNA barcodes 71 DNA hybridization 247 DNA probes 247 DNA replication 42 DNA RNA aptamers 110 bio-recognition element in optical sensors 110 DNA sequences 246 DNA tumor viruses 37 Drug 219,221 from novel delivery materials 219 response of living cells to 221 Drug delivery 241,295 modeling of environmentally sensitive hydrogels for 295 Drug development 5 biomarker in 6 DNA microarrays in 6 high-dersity single nucleotide polymorphism (SNP) in 6 microarrays in 6 quantitative PCR in 6 Drug discovery 55 based technique 71 clinical proteomics market in 57 dimensional MALDI-MS protein array in 60 1-D/2-D gel electrophoresis in 56 impact of proteomics technologies on 56 in other novel strategies to 79 metabolism assays in 55 protein assay in 55 reverse phase protein microarrays in 59 Drug-induced system damage 13 Dynamic light scattering (DLS) 280 E2F-1 transcription factor 44 role of MCM genes 44

Subject Index

E-cadherin 14,47 80-kDa fragment of 14 Electron microscopy (SEM) 280 Electroosomotic flow (EOF) 284 Electrosonic spray ionization (ESSI) 131 Electrospray ionization (ESI) 122,130,196 as atmospheric pressure ionization (API) techniques 130 application of 133 variants in 133 Encapsulated biomolecules 273 combinational chemistry 273 for high-throughput screening 273 using sol-gel reaction 273 Enzyme-immobilization technique 274 Encapsulated microfluidic 287 Encapsulation systems 337 Endocrinopathies 65 Endothelial leukocyte adhesion molecule (ELAM) 231 Enzymatic assays 73 in microsphere environment 73 Epithelial type II pneumocytes (A549 cells) 267 Epidermal growth factor receptor (EGFR) 104 Epithelial ovarian cancer biomarkers 9 by quantitative PCR 9 Escherichia coli 261 Fourier self deconvolution (FSD) 262 Estrogen receptor (ER) 230 Ethanol 135 Expression arrays 146 Expression proteomics 88 in protein-protein interactions 88 in post-translational modifications in 88 Factor IX 13

Subject Index

FGF-2 9 as biomarker 9 Fibrosis 337 Fick’s law 297 Fiber evanescent wave spectroscopy (FEWS) 266 Flow-injection (FI-) NMR accessory 155 Fluorescence energy transfer 71 Fluorescence in situ hybridization (FISH) 229 Fluorescence resonance energy transfer (FRET) 115 Fluorescent biosensing 254 Fluorescent encoding 254 Fluorescent proteins 25 Fluoroimmunoassay 252 Fluorophore-labelled signaling oligonucleotides 115 Fourier transform infrared (FTIR) spectroscopy 261,279 Fourier transform ion cyclotron resonance (FT-ICR) 203 Functional arrays 148 Functional profiling 194 Functionalized magnetic nanoparticles 251 Fundamental mathematical models 297 Gas chromatography (GC) 195 Gene chips 152 Gene/protein-expression profiles 29 using microarray technology 29 Genetic heterogeneity 231 Genome 195 Genome project 81 mining kinome in 81


Genomics 8,126,193 high-throughput technologies for 8 Genomic technologies 6 in genome-wide analysis of disease-unique gene expression profiles 6 Gentamicin 14 hepatic toxicity of 14 Gibbs-Duhem equation 221 Global fingerprinting 194 Glucose oxidase (GOD) 283 Glutathione S-transferase (GST) fusion 122 Glutathione-S-transferase green fluorescent protein (GST-GFP) 61 GP IIb/IIIa receptor antagonists 12 efficacy of 12 Grave disease 334 coated cells artificial tissue in 334 treatment of 334 Guggenheim Anderson Deboer Young models 326 H1NMR spectroscopy 151 data pre-processing of 165 in metabolite identification 166 in metabonomics 154 in principal component analysis (PCA) 160 role of automation 155 spectral editing methods of 156 Haptoglobin-α subunit 15 identification of 15 HDJ-2 farnesylation 12 inhibition of 12 Hematologic disorders 65 HER2/neu receptor 12 High pressure liquid chromatography (HPLC) 195


High-density lipoprotein (HDL) 9 as disease-related biomarker 9 High-grade cervical disease 49 MCM2 in 49 MCM6 in 49 MCM7 in 49 TOP2A in 49 High-grade cervical dysplasia 43 CDC6 expression in 43 High-grade squamous intraepithelial lesion 36 High resolution magic angle spinning (HR-MAS) 158 High throughput 24 protein expression analysis of 24 High throughput assays 5 High throughput screening (HTS) 2276 Higuchi equation 304 HIV-1 replication 247 Hopfenberg model 321 Horse radish peroxidase (HRP) 287 HPV E6/E7/mRNA 40 detection of 40 Human genome project (HPG) 195 Human immunoglobulins 250 Human papillomavirus (HPV) 35 DNA detection in cervical screening of 37,39 induced cellular transformation 42 infections by 40 open reading frame genes of 40 testing of 37 Human serum albumin (HAS) 250 HUT/CCR cells 247 Hydrazine 13,167 hepatic toxicity of 13 Hydrogel 295 Hydrolysis 275 Hypoxia 337

Subject Index

IL-8 9 as biomarker 9 Immobilized biomolecules 280 characteristics of 280 Immobilized pH gradients (IPGs) 124 Immunoprecipitation 31 anti-mKIAA antibodies in 31 Immunotoxins 80 based therapeutics 80 In situ hybridization (ISH) 229 Inflammation 334 Inflammation-associated genes 231 Inflammatory signals 231 Interaction arrays 147 Interleukin-1 (IL-1) 231 Intracellular pathway database 30 based on mKIAA protein-protein interactions 30 Ion evaporation model (IEM) 131 Ion sensitive field effect transistors (ISFETs) 228 Isoelectric focusing (IEF) 123 JAK2 gene 77 JAK2 specific inhibitors 77 Janus kinase (JAK) family 77 JNJ-10198409 11 Keratins 125 from human skin/hair 125 Kidney diseases 87 by congenital defects 89 by hemodynamic dysregulation 89 by infection 89 by inflammation 89 by metabolic derangement 89 by neoplasm 89 by physical injury 89 by toxicity from drugs 89 capillary electrophoresis coupled

Subject Index

to MS (CE-MS) in 90 for 90 gel-based methods to study 89 gel-free methods to study 89 integrative OMICS for 100 mass spectrometric immunoassay (MSIA) in 91 pharmacoproteomics for drug design/discovery in 99 protein chip technology to 90 proteome profiling in 92 proteomic screening for novel therapeutic targets in 87 role of liquid chromatography (LC) coupled to tandem MS (MS/MS) 89 screening for novel therapeutic targets of 92 surface-enhanced laser desorption/ ionization (SELDI) 90 Kinase activity 60 protein peptide arrays for 60 Langmuir theory 308 Laser desorption/ionization (LDI) 122 L-guluronic 337 Light addressable potentiometric sensor (LAPS) Lipid membranes `220 interactions of 220 Lipidomics 67 in cancer research 67 Lipid-protein interactions 62 arrays for detection of 62 Lipoxygenase activating protein (FLAP) 5-10 gene encoding of 10 Liquid chromatography-mass applications of 137 in pharmacokinetics of 137 in proteomics 137 spectrometry (LC-MS) 137


Liver proteins 125 two-dimensional gel-pattern of 125 Liver toxicity 13 Low-density lipoprotein (LDL) 9 as disease-related biomarker 9 Magnetic resonance imaging (MRI) 252 Contrast agents (Cas) in 252 Magnetic resonance spectroscopy (MRS) 158 Magnetospirillum gryphiswaldense 251 Magnetospirillum sp. AMB 251 Magnetospirillum sp. MGT-1 251 MALDI time of flight (TOF) 197 MALDI-ToF-MS 125 Mass mapping 126 Mass spectrometers 127 Mass spectrometry (MS) 88,195 Mass spectroscopy 121 application of 129 developments in 121 in proteomics research 122 ion source in 128 limitations of 142 mass analyzer in 128 mass spectrometer in 127 role of detector 129 tool in promteomics 126 Matabolome 198 Matabolomic analysis 204 Escherichia coli strain DH5-α 204 of islets of langerhans 204 Matabolomics 199 NMR-based 199 Matrix assisted laser desorption ionization (MALDI) 133,196,242 Matrix-assisted laser desorption mass spectrometry (MALDI-MS) 56


Matrix-assisted laser desorption/ ionization (MALDI) 129 Maxwell model 314 MCM family 42 Messenger ribonucleic acids (mRNA) 194 Metabolic disorders 65 Metabolic profiling 153,266 Methylmethane sulfonate (MMS) 267 Metabolomic 8,64,65, 68,151,193, 200,205 application to nutritional studies 168 applications of 151 biological challenges in 167 for cancer research 65 in biofluid studies 155 in disease processes 68 in normality studies 168 in toxicological studies 167 in understanding global system biology 68 metastatic bone disease 11 NMR spectroscopy based 151 NMR spectroscopy in 154 nomenclature of 153 of small molecules aiding biomarker discovery 64 role in tumor metabolome 65 role of automation 155 technology of 151 to study disease in man 168 tool for biomarker discovery 64 Methylene blue (MB) 115,250 Methyltrimethoxysilane (MTMS) 284 Microarray chips 287 Microarray technology 72 molecular profiling by 72 Microencapsulation 336 Microphysiometry 227 Microtiter plates 287

Subject Index

Minichromosome maintenance (MCM) 43 mKIAA proteins 30 MMP-13 9 transgenic over-expression of 9 Molecular aptamer beacons 114 protein detection by 114 Monoclonal antibody 79 as inhibitors for PTKS 79 Monolith 276 structure of 276 mRNA array technologies 126 Multidiemensional liquid chromatography mass spectrometry (MD LCMS) 56 Multidimensional 56 liquid chromatography 56 Multiplexed elisas 59 Mutualistic associations of knowledge by computational comparative technology 31 Nano technology 68 in biomedical science 68 Nanobiotechnology research 68,71 Nanoparticle-aptamer bioconjugates 69 Nanoscience 68 in biomedical science 68 Nanotechnology based assays 70 application of 70 for detecting protein DNA 70 Natural biopolymer nanoparticle hybride approaches in 241 bio-detection applications of 241 functional materials 241 N-cyclopentyladenosine analogs 13 in vivo effects of 13 Nernst Planck equations 324

Subject Index

Neural networks 164 for prediction of metabolite concentration 164 Nitro oxide (NO) 337 N-methylnicotinate 188 NMR-based metabonomics 175 of urine 175 in healthy volunteers patients with type 2 diabetes mellitus 175 Nocogene proteins 37 Non-receptor tyrosine kinase 75 classification of 75 Nosine mono phosphate dehydrogenase 110 Noyes Whitney model 325 Nuclear magnetic resonance (NMR) 195,279 Nucleic acid-based compounds 104 Nutriceutical 154 2’-O-alkyl nucleotides 107 Omics technologies 153 Optical waveguide lightmode applications to drug discovery 211,216 lightmode spectrum in 214 molecular interactions 211 technology of 212 Orthogonal signal correction (OSC) 166 application of 166 Osteoarthritis (OA) 9 type II collagen peptide as biomarker for 9 p16INK4A(p16) 42 Pappas model 309 Parkinson’s disease 338 monkey models of 338 Partial least squares discriminant analysis (PLS-DA) 179


Partial least squares reguares regression (PLS) 162 for data analysis 162 Partial squares-discriminant analysis (PLS-DA) 179 PDGF B-chain (PDGF-BB) 107 Peptides 143 mass values of amino acid residues in 143 Peptidoglycan 260 proportion of 260 Peptidomimetics 80 Peripheral blood mononuclear cells (PBMC) 17 Peroxidases 25 Pharmacokinetics mode 12 in drug discovery 12 Phenylketonuria (PKU) 65 as metabolic disorder 65 Phorbol myristate acetate (PMA) 231 Photo SELEX 112 as capture agents 112 Photoaptamer technology 113 application of 113 to proteomics 113 Photoaptamer-based chips 112 Photoaptamer-protein photocross-linking reactions 113 Photo-crosslink 112 Photopolymerized sol-gel (PSG) 277,287 Pichia pastor 145 PLA polymer system 70 Platelet derived growth factor (PDGF) 107 Platelet-derived growth factor receptor tyrosine kinase (PDGF-RTK) 11 JNJ-10198409 as 11 Platinum palladium metallic


nanoparticles 245 PLS-based uninformative variable elimination (PLS-UVE) 166 Polu-l-lysine (PLL) 340 Poly amidoamine (PAMAM) 247 Poly ethylene oxide 277 Polyacrylic acid (PAA) 337 Polyanion 340 Polydimethylsiloxane (PDMS) 287 Polyelectrolyte multiplayer-based drug delivery 342 Polyelectrolyte nanocapsules 333 in development of new drug therapies 333 Polyethlenimine (PEI) 337 Polyethylene glycol (PED) 277 Polymer-based drug delivery Polymers chain reaction (PCR) 196 Porous monolith 276 Postprandial hyperglycemia 247 Posttranslation modifications 225 Post-translational modifications 196 Power law 306 PPARα agonists 176 Primary cervical carcinoma lesions 42 Principal component analysis (PCA) 179 Principal component discriminant analysis (PCDA) 161 Progesterone receptor (PR) 230 Protein 8,104,266 in cell growth 104 in post-translational modifications 8 methods for 266 Protein analysis 7 Protein arrays 57,62,121,144, developments in 121 Protein arrays based 62 on SELDI-TOF-MS 62

Subject Index

Protein detection 113 by aptamer-based proximity ligation assays 113 Protein expression 23 immunohistochemical analysis of 23 role of western blot 23 Protein ions 126 by gas-phase activation of 126 Protein kinase assays 78 microtiter plate array system for 78 Protein kinase drug design 76 crystallography for 76 Protein microarray 25 in interactions with peptides 25 role in oligosacchaloides 25 role in or DNA 25 role in small molecules 25 Protein misfolding 104 in Alzheimer’s disease (AD) 104 in Huntington’s disease 104 in Parkinson’s disease (PD) 104 in Prion disease 104 Protein-protein interactions 56 proteomics technologies for 56 two-hybrid system for 56 Protein tyrosine kinase 74,76 Protein/antibody microarray platforms 25 classification of 25 Protein-derived peptides 90 role of strong cation-exchange (SCX) chromatography 90 Proteolysis 42 Proteome 196 Proteomic technologies 89 for analysis of kidney/urine 89 Proteomics 56,57,88,103,121,193 associated interventions in clinical screening 56 developments in 121

Subject Index

in diagnosis 103 in therapy 103 solution-based 125 to study pathomechanisms of human disease 57 Proximity ligation 114 mechanism of 114 PSMA-positive prostate cancer cells 70 Purvalanol A 77 Quantum. 254 Quartz-crystal microbalance (QCM) 111 R115777 12 anticancer activity of 12 Raman spectroscopy 262 Ras binding domain (RBD) 107 of Raf-1 107 Reading frames (ORFs) 122 Receptor tyrosine kinases 75 classification of 75 Reverse-phase protein microarrays 24,59 Reverse transcription polymerase chain reaction (RT-PCR) 233 Rheumatoid arthritis 17 RhoB 12 up-regulation of 12 RNA aptamers 107 with 2’-fluoroaminopyrimidine modifications 107 Saccharamyces cerivisae 61,145 Sandwich assay 110 Sarface plasmon resonance (SPR) 145 combination of 145 Scanning angle reflectometry (SAR) 215 Scanning electron microscopy (SEM) 244 Scott’s seconds order diffusion


model 323 Serial analysis of gene expression (SAGE) 196 Sickle cell disease 65 Single nucleotide polymorphisms (SNPs) 57 functional effects of 57 Skeletal metastases 11 Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE 197 Soft laser desorption (SLD) 129 Sol-gel glasses 289 chemical applications of 289 medical application of 290 whole cell in 289 Sol-gel reaction 275 application of 282 in study for protein characteristics 282 Spectroscopic methods 260 applications of 260 foundations of 261 S-phase genes 45 Spinodal decomposition 277 schematics of 277 Squamous cell carcinoma 42 Stable isotope labeling 226 Structural profiling 194 Supercritical fluid chromatography (SFC) 201 in extraction procedures 201 Superoxide dismutase (SOD) 232 electroinsertion of 232 Surface plasmon resonance (SPR) 24,111 based antibody microarray system 24,27 Surface plasmon resonance imaging 62 System biology 193


Systematic evolution of ligands by expontial entrichment (SELEX) technology 104 as in vitro combinatorial chemistry process 105 used to identify aptamers 105 Systems biology 152 Systeomics 152 Tanaka-Fillmore model in 320 Tandem mass spectrometry (MS-MS) 64,138 applications of 139 peptide sequencing by 139 newborn screening by 64 typical peptide mass mapping using 138 Target profiling 194 Target-related biomarkers 6 to elucidate mechanism of action (MOA) 6 used to screen lead compounds 6 Target-related biomarkers 9 Thrombin 115 Tie-of flight (TOF) analyzer 128 Time-of-flight mass spectrometer 136 Tissue microarrays 229 applications of 229 in cancer research 230 Tissue NMR spectroscopic data 159 for multivariate analysis (MVA) 159 Topoisomerase II alpha (TOP2A) 42 Toxicoproteomics 56 Trans-activator of transcription (Tat) 111 of human immunodeficiency virus type 1 (HIV-1) 111 Transcriptional profiling 26 Transcriptomics 126 Transfected cell microarrays 231

Subject Index

Transmission electron microscopy 280 Transplantation 334 semipermeable microcapsules for 334 Trifluroacetate (TFA) 135 Triglyceride (TG) 176 Trimethylamine-n-oxide (TMAO) 187 Trypsin 287 on microfluidic chip 287 Tumor necrosis factor-α (TNF-α) 231 Two dimensional polyacrylamide gel electrophoresis (2-PAGE) 123,226 in separation/detection of proteins 123 is membrane proteins 124 Type 2 diabetes mellitus 178 demographics for 178 Tyrosine kinases 75 classification of 75 Urinary proteome profiling 91 for biomarker discovery 91 in clinical diagnostics 91 Urine 178 role of NMR 175 Uterine-cervix 35 VEGF 12 inhibition of 12 Vide supra 8 Viral genotyping 39 using liquid-based cervical cytology specimens 39 Watson-Crick base pairing 80 Xenobiotic 154 Xerogel 276 X-ray crystallography 217

Subject Index

Zero-order release 310 Zoledronic acid 11 in management of


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