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ADVANCES IN CLINICAL CHEMISTRY VOLUME 43

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Advances in CLINICAL CHEMISTRY Edited by GREGORY S. MAKOWSKI Department of Pathology and Laboratory Medicine Hartford Hospital Hartford, Connecticut

VOLUME 43

AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, California 92101-4495, USA 84 Theobald’s Road, London WC1X 8RR, UK

This book is printed on acid-free paper. Copyright ß 2007, Elsevier Inc. All Rights Reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the Publisher. The appearance of the code at the bottom of the first page of a chapter in this book indicates the Publisher’s consent that copies of the chapter may be made for personal or internal use of specific clients. This consent is given on the condition, however, that the copier pay the stated per copy fee through the Copyright Clearance Center, Inc. (www.copyright.com), for copying beyond that permitted by Sections 107 or 108 of the U.S. Copyright Law. This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale. Copy fees for pre-2007 chapters are as shown on the title pages. If no fee code appears on the title page, the copy fee is the same as for current chapters. 0065-2423/2007 $35.00 Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: (þ44) 1865 843830, fax: (þ44) 1865 853333, E-mail: [email protected]. You may also complete your request on-line via the Elsevier homepage (http://elsevier.com), by selecting ‘‘Support & Contact’’ then ‘‘Copyright and Permission’’ and then ‘‘Obtaining Permissions.’’ For information on all Academic Press publications visit our Web site at www.books.elsevier.com ISBN-13: 978-0-12-373703-8 ISBN-10: 0-12-373703-6 PRINTED IN THE UNITED STATES OF AMERICA 07 08 09 10 9 8 7 6 5 4 3 2 1

CONTENTS CONTRIBUTORS

................................................................................

ix

PREFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xi

The Heme Catabolic Pathway and Its Protective Effects on Oxidative Stress-Mediated Diseases LIBOR VI´TEK AND HARVEY A. SCHWERTNER 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Abstract ... ................................................................................... Introduction ................................................................................. Heme Catabolism ........................................................................... Heme Oxygenase ............................................................................ Hemodynamic and Cytoprotective Effects of Carbon Monoxide ..................... Cytoprotective Effects of Biliverdin Reductase.. ........................................ Biological Effects of Bilirubin ............................................................. Factors Affecting Serum Bilirubin Concentrations ..................................... Therapeutic Agents Affecting HO-1 Activity, CO, and Bilirubin Production ....... Conclusions .................................................................................. References. ...................................................................................

2 2 4 5 13 16 17 34 35 37 38

Cyclooxygenase-2 and Tumor Biology SHIGERU KANAOKA, TETSUNARI TAKAI, AND KEN-ICHI YOSHIDA 1. 2. 3. 4. 5. 6.

Abstract ... ................................................................................... Introduction ................................................................................. Proposed Roles for COX-2 in Carcinogenesis ... ........................................ COX-2 Expression and Clinicopathological Factors ................................... Fecal COX-2 Assay ......................................................................... Conclusions .................................................................................. References. ...................................................................................

v

59 60 60 65 68 70 71

vi

CONTENTS

Oligonucleotide Probes for RNA-Targeted Fluorescence In Situ Hybridization ADAM P. SILVERMAN AND ERIC T. KOOL 1. 2. 3. 4. 5. 6.

Abstract....................................................................................... Introduction.................................................................................. Principles of Fluorescence In Situ Hybridization ........................................ Types of Probes.............................................................................. Applications.................................................................................. Conclusions .................................................................................. References ....................................................................................

79 80 81 90 98 102 103

Activin A in Brain Injury PASQUALE FLORIO, DIEGO GAZZOLO, STEFANO LUISI, AND FELICE PETRAGLIA 1. 2. 3. 4. 5. 6. 7.

Abstract....................................................................................... Introduction.................................................................................. Biochemistry ................................................................................. Activin A After Brain Injury in Animals ................................................. Activin A and Neuroprotection: Findings from Animal Studies....................... Human Studies............................................................................... Conclusions .................................................................................. References ....................................................................................

118 118 119 119 123 125 127 127

Methods for Predicting Human Drug Metabolism LARRY J. JOLIVETTE AND SEAN EKINS 1. 2. 3. 4. 5. 6. 7. 8. 9.

Abstract....................................................................................... Introduction.................................................................................. In Vitro Techniques ......................................................................... High-Throughput Assays ................................................................... In Vivo Predictions from In Vitro.......................................................... Computational Metabolism Methods ..................................................... Integration of Drug Metabolism Data and Interpretation ............................. Newer Technologies ......................................................................... Conclusions .................................................................................. References ....................................................................................

131 132 134 137 138 139 152 153 156 158

CONTENTS

vii

A Summary Analysis of Down Syndrome Markers in the Late First Trimester GLENN E. PALOMAKI, GERALYN M. LAMBERT-MESSERLIAN, AND JACOB A. CANICK 1. 2. 3. 4. 5.

Abstract ... ................................................................................... Introduction ................................................................................. Patients/Methods ........................................................................... Results........................................................................................ Discussion. ................................................................................... References. ...................................................................................

177 178 179 183 198 207

Estrogen Hydroxylation in Osteoporosis NICOLA NAPOLI AND REINA ARMAMENTO-VILLAREAL 1. 2. 3. 4. 5. 6.

Abstract ... ................................................................................... Introduction ................................................................................. Pathways and Products of Estrogen Metabolism ....................................... Factors Influencing Estrogen Hydroxylation ............................................ Role of Estrogen Hydroxylation in Bone Density and Osteoporosis ................. Summary .. ................................................................................... References. ...................................................................................

211 212 212 215 217 222 222

Cytochrome P450: Another Player in the Myocardial Infarction Game? RAUTE SUNDER-PLASSMANN 1. 2. 3. 4. 5. 6. 7. 8.

Abstract ... ................................................................................... Introduction ................................................................................. Cytochrome P450 Enzymes ................................................................ Regulation of CYP Enzyme Activity ..................................................... CYP Gene Variants Interacting with Vascular Homeostasis .......................... CYP Enzymes and Environmental Risk Factors for Cardiovascular Disease ....... CYP Gene–Drug Interactions in Cardiovascular Disease.............................. Summary .. ................................................................................... References. ...................................................................................

230 231 232 234 245 247 249 252 254

INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

281


CONTRIBUTORS Numbers in parentheses indicate the pages on which the authors’ contributions begin.

REINA ARMAMENTO-VILLAREAL (211), Division of Bone and Mineral Diseases, Washington University School of Medicine, St. Louis, Missouri JACOB A. CANICK (177), Department of Pathology and Laboratory Medicine, Women and Infants Hospital, Brown University, Providence, Rhode Island SEAN EKINS (131), ACT LLC, Jenkintown, Pennsylvania; Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland PASQUALE FLORIO (117), Department of Pediatrics, Obstetrics and Reproductive Medicine, University of Siena, Siena, Italy DIEGO GAZZOLO (117), Department of Pediatrics, G. Gaslini Children’s University Hospital, Genoa, Italy; Department of Maternal, Fetal and Neonatal Health G. Garibaldi Hospital, Catania, Italy LARRY J. JOLIVETTE (131), Preclinical Drug Discovery, Cardiovascular & Urogenital Centre of Excellence in Drug Discovery, GlaxoSmithKline, King of Prussia, Pennsylvania SHIGERU KANAOKA (59), First Department of Medicine, Hamamatsu University School of Medicine, 1-20-1 Handayama, Hamamatsu 431-3192, Japan ERIC T. KOOL (79), Department of Chemistry, Stanford University, Stanford, California GERALYN M. LAMBERT-MESSERLIAN (177), Department of Pathology and Laboratory Medicine, Women and Infants Hospital, Brown University, Providence, Rhode Island STEFANO LUISI (117), Department of Pediatrics, Obstetrics and Reproductive Medicine, University of Siena, Siena, Italy

ix

x

CONTRIBUTORS

NICOLA NAPOLI (211), Division of Bone and Mineral Diseases, Washington University School of Medicine, St. Louis, Missouri GLENN E. PALOMAKI (177), Department of Pathology and Laboratory Medicine, Women and Infants Hospital, Brown University, Providence, Rhode Island FELICE PETRAGLIA (117), Department of Pediatrics, Obstetrics and Reproductive Medicine, University of Siena, Siena, Italy HARVEY A. SCHWERTNER (1), Clinical Research, Wilford Hall Medical Center, Lackland AFB, Texas ADAM P. SILVERMAN (79), Department of Chemistry, Stanford University, Stanford, California RAUTE SUNDER-PLASSMANN (229), Institute for Medical and Chemical Laboratory Diagnostics, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria TETSUNARI TAKAI (59), First Department of Medicine, Hamamatsu University School of Medicine, 1-20-1 Handayama, Hamamatsu 431-3192, Japan LIBOR VI´TEK (1), Fourth Department of Internal Medicine and Institute of Clinical Biochemistry and Laboratory Diagnostics, Charles University of Prague, U Nemocnice 2, Praha 2, 128 08 Prague, Czech Republic KEN-ICHI YOSHIDA (59), First Department of Medicine, Hamamatsu University School of Medicine, 1-20-1 Handayama, Hamamatsu 431-3192, Japan

PREFACE Volume forty-three of the Advances in Clinical Chemistry series contains review articles of wide interest to clinical laboratory scientists and diagnostic adventurers. In this volume, the biochemistry of bilirubin, the end product of heme metabolism, is explored with respect to its potential beneficial role in preventing oxidative changes associated with a variety of pathological conditions including atherosclerosis, cancer, inflammatory, autoimmune, and other degenerative diseases. The role of cyclooxygenase-2 (COX-2) in causation and prevention of cancer is critically investigated as a prognostic biomarker in tumor evaluation. Two chapters within this volume deal with technological advances relevant to clinical laboratory diagnostics. In the first chapter, the use of eVective oligonucleotide probe design for fluorescent in situ hybridization (FISH) technology is examined. The second technological chapter deals with advances in computational methods for predicting human drug metabolism. The role of drug metabolism is further explored in additional reviews that investigate the pleiotropic importance of the cytochrome P450 family of enzymes in physiological and pathological processes including myocardial infarction and estrogen hydroxylation in osteoporosis. The role of activin A in acute brain injury is also highlighted as to its potential application as a biochemical index to indicate presence, location, and extent of injury. Finally, a stimulating review evaluates the current state of Down syndrome testing with specific emphasis on first trimester screening. I extend my appreciation to each contributor of volume forty-three and thank colleagues throughout the world who found time to participate in the peer review process. I extend my sincere thanks to my editorial liaison at Elsevier, Ms. Pat Gonzalez, for her continued support and dedication to the series. I hope the first volume of 2007 will be enjoyed and used. The comments and thoughts of the readership are always appreciated and necessary to maintain the high quality of manuscripts for the Advances in Clinical Chemistry series. In keeping with the tradition of the series, I would like to dedicate volume forty-three to the memory of H. Ross Picard. GREGORY S. MAKOWSKI

xi

ADVANCES IN CLINICAL CHEMISTRY, VOL.

43

THE HEME CATABOLIC PATHWAY AND ITS PROTECTIVE EFFECTS ON OXIDATIVE STRESS‐MEDIATED DISEASES Libor Vı´tek* and Harvey A. Schwertner{ *Fourth Department of Internal Medicine and Institute of Clinical Biochemistry and Laboratory Diagnostics, Charles University of Prague, U Nemocnice 2, Praha 2, 128 08, Prague, Czech Republic { Clinical Research, Wilford Hall Medical Center, Lackland AFB, Texas

. 1. 2. 3. 4.

5. 6. 7.

8. 9. 10.

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heme Catabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heme Oxygenase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Heme Oxygenase Induction, Upregulation, and Gene Transfer . . . . . . . . . . 4.2. HO‐1 Gene Promoter Polymorphism in the Pathogenesis of Oxidative Stress‐Mediated Conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hemodynamic and Cytoprotective EVects of Carbon Monoxide . . . . . . . . . . . . . Cytoprotective EVects of Biliverdin Reductase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biological EVects of Bilirubin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Experimental Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. Human Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Factors AVecting Serum Bilirubin Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . Therapeutic Agents AVecting HO‐1 Activity, CO, and Bilirubin Production . . . Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

38

Abbreviations cGMP CHD CO CORM

cyclic guanylate monophosphate coronary heart disease carbon monoxide carbon monoxide‐releasing molecule 1

0065-2423/07 $35.00 DOI: 10.1016/S0065-2423(06)43001-8

Copyright 2007, Elsevier Inc. All rights reserved.

2 CVD GS HDL HO hs‐CRP HSP IL I/R LDL MAPK OR ROS TNF VCAM VSMC

VI´TEK AND SCHWERTNER

cardiovascular disease Gilbert syndrome high‐density lipoprotein heme oxygenase highly sensitive C‐reactive protein heat shock protein interleukin ischemia/reperfusion low‐density lipoprotein mitogen‐activated protein kinase odds ratio reactive oxygen species tumor necrosis factor vascular cell adhesion molecule vascular smooth muscle cells

1. Abstract Bilirubin, the principal bile pigment, is the end product of heme catabolism. For many years, bilirubin was thought to have no physiological function other than that of a waste product of heme catabolism—useless at best and toxic at worst. Although hyperbilirubinemia in neonates has been shown to be neurotoxic, studies performed during the past decade have found that bilirubin has a number of new and interesting biochemical and biological properties. In addition, there is now a strong body of evidence suggesting that bilirubin may have a beneficial role in preventing oxidative changes in a number of diseases including atherosclerosis and cancer, as well as a number of inflammatory, autoimmune, and degenerative diseases. The results also suggest that activation of the heme oxygenase and heme catabolic pathway may have beneficiary eVects on disease prevention either through the action of bilirubin or in conjunction with bilirubin. If so, it may be possible to therapeutically induce heme oxygenase, increase bilirubin concentrations, and lower the risk of oxidative stress‐related diseases.

2. Introduction Bilirubin is the major end product of heme catabolism with heme oxygenase (HO) as the rate‐limiting enzyme in bilirubin production. For many years, bilirubin was thought to have no physiological function other than that of a metabolic waste product of heme catabolism. High concentration of

THE HEME CATABOLIC PATHWAY AND ITS PROTECTIVE EFFECTS

3

serum bilirubin, however, have been shown to cause potentially irreversible damage to the central nervous system, whenever the intracellular concentrations of ‘‘unbound’’ or ‘‘free’’ unconjugated bilirubin (UCB) concentrations exceed its aqueous solubility (about 70 nM). As a result, only serum bilirubin concentrations greater than 17.1 mmol/L were of concern to medical practitioners. During the past two decades, many studies have shown that bilirubin is a powerful antioxidant and anti‐inflammatory agent. As a result, it could have a protective role in diseases associated with oxidative stress. Indeed, serum bilirubin concentrations in the upper reference interval have been shown to be associated with a reduced risk of cardiovascular disease (CVD), whereas those in the lower quartile have been shown to be associated with an increased risk of CVD [1]. This was the first time that low serum bilirubin concentrations were found to be diagnostically useful. Prior to this time, bilirubin concentrations in the normal range were thought to signify health and only elevated serum bilirubin concentrations were of diagnostic significance in the diagnosis of jaundice. Low serum bilirubin concentrations have also been found in certain forms of cancer as well as in other chronic diseases associated with oxidative stress. The accumulating body of evidence suggests that serum bilirubin is an independent risk factor for many oxidative stress‐related diseases. The mechanisms by which bilirubin protects against disease and disease mortality are not known; however, many in vitro and in vivo studies have shown that bilirubin has both potent antioxidant [2–4] and anti‐inflammatory properties [5, 6]. HO, which is the rate‐limiting enzyme in heme catabolism, also appears to protect against coronary heart disease (CHD). HO degrades the pro‐oxidative heme molecules and produces biliverdin and CO. Biliverdin is then converted by another cytoprotective enzyme, biliverdin reductase (BVR), to bilirubin. In this regard, bilirubin and HO‐1 have the potential as novel diagnostic markers as well as novel therapeutic targets for treating CVD and other chronic degenerative diseases involving the overproduction of reactive oxygen species (ROS) or inflammation. These fascinating discoveries were the subject of several recent outstanding reviews that focused specifically on the biological role of heme catabolic pathway, and on HO, CO, and bilirubin [7–9]. The aim of the present chapter is to provide a more global view of the rapidly evolving field of heme metabolism with the focus on the interrelationships between bilirubin, HO, and CO in the pathogenesis of oxidative stress‐mediated diseases. In this chapter, we try to summarize both experimental and clinical data obtained during past few years on the salutary eVects of HO‐1 induction, HO‐1 gene promoter polymorphisms, and bioactive role of BVR as well as CO for clinical medicine. We also examine current experimental and clinical studies that suggest that bilirubin may have a protective function in preventing atherosclerosis, CHD, certain forms of cancer, as well as other chronic


4

oxidative stress‐related diseases. We specifically discuss recent epidemiological studies that show an association between serum bilirubin, CVD risk, and mortality. Finally, we discuss the factors known to induce heme metabolism and their possible therapeutic use in inducing HO‐1, increasing CO and/or bilirubin, and in the prevention of disease.

3. Heme Catabolism Heme, the major substrate for the antioxidant compounds biliverdin and bilirubin, is a cyclic tetrapyrrolic compound with a centrally bound iron atom. Free heme, produced mainly from hemoglobin originating from senescent red blood cells, can be toxic because of its pro‐oxidant eVects [10]. As a result, it is desirable to eliminate heme from the body as fast as possible. Heme is first degraded to biliverdin IX by the action of the microsomal

a H C

V M

D

A N

d HC

V CH b

Fe

H2 O

N

C P

O2

N

N M

M

B C H g

NADPH

Fp M.E.T

NADP Fp

Microsomal heme oxygenase

M

P

CO (Excreted via lungs) Fe (Reutilized)

Heme V

V

M OO NH HN CH

HC

Biliverdin reductase

V N

M P

C H2

CH N HN

NADP

NADPH

M

M P

P

Bilirubin IXa

N

HC

NH HN M

M

M

V

M

OO

M = −CH3 V = −CH =CH2 P = −CH2 −CH2 −COOH Fp = Flavoprotein FIG. 1. Heme catabolism.

C H

P

Biliverdin IXa


5

enzymes, HO, and NADPH‐cytochrome P450 reductase (Fig. 1) [11]. These enzymes catalyze the oxidative cleavage of the ‐methenyl bridge between pyrroles I and II of the porphyrin ring releasing CO and an atom of iron [11, 12]. Subsequently, BVR reduces the central methenyl bridge of biliverdin IX to produce bilirubin IX (Fig. 1).

4. Heme Oxygenase 4.1. HEME OXYGENASE INDUCTION, UPREGULATION,

AND

GENE TRANSFER

Two HO isoforms (HO‐1 and HO‐2) have been identified and found to be products of diVerent genes. HO‐1 is a member of the heat‐shock protein family (HSP 32). It is induced by a variety of stimuli provoking oxidative stress [10, 13], including free oxygen radicals, heavy metals, bacterial lipopolysaccharides, hydrogen peroxide, and ultraviolet light [10]. HO‐2 is a noninducible isoform [6] constitutively expressed in various tissues and organs. One of the roles of heme degradation is the preservation of iron in the body [14]. Second, and probably more importantly, it has been postulated that bile pigments produced by action of HO may have a beneficial role as circulating endogenous antioxidants [7, 8]. In addition, CO has been shown to function as a vasodilator in the splanchnic circulation [15] and as a second messenger and neurotransmitter in the brain [16]. A number of studies have shown that HO plays an important role in attenuating the production of ROS through its ability to produce bilirubin, degrade heme, and release free iron (Table 1). HO‐1 induction with metalloporphyrins [17] and HO‐1 gene transfer [17, 18] have been shown to protect against ischemic liver injury in animal models. HO‐1 was also reported to be induced in heart tissue by hemodynamic stress such as from right‐sided heart failure [19], heart [20], renal [21], and intestinal [22] ischemia and reperfusion (I/R), and hypertension [23, 24]. This is consistent with the significant elevation of UCB described in patients with these acute conditions [25] as well as hemorrhagic stroke [26] and neurotrauma [27]. Although the precise mechanisms involved have not been identified, this observation might be due to HO‐1 upregulation accompanying acute myocardial ischemia. Furthermore, an abundance of HO‐1 mRNA and protein was identified in human atherosclerotic plaques [28] as well as in vascular endothelial and smooth muscle cells exposed to oxidized low‐density lipoprotein (LDL) [29] indicating the in vivo relevance of this enzyme in atherosclerosis. HO‐1 overexpression in the endothelial cells of coronary microvessel exerts a protective eVect against toxicity of heme [30] accumulating in atherosclerotic lesions [31]. Similarly, HO‐1 overexpression in mouse cardiac allografts was shown to


6

TABLE 1 BIOLOGICAL EFFECTS HO‐1 status

OF

HO‐1

EVect

References

HO‐1 induced/ overexpressed in/by

Hemodynamic stress

Metalloporphyrins

Human and murine atherosclerotic plaque’s VSMC and macrophages Human aortic VSMC and endothelial cells pretreated with oxidized LDL Atherosclerotic lesions of rabbits fed high‐cholesterol diet Injured blood vessels in a porcine model, reduces vasoconstriction, and inhibits cell proliferation Trinitrobenzene sulfonic acid‐induced‐ colitis in rats Gastric ulcers in rats Oral squamous cell cancer, HO‐1 expression inversely associated with grade and stage Tongue squamous cell cancer, HO‐1 expression inversely associated with grade and stage Various cancers Orthotopic lung transplantation in rats In right‐sided heart failure in dogs In heart I/R in pigs In renal I/R in rats, HO‐1 induction detected in heart, aorta, kidneys In arterial hypertension‐induced by angiotensin II in rat aortas Protects from I/R liver injury in Zucker rats Protects mice from cardiac allograft rejection Ameliorates experimental murine colitis Protects rat liver allografts preconditioned with HO‐1 inducer from rejection Protects from development of atherosclerotic lesion formation in LDL‐receptor knockout mice Results in increased plasma and tissue lipid peroxide levels in Watanabe heritable hyperlipidemic rabbits Improves function of transplanted pancreatic islets in mice Protects mice from apoptotic liver damage

[28] [29] [44] [45]

[49] [51] [87] [88]

[90] [112] [19] [20] [21] [23, 24] [17] [32] [50] [41]

[37]

[42]

[114] [119]


7

TABLE 1 (Continued) HO‐1 status Hemin

Heme arginate

Cytokines IL‐10 IL‐13

Th2 cytokines Cobra venom factor and cyclosporine A HO‐1 gene transfer

EVect

References

Protects from intestinal I/R injury in rats Protects bovine VSMC from oxyradical damage Protects rat hearts exposed to ischemia Protects from H2O2‐induced I/R tissue injury in rats with suppression of venular leukocyte adhesion Attenuates LPS‐induced expression of endothelial cell adhesion molecules in rats Attenuates vascular endothelial activation and dysfunction in vascular endothelial cells

[22]

Mediates protection from LPS‐ induced septic shock Protects from I/R injury following rat orthotopic liver transplantation Protects mice from cardiac allograft rejection Protects rats from mouse cardiac allograft rejection Protects from I/R liver injury in Zucker rats Prolongs survival of orthotopic liver transplants in Zucker rats Prevents rabbit coronary microvessels from heme/hemoglobin toxicity Prolongs mouse‐to‐rat cardiac allograft survival and inhibits graft arteriosclerosis and interstitial fibrosis Into rat heart grafts prolongs allograft survival Prolongs survival of transplanted cardiac allografts Into rat aortic vessel cells attenuates chronic rejection of allografts and prevents aortic atherosclerosis

[52]

[175] [176] [184]

[197]

[187]

[40]

[32] [33, 34] [17] [18] [30] [34]

[35] [37] [38]

(continues)


8

TABLE 1 (Continued) HO‐1 status

Transplantation of cardiac allografts from transgenic mice with overexpressed HO‐1 Due to HO‐1 gene polymorphism associated with

HO‐1 inhibition/ absence/low activity Inhibition by metalloporphyrins

EVect

References

Inhibits the development of atherosclerosis in apolipoprotein E‐deficient mice Into rat hepatic stellate cells attenuates liver fibrosis Protects from hyperoxia‐induced lung injury in rats Into rat lung microvessel endothelial cells from oxidative stress toxicity Improves liver allograft survival and function in rats Improves pancreatic islets viability in cell culture Into normal rat hearts results in cardioprotection from I/R injury Prolongs survival of transplanted allografts

[43]

Resistance of human lymphoblastoid cell lines to hydrogen peroxide‐ induced apoptosis Longevity of Japanese men but not women Reduced rate of restenosis after balloon angioplasty Reduced risk of cerebrovascular events Reduced risk of coronary events in patients with advanced peripheral artery disease Reduced risk of CHD with multiple risk factors for atherosclerosis Renal allograft survival and function Increased incidence of arterial hypertension in women Decreased incidence of CHD

[57]

Results in VSMC proliferation and vascular hyperplasia in injured femoral arteries

[45]

[46] [47] [48] [106] [202] [332] [37]

[59] [62, 63] [64] [66]

[67]

[72, 73] [75] [76]


9

TABLE 1 (Continued) HO‐1 status In transgenic HO‐1/ mice

EVect Results in VSMC proliferation and vascular hyperplasia in injured femoral arteries Leads to lethal ischemic lung injury Increased susceptibility to CHD in type 2 diabetic patients Increased susceptibility to chronic pulmonary emphysema Increased susceptibility to abdominal aortic aneurysm Increased risk of restenoses after coronary stenting Higher levels of proinflammatory CRP and IL‐6 Low incidence of cerebral malaria Increased risk of lung cancer Increased risk of oral squamous cell cancer

References [45]

[125] [56] [58] [61] [68, 69] [70, 71] [77] [86] [89]

prevent transplant atherosclerosis [32, 33]. It was also found that HO‐1 gene expression [29] or transfection [34, 35] is essential for heart graft survival and that this eVect is at least partly mediated through CO [36] (see below). Indeed, increased cardiac allograft survival was described in mice with hearts transplanted from HO‐1 transgenic animals [37]. Similar results were demonstrated in a rat aortic allograft model treated with HO‐1 gene transfer [38]. It was even reported that a bilirubin rinse of the liver grafts protects them from rejection after transplantation [39]. Similarly, HO‐1 overexpression was found to protect from hepatic I/R injury following rat orthotopic transplantation [40]. In other important studies, Ishikawa et al. [41] have shown that the modulation of HO‐1 expression in LDL‐receptor knockout mice as well as in Watanabe heritable hyperlipidemic rabbits [42] fed a high‐fat diet had an eVect on atherosclerotic lesion formation in the proximal aorta. Similarly, HO‐1 gene transfer was found to inhibit the development of atherosclerosis in apoE deficient mice [43]. In fact, induction of HO‐1 mRNA was found to be associated with production of bilirubin in foam cells, indicating that heme is actually degraded in atherosclerotic lesions [44]. Moreover, HO‐1 gene expression has been recently reported to inhibit vascular proliferation [45], development of liver cirrhosis in rats [46], as well as to protect the lungs from hyperoxia‐induced injury in a rat model [47, 48]. HO‐1 also appears to be involved in the pathogenesis of inflammatory bowel disease. HO‐1 is

10


upregulated in a trinitrobenzene sulfonic acid‐induced colitis model in rats [49]. On the other hand, inhibition of HO‐1 activity with tin mesoporphyrin was found to result in decreased HO‐1 activity, exacerbation of damage in the colon along with increased neutrophil activity, increased free radicals, and increased expression of inducible nitric oxide synthase [49]. Similar results were published in a study on experimental murine colitis induced by dextran sulfate [50]. HO‐1 overexpression was also detected during the healing of gastric ulcers in rats [51]. The cytoprotective eVects of HO‐1 were thought to be at least partially mediated by its anti‐inflammatory eVects of HO‐1 [5]. It has been suggested recently that IL‐10, a key molecule in controlling inflammation, mediates many of its anti‐inflammatory eVects via upregulation of HO‐1 [52]. Another important function of HO‐1 is regulation of iron metabolism via increased ferritin synthesis. In this capacity, HO‐1 may have a role in preventing inflammation and eliminating pro‐oxidative action of iron [53]. 4.2. HO‐1 GENE PROMOTER POLYMORPHISM IN THE PATHOGENESIS OF OXIDATIVE STRESS‐MEDIATED CONDITIONS The evidence presented above shows that an increased HO‐1 expression is associated with protection against oxidative stress. There are also many human studies demonstrating that a low expression of HO‐1 gene due to its promoter polymorphism is associated with various pathological conditions (for review see Ref. [54]; Table 1). The HO‐1 gene promoter contains a (GT)n repeat responsible for HO‐1 gene transcription that is highly polymorphic [55]. Subjects with the large size of a (GT)n (n ¼ 30) in the HO‐1 gene promoter categorized as a class L allele, for example, were shown to have eight times higher transcriptional activity than those with the (GT)n ¼ 22 [56]. The eVects are thought to be due to conformational changes of the HO‐1 gene promoter [54]. The functional importance of the HO‐1 gene promoter polymorphism was also shown in a study by Hirai et al. [57], who described that lymphoblastoid cell lines from subjects possessing S/S genotype exhibit increased HO activity with substantial resistance to hydrogen peroxide‐induced apoptosis. Indeed, the presence of the class L allele was shown to be associated with susceptibility to chronic pulmonary emphysema [58] as well as longevity in the Japanese population [59], although the link to the decline of lung functions in smokers with class L allele was not confirmed in a Canadian study [60]. Furthermore, Austrian investigators recently observed a low frequency of short (GT)n repeats (categorized as class S allele) in HO‐1 gene promoter in patients with abdominal aortic aneurysm as compared to healthy controls [61]. In other studies, patients with short (GT)n repeats were shown to have an attenuated inflammatory response to balloon injury and a reduced rate of restenosis after balloon angioplasty [62, 63]. The same group [64] also found


11

a reduced risk for cerebrovascular events in individuals with normal plasma lipid levels and with short repeats in the HO‐1 gene promoter. Similarly, patients with longer GT repeats on either allele had significantly more frequent angiographic restenoses. In a study by Endler et al. [65], carriers of short allele in the promoter of HO‐1 gene had higher bilirubin and high‐ density lipoprotein (HDL) levels, although diVerences in CHD prevalence were not detected in this study. The Austrian group also reported in their recent prospective study of almost 500 patients with advanced peripheral artery disease [66] that carriers of class S allele had more than twice reduced adjusted hazard ratio for coronary events as compared to noncarriers. Patients with hypercholesterolemia, diabetes, or smoking and shorter GT repeats in HO‐1 gene promoter were also less likely to have CHD than those with longer GT repeats [67]. Similar results were described by Chen et al. [56] in a group of Taiwanese patients with type 2 diabetes. Those patients with longer GT repeats in HO‐1 gene promoter were more susceptible to developing CHD. The same authors also described in their prospective study of 289 patients with CHD [68] an almost fourfold increased risk of angiographic restenoses after coronary stenting and adverse cardiac events during follow‐ up in class L allele carriers. Similar results were also described in a recent prospective study on 199 Austrian patients [69]. Interestingly, the link of class L allele carrier state to the increased serum levels of CRP [70] as well as IL‐6 [71] was described recently providing further mechanism, by which increased HO‐1 activity might protect against atherosclerotic diseases. In an interesting study by Baan et al. [72] involving renal transplant patients, graft survival was found to be associated with donor HO‐1 gene polymorphism. Patients who received a kidney from L class homozygotes lost their graft significantly more often to chronic allograft nephropathy than carriers with the S alleles. In contrast, the risk for graft failure was reduced twofold in kidneys with S alleles [72]. Better renal functions of class S allele renal allograft recipients were observed also in another study [73], although class S vs L allele recipients did not diVer significantly with respect to the incidence of allograft rejection. However, controversial finding was observed in another recent retrospective study on heart transplantation. In this study, the frequency and severity of heart cardiac allograft vasculopathy was not diVerent between class S allele recipients and nonrecipients [74]. Interestingly, diVerent single nucleotide promoter polymorphisms in HO‐1 gene promoter [T(‐413)A] has been shown to aVect the activity of HO‐1. In fact, eightfold higher HO‐1 activity was described in A allele promoter variant as compared to T allele variant [75]. AA genotype was found to be associated with decreased occurrence of CHD in Japanese study groups [76]. This genotype, however, was found to be associated with increased incidence of arterial hypertension in women with no clear explanation of this observation [75].

12


Interestingly, long (GT)n repeats in the HO‐1 gene promoter resulting in decreased expression of HO‐1 were shown to be associated with a lower incidence of cerebral malaria in Karen ethnic groups [77]. The lower incidence of malaria was thought to be due to the fact that low expression of HO‐1 in these subjects results in decreased heme catabolism, and thus decreased availability of iron essential for growth and proliferation of Plasmodium falciparum, which itself is unable to cleave heme [78]. Unfortunately, only one study [65] from those discussed above correlated HO‐1 gene promoter polymorphism with serum bilirubin levels. Although in this study [65] positive association between these variables was detected, this was not true in a recent Japanese study on infant patients with neonatal jaundice [79]. Although HO‐1 might regulate BVR activity and thus bilirubin production [79], these eVects may not always result in elevation of serum bilirubin, but rather in enhanced production of bilirubin in sites with increased ROS formation [80]. As discussed in a previous chapter, bilirubin, indeed, accumulates in atherosclerotic lesions presumably due to HO‐1 overexpression. It is likely that subjects with decreased HO‐1 activity due to class L allele of the HO‐1 gene promoter do not respond appropriately to oxidative stress stimuli leading to more severe clinical picture of atherosclerotic diseases. Although the majority of published clinical data are in favor of cytoprotective protection provided by class S allele, some studies did not support this observation [58, 60, 74, 81–83]. There might be several reasons accounting for this controversy, in particular diVerences in study designs, eVort to find the association in nonrelated diseases and nonrelated ethnic groups. As correctly stated by Dick et al. [66], many human diseases exhibit complicated phenotypes, which are aVected by the interaction of multiple genes, environmental factors, and treatments. It also seems that HO‐1 gene promoter polymorphism‐ mediated protection is displayed mainly in high‐risk subjects, whereas in the normal population this link may not be always apparent [66]. While many studies have shown a relationship between HO‐1 and CVD, the role of HO‐1 in cancerogenesis seems to be more uncertain. HO‐1 has been shown to exert a potent antiproliferative eVect on vascular smooth muscle cells (VSMC) [45] and on kidney epithelial [84] and pulmonary cells [85]. According to one recent study, the proportion of class L allele frequencies was found to be significantly higher in patients with lung adenocarcinoma than in the control subjects [86]. In this regard, it is interesting to note the results of a Japanese study that found the HO‐1 expression to be inversely associated with the grade and stage of oral squamous cell carcinoma [87] as well as tongue squamous cell carcinoma [88]. Similar results were found by Taiwanese investigators [89]. In this study, they found the L allele in HO‐1 promoter to be associated with an increased risk of oral squamous cell carcinoma [89]. On the other hand, HO‐1 overexpression has been


13

observed in various tumor cells including hepatoma, glioblastoma, melanoma, pancreatic, lung, renal, and prostate cell carcinoma [90], and HO‐1 inhibitors were suggested as potential novel antitumor agents [91]. These studies indicate that the role of HO‐1 in cancerogenesis is more complex and further studies of possible contributing molecular mechanisms are needed. Of interest with this respect is a recent study by Abraham et al. [92] who demonstrated that HO‐1 overexpression resulted in modulation of numerous genes associated with cell cycle progression. Based on data available, it seems that HO‐1 gene promoter polymorphism may indeed provide protection, although large prospective epidemiological studies are definitely required to uncover all the pathophysiological mechanisms and consequences.

5. Hemodynamic and Cytoprotective Effects of Carbon Monoxide There is a growing evidence showing that CO, a by‐product of heme catabolism, exerts an enormous eVect on tissue and cell protection (Table 2). CO has been shown to activate soluble guanylate cyclase and increases intracellular cGMP concentrations [93]. The increased cGMP levels in turn inhibit platelet aggregation [94], cause smooth muscle relaxation [95], and subsequent changes in blood pressure [23, 24]. CO also has an indirect eVect on vascular tone through the inhibition of the vasoconstrictors endothelin‐1 and platelet‐derived growth factor‐B [95]. In addition, CO has been shown to reduce inflammation by inhibiting of proinflammatory mediators TNF and IL‐1 and by augmenting anti‐inflammatory cytokine IL‐10 expression [95]. CO also has a direct antiapoptotic eVect on endothelial cells mediated by the activation of the p38 mitogen‐activated protein kinase (MAPK)‐signaling transduction pathway [96, 97]. On the other hand, CO suppresses the proliferation of T‐cells [98], and vascular [99] and airway [100] smooth muscle cells. Besides the well‐known modulation of guanylyl cyclase/ cGMP, CO aVects numerous intracellular signaling pathways including p38 MAPK and ERK1/ERK2 kinase pathways, expression of cyclin D1 [96, 97, 100, 101] and cyclin A [96], p21(Cip1)‐dependent caspase activity, and in particular, caspase‐8 [100], NF‐B, hypoxia‐inducible factor 1, vascular endothelial growth factor [102], JNK‐signaling pathway, and the transcription factor AP‐1 [103]. Carbon monoxide has been shown to suppress the development of atherosclerotic lesions associated with chronic aortic graft rejection [104] as well as with carotid balloon injury [105] and to improve liver allograft survival by preventing CD95/Fas ligand‐mediated apoptosis in animal models [106].


14

TABLE 2 BIOLOGICAL EFFECTS CO therapy CO exposure (10–1000 ppm)

CORM administration

Methylene chloride administration

OF

CO

EVect Prolongs mouse‐to‐rat cardiac allograft survival and inhibits graft arteriosclerosis and interstitial fibrosis Prevents hepatic I/R injury of isolated perfused rat liver Suppresses stenosis after carotid balloon injury in rats and mice, and prevent atherosclerotic lesions following aorta transplantation Provides cytoprotection in orthotopic lung transplantation in rats by downregulating proinflammatory genes Improves function of transplanted pancreatic islets in mice Improves rat heart and kidney graft survival Protects against LPS‐induced lethal endotoxemia in rats Protects against hyperoxia‐induced lung injury in rats Protects against I/R lung injury in mice Protects against I/R injury of intestinal grafts in rats Protects against bleomycin‐induced pulmonary fibrosis in mice Protects against LPS‐induced liver dysfunction in rats Protects against development of postoperative ileus in mice, rats, and swines Protects against transplantation‐induced intestinal dysmotility in rats Protects against necrotizing enterocolitis in rats Protects rat cardiac cells from oxidative stress‐induced damage and decreases infarct size in isolated rat hearts and I/R mice model Protects against ischemia‐induced acute renal failure Attenuates chronic rejection of rat aortic allografts and prevents aortic atherosclerosis Improves liver allograft survival and function in rats Protects mice from apoptotic liver damage

References [36]

[101] [105]

[112]

[113, 114] [115–117] [129] [124] [125] [126] [127] [128] [130, 131] [132] [133] [136–138]

[139] [104] [106] [118, 119]


15

Exposure of isolated perfused rat liver to CO has also been shown to prevent I/R injury through the p38 MAPK‐signaling transduction pathway [101]. The heme oxygenase–carbon monoxide metabolic pathway also has been found to be associated with diabetes. Glucose was found to directly modulate HO activity and CO production and, in turn, to promote insulin secretion [107]. In these studies, the exhaled CO concentrations were found to be higher in patients with diabetes and to positively correlate with the blood glucose levels. The levels of CO were also increased by acute elevations of blood glucose level after oral glucose tolerance tests even in healthy nondiabetic subjects [108]. Increased levels of exhaled CO were also found to be higher in patients with asthma [109], cystic fibrosis [110], or critically ill patients [111] suggesting the universality of CO as a marker of oxidative stress and of HO induction as a major defense system against oxidative stress. The eVect of CO on protection from the rejection of mouse to rat cardiac [36], lung [112], and pancreatic islets transplants [113, 114] further underlines the polymorphic biological action of CO. Similar results were also demonstrated in recent studies by Neto et al. [115, 116], who observed attenuation of inflammatory response, apoptosis, and improved renal functions in rat kidney grafts of CO‐treated recipients. The same authors also described protective eVects of dual therapy of CO and biliverdin on this kidney transplant model [117]. Exogenous CO administration or treatment with CO‐releasing methylene chloride also protected mice from apoptotic liver damage [118, 119] or rejection of rat aortic allografts [104]. Smoking, which is accompanied with high levels of blood CO, has been found to be associated with low prevalence of ulcerative colitis [120]. More interestingly, high blood CO levels were found to be associated with a low prevalence of primary sclerosing cholangitis [121], a condition commonly associated with ulcerative colitis. Moreover, an inverse relationship between cigarette smoking during pregnancy and incidence of preeclampsia was described in a recent meta‐analysis study [122]. Similar clinical associations were also observed in patients with Parkinson’s disease [123]. Although other factors, such as nicotine [122], have been implicated in the protection mediated by cigarette smoking, increased levels of CO may also contribute to the protective eVects. These findings were corroborated by studies showing that inhaled CO protects against hyperoxic [124] or I/R lung injury [125], I/R injury of the rat intestinal grafts [126], bleomycin‐induced pulmonary fibrosis [127], bacterial lipopolysaccharide‐induced liver dysfunction [128] and lethal endotoxemia [129], the development of postoperative ileus in mice [130], rats and swine [131], as well as transplantation‐induced intestinal dysmotility [132] and development of necrotizing enterocolitis in rats [133]. In this regard, it would be very interesting to see the potential clinical eVects of water soluble CO‐releasing molecules (CORMs) targeted to selected tissues [134]. These compounds have been demonstrated to have substantial

16


vasodilation [135] and cardioprotective eVects [136–138]. In fact, rat cardiac cells pretreated with CORM‐3, a water‐soluble CORM, became more resistant to the oxidative stress‐induced damage than those not treated with CORM‐3 [136]. In addition, isolated rat hearts reperfused in the presence of CORM‐3 after an ischemic event, displayed a significant recovery in myocardial performance and significant reduction in cardiac muscle damage and infarct size [136]. Similar results were found in in vivo studies on mice after 30 min of coronary occlusion. In those studies, the animals pretreated with CORM‐3 exhibited a markedly reduced infarct size [137, 138]. CORM‐3 pretreatment was also reported to reduce ischemia‐induced acute renal failure [139].

6. Cytoprotective Effects of Biliverdin Reductase As described above, the only function of this enzyme was believed to be based on its reductase activity and conversion of the open tetrapyrrole biliverdin to bilirubin. Later on, the concept of bilirubin/biliverdin redox cycle catalyzed by BVR was raised by Baranano et al. [see above, 80]. However, an increasing body of evidence suggests that BVR displays multiple bioactive functions [140]. Although primarily localized in the cytoplasm, nuclear translocation of BVR was described to function as a oxidative stress‐ induced transcription factor for activator protein 1‐regulated genes including HO‐1 and activation of a variety of genes belonging to the diVerent signaling pathways including TGF‐, NF‐B, stress response, survival, or Jak‐Stat pathways [141, 142]. BVR has been described to have also protein kinase B/Akt‐like activity and to activate protein kinase C. This fact might account for the observation that BVR transfection of the normally undiVerentiated MCF7 breast cancer cells causes them to display morphological characteristics of diVerentiated cells [140]. Based on its unique properties, BVR has been classified as a member of the bZip DNA‐binding family of transcription factors [143]. Furthermore, BVR has been identified as a member of the rare family of dual‐specificity (serine/threonine/tyrosine) kinases contributing to the cell signaling as an adaptor/scaVold protein with relevance to the HO‐1 expression, or even glucose homeostasis [144]. Human BVR is also capable of autophosphorylation and this triggers an increase in its reductase activity with augmented production of bilirubin [145]. In addition, BVR has been described to function also as a surface protein in macrophages maintaining its enzymatic activity. On the cell surface, it mediates signaling of biliverdin through PI3K/Akt pathway and acts also as a negative adapter of toll‐like receptor 4 suppressing proinflammatory cytokines [146]. BVR belongs thus to the family of so‐called amphitropic proteins being localized and acting both on the cell membrane as well as within the interior of the cell.


17

Several other recent studies suggest a possible therapeutic role for BVR. Liu et al. [147] found that the subcutaneous administration of BVR ameliorated experimental autoimmune encephalomyelitis in the rat model even more eYciently than with treatments with traditional antioxidant enzymes, including superoxide dismutase, catalase, glutathione reductase, and HO‐1. In the same study, BVR deficient cells manifested significantly increased ROS levels and depletion of BVR reduced cell viability by >60% [147]. Also other studies suggested the protective role of BVR on many biological systems including gastrointestinal mucosa [148], kidneys [142], and central [149] as well as peripheral nervous system [150]. In addition, both biliverdin and bilirubin were shown to activate aryl hydrocarbon nuclear receptors [151] involved in the biological eVects of various xenobiotics including control of cellular proliferation, cell‐cycle arrest in G1 phase, protein kinase phosphorylation, and transcriptional activation of cell cycle regulators indicating further consequences of BVR action [152]. 7. Biological Effects of Bilirubin 7.1. EXPERIMENTAL STUDIES During past 20 years, numerous studies have shown that bilirubin and other bile pigments have a biological eVect in vitro and in vivo (Table 3). The first suggestion that bilirubin might have antioxidant properties was raised by Bernard et al. [153] in 1954. In those studies, small quantities of bilirubin were found to prevent the autooxidation of vitamin A and to prevent ultraviolet light‐induced autooxidation of linoleic acid and other unsaturated fatty acids. The results indicated that bilirubin was more eVective than vitamin E in preventing the oxidation of vitamin A and fatty acids [154]. Similar results on the inhibition of linoleic acid oxidation by bilirubin were published decades later by Japanese investigators who apparently were unaware of the earlier studies [155–157]. The antioxidant properties of bilirubin and biliverdin were found in many other studies. For example, bilirubin was found to be an eYcient scavenger of singlet oxygen [158–160], to react with superoxide anion [161], and to serve as a substrate for peroxidases in the presence of hydrogen peroxide or organic hydroperoxides [162]. Important in vitro studies on the antioxidant action of bilirubin were published independently by Bliuger et al. [163] and, in particular, by Stocker et al. about 20 years ago [2, 164–167]. In those studies, bilirubin at physiological concentrations was shown to prevent the oxidation of fatty acids or their derivatives. Bilirubin was found to reduce peroxyl radicals generated chemically in homogenous solutions and in multilamellar liposomes [2], and


18 BIOLOGICAL EFFECTS

Treatment In vitro studies UCB and biliverdin

UCB bound to albumin UCB

Biliverdin, UCB, and bilirubin monoglucuronide Biliverdin, bilirubin, and urobilin Biliverdin In vivo studies UCB

OF

BILIRUBIN

TABLE 3 OTHER BILE PIGMENTS, EXPERIMENTAL STUDIES

AND

Study characteristics

Bile pigment concentration

References

7.5–15 mM

[29]

0–100 mM

[173]

6–25 mM

[172]

0.25–5 mM

[175]

50 and 100 nM

[176]

1–10 nM, þ/ albumin 0.5–50 mM

[179]

0–50 mM

[277]

0–160 mM

[180]

Inhibit replication of human herpes virus 6 Exhibits anti‐HIV‐1 activity

10 mg/mL

[200]

22 mg/mL

[199]

Protects rat liver allografts undergoing pretransplant rinse with UCB from rejection

Beneficiary concentrations 1–5 mM, higher concentrations harmful 115 mg/kg bid i.v. þ 5% albumin for 4 weeks 45 mg/kg b.wt. þ 5% albumin i.v. administration 5–35 mM kg/b.wt. i.p. administration of 30 mg/kg b.wt.

[39]

Reduced human monocyte chemotaxis in response to LDL oxidation Protect rat hepatocytes and human erythrocytes from oxyradical damage Protects human ventricular myocytes from oxyradical damage Protects bovine VSMC from oxyradical damage Protects rat hearts exposed to ischemia Is neuroprotective against H2O2‐induced toxicity Inhibits proliferation of breast cancer cells Induces apoptosis of colon cancer cells Has anticomplement eVects

Ameliorates bleomycin‐ induced pulmonary fibrosis Ameliorates I/R injury in rats Has anticomplement eVects in rats Ameliorates LPS‐induced liver injury in rats

[273]

[177]

[178] [181] [183]


19

TABLE 3 (Continued) Treatment

Biliverdin

Study characteristics Ameliorates VCAM‐1‐ mediated airway inflammation in murine asthma model Attenuates vascular endothelial activation and dysfunction in vascular endothelial cells Protects from experimental autoimmune encephalomyelitis in rats Ameliorates experimental murine colitis Improves rat heart and kidney graft survival when used with CO therapy, downregulates proinflammatory cytokines Protects mice from apoptotic liver damage Protects guinea pigs from anaphylaxis Protects from H2O2‐induced I/R tissue injury in rats with suppression of venular leukocyte adhesion Attenuates LPS‐induced expression of endothelial cell adhesion molecules in rats Improves function and survival of small intestinal transplants after prolonged ischemia in rats Leads to long‐term survival of pancreatic islet allografts in mice


References

6 i.p. doses of 30 mg/kg b. wt.

[185]

5 mM/L

[187]

5 i.p. doses of 50–200 mg/kg b.wt.

[210]

50 mM/kg b.wt. i.p.

[50]

Two i.p. doses (pre‐ and posttransplantation, 50 mg/kg b.wt. Bolus i.p. administration of 25 mg/kg b.wt. Oral and i.v. administration of 5 mg/kg b.wt. i.v. administration of 2.5–10 mM/L

[117]

i.p. bolus of 40 mM/kg b.wt.

[197]

i.p. bolus of 50 mg/kg b.wt. given to both donor and recipents i.p. administration of 8.5–17 mM/kg b.wt. given to either donor or recipients

[198]

[119]

[6]

[184]

[201]

(continues)


20

TABLE 3 (Continued) Treatment

Study characteristics Leads to long‐term survival of cardiac allografts in mice

Protects against LPS‐ induced acute lung injury in rats Inhibits immediate‐type hypersensitivities in animal models Prevents balloon injury‐ induced neointima formation in rats


References

i.p. administration of 50 mM/kg b.wt. given to either donor or recipients Two i.p. doses of 35 mg/kg b.wt.

[209]

Chicken bile containing predominantly biliverdin 20–1000 mM/kg i.p. and i.v.

[219]

Consequences of constitutive hyperbilirubinemia (studies on Gunn rats) Reduction of oxidative injury of neonatal Gunn rats exposed to hyperoxia Attenuation of pressor and pro‐oxidant eVects of angiotensin II Prevention of balloon injury‐ induced neointima formation

[218]

[228]

[226]

[227]

[220]

In all studies except those under Refs. [183, 185, 227], and probably [273] unpurified bile pigments were used. b.wt. ¼ body weight; i.p. ¼ intraperitoneally; i.v. ¼ intravenously.

to protect human LDL from oxidation [3, 4, 168]. The antioxidant eVects of bilirubin were confirmed by Wu et al. [169] who demonstrated that UCB is 20 times more eVective than Trolox, a vitamin E analogue, in preventing LDL oxidation. Additionally, bilirubin was found to protect albumin from oxidative damage and to strongly inhibit the formation of protein carbonyls [170]. Farrera et al. [171] compared the peroxyl antioxidant trapping potential of various substances and found that the free bile pigments were more eVective as antioxidants than Trolox. These eVects were described not only for bilirubin and biliverdin but also for the non‐vinyl substituted bile pigments, mesobilirubin, and mesobiliverdin [171], as well as bilirubin ditaurate [163, 167].


21

In addition, Wu et al. [172] demonstrated that albumin‐bound bilirubin, as well as delta bilirubin, protected human ventricular myocytes from oxyradical damage. Biliverdin and albumin‐bound bilirubin were also found to protect rat hepatocytes and human erythrocytes from oxyradical damage [173]. Furthermore, bilirubin concentrations as low as 10 nmol/L were found to be eVective in protecting neuronal cultures from the oxidative stress induced by 10,000 times higher concentrations of hydrogen peroxide. According to the authors, the biliverdin–bilirubin redox cycle, in which bilirubin is continuously regenerated and reutilized, enables such low concentrations of bilirubin to protect against a much higher concentration of hydogen peroxide [80]. It is important to note that bilirubin can inhibit superoxide producing NADPH oxidase [174]. In other studies, Clark et al. [175] showed that the addition of submicromolar concentrations of bilirubin to the culture medium of VSMC could markedly reduce hydrogen peroxide‐induced cytotoxicity. Protective eVects of as little as 100‐nM concentration of bilirubin were also found in rat hearts exposed to ischemia [176]. In addition, intravenous administration of bilirubin was also shown to ameliorate the eVects of bleomycin‐induced pulmonary fibrosis [177], as well as intestinal I/R injury in rats [178]. In other interesting and important studies, bilirubin formed by activation of HO‐2 was found to protect neurons against oxidative stress injury [179]. There is increasing evidence that bilirubin has anti‐inflammatory properties [5, 6]. Upregulation of HO activity, which results in higher bilirubin concentrations, has been linked to a faster resolution of inflammation, whereas inhibition of this enzyme was found to result in increased inflammation [5]. Bilirubin, by its anticomplement action, was also found to protect tissue against inflammatory damage [6, 180, 181]. These eVects were described both in vitro [5, 180] and in vivo in guinea pigs [6] and Wistar rats [181]. Bilirubin was thought to interfere with the interaction between IgG or IgM and complement C1 component [180] and the inhibition of the hemolytic activity of complement C1 component. A dose–response inhibition of complement‐dependent hemolysis in the classical pathway as well as antibody‐dependent cell‐mediated cytotoxicity in the presence of bilirubin was observed also recently in another study [147]. It has been also reported that albumin, which appears in inflammatory exudates, carries bilirubin across the vascular wall into the sites of potential oxyradical damage by phagocytic cells [182]. Bilirubin treatment has also been shown to improve rat hepatocyte survival and to attenuate liver injury in response to lipopolysaccharide‐induced endotoxemia in rat models [183]. Bilirubin treatment has also been shown to enhance prostaglandin synthesis independently of cyclooxygenase expression [183]. Bilirubin has been shown to have a number of other eVects. For instance, it has been shown to block oxidant‐mediated venular leukocyte adhesion [184],

22


the transendothelial migration of monocytes [29, 185], and the activation of leukocytes [186]. In addition, i.p. administration of biliverdin has been shown to have an ameliorating eVect on intestinal inflammation in dextran‐sulfate‐ induced experimental murine colitis [50]. Furthermore, bilirubin has been shown to attenuate vascular endothelial activation and dysfunction in vitro [187] with inhibition of the NF‐B activation pathway as a likely contributing factor [188]. These findings further support the anti‐inflammatory and antiatherogenic role of bilirubin. It is important to note that bilirubin‐ induced inhibition of transendothelial leukocyte migration is mediated via suppression of vascular cell adhesion molecule (VCAM) signaling [185], a process implicated in the pathogenesis of several pathological conditions, including inflammatory bowel disease [189], conjunctivitis [190], nephropathy [191], arthritis [192], systemic collagenoses [193, 194], and possibly cancer [195, 196]. In another study, i.p. administration of biliverdin was shown to attenuate LPS‐induced P‐ and E‐selectin expression in the lung, kidneys, liver, and intestine in rats [197], and this treatment also improved survival of small intestinal graft recipients after prolonged ischemia presumably due to modulation of proinflammatory cytokines and cytoadhesive molecules [198]. All of these eVects indicate a role of bilirubin in the inflammatory processes [183]. Besides the eVects of bile pigments on immune response, direct antiviral activities against human herpes virus 6 and human immunodeficiency virus type 1 were described for biliverdin, bilirubin, and urobilin [199, 200]. Salutary eVects of bilirubin treatment on pancreatic islets allografts have also been described [201], and the eVects were found to be similar to those found with CO treatment [113, 114] and HO‐1 overexpression [202]. These data fit perfectly with our recent findings showing that subjects with chronic inflammatory conditions such as rheumatoid arthritis [203], systemic lupus erythematosus [204], or Wegener granulomatosis (Vı´tek L., unpublished results) have substantially lower serum bilirubin levels than individuals without these inflammatory conditions (see below). VCAM‐1‐mediated leukocyte recruitment has also been found to contribute to the pathogenesis of atherosclerosis [205, 206]. VCAM‐1 is detectable in atherosclerotic plaques [207] and the endothelial expression of this molecule has been found to be an early event at sites predisposed to atherosclerosis [208]. The HO‐1/biliverdin/bilirubin system has been shown to suppress the expression of adhesion molecules in rodents [197]. These findings suggest that modulation of intercellular adhesion, which plays an important role in the atherogenesis [205], may also contribute to the protective eVects of bilirubin. The immunological eVects of biliverdin, the precursor of bilirubin, comprising modulation of the alloimmune response, T cell proliferation, and IL‐2 production via NF‐B activation, were described to result in improved survival of mice cardiac allograft recipients [209]. UCB has been also shown


23

to prevent experimental autoimmune encephalomyelitis in the rat model for human multiple sclerosis [210]. Immunomodulatory eVects of bilirubin have been shown in a number of in vitro and in vivo studies [211–217]. In those studies, bilirubin has been found to inhibit the in vitro chemotactic activity of human granulocytes [211, 212], modulate the expression of Fc receptors on peritoneal macrophages [213], inhibit human lymphocytes and granulocytes [214], suppress the diVerentiation of immunocompetent cells [215], modulate the phagocytic activity of granulocytes and monocytes [216], modulate antibody production and aVect response to alloantigens [217]. Anti‐inflammatory eVects of biliverdin treatment were described also in a rat model of LPS‐induced shock [218] and beneficiary eVects of biliverdin were also demonstrated by Japanese authors, who had found that chicken bile containing biliverdin as a major bile pigment had markedly inhibited immediate‐type hypersensitivities in several experimental in vitro and in vivo models [219]. Both clinical laboratory and experimental evidence indicate that the chronic inflammatory processes contribute to atherogenesis [220]. In this regard, elevated hs‐CRP levels have been shown to be one of the most valuable markers of chronic inflammation and atherosclerosis [221]. It is interesting to note that an inverse relationship between serum bilirubin and hs‐CRP levels has been recently described [222, 223]. These findings further support the possible antioxidant and anti‐inflammatory role of serum bilirubin in protecting individuals from atherosclerosis. It is likely that other mechanisms, such as inhibition of protein kinase C activity [224], known to importantly contribute to atherosclerosis processes may also be involved in the bilirubin‐mediated protection against atherogenesis [225]. The eVect of elevated bilirubin levels on the oxidative stress has been studied in hyperbilirubinemic Gunn rats lacking bilirubin UDP‐glucuronosyltransferase and the ability to conjugate bilirubin with glucuronic acid [226, 227]. Dennery et al. [226] found reduced oxidative injury in neonatal hyperbilirubinemic rats as well as lower concentrations of lipid peroxides, conjugated dienes, and carbonyl proteins. Similarly, Pflueger et al. [227] demonstrated an attenuation of the pressor and pro‐oxidant eVects of angiotensin II in the hyperbilirubinemic Gunn rat presumably due to the scavenging of superoxide anion by bilirubin. In another recent study, Gunn rats as well as wild‐type rats treated with biliverdin exhibited decreased susceptibility to balloon injury‐induced neointima formation compared to control animals presumably due to inhibition of smooth muscle cell cycle progression [228]. This is in accord with results by Clark et al. [176], who demonstrated that exogenously administered bilirubin at concentrations as low as 100 nM significantly restored myocardial function and minimized infarct size in the I/R‐isolated rat heart model. In studies discussed above, i.p. administration of biliverdin was found to

24


induce tolerance to cardiac allografts in mice possibly due to modulation of inflammatory eVectors [209]. Similarly, it was shown that a bilirubin rinse of the liver grafts provided protection from their rejection after transplantation [39]. Combined treatment with biliverdin and CO has been shown to improve survival of heterotopic heart and orthotopic kidney grafts in a rat model [117]. 7.2. HUMAN STUDIES 7.2.1. Bilirubin and Serum/Plasma Antioxidant Capacity Both in vitro and in vivo studies have shown that bilirubin is directly correlated with serum and plasma antioxidant capacity (Table 4). Frei et al. [229] performed studies to quantify the serum antioxidant capacity contributed by serum bilirubin. In their studies, they found that bilirubin constitutes approximately 10% of the total antioxidant capacity in normobilirubinemic adults. Likewise, serum bilirubin was found to correlate with the serum antioxidant capacity of neonates. In term infants, Belanger et al. [230] detected a decrease in total antioxidant capacity after exchange transfusion. These findings suggested a correlation between bilirubin removal and antioxidant capacity. Gopinathan et al. [231] also observed a direct correlation between serum bilirubin and total antioxidant potential at birth in term infants. A similar relationship was reported by Hammerman et al. [232] in studies involving premature neonates. In those studies, a direct association between serum bilirubin levels in premature neonates and the serum peroxyl radical‐ trapping capability was found. Similar associations have been also reported by Drury et al. [233]. Wiedemann et al. [234] also found a substantially lower plasma oxidazibility in hyperbilirubinemic neonatal plasma than in normobilirubinemic plasma from adults. An association between plasma bilirubin and oxidative stress, however, was not found in two other studies involving preterm infants [235, 236]. On the other hand, the relation between serum bilirubin levels and antioxidant capacity was also found in children with sickle cell anemia [237]. In other studies, human meconium was found to have potent antioxidant properties and the antioxidant properties were ascribed to high concentrations of bilirubin IX and ubiquinol‐10 [238]. We have also found a correlation between serum bilirubin concentrations and total serum antioxidant capacity in individuals with Gilbert syndrome (GS) [239]. In addition, the bilirubin and total serum antioxidant capacity relationship was confirmed in an in vitro study evaluating the eVect of increasing bilirubin concentrations on the total serum antioxidant capacity [239]. 7.2.2. Bilirubin and CHD Recently, much attention has been focused on the role of lipid peroxides, oxidized lipoproteins, and anti‐inflammatory agents in atherosclerosis and


BIOLOGICAL EFFECTS

TABLE 4 BILE PIGMENTS, HUMAN STUDIES

OF

Study characteristics Elevation of serum bilirubin

Low serum bilirubin levels as a risk factor for

Hyperbilirubinemia due to Neonatal jaundice is associated with

Sickle cell anemia Gilbert syndrome is associated with

Higher serum bilirubin levels (cause undefined) are associated with Low risk of CHD Low risk of carotid atherosclerosis

25

In acute myocardial infarction and angina pectoris Hemorrhagic stroke Neurotrauma CHD Associated with all‐cause mortality Rheumatoid arthritis Systemic lupus erythematosus Wegener granulomatosis Diabetes Arterial hypertension Cancer (colorectal, breast, endometrial . . .) Depressions

Higher total serum antioxidant capacity With no eVect on total serum antioxidant capacity in two other studies Low‐incidence circulatory failure, asphyxia, and sepsis Low incidence of necrotizing enterocolitis, bronchopulmonary dysplasia, and intraventricular hemorrhage Is associated with higher total serum antioxidant capacity Low serum levels of hs‐CRP Higher total serum antioxidant capacity Decreased urinary excretion of biopyrrins Low serum levels of pentosidine and carboxymethyllysine (advanced glycation end products) Low prevalence and incidence of CHD Low risk of carotid atherosclerosis Low risk of colorectal cancer Low risk of schizophrenia (for details, see text)

References [25] [26] [27] [1, 243–249, 251, 254] [250, 253] [203] [204] Vı´tek L., unpublished [264] [265] [253, 256–262] [280]

[229–234] [235, 236] [281] [282]

[238] [222, 223] [239] [313] [315]

[239] [263] [268] [294]

[254] [225, 257–262]

26


CVD [240–242] (Table 4). In 1994, Schwertner et al. [1] first reported an inverse association between fasting serum bilirubin concentrations and CHD in 877 males. Up to this time, there were no reports on low serum bilirubin concentrations and any disease. The results of the studies indicated that low serum bilirubin was associated with an increased risk of CHD, whereas bilirubin in the upper normal range was associated with a low risk of CHD. The strength of the association between bilirubin and CHD was similar to that of smoking, systolic blood pressure, and HDL‐cholesterol. In those studies, serum bilirubin was found to be an independent risk factor and to be independent of age, cholesterol, HDL‐cholesterol, blood pressure, and smoking. Since bilirubin is an antioxidant, it was thought that bilirubin might protect LDL from oxidation and subsequent atherosclerotic plaque formation [1]. The causes of the low serum bilirubin concentrations were not identified; however, increased oxidative activity or increased iron stores were thought to result in the decreased serum bilirubin levels [1]. Genetic factors resulting in low serum bilirubin levels could also account for the low serum bilirubin concentrations. 7.2.2.1. Retrospective Studies. Schwertner et al. [1] first reported on the inverse association between serum bilirubin concentrations and the presence and extent of angiographically proven CHD. Hopkins et al. [243] confirmed these findings in a retrospective case‐controlled study of men and women with early familial CHD. The odds ratio (OR) of CHD in individuals in the lowest quintile (5.1–6.9 mmol/L) was found to be fourfold higher than that for individuals in the highest quintile (16–17 mmol/L). This meant that individuals in the top quintile of bilirubin levels had an 80% reduction in CHD risk compared to individuals in the lowest quintile. Other investigators have found similar inverse relationships between bilirubin and CVD [244–246]. An inverse relation between serum bilirubin and coronary artery disease in men was reported by Levinson [244]; however, they found little practical discrimination for CHD compared to the lipoprotein markers. Madhavan et al. [245] found that young asymptomatic oVsprings of parents with a history of heart attack and hypertension had consistently lower serum bilirubin concentrations than did the oVsprings of parents without a history of heart disease and hypertension. The results suggested that low serum bilirubin is a marker for oxidative stress and future CVD. Hunt et al. [246] found that decreased bilirubin is mildly related to CHD in males but not in females. It was concluded that the inferred major gene for bilirubin may protect against CHD, since elevated levels were associated with the inferred gene. 7.2.2.2. Prospective Studies. Several prospective studies have shown that low serum bilirubin predicts future CVD. In 1995, Breimer et al. [247] performed a prospective study of 7685‐middle‐aged men enrolled in the British Regional Heart Study (BRHS). They found that both low and high


27

levels of bilirubin were associated with an increased risk of CHD relative to the intermediate levels. The hyperbilirubinemic subgroup, however, was not well defined in that it included individuals with elevated hepatic enzyme activities. As a result, liver disease could have been partly responsible for the increased prevalence of CHD seen in this subgroup. In addition, standardized blood collections conditions were not used. Djousse et al. [248] have recently reported on their prospective study of serum bilirubin and myocardial infarction in the Framingham OVspring Study. In addition, they evaluated the relationship between serum bilirubin and myocardial infarction or coronary death and any cardiovascular event (any CHD event, congestive heart failure, intermittent claudication, or stroke). A higher total serum bilirubin was found to be associated with a lower risk of myocardial infarction, CHD, and any CVD event in men; however, there was only suggestive evidence for a lower CHD risk in women. The authors concluded that the limited number of myocardial infarction and CHD cases and the relatively young age of the women at baseline, 39.5 years, indicating that most women were premenopausal, might have accounted for the diVerences in men and women. In another study, Djousse et al. [249] reported on an inverse relationship between serum bilirubin and albumin and myocardial infarction. Albumin has been shown to be an important antioxidant [3, 170] and to be a major transporter of bilirubin in the intravascular compartment. In that study, the combination of both low serum albumin and serum bilirubin was associated with a higher risk of myocardial infarction in men and women, whereas the group with high serum albumin and bilirubin was found to have the lowest risk. In prospective studies, Wei et al. [250, 251] also found that low serum bilirubin concentrations were associated with a higher risk of CHD mortality and all‐cause mortality. These studies as well as the prospective studies by Vı´tek et al. [239] are discussed in more detail in the sections that follow. 7.2.2.3. Protective EVect of Elevated Bilirubin Levels. Most studies of serum bilirubin and CHD have been performed under conditions of normal serum bilirubin concentrations (17.1 mmol/L, i.e., 1.0 mg/dL). In 1994, Schwertner et al. [1] found protective eVects between slightly elevated serum bilirubin and the presence of CHD. Several more recent studies have found that slightly elevated bilirubin concentrations are associated with a lower prevalence of CVD and cardiovascular mortality [239, 243, 250, 251]. Wei et al. [250] also found a protective eVect between baseline fasting serum bilirubin and subsequent all‐cause mortality in men without baseline liver dysfunction. Compared to men with low bilirubin 0.05b

Location Cecum Ascending Transverse Descending Sigmoid Rectum

80% (4/5) 91% (10/11) 100% (2/2) 67% (2/3) 100% (13/13) 90% (18/20)

28–99% 59–100% 22–100% 9–99% 79–100% 68–99%

p > 0.05b

a

Fisher exact test. Chi‐Square test. CI ¼ confidence interval. b

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significant diVerence for COX‐2 expression numbers among Dukes’ stages ( p 0.05, by Chi‐Square test). For patients with stage A or B cancer, who are expected to undergo curative operations by endoscopic or surgical resection, the sensitivity of the assay was 92% (34/37; 95% CI 78–98%). Positive results were obtained in 4 of 5 patients with tumors located at cecum, 10 of 11 patients at ascending colon, 2 of 2 patients at transverse colon, 2 of 3 patients at descending colon, 13 of 13 patients at sigmoid colon, and 18 of 20 patients at rectum (Table 1). There was no significant diVerence for COX‐2 expression numbers among tumor locations ( p 0.05, by Chi‐Square test). It appears that COX‐2 mRNA detection is generally high in patients with CRC irrespective of the clinical stage and location. There was no correlation between CEA expression levels and COX‐2 expression levels in feces from CRC patients ( p > 0.05, Spearman’s coeYcient by rank test) or between COX‐2 expression levels in feces from CRC patients and Dukes’ stage, location, or size of the tumor ( p > 0.05, by the Kruskal–Wallis test). The fecal occult blood test (FOBT) is a noninvasive and simple examination, which helps to reduce the incidence, morbidity, and mortality of colorectal cancer patients [143–146]. However, FOBT appears to be insensitive to the detection of early stage and/or proximal tumors. The fecal COX‐2 assay, however, has high sensitivity and high specificity, and is able to detect early stage and/or proximal tumors. 6. Conclusions Increased expression of COX‐2 is seen in premalignant and malignant lesions in a number of tissues. However, the underlying mechanisms are numerous and complex. Although COX‐2 mediates its eVects early in carcinogenesis, overexpression of COX‐2 is an important prognostic factor in a variety of cancers. Nevertheless, the use of COX‐2 as a prognostic factor in some cancers is still controversial and a large‐scale study is required to clarify this issue, especially since both NSAIDs and COX‐2 selective inhibitors are important chemopreventive agents for a variety of cancers. The fecal COX‐2 assay is a novel noninvasive screening test for colorectal neoplasms. This assay is sensitive to early stage and/or proximal tumors as well as advanced cancer while maintaining high specificity. Future studies are required to compare the sensitivity and specificity of this test with those of the fecal occult blood test. This assay would be a promising approach for detecting colorectal neoplasms as well as fecal DNA testing. ACKNOWLEDGMENTS Funded by Kurozumi Medical Foundation, Osaka Cancer Research Foundation, Aichi Cancer Research Foundation, and Hamamatsu Foundation for Science and Technology Promotion.

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OLIGONUCLEOTIDE PROBES FOR RNA‐TARGETED FLUORESCENCE IN SITU HYBRIDIZATION Adam P. Silverman and Eric T. Kool Department of Chemistry, Stanford University, Stanford, California

1. Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Principles of Fluorescence In Situ Hybridization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Hybridization AVinity and Specificity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Base and Backbone Modifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Target Site Accessibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Types of Probes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Oligonucleotide Probes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Molecular Beacons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Quenched Autoligation Probes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Bacterial Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Human Cell Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79 60 60 60 61 62 63 64 65 65 65 66 66 66 67 103

1. Abstract The need for accurate and rapid methodology for detecting cells in environmental and clinical samples has led to the development of in situ detection methods, where fixed or intact cells can be imaged directly. In this chapter, we focus on the use of labeled oligonucleotide probes in fluorescence in situ hybridization (FISH). We give an overview of FISH probe design, covering issues of aYnity and specificity of probes, probe backbone options, cellular targets, and accessibility of target sequences. Decisions that must be made to design optimal probes are evaluated, and available resources to assist in probe design, such as secondary structure, Tm calculation, and site accessibility software, are discussed. We cover diVerent types of FISH probes that have 79 0065-2423/07 $35.00 DOI: 10.1016/S0065-2423(06)43003-1


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been reported in the recent literature, including standard fluorescently labeled oligonucleotide probes and newer classes of quenched oligonucleotide probes: molecular beacons and quenched autoligation probes. Advantages and disadvantages of the diVerent probe types are examined and recent literature applications are discussed. The current state of the art in the field as well as limitations and challenges in detection are evaluated.

2. Introduction Identification and classification of cells in environmental and clinical samples can be critical for determining both prophylactic and curative responses. In dealing with contamination of food or water supplies by microorganisms, bacterial infections in tissues, and cancer‐causing mutations in biopsy samples, a rapid diagnosis may be a life or death issue. Standard detection of cells in clinical or environmental samples requires any of a number of methods including culture, antigen detection, serology, nucleic acid amplification, and other biochemical assays [1, 2]. Despite the widespread use of these detection strategies, many can be time‐consuming, diYcult, and yield inconclusive results. As a result, a great deal of research in clinical and environmental chemistry focuses on developing rapid and specific methods to identify cells and gain information about cell type and characteristics. One of the most promising techniques for doing this is in situ detection of nucleic acids. Oligonucleotide and polynucleotide probes have long been used in detection of nucleic acids in cells; the first report of RNA‐targeted in situ hybridization came in 1989 by DeLong et al. [3], and over the past two decades, RNA‐targeted fluorescence in situ hybridization (FISH) has become a critical biotechnological tool in environmental sampling and clinical diagnostics. This chapter will focus on short, well‐defined oligonucleotide probes, which display much higher sensitivity to small sequence variations in the target than do the much longer enzymatically synthesized polynucleotide probes. The applications of oligonucleotide probes are numerous, and they are becoming commercially available in rapidly increasing numbers. Despite the growing availability and applications of oligonucleotide probes for in situ hybridization, there remain several important factors that limit their use, and these issues are at the forefront of modern research. Among these issues is the diYculty in predicting whether a specific target site will yield useful signal or not; this is important because searching for a target can be costly in time and money, and because detecting genetic diVerences may only be possible at locally defined sites. A second important issue is sequence specificity; as little as a single nucleotide diVerence may define

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an important genetic variation, and such high specificity is often diYcult to achieve by hybridization alone. Third is the problem that many, perhaps even most, potentially useful target RNAs exist in low copy numbers, which means that signal intensity may be low and background signal may interfere. Finally, it is currently diYcult to perform FISH in living cells; this would be useful in the study of the dynamics and localization of RNAs, and also might be applied to imaging gene expression in the whole organism. One of the chief aims of this chapter is to define these problems in more detail and to outline current and emerging molecular FISH approaches that are addressing these issues. This chapter will outline the current FISH standard approach, employing oligonucleotides with fluorescent labels attached, and will then introduce emerging strategies involving the newer classes of quenched probes. The most common strategy for RNA‐targeted FISH employs static fluorescent probes to sense sequences, requiring cell fixation and washing steps to remove unbound probes which would otherwise interfere with the desired signal. Quenched probes use a dark quencher, such as dabcyl or black hole quencher [4], to quench the fluorescence of the fluorophore when the probe is not bound to template. Once the probe hybridizes to its template, the quencher is separated from the fluorophore either by conformational or chemical change in the structure of the probe, thereby establishing fluorescence signal [5]. Nonhybridized quenched probes are therefore nonfluorescent and do not need to be washed out of cells, which raises the possibility of applications in living cells and tissues. In the following sections, we outline the fundamentals that are critical to understanding and developing oligonucleotide‐based FISH techniques, give an overview of the scope and limitations of current and emerging methods, and finally describe applications of FISH probes toward in situ diagnostics targeted to cellular RNAs, with an emphasis on probe design and the scope and limitations of various methodologies. We begin with a discussion in Section 3 of the basic principles of RNA‐targeted FISH: hybridization aYnity and specificity, nucleic acid backbone eVects, targets and site accessibility. In Section 4.1, we discuss the most widely used FISH technique, employing oligonucleotide probes, and then we discuss quenched probes—molecular beacons and quenched autoligation probes—in Sections 4.2 and 4.3. Finally, we discuss some of the numerous applications of RNA‐targeted FISH probes in molecular diagnostics for bacterial and human cells in Section 5. 3. Principles of Fluorescence In Situ Hybridization Probe design and determination of optimal experimental conditions for hybridization to cellular RNAs are often not trivial, and both are critical to employing FISH successfully. Determining the utility of a given probe set is

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SILVERMAN AND KOOL TABLE 1 ONLINE TOOLS FOR PROBE DESIGN URL

References

Secondary structure and site accessibility prediction Dynalign http://rna.urmc.rochester.edu/ mFOLD http://www.bioinfo.rpi.edu/applications/mfold/ Pfold http://www.daimi.au.dk/compbio/rnafold/ sFOLD http://www.bioinfo.rpi.edu/applications/sfold/index.pl Sequence alignment ClusalW http://www.ebi.ac.uk/clustalw/# Tm prediction DINAMelt http://www.bioinfo.rpi.edu/applications/hybrid/hybrid2.php HYTHER http://ozone2.chem.wayne.edu/ Probe construction ARB http://www.arb‐home.de/ PRIMROSE http://www.cardiV.ac.uk/biosi/research/biosoft/ probeBase http://www.microbial‐ecology.net/probebase/

[86] [79, 80] [85] [81, 218] [77] [17, 18] [19, 20] [78] [103] [70]

largely empirical [6], and low fluorescent responses can be caused by a number of factors. Low aYnity of the probe for its target can be caused by poor probe design, inappropriate hybridization conditions, and inaccessibility of target sites due to either secondary or tertiary RNA structure or associated proteins. Low signal can also result from low RNA content of the cells and from diYculty permeating cell walls. Some useful probe design tools available online are summarized in Table 1.

3.1. HYBRIDIZATION AFFINITY

AND

SPECIFICITY

Recent advances in the biophysics and biochemistry of nucleic acids over the past decades have paved the way for the development of hybridization‐ based approaches in detecting cells. The intricately related issues of aYnity and specificity are critical to the design of nucleic acid probes for detection applications [7]. AYnity is the propensity of the probe to bind to its target under a given set of hybridization conditions, defined as the overall free energy change of the probe binding to its target site (G overall) [8, 9]. Specificity is the propensity of the probe to bind only to its target and not to other sites with similar sequences. Typically, aYnity and specificity are inversely related, meaning that as aYnity for the selected target increases, so does the likelihood of binding to similar nontarget sequences [10]. The traditional approach for determining probe aYnity has been the use of probe melting temperature (Tm), the temperature at which half of the


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population of oligonucleotides is hybridized, an indicator of the stability of the probe‐target duplex [11]. A number of factors aVect Tm, including length, GC content, and nearest neighbors, particularly at the site of a mismatch [12–15]. However, because enthalpic and entropic contributions may be very diVerent for unrelated sequences, Tm is not always a good predictor for probe aYnity. More recently, Yilmaz and Noguera demonstrated a free energy‐based approach to designing high‐aYnity FISH probes [9]. The proposed reaction scheme for FISH, shown in Fig. 1, consists of three equilibriums: reaction 1 represents binding of the unfolded probe to the unfolded complementary site, reaction 2 represents unfolding or folding of the probe, and reaction 3 represents unfolding or folding of the target site. Probes are typically designed with minimal self‐complementarity, so the contribution from reaction 2 is often small, but target RNAs typically have a great deal of higher order structure, so the contribution of reaction 3 may be very significant, depending on the target site (Section 3.4). Using these considerations, the authors were able to demonstrate a relationship between probe aYnity (G overall) and hybridization eYciency, as measured by probe brightness [9]. Thus, by predicting G overall for a FISH reaction, it may be possible to predict whether a given probe will have suYcient aYnity to generate signal. A value of 13.0 kcal/mol is suggested for maximizing aYnity while maintaining reasonable specificity. Using this value as a threshold, Yilmaz et al. demonstrated that signal‐generating probes can be designed even for RNA regions previously believed to be inaccessible [16] (Section 3.4). While free energy‐based approaches show promise for generating high‐ aYnity probes, preparation of extremely high‐specificity probes remains problematic. In these applications, Tm remains a useful tool. When comparing closely related systems (same probe length and hybridization conditions, similar sequence), Tm can be a useful predictor in the design of high‐ sensitivity probes. For cases where only one or two mismatches are present between probe and target, the probe may have significant aYnity for the

Pf

Tf 2

3 1

Pu

+

Tu

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FIG. 1. Proposed reaction scheme for FISH [8, 9]. P, T, and PT denote the DNA probe, the target, and the probe/target hybrid, respectively. The subscripts f and u indicate the folded and unfolded conformations.

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mismatched target (and thus low specificity), so the stability of the resulting duplex is significant in determining whether unwanted background signal from binding will be generated. Several programs for predicting both free energies and Tm’s are available: the DINAMelt Server predicts full melting profiles and thermodynamic parameters for DNA/DNA and RNA/RNA hybridization [17, 18], and HYTHER provides thermodynamic data but not a full melting profile for DNA/DNA, DNA/RNA, and RNA/RNA hybridization [19, 20]. There are a few significant limitations to calculation of Tm by these methods that are worth noting. First, target secondary structure is not considered; only the stability of the probe‐target duplex is calculated. Second, although there are a number of studies that determine the eVects of mismatches on DNA/DNA duplexes [20–24] and RNA/RNA duplexes [25, 26], there is only one such study for DNA/RNA duplexes [12], and it is not fully exhaustive in the combinations of mismatches examined. AYnity may be increased by increasing the probe length, or in some cases by altering the hybridization conditions (e.g., raising the concentration of Mg2þ or lowering the hybridization temperature). However, increasing specificity while maintaining high aYnity tends to be more diYcult. A single mismatch has a stronger impact on shorter probes than on longer ones. This may beg the question: why not simply use very short probes when very high specificity is required? The answer is twofold. First, the genetic code is made up of only four letters, so the likelihood for undesired target sequence redundancy is greater for shorter probes. Second, aYnity is lowered with decreasing probe lengths, making very short probes incompatible with FISH protocols. 3.2. BASE

AND

BACKBONE MODIFICATIONS

There are a number of methods for improving aYnity of nucleic acid interactions, the most widely studied of which are using backbone‐ or base‐ modified probes. Modified bases include C‐5 propynyl pyrimidines [27–29], 5‐Me‐C [30–32], 2‐amino‐A [30, 32], and 7‐propynyl‐8‐aza‐7‐deazapurines [33] (Fig. 2). C‐5 propynyl pyrimidines, for example, have been shown to increase mismatch penalties by as much as 4.7–10.2 kcal/mol for a single mismatch, resulting in a Tm diVerence of up to 14 C [34]. Although modified bases can improve melting temperatures of matched sequences by 0.5–3 C per individual substitution, they can be prohibitively expensive, especially when multiple substitutions are desired, and may not be available for incorporation into commercially purchased probes. Common modified backbones for enhancing aYnity include 20 ‐O‐methyl (20 ‐OMe) RNA, 20 ‐fluoro (20 ‐F) RNA, locked nucleic acid (LNA), and


O

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N

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N R

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H2 N

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HN

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FIG. 2. DNA base modifications for improving mismatch selectivity.

OR O P OO O

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O O P OOR DNA OR O P OO O

OR O P OO O

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B

OR O P OO O

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RHN

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F O O P OOR 2⬘-F RNA

O O O P OOR LNA

O

O NHR PNA

FIG. 3. Common oligonucleotide backbone modifications used in fluorescence in situ hybridization probes.

peptide nucleic acid (PNA) (Fig. 3). The thermodynamic stability of diVerent backbone‐containing nucleic acids hybridized to RNA template goes roughly in the order DNA < RNA < 20 ‐OMe RNA < 20 ‐F RNA < LNA/PNA [35].

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Of these, all have been used for in situ hybridization studies except 20 ‐F RNA, possibly because it is extremely expensive. 20 ‐F RNA is known to increase Tm’s approximately 1.8 C per substitution, with a slight cooperative eVect (i.e., the Tm per substitution increases the more 20 ‐F RNA bases that are incorporated into the probe) [36], but does not have any other properties that make it superior to other options for FISH. The natural RNA backbone is also not used much for in situ hybridization experiments because of its instability and the requirement that RNase‐free reagents and techniques must be employed when handling it to prevent degradation. With the development of phosphoramidite chemistry, all of these backbones (except PNA) can be readily incorporated into oligonucleotides in combination [37]. PNA can be synthesized using Boc or Fmoc chemistry for solid‐phase peptide synthesis [38]. 20 ‐OMe RNA is an attractive backbone modification because it hybridizes more strongly than DNA and, unlike natural RNA, is extremely stable to RNases and DNases [39]. Oligonucleotide probes containing 20 ‐OMe RNA are particularly useful in live cell applications because of this stability. The enzymatic resistance is explained by the decreased nucleophilicity and increased steric bulk of the methoxy group as opposed to the hydroxyl group in natural RNA, and even greater stability has been observed with larger alkyl groups at the 20 position [40, 41]. Furthermore, 20 ‐OMe RNA oligonucleotides have been reported to exhibit faster hybridization kinetics than DNA probes, and due to their increased Tm’s, can in some cases bind to RNA targets that contain significant secondary structure [42]. LNA is a class of conformationally restricted oligonucleotide analogs in which a ribonucleoside is linked with a methylene group between the 20 ‐O and the 40 ‐C [43, 44]. It resembles natural nucleic acids with respect to base‐ pairing and duplex formation, but LNA duplexes with complementary nucleic acids are very thermally stable compared to the respective DNA or RNA duplex [45]. Single or multiple, but separated, LNA modifications appear to have a greater impact on probe aYnity than contiguous stretches of LNA bases, so most probes incorporate one or a few LNA nucleotides into a DNA strand for hybridization [46]. The Tm of hybridization to RNA may be increased by as much as 8.3 C for a single LNA nucleotide incorporation [47], and as such, shorter probes can be used, thereby increasing sensitivity to SNPs [48]. The diYculty of using LNA probes lies in the fact that LNA–LNA pairing is extremely strong, and poorly designed probes may self‐anneal and be useless [49]. Fortunately, an LNA design tool is available on the web (see www.exiqon.com) to assist in LNA probe design. Like 20 ‐OMe RNA, LNA has been shown to be resistant to cleavage by nucleases [50–52]. Despite these advantages, LNA has found more adoption in the fields of antisense and RNA interference (RNAi) than for FISH applications [53].


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This probably stems from the expense of LNA‐spiked oligonucleotide probes, the lack of many facilities that will make them, and the problems often experienced with binding that is too tight, preventing washing away of unbound probes. In contrast, PNA has been widely adopted in the field of RNA‐targeted diagnostics. The PNA backbone is based on 2‐aminoethyl glycine units replacing the normal phosphodiester backbone [54]. As such, it is uncharged, yet PNA still recognizes complementary nucleic acid sequences by Watson– Crick base‐pairing in an antiparallel geometry [54–56]. Since there is no electrostatic repulsion to destabilize hybridization between PNA and DNA or RNA, the rate of PNA hybridization is increased by up to 48,000‐fold (under low ionic strength conditions) relative to that for analogous DNA oligonucleotides [57]. Furthermore, the Tm is substantially raised and is less dependent on the presence of mono‐ or divalent cations [58]. Specificity is reported to be higher than analogous DNA sequences; a single mismatch in a 16‐mer PNA–DNA duplex can reduce the Tm up to 15 C [54]. The increased Tm requires that shorter probes are used relative to DNA oligonucleotides, since the Tm of long PNA probes would be too high and prevent stringent hybridization and washing. 3.3. TARGETS Ribosomal RNA (rRNA) is currently the most common target for in situ hybridization. Ribosomes are made up of two subunits; in prokaryotes, the two subunits are referred to as 50S, which contains 23S and 5S rRNA, and 30S, which contains 16S rRNA. In eukaryotes, the respective subunits are 60S, containing 28S, 5.8S, and 5S rRNA, and 40S, containing 18S rRNA. In Escherichia coli, roughly 30% of the cellular mass is accounted for by ribosomes and rRNA accounts for about 80% of the total cellular RNA [59]. In all, a typical E. coli cell has between 10,000 and 60,000 ribosomes, depending on growth conditions [59]. The number of ribosomes in eukaryotic cells may range dramatically depending on the cell’s function, but is typically in a similar range [60]. The natural abundance of ribosomal RNA makes it a particularly attractive target for in situ hybridization assays. There is little need for signal amplification to increase signal‐to‐background, as observable fluorescence is generated simply by binding fluorescence probes to available target. Messenger RNAs (mRNAs) are, at present, much less commonly used as a target because they are typically at least an order of magnitude less abundant than rRNA, and in some cases may be present in just a few copies per cell [61]. Sensing such low abundance target is impossible without some sort of amplification strategy, and there are currently no reliable methods

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for in situ sensing of very low abundance mRNAs without an intermediate amplification step. However, more abundant mRNAs, such as ‐actin [62] and dps [63], have been sensed by some FISH techniques. The widespread availability of RNA sequences for a large number of organisms in databases, such as GenBank [64], EMBL [65, 66], the European ribosomal RNA database [67], the Ribosomal Database Project (RDP) [68, 69], probeBase [70], 5S subunit databases [71], small‐subunit rRNA databases [72, 73], and large‐subunit rRNA databases [74–76], has significantly enhanced the possibilities for comparative sequence analyses (Table 2). Programs for sequence alignment, phylogenic analysis, and structure prediction, such as ClustalW [77], ARB [78], and mFOLD [79, 80], respectively, are critical to analysis of the tremendous volume of available sequence data. 3.4. TARGET SITE ACCESSIBILITY In order for any FISH assay to be successful, the probe must find and hybridize to its target. Unfortunately, this is not always a simple matter, as RNA has complex secondary and tertiary structure elements that can prevent FISH probes from binding to their intended site. Despite the availability of many RNA secondary structure‐prediction algorithms [80–87], long‐ chain RNAs may exist as a population of structures [81], and it is substantially more diYcult to predictively determine tertiary folds and the eVects of any protein interactions on structure. Even in cases where tertiary structure and protein interactions are minimal, the expected accessibility may not always match the empirical result. Three‐dimensional structures for some RNAs have

TABLE 2 ONLINE RNA SEQUENCE DATABASES Database EMBL‐Nucleotide Sequence Database European Ribosomal RNA Database GenBank probeBase Ribosomal Database Project II 5S Ribosomal RNA Database

URL http://www.ebi.ac.uk/embl/ http://www.psb.ugent.be/rRNA/ http://www.ncbi.nlm.nih.gov/Genbank/ http://www.microbial‐ecology.net/probebase/ http://rdp.cme.msu.edu/index.jsp http://biobases.ibch.poznan.pl/5SData/

References [65, 66] [67] [64] [70] [68, 69] [71]


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been determined by X‐ray crystallography [88–91] or NMR [92–94], and this information can be extremely useful in determining the possible accessibility of a target site. As discussed in Section 3.1, probe aYnity depends not only on sequence but also on target structure. A straightforward method for designing probes to accessible target sites might involve determining several energy‐minimized secondary structures using mFOLD [80] or other program, then considering regions of potential accessibility based simply on predicted secondary structure elements, for example, avoiding hairpins and long regions of base pairs, while targeting loops or bulges [95]. However, such an approach may require several time‐consuming trials. Fortunately, a great deal of empirical data on the accessibility of rRNA to oligonucleotide probes is available, largely from the work of Amann and coworkers [95–101]. Using flow cytometric analysis of fixed E. coli using overlapping adjacent probe sets spanning the entire 16S [95, 101] and 23S rRNA [98] subunits, they generated rRNA accessibility maps. This work was expanded to include small subunit rRNA (16S or 18S) of members of the domains Bacteria, Archaea, and Eucarya, enabling the generation of an in situ accessibility consensus map [99, 101]. Considering the high sequence conservation of rRNA, these profiles are also useful for rational probe design for other organisms. Furthermore, a significant improvement in target site accessibility can sometimes be obtained by use of ‘‘helper probes,’’ unlabeled oligonucleotides, typically around 18 nucleotides long, which bind adjacently to the labeled probe [97]. Helper probes are intended to disrupt secondary structure around the target site, and were shown to lead to 4‐ to 25‐fold increase in signal when applied to E. coli 16S RNA target sites [97]. Much of these data have been integrated into the ARB software package PROBE Design Tool (PDT) [102]. Using this program, the user may select a group or species of interest, indicate probe parameters (length, GC content, and so on), and search for site candidates based on predicted probe quality. Although probes designed using PDT still must be tested empirically, by identifying and excluding inaccessible regions, the number of trials can be reduced. Other software packages for the design of RNA‐targeted oligonucleotide probes are also available [103–105]. Because of the large numbers of diVerent mRNAs that may be of interest for FISH applications, accessibility is typically determined by testing probe sets that were rationally designed using secondary structure information. Several labor‐intensive biochemical methods for determining site accessibility in mRNA have been reported, but they tend to be more useful for antisense, knockdown, or RNA interference applications, where it is necessary to know beforehand whether probe will bind target [106–111]. In FISH applications, accessibility is to a large extent inherently determined over the

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course of the hybridization experiment, so it is unnecessarily laborious to use such methods in most cases. Indeed, FISH is one method that may be used to empirically determine accessibility of mRNA sites for such other mRNA‐ targeting applications, assuming the mRNA is abundant enough to generate signal. A study by Yilmaz et al. uses an aYnity‐based approach to generate probes that generate signal across the entire E. coli 16S rRNA, including sites that were shown to be inaccessible in the Amann study [16]. Using calculations to design probes with values for G overall around 13 kcal/ mol, the authors demonstrated that there are no truly inaccessible regions so long as probes with great enough aYnity can be designed. This method oVers significant new possibilities for rational probe design, but due to several limitations, the need for empirical determination of site accessibility is not yet trumped in many cases. First, diVerent methods for estimating G2 , (Fig. 1, unfolding of target site) give diVerent results, so calculation errors can result in errors in the determination of probe aYnity. Second, these calculations do not take into account tertiary structure or the influence of associated proteins. Third, the assumption of chemical equilibrium behind the calculations of G overall may be invalid after normal hybridization times for some target sites when the kinetics of hybridization are slow [9]. As a result, hybridization times had to be increased to as much as 96 hours in order to generate signal with some target sites, an unacceptably long time for many diagnostic applications. 4. Types of Probes 4.1. OLIGONUCLEOTIDE PROBES FISH probes several hundreds or thousands of nucleotides long complementary to nearly the entire 16S or 23S rRNA and containing multiple fluorophore labels have long been used to detect microorganisms in environmental samples because such lengthy probes oVer reliable hybridization and intense signal. These probes are prepared enzymatically, typically by PCR [112] or in vitro transcription [113, 114], at which time multiple fluorophores are incorporated. Although polynucleotide probes allow the visualization of a significantly higher percentage of prokaryotes in a sample compared to singly labeled oligonucleotide probes [115], polynucleotide probes are only able to discriminate between distantly related groups, such as Bacteria, Crenarchaeota, and Euryarchaeota [113, 116], because their sensitivity to small sequence diVerences is very low. Furthermore, long polynucleotide probes must be produced in the laboratory using cost‐ and labor‐intensive protocols. Typical problems encountered include nonspecific binding of


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probe [116], high autofluorescence vs specific fluorescence [117], poor signal‐ to‐background [118], low cellular detection compared to total cell count [119], and enzymatic degradation in situ [120]. More commonly, RNA‐targeted FISH probes employ oligonucleotides 15–30 nucleotides long, with a DNA, PNA, or modified nucleic acid backbone (Section 3.2), prepared synthetically (Fig. 4A). Fluorescence is typically observed directly, using a fluorophore attached to the 50 ‐terminus, though in some cases 30 ‐ or internally labeled probes are used. Common fluorophores used in RNA‐targeted FISH diagnostics include fluorescein, tetramethylrhodamine (TAMRA), Texas Red, Cy3, and Cy5. Choice of dye is typically determined by its spectral properties and the availability of equipment for imaging. Labeled oligonucleotides are available from a variety of commercial sources, so it is typically not necessary for investigators to synthesize or purify probes. In some applications, indirect sensing is used instead of directly coupling the fluorophore to the probe. Indirect sensing strategies typically involve coupling an enzyme to the oligonucleotide probe, hybridizing to targets, then

FIG. 4. Standard oligonucleotide FISH probes. (A) Standard probe hybridization. (B) Tyramide signal amplification using oligonucleotide FISH probes conjugated to horseradish peroxidase (HRP).

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adding a fluorophore moiety that is recognized by and covalently binds to the enzyme [121]. These approaches can oVer the significant advantage of brighter signals, but they tend to have low specificity [122, 123]. The most well‐studied approach employs horseradish peroxidase (HRP)‐labeled oligonucleotide probes [124–127]. HRP reacts with hydrogen peroxide and tyramide to produce a free radical on the tyramide, which covalently binds to a nearby tyrosine residue (Fig. 4B) [128, 129]. A number of fluorophore‐ conjugated tyramides are available, thus allowing fluorescence detection of enzymatically deposited tyramide [129]. 4.1.1. Standard Oligonucleotide FISH Protocols Standard FISH protocols consist of four steps: (1) fixation and permeabilization of the sample, (2) hybridization of fluorescent probe, (3) washing away unbound probe, and (4) detection of labeled cells by microscopy or flow cytometry [116]. The first issue when setting up an RNA‐targeted FISH experiment is getting the probes into the cells. Cells typically must be fixed so that high stringency washing steps may be performed to remove unbound probes. However, it is usually desirable to select fixatives that will disrupt cellular morphology as minimally as possible. The most common fixatives fall into two classes: cross‐linking reagents, such as aldehydes, and precipitants such as methanol and ethanol [6]. Cross‐linking reagents like formalin and paraformaldehyde are quite commonly used for permeabilization of gram‐ negative bacteria [130] and human cells [131], but may be ineVective in permeabilizing the cell walls of gram‐positive bacteria [132]. Several possibilities exist to permeabilize gram‐positive bacteria, and often diVerent procedures are required for diVerent species. Treatment of paraformaldehyde‐fixed bacteria with cell wall‐lytic enzymes, such as lysozyme or proteinase K, has been shown to increase the cellular permeability of Lactococci, Enterococci, and Streptococci [133]. Permeabilization by treatment with ethanol/formalin [134], high concentrations of ethanol or methanol [135], or heat [136], also has been successful in many cases. Hybridization and washing conditions are highly dependent on the probe aYnity and Tm and the cell type being examined, and optimal conditions must be determined empirically. Hybridization is performed at a few degrees lower than the probe Tm, typically in the 40–60 C range, in a buVer containing a relatively high salt concentration. For PNA FISH probes, higher hybridization temperatures can be used when the probes have higher Tm’s [54]. The advantages to using higher hybridization temperatures are better disruption of target structures and better probe specificity. Washing is carried out in similar temperature ranges, often with the addition of higher concentrations of detergents such as SDS, Triton X, or Tween, or


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of formamide [116]. More stringent conditions may be required for PNA FISH probes. The washing step is typically diYcult to optimize, but the most important in order to minimize false positives from unbound probes. InsuYciently low signal, on the other hand, can be caused by a number of factors, including low ribosome or mRNA count, poor target accessibility (Section 3.3), or impermeability of cells (see above). Low RNA content potentially can be circumvented by using strategies such as tyramide amplification, but this method requires conditions which tend to cause lysis of fixed cells [122, 137]. When target accessibility appears to be an issue, helper probes can be used or a diVerent target site may need to be selected (see Section 3.3) [97]. In some cases, addition of low concentrations of formamide to the hybridization buVer may improve the result, as formamide lowers the Tm of secondary structures (but also lowers the Tm of the probes) [6]. As a result, hybridizations in formamide‐containing buVer must be run at lower temperatures. After washing, cells may be analyzed by fluorescence microscopy or flow cytometry. Microscopy has the advantage of being rapid and simple, but an untrained eye can lead to incorrect reporting of data, and results are usually qualitative. Flow cytometry provides quantitative data on the fluorescence of individual cell populations, but instruments are quite expensive. The greatest advantages of standard oligonucleotide probes for RNA‐ targeted diagnostics are that they are commercially available, relatively inexpensive, and well established in the literature for a plethora of applications. However, standard oligonucleotide probes do have several disadvantages. As discussed in Section 3.1, they are typically unable to distinguish related RNA sequences unless there are multiple nucleotide diVerences [5, 116, 138]. In addition, careful handling is required to avoid nonspecific signals, especially during washing away of unbound probes [139]. The washing step increases the chances of error and nonspecific signal, and prevents application to live cells. 4.2. MOLECULAR BEACONS Molecular beacons (MBs) were first developed in 1996 as tools for real‐ time PCR assays [140, 141]. They have since been developed for multiplex PCR assays [142, 143], solid‐phase hybridization assays [144–146], biosensing [147], and FISH applications with both prokaryotic [148] and human cells [149]. Molecular beacons are oligonucleotides, typically with DNA, 20 ‐OMe RNA, or PNA backbones that have a stem‐loop hairpin conformation in their native state, with a fluorophore and quencher at either end such that the probe is quenched while in the hairpin state [140, 150, 151]. The loop region of the probe, or sometimes the loop and part of the stem [152], is

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complementary to an RNA target site, and upon binding, the hairpin is disrupted, separating the fluorophore and quencher, enhancing fluorescence signal (Fig. 5A). The quenching eYciencies for several quenchers with many diVerent fluorophores have been examined, allowing for the design of optimal fluorophore–quencher pairs [4]. A careful balance must be reached between the stem length and loop length to design optimal molecular beacons for mismatch discrimination. Solution experiments demonstrated that mismatch discrimination increases as the number of bases in the stem increases [153]. However, if the stem is too long, the kinetics of hybridization to target will be slow [154]. MBs with longer loop lengths tend to have lower hairpin Tm’s, and thus increased kinetics of hybridization and decreased specificity, and MBs with very short stem lengths have lower signal‐to‐background ratios [154]. Design of MBs is substantially simplified by software, typically oVered by companies that sell custom MBs. MBs oVer several advantages over standard oligonucleotide probes. Because MBs are quenched, no washing steps are required to remove unbound probe, and they may therefore be applicable to living cells assuming

FIG. 5. Molecular beacons. (A) Standard MB hybridization. (B) Wavelength‐shifting MB. (C) Dual FRET MB.


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the cells can be permeabilized. Second, MBs have higher mismatch sensitivity than standard oligonucleotide FISH probes as a result of their conformational restraints [153]. The main sources of nonspecific signal in MBs are: (1) incomplete quenching, (2) hairpin–hairpin binding between two beacons, (3) nuclease degradation that separates the quencher and the fluorophore, and (4) nonspecific interactions with proteins and other small molecules within the cell that disrupt the hairpin structure [5]. The last may be the biggest problem in cellular diagnostics, since molecular beacons are known to interact with certain nucleic acid‐binding proteins, disrupting the MB secondary structure and giving nonspecific signal [155]. Several new approaches to MBs recently have been developed to improve signal‐to‐background. Most notably, fluorescence resonance energy transfer (FRET) approaches have the potential to decrease background signal if the spectral overlap between the donor and acceptor is minimal [156]. This approach was first examined with the so‐called ‘‘wavelength shifting molecular beacons,’’ in which an acceptor fluorophore, such as a rhodamine, was tethered to the fluorescein donor via a linker (Fig. 5B) [157]. Wavelength shifting MBs were found to be useful in multiplex PCR assays, but have not been employed for FISH assays, possibly because the overlap between the donors and acceptors studied is too great to give a substantial signal‐to‐ background advantage. A similar approach uses an acceptor fluorophore instead of a dark quencher, thereby changing the maximum emission wavelength between hairpin and bound states [158]. Going one step further, Bao and coworkers developed ‘‘dual FRET MBs,’’ in which two MBs bind side‐ by‐side, with a donor fluorophore on one beacon thereby being brought into proximity with an acceptor fluorophore on the other beacon (Fig. 5C) [159, 160]. Since both donor and acceptor fluorophores are quenched in their native state, and both need to hybridize adjacently in order for FRET to occur, background from nonspecific hairpin opening is substantially reduced. 4.3. QUENCHED AUTOLIGATION PROBES Quenched autoligation (QUAL) probes are a relatively new class of in situ hybridization probes that were developed for detection of sequences with high specificity [161–163]. Whereas molecular beacons rely on a conformational change to initiate fluorescence signal, QUAL probes utilize a chemical reaction [163]. QUAL probes consist of two oligonucleotide strands, the ‘‘dabsyl’’ probe and the phosphorothioate (thioate) probe (Fig. 4C). The dabsyl probe is short, typically 7–10 nucleotides, while the thioate probe is longer, around 15–20 nucleotides. The dabsyl probe is modified such that it has a dabsyl group at its 50 ‐terminus, attached through an electrophilic

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sulfonate ester linkage, and a fluorescein or other fluorophore internally attached to a uridine base. Dabsyl is a dark quencher that eYciently quenches fluorescein, so the background fluorescence of the dabsyl probe is very low [4]. The thioate probe is modified such that it has a phosphorothioate group at its 30 ‐terminus. The two probes are designed such that they bind adjacently on their target, bringing the nucleophilic 30 ‐phosphorothioate close to the electrophilic 50 ‐dabsyl group. The phosphorothioate reacts to displace the dabsyl group, thereby ligating the two strands and unquenching the fluorophore (Fig. 6A and B). It should be noted that both the 30 ‐terminal phosphorothioate in the nucleophilic probe and the bridging phosphorothioate linkage formed in the ligation product are quite stable to nucleases and hydrolysis [164]. The 50 ‐dabsyl group, however, is subject to slow hydrolysis in buVer, particularly in basic conditions or at high temperatures [165]. QUAL probes take advantage of the ability of very short oligonucleotides to sense single nucleotide mismatches while avoiding issues of redundancy and lack of aYnity. The highest specificity is achieved when the mismatch is placed in the center of the short probe; substantially less discrimination is observed when the mismatch is at the end of the short probe or in the long probe [161]. Thus, specificity of QUAL probes is determined mostly by

FIG. 6. QUAL Probes. (A) Ligation chemistry. (B) QUAL probe hybridization and reaction. (C) FRET–QUAL probes.


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binding aYnity as opposed to geometry of the reaction site, as mismatches in the center of short probe lead to the greatest Tm diVerence for binding to a matched vs mismatched target [161, 162]. Naturally, probing for sites with multiple mismatches will increase the specificity of QUAL probes. When very short probes are used, there is a high probability for sequence redundancy. This issue is made moot in QUAL probes by requiring the two probes that bind adjacently. Thus, even if the short probe represents a sequence that has redundancy in the target RNA, no fluorescence is observed unless the longer probe binds adjacent to it. The lack of aYnity of very short probes is dealt with by running the hybridization at relatively low temperatures (37 C or lower) and selecting sequences that have a Tm high enough to hybridize under the desired conditions. Since unbound probes are not fluorescent, no washing steps are required, and ligated fluorescent products are typically 20‐mers or longer, which have high aYnity for their target. It should be noted, however, that it is not actually necessary that probes remain bound after ligation occurs in QUAL probes, as ligation permanently switches on fluorescent signal. In fact, if ligated probes dissociate from their target and new probes bind and ligate on the same template, signal amplification may occur [166]. Signal amplification by turnover is highly desirable for the detection of extremely low abundance targets such as mRNAs. A strategy of using ‘‘universal linkers’’ to attach the quencher to the 50 ‐terminus of the dabsyl probe (Fig. 5A) yielded turnovers of nearly 100‐fold on RNA templates in solution [166], and was used to detect mRNAs in human cells [167]. The product of the ligation reaction using the universal linker is destabilized compared to natural DNA, apparently because the alkane linker adds flexibility to the strand. This decreases product inhibition so that the target RNA can become a catalyst for generating multiple signals per target. Furthermore, the linker has been shown to destabilize the product without destabilizing the transition state of the reaction; in fact, the reaction rate is sped up by a factor of 4–5 [166]. In solution and solid‐phase assays, QUAL probes were shown to accurately discriminate between all possible mismatches [163, 165]. QUAL probes oVer several advantages over other RNA‐targeted diagnostics strategies. Like MBs, unbound probes do not need to be washed out of cells, which reduces experimental time and decreases chances for error. QUAL probes may be less prone to nonspecific signals than MBs because turning on fluorescence requires a chemical reaction; thus, binding to proteins should not lead to nonspecific signals. Disadvantages of QUAL probes include the slow hydrolysis of quencher leading to nonspecific fluorescence, the requirement that multiple probes be used, and the limited number of systems that they have been applied to thus far. The main sources of nonspecific signal in QUAL probes

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are: (1) incomplete quenching, (2) nuclease degradation in the 1–3 nucleotides between the fluorophore and quencher, and (3) hydrolysis of the quencher [5]. The last of these appears to be the greatest problem, requiring careful handling and storage of the probe. Abe and Kool recently described a method to improve signal‐to‐background by using FRET–QUAL probes (Fig. 6C) [167]. In these specialized QUAL probes, Cy5 was attached internally to the thioate probe. The ligation was monitored by excitation at 488 nm, which gives almost no excitation for Cy5. When the probes ligated, FRET between fluorescein and Cy5 allowed emission to be monitored at 665 nm, beyond the emission wavelength for fluorescein. Thus, background from incomplete quenching or nonspecific hydrolysis of the quencher was minimized, leading to substantially higher sensitivity. QUAL probes are not yet commercially available, so they have not yet been adopted wide use in diagnostic settings.

5. Applications 5.1. BACTERIAL TARGETS RNA‐targeted FISH may be used for the identification of bacteria in environmental samples, clinical specimens, or cell culture. All three applications are important in diagnostics: environmental detection is critical in identifying contamination, while clinical specimens and cultured cells are important in determining and diagnosing disease. 5.1.1. Oligonucleotide Probes Oligonucleotide FISH probes can be classified as generic, group specific, genus specific, or species specific, depending on the intended targets. Generic probes are designed to target consensus regions across many bacteria groups, while group‐specific probes should only hybridize in the presence of related microorganisms. Genus‐ and species‐specific probes are the most highly specific and therefore often the most useful. However, it should be noted that these probes are typically only tested against a few related bacteria, and their general scope of applicability may still be limited. Early experiments focused on developing generic probes that are complementary to the rRNA in a large number of bacteria species, and generate very strong signal. These probes, such as EUB338 [96], UNIV1392 [168], and ARCH915 [169], can be used in general diagnostic applications or as positive controls to evaluate the brightness or eYciency of new probes. For example, EUB338 is widely used in identification of microorganisms in


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plankton [137, 170], biofilms [171–173], sludge [174, 175], sediments [176], soil [177], wastewater [178, 179], and food [180]. A plethora of papers have appeared on development of RNA‐targeted FISH probes to identify various organisms or groups in clinical and environmental samples [2, 116, 139, 181, 182], and it would be impossible to cover all examples here. Instead, we will provide some illustrative cases and point out some of the challenges in development of useful diagnostic probes. It is interesting to note that greater than 90% of cases of bacteremia are caused by a very limited number of pathogens including Staphylococcus spp., Streptococcus spp., Enterococcus spp., E. coli, Klebsiella pneumoniae, Pseudomonas areruginosa, and Candida spp. [183, 184], and a significant number of clinical studies focus on developing FISH probes for these microorganisms. Group‐specific oligonucleotide probes have been developed for a wide range of targets, including Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Firmicutes, Cyanobacteria, Cytophagales, Fibrobacter, Planctomycetes, Verrucomicrobiales, Nitrospira, Acidobacteria, Actinobacteria, domain Bacteria, Crenarchaeota, Euryarchaeota, and Archaea [70, 185]. These probes have good to excellent sensitivities, and target coverage of up to 100% [70, 185]. Some gram‐positive groups are diYcult to stain using DNA oligonucleotide probes, but PNA probes have shown improvement in some cases [168, 186–188]. It is worth noting that while FISH gives information about the presences of microorganisms in a sample, quantitative data on the abundance of microorganisms in complex samples tends to have low accuracy [120]. Genus‐specific identification of Chlamydiae [189], Brachyspira [190], Brucella [191], Enterobacteriaceae [192], and Candida [193] in culture using specific oligonucleotide FISH probes has been achieved, and in several cases these probes have been applied directly to smears of biopsies. Species‐ specific oligonucleotide FISH probes have been developed for identification of several species of clinically important bacteria [194]. Particularly useful in the clinic are probes that can be directly applied to clinical samples and do not require culturing the bacteria. This is particularly important in the case of slow‐growing organisms such as Legionella or Mycobacteria. PNA FISH probes are particularly appropriate for use in the clinic because their hydrophobicity makes them more compatible with standard smear preparations [188]. However, cells must still be fixed prior to staining. Species‐specific PNA or DNA probes have been applied for identification of Staphylococcus aureus [186], Candida albicans [195], Enterococcus faecalis [196], Helicobacter pylori [194], and Legionella pneumophila [197], directly from blood smears, giving diagnostic sensitivities of 93–100%. Rapid diagnosis of bacterial infections can be particularly important in immunocompromised individuals such as individuals with cystic fibrosis.

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A recent study by Hogardt et al. used RNA‐targeted probes and flow cytometry to identify bacteria from 8 groups in sputum samples from 75 cystic fibrosis patients [198]. In this study, they observed 100% accuracy for identifying P. aeruginosa, Burkholderia cepacia, Stenotrophomonas maltophilia, Haemophilus influenzae, and Staphylococcus aureus. A poignant example of the challenges of employing FISH in clinical diagnostics is the development of probes for identifying Mycobacterium tuberculosis. Mycobacterium tuberculosis remains one of the most important clinical targets because 8 million new cases of tuberculosis (TB) occur annually, producing nearly 3 million deaths, mostly in developing nations [199]. Diagnosis of Mycobacterium tuberculosis is extremely time‐consuming, requiring initial examination of sputum stained for acid‐fast bacilli, followed by diVerentiation of Mycobacterium tuberculosis from nontuberculosis mycobacteria using biochemical tests on cultures, which often takes 4–6 weeks [200]. Several academic laboratories and companies are working to develop FISH probes specific for Mycobacterium tuberculosis 16S rRNA [200–203]. The first diYculty in applying FISH to Mycobacteria is their extremely robust mycolic acid‐containing cell wall, which is extremely diYcult to permeabilize to oligonucleotide probes without destroying cellular morphology. As a result, most attempts at Mycobacterium tuberculosis‐ specific probes have employed PNA. While such probes oVer reasonable results, sensitivities may be as low as 57% because of contamination by non‐Mycobacterium tuberculosis Mycobacteria species which are not identified by the probe set [204]. Furthermore, the non‐Mycobacterium tuberculosis probes have been shown to nonspecifically identify species of Actinomyces and Rickettsia [201]. Thus, these probes have not been adopted for widespread use because they are not suYciently accurate for identifying Mycobacterium tuberculosis or non‐Mycobacterium tuberculosis Mycobacteria infections. Nevertheless, initial probe sets with reasonable sensitivity prove that FISH is a potential method for more rapid identification of Mycobacteria infections. 5.1.2. Quenched Probes MBs have been used primarily indirectly in real‐time PCR monitoring of rRNA or mRNA sequences, but several attempts have been made for using MBs for in situ detection of ribosomal rRNA in bacteria. Most FISH applications of MBs use PNA beacons. Xi et al. were able to discriminate cultured and fixed E. coli from Methanosarcina acetivorans very accurately [148]. Another study used MBs to detect Lactobacillus reuteri in stomach biopsies [205]. Despite their potential for use with intact organisms, there are presently no reports of applications of MBs with live nonfixed bacteria.


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QUAL probes targeted to their 16S rRNA of E. coli have been shown to generate signals within 3 hours of incubation [206]. No signal was observed when the thioate probe was not included, nor when probes that bound nonadjacently were used. Similarly, live E. coli could also be detected with QUAL probes, as the cells can be permeabilized with 0.05% sodium dodecyl sulfate (SDS) [165]. QUAL probes have been used to discriminate three diVerent species of closely related gram‐negative bacteria, E. coli, Salmonella enterica, and P. putida, based on very small diVerences in their 16S RNA sequences [207]. The E. coli and Salmonella enterica probes diVered by a single mismatch, and the P. putida probes diVered by a few mismatches, yet bacteria treated with probes only fluoresced when treated with fully matched sequences. Thus, QUAL probes have been shown to be useful for rapid and specific detection of closely related bacteria species based on very small rRNA sequence diVerences [207]. It remains to be seen whether QUAL probes will work as well in discriminating clinically important strains. 5.2. HUMAN CELL TARGETS RNA‐targeted FISH in human cells is typically aimed at identification of mRNAs. Although mRNA expression may not directly correlate with expression of the corresponding protein, mRNA expression levels are quite useful for gaining an understanding of cellular activities [208, 209]. In particular, tumor cells are known to express or overexpress mRNAs for certain oncogenes [210], and detection of such mRNAs could be extremely useful for rapid diagnosis of cancers. Furthermore, a reliable method to quantitatively or even qualitatively monitor mRNA expression levels would be extremely useful for understanding cellular behavior in response to stimuli, thereby potentially helping determine treatment options. Although no such method exists now for native cells, FISH holds a great deal of promise. Standard olignucleotide probes cannot be used in live cells, so any mRNA detected is the expression level at the time of fixation, though it is likely that the fixation event triggers cellular responses that alter mRNA expression levels. MBs and QUAL probes, on the other hand, give a great deal of promise for real‐ time detection of mRNA expression levels. However, only a handful of papers on this topic have appeared in the literature, and results are largely preliminary. Part of the diYculty in sensing mRNAs lies in the fact that in applying FISH probes to live cells, not only the abundance of target but also its stability must be considered. Ribosomes are quite stable and degradation is typically not an issue on the timescale of the FISH experiment, but mRNA is potentially more problematic since its half‐life may be as short as 3 min [61].

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In addition, introducing probes into human cells can be quite diYcult, requiring microinjection [62, 211, 212], attachment of a cell‐permeable peptide [213, 214], fixation [215], or use of reversible permeabilization such as by streptolysin O (SLO), which can severely disrupt cellular morphology [159, 216]. The first examples of MBs being used to detect abundant mRNAs in human cells have appeared in the literature over the past few years. In one of the earliest examples, human trabecular cells were treated with an MB complementary to basic fibroblast growth factor (bFGF) mRNA fluoresced, while cells treated with a noncomplementary MB gave less signal [217]. This initial study suggested that mRNA levels could be qualitatively examined using MB probes. More recently, MBs have been applied to detect c‐fos mRNA in transfected Cos7 cells [211], ‐actin mRNA in K562 human leukemia cells [212], PTK2 cells [62], and fibroblasts [213], GADPH and survivin mRNA in HDF cells [214], cyclin D1 and survivin in human breast cancer cells [215], and K‐ras and survivin in HDF and MIAPaCa‐2 cells [159]. The latter study employed dual FRET MBs (see Section 4.2), which helped reduce nonspecific background signals [159]. mRNA‐targeted studies using MBs have been largely preliminary, but suggest that MBs, and especially dual FRET MBs, merit more study for real‐time monitoring of cellular mRNA expression levels. QUAL probes have been reported to be applicable for sensing abundant mRNAs in intact human HL‐60 cells [167]. This study employed FRET– QUAL probes to minimize background in sensing GAPDH, ‐actin, histone H3, and JUND mRNAs. The RNAs were detected both by fluorescence microscopy and by flow cytometry. Not surprisingly, there was a high dependence of probe target site accessibility on signal‐to‐noise, suggesting that better methods for determining mRNA accessibility would still be useful.

6. Conclusions RNA‐targeted FISH has become an extremely significant diagnostic method over the past two decades, and recent advances in protocols and probe design have had a tremendous impact on maximizing the utility of the approach. The empirical determination of RNA accessibility and availability of probe design tools have made FISH probe design substantially more facile, and the many approaches available for improving the aYnity, specificity, and in situ stability of probes give researchers a great deal of flexibility in developing new probes. The development of quenched probes has substantially added new possibilities for live cell and very high‐specificity detection.


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Nevertheless, there are still many challenges to overcome. In the area of microbiology, preliminary results have shown promise for highly specific detection of closely related microorganisms, but it remains to be seen if these probes will be applicable to a wide range of systems or be useful in a clinical setting. Specific detection of gram‐positive bacteria, particularly those with very robust cell walls, remains problematic. There are no examples in the literature demonstrating specific detection of low abundance mRNAs in bacteria, an advance that is critical for detecting many virulence factors such as the Shiga‐like toxin in E. coli O157:H7. Early results in human cells using quenched probes have demonstrated the possibility of detecting abundant mRNA in living systems; it remains to be seen whether expression levels can be quantitated or if very low‐abundance transcripts can be observed, and whether cancerous cells can be diVerentiated from normal cells based on mRNA expression.

ACKNOWLEDGMENTS The authors thank the National Institutes of Health, the Army Research OYce, and the Stanford Collaborative Translational Research Program for support. APS acknowledges National Science Foundation Fellowship and a GJ Lieberman Fellowship.

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ACTIVIN A IN BRAIN INJURY Pasquale Florio,* Diego Gazzolo,{,z Stefano Luisi,* and Felice Petraglia* *Department of Pediatrics, Obstetrics and Reproductive Medicine, University of Siena, Siena, Italy { Department of Pediatrics, G. Gaslini Children’s University Hospital, Genoa, Italy z Department of Maternal, Fetal and Neonatal Health G. Garibaldi Hospital, Catania, Italy

1. 2. 3. 4.

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biochemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Activin A After Brain Injury in Animals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Activin A After Acute Excitotoxic Brain Injury. . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Activin A After Hypoxic/Ischemic Brain Injury . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Activin A After Mechanical Brain Injury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Activin A After Stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Activin A‐Binding Proteins After Brain Injury . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6. Activin A After Seizure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7. Activin A After Meningitis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Activin A and Neuroprotection: Findings from Animal Studies . . . . . . . . . . . . . . . 6. Human Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

118 80 81 82 84 87 88 90 90 93 95 98 98 101 127

Abbreviations ActRI ActRII ActRIB ActRIIB bFGF CNS CSF

Activin type I receptor Activin type II receptor Activin type IB receptor Activin type IIB receptor Basic fibroblast growth factor Central nervous system Cerebrospinal fluid 117

0065-2423/07 $35.00 DOI: 10.1016/S0065-2423(06)43004-3


118 FLRG FSH HIE IVH PAI‐1 TGF‐

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Follistatin‐related gene Follicle‐stimulating hormone Hypoxic/ischemic encephalopathy Intraventricular hemorrhage Plasminogen activator inhibitor‐1 Transforming growth factor

1. Abstract Activin A is a growth factor composed of two A subunits belonging to the transforming growth factor (TGF‐) superfamily of dimeric proteins. The biological activity of activin A is mediated by two diVerent types of receptors, the type I (ARI and ARIB) and the type II receptors (ARII and ARIIB), and by two activin‐binding proteins, follistatin and follistatin‐ related gene. These factors bind to activin A and thereby inhibit its biological eVects. Activin A, its receptors, and binding proteins are widely distributed throughout the brain. Studies employing models of acute brain injury strongly implicate enhanced activin A expression as a common response to acute neuronal damage of various origins. Hypoxic/ischemic injury, mechanical irritation, and chemical damage of brain evoke a strong upregulation of activin A. Subsequent experimental studies have shown that activin A has a beneficial role to neuronal recovery and that, by activating diVerent pathways, activin A has robust neuroprotective activities. Because activin A induction occurs early after brain injury, its measurement may provide a potential biochemical index of the presence, location, and extent of brain injury. This approach may also facilitate the diagnosis of subclinical lesions at stages when monitoring procedures are unable to detect brain lesion and furthermore establish a prognosis.

2. Introduction The brain produces several neurotrophic factors whose expression is greatly increased in response to brain injury in order to restrict extent of neuronal loss and promote strong recovery [1, 2]. Evidence has also suggested that measurement of these neurotrophic factors as biochemical indexes to: (1) detect cases at risk of adverse neurological outcome, (2) know timing of damage to the central nervous system (CNS), and (3) diagnose subclinical lesions at stages when monitoring procedures are still unable to detect damage in order to enhance therapy and intervention [1].

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In this chapter, we discuss in vitro and in vivo data on the increased expression of activin A in brain injury and its potential use as a biomarker to indicate presence of active CNS damage. 3. Biochemistry Activins are members of the transforming growth factor (TGF‐) superfamily of dimeric proteins, consisting of A and B subunits connected by disulfide linkages. Three diVerent forms of activin include homodimeric activin A (AA), activin B (BB), as well as heterodimeric activin AB (AB) [3]. Recently, C, D, and E chains have also been discovered [4]. Activins were first isolated in 1986 from porcine follicular fluid [5] and were found to retain gonadal proteins. Activins are synthesized in the ovaries and testes and stimulate pituitary follicle‐stimulating hormone (FSH) synthesis and secretion. Because of this observation, the three forms of activin were initially considered members of the hypothalamus–pituitary–gonadal axis [6]. In contrast to inhibins, these factors were named activins because of their stimulatory action on the pituitary secretion of FSH. However, several lines of research have found that activins are synthesized also in specific regions of the brain [3]. The biological activity of activin is mediated by heteromeric receptor complexes consisting of two types of receptor, type I (ARI and ARIB) and type II (ARII and ARIIB), characterized by an intracellular serine/threonine kinase domain [4, 7]. A soluble activin‐binding protein, follistatin, has also been discovered [4, 7, 8]. However, the biological eVects of follistatin are opposite to those of activins and in many cases similar to inhibins [4, 7, 8]. Recently, a new protein composed of 70 amino acids has been identified and named follistatin‐related gene (FLRG) due to its primary sequence homology and modular architecture to follistatin [9]. Like follistatin, FLRG interacts physically with activin A, prevents binding to ActRs, and regulates its function on cellular processes [7, 8]. Activin has been shown to aVect growth and diVerentiation of many target cells of various origins [4–7]. For example, activin can act as a mesoderm‐ inducing factor during early embryonic development. Recent results obtained with mice lacking activin or its receptors revealed a role of activin in organogenesis [7]. During the past few years, increasing evidence has provided information regarding the novel and important roles of activin in brain injury. 4. Activin A After Brain Injury in Animals A large body of evidence has shown that brain lesions upregulate the expression of various growth and diVerentiation factors [10] and that their

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− Excitotoxic injury − Hypoxic/ischemic injury − Mechanical irritation

Brain damage

Increased activin A expression

+ Expression of Ca2++ channels + Expression of PAI-1 modulation of bFGF action

Neuroprotection FIG. 1. The increased expression of activin A is a common response after diVerent stimuli causing brain injury. Nevertheless, activin A is able to exert a neuroprotective action through the activation of diVerent pathways. (Abbreviations: PAI‐1, plasminogen activator inhibitor‐1; bFGF, basic fibroblast growth factor).

temporal and spatial interplay appears crucial for orchestration of postlesion restructuring. Studies employing models of acute brain injury strongly favor the notion that enhanced activin A expression represents a common response to acute neuronal damage of various origin (Fig. 1). 4.1. ACTIVIN A AFTER ACUTE EXCITOTOXIC BRAIN INJURY Strong upregulation of activin A subunit mRNA occurs within 6 h of injury following intracerebroventricular injection of kainic acid that led to neuronal death in the ipsilateral hippocampal region of adult mouse [11]. The expression of B chain was not, however, induced by the lesion. This finding suggested that the A transcripts most likely give rise to activin A, but not to other members of the activin family. Because both A mRNA and activin protein were detected almost exclusively in neurons adjacent to the site of the lesion, it was suggested that increased production occurs in neuronal rather than glial cells [11]. 4.2. ACTIVIN A AFTER HYPOXIC/ISCHEMIC BRAIN INJURY Hypoxic/ischemic injury produced patterns of activin A induction very similar to the one observed after excitotoxic lesion. Indeed, Lai et al. [12]


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using a unilateral model of hypoxic/ischemic brain injury (double ligation of the right carotid artery of 21‐day‐old Wistar rats) reported a strong induction of activin A subunit mRNA as early as 1 h after injury in the dentate gyrus of the nonligated hemisphere, and 24 h after injury in the hippocampus, piriform cortex, and amygdala on the nonligated hemisphere. The expression of A subunit was seen in the center of the infarcted region 5 days after injury. However, the pattern of mRNA distribution strongly correlated with the expected distribution pattern for blood capillaries, thus suggesting a new role for activin A in the response to brain injury as local mediator of angiogenesis during the repair process [12]. Ribonuclease protection assay was used also to quantify the time course of the mRNA expression of activin A subunit and follistatin, following a 60‐min hypoxic/ischemic brain injury in 21‐day‐old Wister rats. Activin A subunit mRNA level increased in the contralateral hemisphere 5 h after injury and returned to normal 10‐h postinjury [13]. 4.3. ACTIVIN A AFTER MECHANICAL BRAIN INJURY Unilateral mechanical brain injury via saline injection induced activin A mRNA in dentate gyrus neurons. Expression was dependent on NMDA receptor activation since pretreatment with MK801 (5 mg/kg, i.p.) largely attenuated the signal. Induction also required de novo protein synthesis, as cycloheximide (10 mg/kg, i.p.) pretreatment abolished the expression of activin A mRNA 1 h after injury [14]. Expression levels of activin A mRNA significantly increased in damaged regions of brain tissue of rat embryos after penetrating injury, coincident with FLRG [15]. 4.4. ACTIVIN A AFTER STROKE With respect to stroke injury, the possible function of activin A has been recently investigated in female Sprague–Dawley rats that underwent permanent middle cerebral artery occlusion (MCAO). In this study, the expression of activin A mRNA was evaluated by semiquantitative reverse transcriptase‐polymerase chain reaction (RT‐PCR) at 24 h following onset of ischemia. Activin A mRNA expression was upregulated in response to injury. Dual‐label immunocytochemistry followed by confocal z‐stack analysis showed that activin A expressing cells comprised neurons. Induction of activin A was observed in many regions of the cortex including the cingular, frontal, and parietal areas in response to cortical infarction but not in the respective contralateral regions, thus supporting the concept that changes of activin A mRNA were translated into protein. Furthermore, because estradiol replacement protects against MCAO‐induced cell death, the influence of estradiol replacement on activin A gene expression was also investigated at 4, 8, 16,

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and 24 h after MCAO by in situ hybridization in estradiol‐ or vehicle‐treated rats. Activin A mRNA was found to be upregulated in cortical and striatal areas in the ipsilateral hemisphere 4 h after injury. In addition, activin A mRNA levels in the cortex increased dramatically with time and were highest at 24 h after the insult. Estradiol replacement did not, however, influence this increase [16]. 4.5. ACTIVIN A‐BINDING PROTEINS AFTER BRAIN INJURY Ribonuclease protection assay was used to quantify the time course of the mRNA expression of follistatin following a 60‐min hypoxic/ischemic brain injury in 21‐day‐old Wister rats [13]. This study showed that follistatin mRNA levels decreased in the injured hemisphere at 5 and 10 h after injury [13]. Expression levels of FLRG mRNA and protein were also analyzed in brain tissue of rat embryos using quantitative PCR and immunohistochemical methods [15]. FLRG and follistatin mRNAs were found to be mainly expressed in astroglial cells, while activin A mRNA was abundant in primary neurons. Expression levels of FLRG mRNA were significantly increased in damaged regions after penetrating injury. Immunohistochemical observations showed that positive signals of FLRG protein were colocalized in glial fibrillary acidic protein‐positive reactive astroglial cells located in damaged regions after penetrating injury. Expression of follistatin mRNA was shown to decrease in damaged regions after the brain injury [15]. In situ hybridization analysis of the expression of two forms of activin receptor types II and IIB revealed their wide distribution throughout the brain supporting results obtained from binding studies [17, 18]. ActR‐1 mRNA is upregulated in neurons in the dentate gyrus 6 h after a mild cerebral contusion injury [19] as well as in the cerebral ipsilateral and contralateral cortex 24 h after MCAO injury [16]. 4.6. ACTIVIN A AFTER SEIZURE The early induction of activin A is associated with seizure activity since administration of anticonvulsants, such as carbamazepine (8 mg/kg) [12] and glutamate N‐methyl‐D‐aspartate (NMDA) receptor antagonist, MK801 [14], markedly attenuated A subunit mRNA expression after both mechanical [14] and hypoxic/ischemic [12] brain injury. Seizure‐induced expression of A subunit correlated well with distribution of activin receptor subtypes ActRII and ActRIIB in the brain [11, 17]. It has been shown that convulsive seizure caused by kainate or pentylenetetrazol significantly induced expression of activin A mRNA in the hippocampus of male Wistar rats [20]. Furthermore, high frequency stimulation of perforant pathway, which produced a persistent long‐term potentiation (>10 h), caused a marked increase in the level of activin


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A mRNA within the dentate gyrus of urethane‐anesthetized rat. This increase was NMDA receptor dependent. These data have shown that two forms of neural activity (seizure and long‐term potentiation) lead to the expression of activin A mRNA in the hippocampus and suggest a role for activin in the maintenance of neuronal function in the adult brain [20]. More recently, the induction of activin A mRNA in amygdala and cortex of Long‐Evans hooded rats at several stages of seizure development was investigated [21]. This study used amygdala kindling, an important model of temporal lobe epilepsy of the complex partial type with secondary generalization. With this technique it is possible to examine the molecular events that accompany the spread of seizure activity from the original focus, especially those responsible for enduring synaptic changes that occur in aVected areas [22, 23]. Activin A has been implicated in neuronal development where it is important for neurotransmitter phenotype expression [24, 25]. Briefly, a strong activin A mRNA induction, measured 2 h after the first stage 2 (partial) seizure, appeared in neurons of the ipsilateral amygdala (confined to the lateral, basal, and posterior cortical nuclei) and insular, piriform, orbital, and infralimbic cortices. Activin A mRNA induction, after the first stage 5 (generalized) seizure, spread to the contralateral amygdala (same nuclear distribution) and cortex, and the induced labeling covered much of the convexity of neocortex as well as piriform, perirhinal, and entorhinal cortices in a nearly bilaterally symmetrical pattern [21]. 4.7. ACTIVIN A AFTER MENINGITIS Using a rabbit model of meningitis, activin A levels in cerebrospinal fluid (CSF) rose progressively and increased approximately 15‐fold within 24 h following intracisternal inoculation with Streptococcus pneumoniae. Subsequent immunohistochemistry showed that activin A was localized to epithelial cells of the choroid plexus, cortical neurons, and the CA3 region of the hippocampus [26]. Analysis of brains and other organs from uninfected and infected animals sacrificed 6–36 h after infection did not, however, reveal any significant diVerences in the distribution and intensity of follistatin mRNA and protein expression [27]. 5. Activin A and Neuroprotection: Findings from Animal Studies Although enhanced activin A expression represents a common response to acute neuronal damage of various origins, the functional implications of enhanced activin A expression are not however known. Several experimental studies have shown that activin A has a beneficial role in neuronal recovery.

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Activin is known to support the survival of neurogenic cell lines and retinal neurons [28] and oVers protection against neurotoxic damage in the cultures of midbrain dopaminergic neurons [29]. Furthermore, activin A has been reported to enhance the survival of rat embryonic hippocampal neurons in vitro [30], to decrease ischemic brain injury in infant rats [14], and to rescue striatal neurons against neurotoxic damage in rats [31]. The neuroprotective action of activin A is exerted by activating diVerent pathways. Increased intracellular Ca2þ concentration ([Ca2þ]i) in neurons has been demonstrated to support neuronal survival [32, 33]. Indeed, depolarization‐induced Ca2þ entry through voltage‐dependent Ca2þ channels mimics and complements the action of neurotrophic factors, including neuronal survival and the maintenance of neurites after neurotrophic factor deprivation [34]. Iwahori et al. [30] showed that activin A was able to support survival of primary cultures of rat hippocampal neurons exposed to KCl, a well‐known depolarizing agent. Such an eVect is directly induced by activin A, since its neurotrophic action is blocked by treatment with genistein, a tyrosine kinase inhibitor, or calphostin C, a protein kinase C inhibitor (activin A receptors are transmembrane protein serine/threonine kinases [6, 7]). The neuroprotective eVect of activin A is driven through the action of a tyrosine kinase and protein kinase C by increasing the expression of voltage‐dependent Ca2þ channel, since nicardipine, a blocker of the L‐type Ca2þ channel, and cycloheximide, a protein synthesis inhibitor, inhibited the neurotrophic eVect of activin A [30]. The neuroprotective action of activin A may also involve basic fibroblast growth factor (bFGF), a protein with a well‐documented neuroprotective and neurotrophic profile [35–39]. In mice, the induction of activin A is an essential step in the signaling cascade of bFGF required for neuroprotection. Exogenous bFGF aVords neuroprotection against excitotoxicity acute brain injury induced by intracerebroventricular injection of kainic acid which selectively destroys CA3 neurons of the ipsilateral hippocampus [40]. Recombinant activin A showed the same eYcacy as exogenous bFGF to abrogate excitotoxic neuronal loss. In the presence of activin‐binding protein follistatin, which neutralizes activin A actions both in vitro and in vivo [4, 7, 8], the beneficial action of bFGF in this lesion model was lost [40]. Another pathway activated by activin A is that of the tissue type plasminogen activator (t‐PA) [41]. t‐PA is a serine protease involved in neuronal plasticity and cell death induced by excitotoxins and ischemia in the brain [42, 43]. Plasminogen activator inhibitor‐1 (PAI‐1) produced by astrocytes mediates the neuroprotective eVect of the TGF‐1 in NMDA‐induced neuronal cell death [44]. Moreover, it has been observed that PAI‐1 expression in mice was also modulated by activin A in astrocytes [44].


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6. Human Studies During development, activin A‐subunit, follistatin, and ActRII and ActRIIB transcripts are coexpressed in CNS [18]. Many studies report that enhanced activin A expression represents a common response to acute neuronal damage of various origins and together have suggested that increase activin A concentration may reasonably be used as a direct measure of increased CNS production. The development of an analytical assay in the last few years has made it possible to measure activin A concentrations after brain injury in humans. Until now, activin A measurement after human brain injury was generally limited to hypoxic/ischemic encephalopathy (HIE) in the perinatal period. The findings that the fetus is the main source of activin A in the umbilical cord blood during pregnancy [45] and that increased umbilical cord activin A levels are an indirect marker of impaired blood flow in the fetal circulation [46] may provide an index of fetal hypoxia in preterm newborn [47]. These observations, together with in vitro evidence showed that induction of activin A occurs early after brain injury, have prompted investigations as to whether activin A could provide additional information to physicians and indicate occurrence of perinatal brain injuries at a stage when diagnostic procedures are of limited benefit. This approach could thus permit implementation of earlier therapeutic and intervention strategies. Indeed, the specific pathologic processes preceding the onset of irreversible cerebral injury appear to be a combination of several complex mechanisms due to the severity and duration of the insult and concomitent biochemical modifications within the brain [48]. Injury in the fetal and neonatal brain results in neonatal mortality and morbidity and long‐term sequelae such as cerebral palsy, mental retardation, epilepsy, and learning disability [49, 50]. As such, the essential steps for establishing a diagnosis of cerebral bleeding or of hypoxic/ischemic events, the main factors involved in the genesis of perinatal brain damage in preterm and term infants, are similar. These steps are generally based on clinical examination, continuous electroencephalographic monitoring, cerebral ultrasound and Doppler velocimetry recordings, CSF assessment, cerebral computerized tomography, magnetic resonance imaging, and proton magnetic resonance spectroscopy [49, 50]. These tools can provide useful and crucial information regarding the presence, location, and extent of brain injury and may be useful in establishing a prognosis. Although there is a wide range of diagnostic possibilities, there are several problems associated with the early diagnosis of cases at risk. The limited interval for diagnosis and therapeutic intervention and the confusing/ambiguous eVects of sedative and anticonvulsant drugs are the main factors involved. This particularly applies

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to neurologic examination [49, 50], electroencephalography [51], and cerebrovascular recordings [52]. Other limitations are poor reproducibility and the need for complex measurements (CSF measurements), infrequent use of longitudinal monitoring, and high technologic cost (computerized tomography, magnetic resonance imaging, and proton magnetic resonance spectroscopy). For these reasons, cerebral ultrasound scanning is the procedure of choice for diagnosis. It should be noted, however, that progression and extent of hemorrhage and brain insult can only be defined at later stages (i.e., after more than 6–12 h), which limits the possibility of intervention [49, 50]. As such, there is a strong need for new markers that enable early and longitudinal monitoring and assess eVectiveness of therapeutic intervention. This limitation has prompted the investigation as to the clinical usefulness of activin A in perinatal brain damage. To accomplish this goal, umbilical cord activin A levels were evaluated in preterm newborns and evaluated in their diagnostic ability to predict occurrence of perinatal IVH [53]. In this study, activin A levels were significantly higher in preterm newborns developing IVH than in those who did not follow up. The cutoV indicated by receiver– operator characteristics (ROC) curve analysis achieved a sensitivity of 100% and a specificity of 93% for activin A as a single marker for prediction of IVH in preterm newborns [53]. In a longitudinal cohort study, activin A levels were measured in CSF collected at birth from healthy babies and from asphyxiated full‐term newborns who experienced HIE within the first 7 days after birth. Full‐ term asphyxiated infants had increased CSF activin A levels in comparison to healthy newborns suggesting that hypoxia/asphyxia triggers activin A secretion. Furthermore, activin A concentrations were higher in those asphyxiated infants who developed severe HIE than in those who did not or in controls. This finding supports the notion that elevated CSF activin A levels provide a reasonable marker to indicate CNS production. Early prediction of hypoxic/ischemic brain lesions was thus possible before the appearance of related biophysical signs, since newborns with activin A levels above the threshold defined by the ROC curve analysis had a probability of developing HIE as high as 100% and 0% if levels were unchanged [54]. More recently, activin A levels were measured in urine collected immediately after birth in asphyxiated full‐term newborns and the diagnostic accuracy in perinatal encephalopathy was evaluated. Activin A levels in urine were significantly (p < 0.0001) higher in asphyxiated newborns with moderate or severe HIE than in those with absent or mild HIE. Moreover, an activin A cutoV concentration of >0.08 ng/L at first urination had a sensitivity of 83.3% and a specificity of 100% for predicting development of moderate or severe HIE [55].


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Finally, follistatin concentrations determined in CSF of patients with bacterial and viral meningitis were found to be elevated during meningitis and correlated with total protein and lactate concentration [27]. 7. Conclusions The neuroprotective role of activin A and its overexpression in the early neuronal response in vitro provides compelling evidence to include this molecule in the list of various growth and diVerentiation factors in brain injury. Activin A may also be of relevance for orchestration of postlesion restructuring and therefore for protecting neurons against the immediate impact of the injury. Although activin A use may have additional potential use in reducing neuronal loss, human studies are still lacking and generally limited to HIE in the perinatal period. Despite this, activin A should be included among biochemical markers of brain damage because of its ability to diagnose subclinical lesions at stages when monitoring procedures are still unable to detect brain lesion and whose measurement could be especially useful in the brain injury prevention and/or management. REFERENCES [1] Ingebrigtsen T, Romner B. Biochemical serum markers for brain damage: A short review with emphasis on clinical utility in mild head injury. Restor Neurol Neurosci 2003; 21:171–176. [2] Hughes PE, Alexi T, Walton M, Williams CE, Dragunow M, Clark RG, et al. Activity and injury‐dependent expression of inducible transcription factors, growth factors and apoptosis‐related genes within the CNS. Prog Neurobiol 1999; 57:421–450. [3] Luisi S, Florio P, Reis FM, Petraglia F. Expression and secretion of activin A: Possible physiological and clinical implications. Eur J Endocrinol 2001; 145:225–236. [4] Bernard DJ, Chapman SC, WoodruV TK. Mechanisms of inhibin signal transduction. Recent Prog Horm Res 2001; 56:417–450. [5] Vale W, Rivier J, Vaughan J, McClintock R, Corrigan A, Woo W, et al. Purification and characterization of an FSH releasing protein from porcine ovarian follicular fluid. Nature 1986; 321:776–779. [6] Boyd FT, Cheifetz S, Andres J, Laiho M, Massague J. Transforming growth factor‐beta receptors and binding proteoglycans. J Cell Sci 1990; 13:131–138. [7] Harrison CA, Gray PC, Vale WW, Robertson DM. Antagonists of activin signaling: Mechanisms and potential biological applications. Trends Endocrinol Metab 2005; 16:73–78. [8] Kumar TR. Too many follistatins: Racing inside and getting out of the cell. Endocrinology 2005; 146:5048–5051. [9] Maguer‐Satta V, Bartholin L, Jeanpierre S, Gadoux M, Bertrand S, Martel S, et al. Expression of FLRG, a novel activin A ligand, is regulated by TGF‐beta and during hematopoiesis. Exp Hematol 2001; 29:301–308.

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[30] Iwahori Y, Saito H, Torii K, Nishiyama N. Activin exerts a neurotrophic eVect on cultured hippocampal neurons. Brain Res 1997; 760:52–58. [31] Hughes PE, Alexi T, Williams CE, Clark RG, Gluckman PD. Administration of recombinant human activin‐A has powerful neurotrophic eVects on select striatal phenotypes in the quinolinic acid lesion model of Huntington’s disease. Neuroscience 1999; 92:197–209. [32] Collins F, Schmidt MF, Guthrie PB, Kater SB. Sustained increase in intracellular calcium promotes neuronal survival. J Neurosci 1991; 11:2582–2587. [33] Koike T, Tanaka S. Evidence that nerve growth factor dependence of sympathetic neurons for survival in vitro may be determined by levels of cytoplasmic free Ca2þ. Proc Natl Acad Sci USA 1991; 88:3892–3896. [34] Franklin JL, Sanz‐Rodriguez C, Juhasz A, Deckwerth TL, Johnson EM, Jr. Chronic depolarization prevents programmed death of sympathetic neurons in vitro but does not support growth: Requirement for Ca2þ influx but not Trk activation. J Neurosci 1995; 15:643–664. [35] Anderson KJ, Dam D, Lee S, Cotman CW. Basic fibroblast growth factor prevents death of lesioned cholinergic neurons in vivo. Nature 1988; 332:360–361. [36] Baird A. Fibroblast growth factors: Activities and significance of non‐neurotrophin neurotrophic factors. Curr Opin Neurobiol 1994; 4:78–86. [37] Tanaka R, Miyasaka Y, Yada K, Ohwada T, Kameya T. Basic fibroblast growth factor increases regional cerebral blood flow and reduces infarct size after experimental ischemia in a rat model. Stroke 1995; 26:2154–2158. [38] Dietrich WD, Alonso O, Busto R, Finklestein SP. Posttreatment with intravenous basic fibroblast growth factor reduces histopathological damage following fluid‐percussion brain injury in rats. J Neurotrauma 1996; 13:309–316. [39] Bethel A, Kirsch JR, Koehler RC, Finklestein SP, Traystman RJ. Intravenous basic fibroblast growth factor decreases brain injury resulting from focal ischemia in cats. Stroke 1997; 28:609–615. [40] Tretter YP, Hertel M, Munz B, ten Bruggencate G, Werner S, Alzheimer C. Induction of activin A is essential for the neuroprotective action of basic fibroblast growth factor in vivo. Nat Med 2000; 6:812–815. [41] Docagne F, Nicole O, Marti HH, MacKenzie ET, Buisson A, Vivien D. Transforming growth factor‐beta1 as a regulator of the serpins/t‐PA axis in cerebral ischemia. FASEB J 1999; 13:1315–1324. [42] Qian Z, Gilbert ME, Colicos MA, Kandel ER, Kuhl D. Tissue‐plasminogen activator is induced as an immediate‐early gene during seizure, kindling and long‐term potentiation. Nature (London) 1993; 361:453–457. [43] Wang YF, Tsirka SE, Strickland S, Stieg PE, Soriano SG, Lipton SA. Tissue plasminogen activator (tPA) increases neuronal damage after focal cerebral ischemia in wild‐type and tPA‐deficient mice. Nat Med 1998; 4:228–231. [44] Buisson A, Nicole O, Docagne F, Sartelet H, MacKenzie ET, Vivien D. Up‐regulation of a serine‐protease inhibitor in astrocytes mediates the neuroprotective activity of transforming growth factor‐1 (TGF‐1). FASEB J 1998; 12:1683–1691. [45] Florio P, Calonaci G, Luisi S, Severi FM, Ignacchiti E, Palumbo M, et al. Inhibin A, inhibin B and activin A concentrations in umbilical cord artery and vein. Gynecol Endocrinol 2003; 17:181–185. [46] Florio P, Reis FM, Severi FM, Luisi S, Imperatore A, Palumbo MA, et al. Umbilical cord serum activin A levels are increased in pre‐eclampsia with impaired blood flow in the uteroplacental and fetal circulation. Placenta 2006; 27:432–437.

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[47] Florio P, Perrone S, Luisi S, Longini M, Tanganelli D, Petraglia F, et al. Activin A plasma levels at birth: An index of fetal hypoxia in preterm newborn. Pediatr Res 2003; 54:696–700. [48] Buonocore G, Perrone S. Biomarkers of hypoxic brain injury in the neonate. Clin Perinatol 2004; 31:107–116. [49] Volpe JJ. Neurology of the Newborn, 4th ed. Philadelphia: WB Saunders, 2001. [50] Volpe JJ. Hypoxic‐ischemic encephalopathy: Clinical aspects. In: Volpe JJ., editor. Neurology of the Newborn. Philadelphia: WB Saunders, 1995: 314–370. [51] Pezzani C, Radvanyi MF, Relier JP, Monod N. Neonatal electroencephalography of the newborn during the first twenty‐four hours of life in full‐term newborn infants. Neuropediatrics 1986; 17:11–18. [52] Ilves P, Talvik R, Talvik T. Changes in Doppler ultrasonography in asphyxiated term infants with hypoxic‐ischaemic encephalopathy. Acta Paediatr 1998; 87:680–684. [53] Florio P, Perrone S, Luisi S, Vezzosi P, Longini M, Marzocchi B, et al. Increased plasma levels of activin A in preterm newborns developing intraventricular haemorrhage. Clin Chem 2006; 52:1516–1521. [54] Florio P, Luisi S, Bruschettini M, Grutzfeld D, Dobrzanska A, Bruschettini P, et al. Cerebrospinal fluid activin A measurement in asphyxiated full‐term newborns predicts hypoxic ischemic encephalopathy. Clin Chem 2004; 50:2386–2389. [55] Florio P, Torricelli M, Luisi S, Moataza B, Iman I, Hala M, et al. High urinary levels of activin A in asphyxiated full‐term newborns with moderate or severe hypoxic ischemic encephalopathy. Clin Chem 2006; in press.


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METHODS FOR PREDICTING HUMAN DRUG METABOLISM Larry J. Jolivette* and Sean Ekins{,z *Preclinical Drug Discovery, Cardiovascular & Urogenital Centre of Excellence in Drug Discovery, GlaxoSmithKline, King of Prussia, Pennsylvania { ACT LLC, Jenkintown, Pennsylvania z Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland

1. 2. 3. 4. 5. 6.

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . In Vitro Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . High‐Throughput Assays. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . In Vivo Predictions from In Vitro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Computational Metabolism Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. 2D‐ and 3D‐QSAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Electronic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Homology Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4. Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5. Expert Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6. Hybrid Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7. Caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.8. Metabolism Prediction Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Integration of Drug Metabolism Data and Interpretation . . . . . . . . . . . . . . . . . . . . 8. Newer Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

131 118 119 119 120 120 121 121 122 122 123 123 125 127 127 127 103 158

1. Abstract Drug metabolism information is a necessary component of drug discovery and development. The key issues in drug metabolism include identifying: the enzyme(s) involved, the site(s) of metabolism, the resulting metabolite(s), and the rate of metabolism. Methods for predicting human drug metabolism 131 0065-2423/07 $35.00 DOI: 10.1016/S0065-2423(06)43005-5


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from in vitro and computational methodologies and determining relationships between the structure and metabolic activity of molecules are also critically important for understanding potential drug interactions and toxicity. There are numerous experimental and computational approaches that have been developed in order to predict human metabolism which have their own limitations. It is apparent that few of the computational tools for metabolism prediction alone provide the major integrated functions needed to assist in drug discovery. Similarly the diVerent in vitro methods for human drug metabolism themselves have implicit limitations. The utilization of these methods for pharmaceutical and other applications as well as their integration is discussed as it is likely that hybrid methods will provide the most success.

2. Introduction We are exposed daily to a vast array of small molecules commonly termed xenobiotics that can be absorbed across the cellular barriers that maintain physiological functions. Some of these molecules may be biologically active and possibly toxic to the cell. The metabolism of molecules by ubiquitously expressed enzymes to hydrophilic metabolites is therefore an important prerequisite for their eventual elimination from the body. The field of drug metabolism has therefore been widely studied and many metabolic pathways have been determined since the early 1800s (Table 1, see the International Society for the Study of Xenobiotics Web site http://www.issx.org/pages/ page04a.html) [1]. From a pharmaceutical perspective, it is important to understand the metabolism of a new chemical entity as metabolic transformations in vivo can modify bioavailability, eYcacy, chronic toxicity, and rate and route of excretion. Both the parent molecule and the products of such metabolic pathways may also be involved in drug interactions where they interfere with metabolism of endogenous or other coadministered compounds. Such drug–drug interactions, or other adverse drug reactions, can have potentially fatal consequences for the patient or be very costly for health care providers [2–6]. Research in this area has certainly advanced over the past 20 years, though fundamentally, we are still trying to answer the same question—how is the molecule metabolized and are the metabolites biologically active and potentially toxic [7]? Naturally the use of drug metabolism data in drug discovery and development has also changed over the years aided by new experimental methods and analytical techniques and will certainly develop in the future into areas that are perhaps predictable from past experience [8]. The important issues that will always be key in drug metabolism include identifying: the enzyme(s) involved, the site(s) of metabolism, the resulting metabolite(s), and the rate of metabolism [9]. In addition,

METHODS FOR PREDICTING HUMAN DRUG METABOLISM

HISTORY

OF

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TABLE 1 XENOBIOTIC METABOLISM, AFTER LEIBMAN [1]

Reaction Hippuric acid formation from benzoic acid Aromatic hydroxylation of benzene and side chain hydroxylation of toluene Sulfate conjugation of phenol Glucuronide conjugation of o‐nitro‐toluene and camphor metabolites Mercapturic acid formation from halobenzenes Reduction of chloral hydrate Methylation of pyridine Nitroreduction and arylamine acetylation N‐dealkylation of methylamine O‐demethylation isoform N‐hydroxylation of acetanilide N‐oxidation of trimethylamine Azoreduction of prontosil Epoxidation of heptachlor

References Keller, 1842 Schultzen and Naunyn, 1867 Baumann, 1876 JaVe, 1874; Schmiedeberg and Meyer, 1879 Baumann and Preusse,1879; JaVe, 1879 Von Mering, 1884 His, 1887 Cohn, 1893 Pohl, 1893 Rohmann, 1905 Ellinger, 1920 Linzel and Hoppe‐Seyler, 1934 Trefouel, Trefouel, Nitti, and Bovet, 1935 Radomski and Davidow, 1953

The references are provided in the original publication of Leibman [1].

the role of species diVerences in metabolism is key to aid in extrapolation from commonly used animals to man. As the majority of drugs as well as other xenobiotics undergo phase I metabolism via the cytochrome P450 (P450) enzymes predominantly in liver, most of the research has been done on this organ although these enzymes are also expressed extrahepatically and demonstrate considerable activity in organs such as the intestine and kidney [10]. Drug‐metabolizing enzymes are capable of either inactivating or activating both xeno‐ and endobiotic molecules. Secondary or tertiary metabolism may then occur via phase II reactions including glucuronidation, sulfation, or other phase II biotransformations, which can result in metabolites that exhibit biological or toxic activity [11]. Despite considerable interest in understanding the role of the phase II enzymes in drug metabolism, pharmaceutical research in this area continues to lag behind that of the P450s [11]. It should be clear that depending on the xenobiotic molecule structure and the enzymes involved in metabolism, there could be a range of possible metabolites. The prediction of the potential metabolites and their subsequent disposition in the human is therefore desirable but also extremely complex. There is an urgent requirement within the pharmaceutical, biotechnology industries, regulatory authorities, and academia to improve the success of molecules selected for clinical trials. Metabolism is just one important component

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contributing to successful drug discovery and development. Since mechanistic metabolism studies in vivo are themselves quite complex and resource intensive, there is a need for in vitro and computational methodologies for prioritization and uncovering the relationships between the structure and metabolic activity of novel molecules. In order for such models to be relevant they should be able to determine the complete xenobiotic biotransformation pathways in the body that define the activity, toxicity, and interactions with normal endogenous metabolism. This in itself is a tall order. To date computational approaches have only been applied to a fraction of the known enzymes involved in human drug metabolism (Table 2). As we will describe, there are numerous experimental and computational approaches that have been developed in order to predict human metabolism which have limitations, and there has been considerable discussion of this topic already in the literature. Our aim is to present a cross section of diVerent methods that are used for human metabolism prediction and how they can be utilized. 3. In Vitro Techniques We and others have previously extensively reviewed in vitro approaches applied to drug metabolism including incubations of new chemical entities with S9 fraction, microsomes, recombinant enzymes, hepatocytes, and tissue slices [12, 13] as well as proposing the early use of computational models for absorption, distribution, metabolism and excretion (ADME) properties before synthesis and in vitro/in vivo testing [13–15]. In the intervening years, there have been many reviews as well as other studies that have documented the use of in vitro metabolism data and ultimately the application of this to predict the in vivo situation. Pelkonen et al. have described two important goals for in vitro studies of metabolism, metabolic stability, and drug–drug interactions as well as the systems and analytical tools [16]. Uncertainties and sources of bias or error when using in vitro studies for prediction of in vivo drug interactions or clearance have been described [17, 18]. Additionally, detailed reviews have provided a selection of examples of in vivo drug–drug interactions predicted from in vitro metabolism calculations [19, 20]. Tiered screens for assessment of P450 inhibition have been proposed in one strategy for drug discovery alongside secondary screens for enzyme profiling and tertiary screening for cell‐based enzyme inhibition [21]. Compounds are classified as potent (IC50 < 1 mM) intermediate (1–10 mM) or weak (>10 mM) inhibitors in these screens [22]. Reaction‐phenotyping strategies have been described which are applicable to P450s and conjugation reactions and build on the work of Rodrigues et al. [23]. In general, these methods may use hepatocytes, microsomes, or recombinant enzymes, and the role of specific

TABLE 2 EXAMPLES

OF

HUMAN ENZYMES INVOLVED COMPUTATIONAL

Enzyme Cytochrome P450

Cellular location Microsomal

Reaction Oxidation

Cofactor

AND

IN

DRUG METABOLISM

WITH

SOURCES

OF

STRUCTURAL DATA Phase

QSAR

Pharmacophore

Homology models

Crystal structures

NADPH

1

[78–81] [82–87, 122]

[89, 90, 108–119]

[154–164]

[145–147, 152, 153]

[262–265]

and reaction Flavin containing

Microsomal

Oxidation

NADPH

1

[256]

Mitochondrial

Oxidation

FAD

1

[257–259]

[260]

[261]

FAD

[267]

[268–270]

monooxygenase Monoamine oxidases

135

Aromatases

Mitochondrial

Oxidation

1

[266]

Esterases

Microsomal,

Hydrolysis

1

[271–275]

Hydration

1

[282]

[283]

NADþ

1

[286]

[287]

NAD[P]H

1

(carboxylesterase,

lysosomes

pseudo‐cholinesterase,

and cytosolic

[276–281]

paraoxonase) Epoxide hydrolases

Microsomal

[284]

[285]

and cytosolic Alcohol dehydrogenase

Cytosol, blood, microsomes

NAD(P)H‐quinone oxidoreductase Dihydropyrimidine

Cytosol, microsomes

Carbonyl

[288–294]

reduction Quinone reduction

Cytosol

Reduction

NADPH

1

Cytosol

Oxidation

NADP(H)

1

Cytosol

Oxidation

O2 or NADþ

1

dehydrogenase Dihydrodiol dehydrogenase Molybdenum hydroxylases

(continues)

TABLE 2 (Continued ) Enzyme

Cellular location

Reaction

Cofactor

Phase

QSAR

Polyamine oxidase

Cytosol

Oxidation

FAD

1

Diamine oxidase

Cytosol

Oxidation

FAD

1

UDPGA

2

[95] [96, 295, 296]

Prostaglandin H‐synthase

Microsomes

Oxidation

UDP‐

Microsomal

Glucuronidation

Sulfotransferases

Cytosolic

Sulfation

PAPS

2

Phenyl‐O‐methyltransferase

Microsomal

O‐methylation

S‐adenosylmethionine

2

Catechol

Cytosolic and

O‐methylation


2

[295, 296, 301, 302]

Cytoplasm

S‐methylation


2

[303]

N‐acetyl transferases

Cytosolic

Acetylation

Acetyl‐coenzyme A

2

[304]

Amino acid conjugation

Mitochondria,

Conjugation

Acyl‐CoA, serine,

Glutathione

Microsomal

Glutathione

Glutathione

2

[257]

Pharmacophore

Homology models

Crystal structures

1 [133–135]

glucuronosyltransferases

136

O‐methyltransferase Thiopurine

[96, 97, 297–300]

microsomal

methyltransferase

microsomes S‐transferases

and cytosolic

[305, 306]

proline [307–311]

conjugation

NADPH ¼ ‐nicotinamide adenine dinucleotide phosphate reduced from NAD, NAD ¼ ‐nicotinamide adenine dinucleotide, FAD ¼ flavin adenine dinucleotide, PAPS ¼ 30 ‐phosphoadenosine 50 ‐phosphosulfate, UDPGA ¼ uridine diphosphate glucuronic acid.


137

enzymes is then delineated by analysis of metabolites produced, correlation studies and chemical or antibody inhibition [24]. Such reaction‐phenotyping eVorts represent just a small part of the process of determining whether a new chemical entity is developable but are also a component of the data submitted to regulators [25]. In such cases, reaction phenotyping is likened to a necessary decision gate [26]. The potential for predictive phenotyping has been more recently suggested which incorporates computational approaches [27]. Others have also suggested that computational methods will allow evaluation of metabolism earlier, and this is a major focus though tempered by realistic expectations of their utility [28].

4. High‐Throughput Assays Due to the escalating cost of bringing new drugs to the marketplace, the pharmaceutical industry has sought mechanisms to identify, early in the drug discovery process, compounds that are likely to fail in the clinic. Thus, in vitro metabolic clearance and P450 inhibition assessment have been implemented to identify those compounds that are likely to demonstrate the desired human exposure profile and a low likelihood of involvement in drug–drug interactions [29]. Implementation of the in vitro clearance assay has been fueled by its simplistic experimental design, compatibility with automated high‐throughput screening formats, and concomitant advances in analytical and data‐processing capabilities [30–34]. These assays tend to be performed in the presence of commercially available hepatic microsomal or S9 fraction, or hepatocytes [35–37] obtained from preclinical animal species and human. Microsomal or S9 fraction incubations tend to be the most commonly used early in the drug discovery process and hepatocyte incubations are placed later in the process. Microsomes are suitable for assessment of P450‐dependent metabolism, while S9 fraction incubations are typically used for phase I and limited phase II metabolism assessment. Hepatocyte incubations add cellular architecture and a more complete phase I and phase II metabolic capacity component to the analysis [36, 38]. Since protein binding can confound interpretation of in vitro clearance data [39–41], comparison of compounds based on in vitro clearance should only be made for compounds with similar structures that are also likely to possess similar protein‐binding properties. Several strategies are typically used to screen new chemical entities for P450 inhibition potential in the preclinical drug discovery paradigm. These include the traditional human hepatic microsomal incubation with specific probe substrates and LC/MS detection, use of heterologously expressed recombinant human P450s and profluorescent probe substrates with fluorescence intensity detection [42], and finally the use of a mix of recombinant

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human P450s and a cocktail of specific probe substrates with LC/MS/MS detection [43]. While human hepatic microsomes are a physiologically more relevant source of P450 activity compared to recombinant P450s, this enzyme source can lead to potentially low P450 inhibition estimation for compounds that are metabolically labile [44]. Furthermore, compound throughput capacity of human hepatic microsomal‐based P450 inhibition assays are limited by analytical (typically LC/MS/MS) capacity, although methodologies have been described to increase analytical capacity for P450 inhibition screening [45] or by using fewer data points [46, 47]. The use of a mixture of recombinant P450s with a cocktail of specific probe substrates is an intriguing approach; nonetheless, compound throughput capacity limitations are the same as the traditional human hepatic microsomal P450 inhibition assay. The ‘‘workhorse’’ of early drug discovery P450 inhibition screening over the past decade has been the fluorometric P450 inhibition assay. Implementation of this assay in early drug discovery has been facilitated by its compatibility with high‐throughput microtiter plate formats, commercially available recombinant P450s, and profluorescent probe substrates, as well as automation of experimental, analytical, and data analysis aspects of the assay. This assay system, formatted as either a single (for increased screening capacity) or multiple (for IC50 value generation) test compound concentration assay has been highly miniaturized and can be run in up to a 1536‐well plate format [48]. The fluorometric P450 inhibition assay can suVer interference issues with test compounds that are also fluorescent or are converted to fluorescent metabolites. Additionally, some fluorometric probe substrates are more prone to yield apparent activation of enzyme activity than others [49] while the physiological significance of this phenomenon remains unclear. 5. In Vivo Predictions from In Vitro There have been many reports on the use of in vitro clearance data to predict drug plasma clearance in vivo [50–55]. Yet, other reports have questioned the reliability of in vitro clearance data to project in vivo clearance [56–59]. In some cases, consideration of plasma and nonspecific microsomal protein‐binding data improves the in vitro–in vivo concordance [60]. However, application of corrections for protein binding cannot be ascertained prospectively; therefore, the projected human clearance from in vitro clearance data should be interpreted with caution. A recent survey of the in vitro clearance literature yielded findings of profound inter‐laboratory diVerences (up to >100‐fold) in intrinsic clearance values [61]. The quantitative value of in vitro clearance obtained by experimentation can be influenced by several factors including variability in P450 activity between batches of hepatic


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microsomes, protein concentration, and choice of solvents, which can adversely aVect the in vitro clearance values obtained [31, 62, 63]. Thus, the relative ability of in vitro clearance data to predict in vivo clearance is likely to diVer from one laboratory to another. Given the uncertainties of the predictivity of in vitro clearance data to accurately project in vivo clearance, it is advisable to investigate and define the in vitro–in vivo correlation with preclinical animal experimentation before committing to the use of in vitro clearance data to assign development prioritization between diVerent chemical series, or maybe even for diVerentiation between compounds within a single chemical series. The ability of in vitro clearance data derived from hepatic S9 fraction or hepatocytes to project human clearance has not been reported as extensively as hepatic microsomal data [61]. Freshly isolated human hepatocytes have been used for predicting in vivo clearance [64] while the use of cryopreserved hepatocytes is finding favor within the pharmaceutical industry due to evidence of minimal changes for some enzyme activities following cryopreservation for at least a year [65]. However, the utility of these human in vitro data to predict in vivo clearance has been questioned [57]. There seems to be a shortage of consistent in vitro data for rat, dog, monkey, and human to evaluate the predictivity of multispecies in vitro scaling [61]. Conversely, there are some sizeable datasets of in vivo kinetic parameters for clearance and volume of disposition in rat, dog, monkey, and human that have been used along with select molecular descriptors to derive rules for predictions of these same pharmacokinetic data [66]. This latter study represents one of the first attempts at using a combination of computational and in vivo methods and may ultimately result in an automated predictive model to select the appropriate species for in vitro testing for scaling to human clearance.

6. Computational Metabolism Methods Enzymes such as the P450s have high structural homology yet with distinct roles in xenobiotic metabolism even though their active sites enable a wide array of structurally diverse substrates to bind [67]. Naturally, the substrate selectivity of human P450s is related to the molecule structure and the key molecular‐binding features of the active sites. The amino acid residues around the heme are thought to have a key role for substrate binding [68]. For a long time in the absence of X‐ray crystal structures for many of these enzymes, the prediction of metabolism was derived from in vitro data. In some respects, these observations are similar for some of the other enzymes involved in drug metabolism. Predicting metabolism via the various phase I and phase II drug‐metabolizing enzymes has progressed in a number of

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directions over more than 30 years and the following are representative of some of the computational technologies that have been used in this field. The prediction of metabolites is also recognized as useful prior to the assessment and detection using analytical methods to actually determine their presence experimentally [69–73]. Metabolism prediction also has a place in the assessment of large virtual combinatorial libraries [74] or before purchasing compounds from suppliers. From a biologist’s or chemist’s perspective, user friendly predictive computational methods for the site(s) of metabolism on a molecule would aid in design of in vitro experiments or the synthesis of more metabolically stable examples or even focus metabolism toward other enzymes and avoid certain undesirable interactions. A recent exhaustive review of computational methods for prediction of P450 metabolism and inhibition across all species has documented how these approaches have been used over nearly 20 years and the reader is encouraged to refer to this [75]. It is important to note that certain researchers have tended to promote their predictive metabolism methods almost in isolation with only brief consideration of other approaches or the insights these methods could provide or already have done so. We have attempted to provide a discussion of virtually all computational metabolism methods that we are aware of and present the advantages and limitations for each. Although no single method is ideal in this respect, a trend we have noted is toward the integration of diVerent methods or hybrid approaches. 6.1. 2D‐

AND

3D‐QSAR

As early as the 1960s, mathematical models describing the relationship between calculated molecular properties for a series of molecules and a particular biological property were generated [76, 77]. Following this, quantitative structure metabolism relationships (QSMR) were pioneered by Hansch et al. [78–81] using small sets of similar molecules and a few molecular descriptors. Later, Lewis et al. [82–87] provided many quantitative structure activity relationships (QSAR) studies for the human P450s that have resulted in a simple decision tree for human P450 substrates [83]. From all of these classic QSAR or QSMR studies, lipophilicity expressed as log P or molecular refractivity were the first important molecular properties related to enzyme substrate binding. Later, steric, electronic, and molecular shape properties were also found as important for enzyme binding and transformation. Conversely, metabolite release likely requires the opposite properties to binding [9]. QSMR or QSAR models have since been constructed for each major human P450 enzyme in the last decade using in vitro data. The wide availability of more computationally complex and graphically intensive software tools in the late 1980s–1990s precipitated and increased the level of


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ligand‐based computational modeling or QSAR analysis applied to drug‐ metabolizing enzymes. Such software for 3D‐QSAR includes Catalyst (Accelrys, San Diego, CA), DISCO, CoMFA, ALMOND (Tripos Associates, St. Louis, MO), and GOLPE (Multivariate Infometric Analysis, S.r.l., Perugia) which have been described in detail [88]. For example, CoMFA was used to describe key molecular features of ligands for human CYP1A2 [89] and CYP2C9 [90] in early 3D‐QSAR studies and more recently for hydrogen peroxide formation by CYP2C9 ligands [91]. These methods provided a foundation for future applications of computational methods to metabolism and introduced researchers to this field that perhaps would not have entered it. Few of the early 2D‐ and 3D‐QSAR methods used large datasets that would likely capture the diversity of chemical space and in addition rarely used test sets of molecules to evaluate predictive capability. These models and methods in general are therefore highly training set dependent unlike some of the other methods that will be described later. Small lipophilic molecules can also undergo glucuronidation which is a further important route for drug clearance [92]. These membrane bound enzymes have not been crystallized to our knowledge. One study described the glucuronidation of 4‐substituted phenols by the human recombinant UGT1A6 and UGT1A9 enzymes [93]. A genetic algorithm and a range of molecular surface and atomic descriptors enabled one of the first computational attempts to predict the Km for these enzymes [93]. More recently, other QSAR algorithm methods, such as support vector machines, have been used with quantum chemical and 2D descriptors for UGTs [94]. The datasets from which the models were constructed are still relatively limited in terms of structural diversity compared with the P450 models, but this situation is likely to improve as more data is generated. A further class of conjugating enzymes are the sulfotransferases which in contrast to UGTs have been crystallized [95, 96], and a QSAR method has also been used to predict substrate aYnity to SULT1A3 [95]. More recently, other types of QSAR methods have been used to generate predictions for metabolic stability as a general property rather than at the individual enzyme level. For example, recursive partitioning is a powerful statistical method that can uncover relationships in large complex data to classify objects into categories based on similar activities [97]. A recursive partitioning model containing 875 molecules with human liver microsomal metabolic stability was used to predict and rank the clearance of 41 drugs [98]. Another approach, a k‐nearest neighbor statistical model finds a subspace of the original descriptor space where activity of each compound in the dataset is most accurately predicted as the averaged activity of its k nearest neighbors in this subspace. This method has also been used with metabolic stability data from human S9 homogenate for 631 diverse molecules and was

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able to adequately classify metabolism of a further set of over 100 molecules [99]. A set of 130 calcitriol analogs with in vitro metabolic stability was used to develop QSAR models with molecular structure descriptors from QikProp and DiverseSolutions [100]. Variable selection was carried out and partial least squares (PLS) regression models were generated. The models were used for prediction of the metabolic stability of 244 virtual calcitriol analogs. Twenty molecules were selected for in vitro testing and correctly predicted metabolic stability for 17 of the 20 selected analogs (85% success rate) [100]. Kohonen maps are a multivariate statistical technique which approximates local geometric relationships of a multidimensional property space on a 2D plot [101]. Kohonen maps have also been useful for diVerentiating high‐ and low‐aYnity CYP3A4 substrates [102]. Neural networks are biologically relevant based on ideas from neuroscience, they include ‘‘neurons’’ which are weighted connecting an input layer, one or more hidden layers, and an output layer [103]. Neural networks have also been used to predict N‐dealkylation rates for CYP3A4 and CYP2D6 substrates [104]. This latter work represents a foundation for a software system to predict metabolites and the enzymes involved from an input molecular structure and has also been applied to the diVerentiation of P450 substrates from nonsubstrates [105, 106]. The development of NMR T1 relaxation data as descriptors for use in QSAR models has also been proposed as a method to describe molecules in the P450 enzyme’s binding site and therefore may be used to optimize combinatorial libraries to avoid interaction with specific enzymes [107]. All of these computational techniques represent eVorts to predict metabolism either focused on diverse or structurally similar training sets of molecules for metabolic stability or a particular P450 reaction and at the same time attempt to address the specific issues with modeling of these complex enzymes. 6.1.1. Pharmacophores Computational pharmacophore models are another method that has been widely applied to predicting metabolism and interactions with P450s. A pharmacophore represents the key features present in ligands necessary for a biological response. In pharmacophore software, such as Catalyst, the ligand molecular features are translated into spheres onto which molecule structures themselves can be mapped in 3D space [88]. Many pharmacophores have been generated for P450s [108] providing insight into the important features for interaction of ligands and the proteins. The human enzymes CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2D6, CYP3A4, CYP3A5, and CYP3A7 [89, 109–119] have all been the focus of pharmacophore‐modeling approaches. The CYP3A enzymes represent the most important human drug‐metabolizing enzymes [120] as they have a very broad substrate specificity and metabolize a large proportion of marketed drugs. Computational


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pharmacophores for CYP3A4 have been developed for substrates [121] and inhibitors [117, 121, 122] using an array of kinetic constants (Km, Ki (apparent), and IC50) [108]. The recent development of benzbromarone analogs that are CYP2C9 [123, 124] and 2C19 [125] inhibitors with Ki values in the nm range has enabled the extension of the pharmacophore/3D‐QSAR models [91]. For CYP2C9, these analogs pointed to an important role for hydrophobic interactions which had also been addressed in an earlier pharmacophore study [115]. The incorporation of such high‐aYnity inhibitors will certainly be useful for improving the model statistics and in turn searching databases for other inhibitors containing the same pharmacophore. As nonhyperbolic kinetics have been reported for numerous P450s [126–128] and UGTs [11, 129], the computational pharmacophore approach has also been used to develop a model for the important features of molecules which increase their own metabolism (autoactivators) via CYP3A4 [121]. Several CYP2B6 reactions have also been reported to display atypical enzyme kinetics with recombinant enzyme [127], namely 7‐ethoxy‐4‐trifluoromethylcoumarin O‐deethylation, testosterone 16‐hydroxylation, and verapamil O‐demethylation. Although there has not been any development of a pharmacophore solely for CYP2B6 autoactivators, based on the CYP2B6 substrate pharmacophore [111] this may have some similarity to the CYP3A4 autoactivator model which also has three hydrophobic features around a hydrogen bond acceptor. Recently, the Catalyst pharmacophore approach has also been applied to heteroactivators (where a molecule can increase the metabolism of another molecule that is metabolized by the same enzyme) of CYP3A4 and CYP2C9 metabolism [130, 131]. These eVorts primarily define the key features necessary for autoactivation and heteroactivation and may relate to a specific binding site(s) in the respective enzyme. Analogous to their use in modeling P450s, pharmacophores have also been applied to various human enzymes involved in glucuronidation using a custom metabolism pharmacophore feature [132–134]. In this way, it was possible to derive pharmacophore models for UDPGT 1A4 [133], UDPGT 1A1 [134, 135], and others [136]. This work has been reviewed in detail by Miners et al. [137]. We envision further attempts at computational modeling of other phase II enzymes in the future as larger datasets are published or the cumulative database of individual studies increase in size. As with the QSAR approaches in general, pharmacophores are limited by the training sets used, and prediction quality is high when the test set molecules resemble those in the training set. Pharmacophores assume a similar binding mode and interaction with the protein which is unlikely to be the situation and they generally do not indicate reactivity (although some pharmacophores have included features for the site of metabolism [138]).

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Although the results suggested by pharmacophores are perhaps too simplistic, they have played a useful rule in representing the shapes and distances between key features in molecules that are likely to be metabolized, and in so doing have allowed researchers to visualize features in molecules to be avoided. 6.2. ELECTRONIC MODELS Other molecular models accounting for electronic eVects of ligands for P450‐mediated metabolism have also been produced [90, 139, 140]. These methods depend on the calculation of ground state energies and in some cases have also combined aliphatic and aromatic oxidation reactions. Using steric and orientation terms, predictions have been generated for metabolic regioselectivities of enzymes in general [139, 141] or for specific enzymes such as CYP2E1 [142] and CYP3A4 [140]. In the latter case, a PLS method was trained with AM1 calculated hydrogen abstraction energy data to rapidly speed up the prediction of these values for molecules. The combination of electronic methods with steric and orientation terms has also been described to limit overfitting of the training data and improve predictions [143]. An electronic model has been developed for hydrogen abstraction for a series of steroidal androgens [144]. Electronic methods have to date been much less widely applied than QSMR methods likely due to their slow calculation speeds, and there have been no comparisons of predictions from electronic models and other QSMR. There is certainly some scope for the further development of these technologies as applied to metabolism prediction either alone or alongside the other methods described here. 6.3. HOMOLOGY MODELS The 3D structure of the membrane bound P450s were largely unknown until the relatively recent crystallization of the rabbit and human CYP2C forms [145–151] as well as the human CYP3A4 [152, 153]. Up to this time, there were many eVorts at homology modeling the various P450s using bacterial P450s as template structures such as P450cam, P450BM3, P450terp, and CYP450eryF, including the extensive studies of Lewis et al. and de Groot et al. [154–169]. These models were used in some cases to dock or manually orientate small numbers of molecules in the theoretical enzyme‐binding sites and deduce likely sites of metabolism [170, 171] or rationalize inhibitor binding [172]. Once the rabbit CYP2C3/5 enzyme became available, this was also utilized for modeling other human P450s [116, 138, 151, 167, 173– 175]. Experimental IC50 data for CYP2D6 inhibition and the ChemScore value derived from docking molecules in the homology models for this


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enzyme were shown to correlate well enough for this model to be proposed as a filter for screening databases [176]. This docking and scoring approach has also been extended to CYP3A4 crystal structure for drugs commonly prescribed to cancer patients [177]. Others have combined docking in CYP3A4 with multidimensional QSAR for 48 substrates or inhibitors to create a model that was used to predict the binding mode and aYnity of a further five compounds [178] as well as suggesting the importance of water‐mediated hydrogen‐bonding interactions. Limitations of docking in homology and crystal structures include the frequent inability of ligands to actually dock in the active site even when side chain flexibility is enabled [179]. The various types of software used for either manual or automated docking with CYP protein models has been comprehensively reviewed alongside discussion of strategies such as water inclusion, consensus methods, and protein flexibility as attempts to improve predictions [75]. The convergence of pharmacophore and homology modeling has been frequently used [116, 151, 168, 169] as a means to both validate and improve the models resulting from each method separately. At the same time, this assists in overcoming some of the criticisms of using the methods alone. The 3D structures of several mammalian P450s are potentially useful for modeling of other enzymes involved in drug metabolism; however, structural changes induced upon substrate binding may have to be considered and accounted for, such that it might otherwise be diYcult to model them accurately [180]. There has not been an exhaustive comparison of all the homology models created previously (except for reviews of homology modeling) and the new X‐ray structures only briefly mention these eVorts [145–147, 152, 153, 181]. It would be of interest to understand to what degree these modeling eVorts were able to predict the actual binding site requirements, volume, key interactions, and ligand‐binding mode of the crystal structures, and it will be interesting to see whether future models and software developments will likely improve these approximations. 6.4. DATABASES There have been very limited public eVorts to organize ADME/Tox data into readily accessible computational databases, with exceptions being PharmaGKB [182], the nuclear receptor database [183], human membrane transporter database [184], and the ADME‐AP database [185]. In the area of commercial oVerings, there are several drug metabolism databases (Metabolite™, Metabolism™, and BioFrontier/P450™) which represent broad collections of metabolic data across multiple species [186]. Such databases may be useful for calculating the probability for a given metabolic reaction [187] to then indicate potential metabolites [188] and the sites of metabolism using statistical or algorithmic approaches [189]. One example of

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software that takes this approach is SpORCalc which highlights on the molecule with color coding the likelihood of metabolism using probabilistic methods to assess the frequency of substructures in a molecule compared to a database (Table 3). These types of methods are less useful for indicating which are major and minor metabolites. It will certainly be interesting to see how these eVorts are integrated into commercial quality software or pharmaceutical in‐house eVorts. The development of a new public database, DrugBank, which merges bioinformatics and cheminformatics and is applied to drug‐like molecules contains some information on the major enzymes involved in their metabolism, but does not appear to be exhaustive in terms of small molecule structures. Although this type of comprehensive database enables numerous search options to retrieve molecule structures and published information, the predictive capabilities seem limited at present [190], but this will likely change on further development. Of course, the limitation of using any database as a predictive method for human metabolism is that they are unlikely to have a complete dataset of reactions and molecular structures to extrapolate for a new molecule which could come from any part of the vast areas of unchartered chemical space. In turn, the user is relying on the quality of the published in vitro or in vivo data which in many cases may predate modern analytical methods with the result that older published metabolic pathways may be incomplete. In reality, such database approaches provide knowledge of most published data and are perhaps limited to interpolation. The utility and development of drug metabolism databases has been discussed more extensively elsewhere [191, 192] and the reader is referred to these publications for further in‐depth details. 6.5. EXPERT SYSTEMS The continuous accumulation of drug metabolism data from the literature has also resulted in the creation of expert systems for metabolism [193] culminating in several commercial rule‐based products such as MetabolExpert™ [194, 195], META™ [196–198], and METEOR™ [199, 200], which have been reviewed previously [199]. The advantages of these approaches are that, in general, new rules can be added to them as they are discovered. One of the disadvantages of expert systems is the combination of data or rules from many diVerent mammalian species, and computer programs using this combined information tend to predict all possible metabolic possibilities for a molecule. However, it is widely known that metabolic pathways can be very diVerent even in close mammalian species, and metabolism of the same drug may further vary substantially between individuals (depending on the expression level of particular enzymes, polymorphisms, and the presence of particular enzymes in normal and disease states as well as diVerent tissues).

TABLE 3 COMPUTATIONAL TECHNOLOGIES FOR DRUG METABOLISM

Software MetaDrug™

Metabolite™ Metabolism™ BioFrontier/P450™ PharmGKB METEOR™

META™ MetabolExpert™

MetaSite™ TIMES SpORCalc

Function Metabolism database, Metabolite prediction, Metabolite prioritization, QSAR models for enzymes, transporters and network building algorithms for Systems‐ADME/Tox Metabolism content database Metabolism content database Metabolism content database Pharmacogenetics and pharma cogenomics knowledgebase Rule‐based Metabolite prediction software. Predicts the metabolic fate of chemicals. Displays results as a metabolic tree. User can filter results for ‘‘likely’’ metabolites. Links directly to MetaboLynx for analysis of mass spectrometry data Rule‐based Metabolite prediction software Rule‐based Metabolite prediction software predicts the most common metabolic pathways in animals, plants or through photodegradation. Results are presented in metabolic tree format. Graphical interface for editing and adding rules Site of metabolism prediction for various P450s A rule‐based method that provides metabolites and pathways Software highlights the site of metabolism on a molecule using a database of substrate and reaction center occurrences in the MDL Metabolite database

Application references

Web site

[215, 312, 313]

www.genego.com

[314, 315] [199, 200]

www.mdl.com www.accelrys.com www.fqspl.com.pl www.pharmgkb.org www.lhasalimited.org

[196–198] [194, 195]

www.multicase.com www.compudrug.com

[179, 207, 208] [205]

www.moldiscovery.com http://www.oasis‐lmc.org/ showsoft.php?item¼4 http://www.chemie.uni‐ erlangen.de/clark/smith/ SPORCalc.html

http://www.chemie. uni‐erlangen.de/ clark/smith/ abstracts/C‐ 2_Hasselgren‐ Arnby.pdf

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Overprediction of metabolites may be less useful to the scientist than a method that can suggest major or minor metabolites or even the lability of certain sites on a molecule. Expert systems for metabolism prediction have also been developed as part of the University of Minnesota biocatalysis/ biodegradation database, which merges a database of molecules with metabolite prediction rules [201–204] for small organic molecules and is being integrated into METEORTM [204]. Another example is the tissue metabolism simulator (TIMES) which combines a database with probability of occurrence of metabolites to produce a metabolic map. TIMES has to date been tested with 179 molecules with published rat data, reproducing 86% of the documented metabolic pathways [205]. Neither of these latter methods has been applied to human metabolism data at present to our knowledge. All four of the rule‐based methods have been described in more detail in a recent review. A comparison of the predictive ability of the methods was limited to seven phenolic compounds but no judgment was made as to which method(s) was closest to the observed metabolism for these compounds [206]. To be of relevance to the pharmaceutical industry these rule‐based methods need more extensive testing with diverse drug‐like structures. 6.6. HYBRID METHODS The history of methods used for the computational prediction of human drug metabolism includes several diVerent approaches described earlier such as databases, QSMR/QSAR, pharmacophores, rule‐based approaches, electronic models, homology models, and crystal structures with docking approaches as described in previous Sections 6.1–6.5. These techniques have been used individually with diVerent levels of success although they could ultimately be combined to improve predictions whether as a consensus‐based approach or a single platform that applies the most appropriate models for the task at hand (e.g., based on the molecule neighborhood for the test molecule compared to training sets). Specific P450‐substrate/inhibitor recognition interactions have been studied extensively, and several QSAR and pharmacophore models have been built for a limited number of these enzymes. These models have generally shown the importance of hydrophobic, hydrogen bonding, and ionizable features for both substrates based on Km data and inhibitors using Ki, IC50, and percent inhibition data [108]. Molecular models that account for electronic eVects of ligands for P450‐mediated metabolism have also been produced [90, 139], and these have combined aliphatic and aromatic oxidation reactions to generate predictions for metabolic regioselectivities. As mentioned earlier, each of the methods described above have advantages and disadvantages for drug metabolism prediction. The combination of approaches may balance the strengths and weaknesses of


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each approach and hence the introduction of hybrid methods would appear to be worth considering. A recent technique called MetaSiteTM (Molecular Discovery, Middlesex, UK) generates GRID field descriptors (used for determining energetically favorable binding sites on molecules of known structure) using crystal structures or homology models for the P450 enzymes, as well as the interaction energy descriptors for the molecules evaluated as substrates [207]. A reactivity component is also considered in the MetaSiteTM calculation which produces a probability for an atom to be metabolized. To date this approach has been applied with AT receptor antagonists to predict the site of metabolism for the P450s CYP2C9 and CYP3A4 [207] while a more recent study has expanded the application to CYP2D6, CYP2C19, and CYP1A2 with 75–86% correct predictions [208]. A further study compared MetaSiteTM with docking of ligands into crystal structures and homology models of CYP3A4. MetaSiteTM had 78% overall prediction success compared with 57% for docking overall [179]. It appears that there were no attempts at consensus methods to try to improve prediction accuracy. In this case, drawbacks of MetaSiteTM include that it cannot predict the absolute or relative amounts of the major and minor metabolites or the rate of metabolite formation for a molecule. Another hybrid method MetaDrug™ includes a manually annotated Oracle™ database of human drug metabolism information including xenobiotic reactions, enzyme substrates, and enzyme inhibitors with kinetic data. The MetaDrug™ database has been used to predict some of the major metabolic pathways and identify the involvement of P450s [105]. This database has enabled the generation of over 85 key metabolic pathways for predicting likely metabolic reactions. Extensive testing of the metabolite predictions with this software have also been summarized in which 66 molecules were used with this rule‐based approach and captured approximately 79% of first‐pass metabolites [209]. It is interesting that this number is very similar to the success rates for MetaSiteTM and possibly represents a current upper limit for these diVerent methods. In addition to metabolite prediction, there are over 40 recursive partitioning QSAR models [122, 210, 211] in MetaDrug™, enabling the prediction of aYnity and rate of metabolism for numerous enzymes such as P450s as well as prediction of other ADME/Tox properties. The user may also upload their own QSAR or QSMR data into the software to oVer a further level of utility. The QSAR methods also provide Tanimoto similarity as a measure of similarity to training set molecules. Structural alerts for likely reactive metabolites [212–214] are also provided. Ultimately, the results of the QSAR model predictions (for individual proteins such as enzymes, transporters, and so on) for molecules can be visualized as nodes on a network diagram. This represents a novel graphical method for presenting potential drug–drug interactions alongside known molecules in the database. To date this method has been used to show the

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predicted binding interactions for 4‐hydroxytamoxifen derived from QSAR models for P‐glycoprotein (P‐gp) and CYP3A4 as well as several other molecules in combination with gene expression data [209, 215]. As this method combines various methods, for example QSAR, rule‐based methods, the user should be aware of their individual limitations. Steps have been undertaken to provide more confidence in predictions. The evaluation of any rule‐based metabolite software with a diverse array of molecules will indicate that it is possible to generate many more metabolites than have been identified in the literature for the respective molecules to date, which could also reflect the sensitivity of analytical methods at the time of publishing the data. In such cases, eYcient machine learning algorithms will be necessary to indicate which of the metabolites are relevant and will be likely to be observed under the given experimental conditions. The development of such machine learning tools that utilize databases of human metabolism information may represent methods for calculating more reliable predictions of metabolites. For example, the Kernel‐PLS (K‐PLS) algorithm devised by Rosipal and Trejo [216] and implemented as a component of the Analyze/StripMiner software [217] could be used in this regard. We have previously described how classification models could be generated for human phase I and II reactions [218]. It is likely that computational metabolism predictions could also be integrated with pharmacokinetic simulation methods such as Cloe PK™, QMPRPlus™, GastroPlus™, SimCYP™, and others [219] to assist in deriving more accurate predictions of human pharmacokinetic parameters. In these cases, the prediction of just the site of metabolism is insuYcient and it will be necessary to predict kinetic parameters for diVerent enzymes. 6.7. CAVEATS A drug metabolizing enzyme may produce multiple products from a single substrate. Thus, predicting the major metabolites becomes a challenge, though a theoretical model has been developed to create a distributed catalysis network in which a substrate produces multiple metabolites with a lower concentration than the initial substrate concentration [220]. The substrate promiscuity of P450s such as CYP3A4 [67] known to metabolize molecules at multiple positions also complicates predictions for metabolite formation. This perhaps provides further weight for combining metabolism methods to predict aYnity for enzymes to improve their overall accuracy. As the previously considered metabolism prediction approaches were rather reductionist, it is important to also consider the impact of biological complexity of the organism. The incorporation of metabolism information and ADME/Tox data in general into systems biology methods will provide


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this alongside predictive models to assess metabolism and binding to diVerent proteins by drug‐like molecules [215, 221]. Methods to reliably predict metabolites and their further eVects on the complete biological system are certainly needed to aid in the selection of molecules to be synthesized and tested in vivo and provide an indication of likely toxicity [222]. Simple outputs or graphical representations of this type of information are also important for the biologist or chemist to understand what action they should take. The limitations of virtually all of the computational methods developed thus far are related to the fact that the experimental measurement of metabolism‐related parameters is inherently prone to errors. Kinetic constants for the same compound vary substantially between studies, depending on the enzyme source (recombinant P450s, purified enzyme or human liver microsomes, and so on) or experimental conditions. In some cases, the reported Vmax values for the same compound may vary by 2–3 orders of magnitude, which can seriously impact regression‐based QSMR or QSAR modeling. Therefore, considerably larger, consistent datasets for each enzyme will be required to increase the predictive scope of such models. These will then be used alongside methods for predicting the site of metabolism that do not use QSAR. 6.8. METABOLISM PREDICTION APPLICATIONS Several of the computational metabolism methods can already be used to aid biologists and chemists. For example, rule‐based methods for metabolite prediction have been used in bioanalytical groups to suggest likely metabolites before mass spectroscopy data analysis [69–72, 223]. Such approaches can combine MS spectra feature prediction software and metabolite prediction software. ApexTM (Sierra Analytics, www.massspec.com) is one example that has been used with METEOR™, MetabolExpert™, and MetaDrug™. Predicting likely metabolite fingerprints for libraries of compounds also suggests a role for software in screening large numbers of molecules eYciently [74]. The integration of metabolism prediction tools (e.g., MetaDrug™) with tools such as PipelinePilot (Scitegic, San Diego) will enable the processing of large libraries of molecules through metabolism and other filters as a common informatics process. As the estimated size of synthetically tractable chemistry space is huge, on the order of 1020 and 1024 molecules [224], we have to limit the user expectations of QSAR models for metabolism that are generally based on only a very small set of molecules, as they will only represent a fraction of this chemical space. Methods to access similarity of testing molecules to those in QSAR models includes the simple Tanimoto similarity score calculated using molecular descriptors for the molecules in both the model training set as well as

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for those molecules predicted [225]. Hence, the metabolism predictions for molecules that are predicted as similar to those in the training set based on this score may be suggested as more reliable. The incorporation of new data into such metabolism models will also be key as we are constantly seeing new structures with metabolic pathways published as well as novel metabolites. Obviously, the proprietary databases inside drug companies are likely to be more readily updated than those commercially available, although of course this may not always be the case. The generation of further enzyme crystal structures may also aid in understanding the promiscuity of the enzymes, assist in the prediction of metabolites, and direct the improvement of the homology models that have been generated already [181]. These further enzyme structures may also be incorporated into predictive computational methods such that they provide important information on the likely site of metabolism of a molecule once docked in the enzyme. The current processing of molecules in pharmaceutical companies is likely to proceed from the fast rule‐based metabolism prediction and site of metabolism prediction, QSAR methods for rate and aYnity predictions prior to the presently slower docking‐based site of metabolism predictions based on the X‐ray structure information. Virtually all computational metabolism prediction eVorts have been on human but in future we should also address the considerable amount of metabolism data for mouse and rat that will be important for understanding species diVerences [226]. This represents an area ripe for computational eVorts and in particular multidimensional methods that could consider data from the multiple species and generate predictions for them all simultaneously.

7. Integration of Drug Metabolism Data and Interpretation Ultimately any pharmaceutical company has to successfully bring together all the preclinical data that has been produced for a molecule. This includes preclinical animal, computational, human in vitro metabolism data, and so on. This will essentially create a ‘‘compound Curriculum Vitae (CV)’’ composed of predicted and empirical information (Table 4). Clearly, metabolism, intrinsic clearance, and enzyme inhibition are major components of this CV. Desirable thresholds for a particular therapeutic target may have some impact on these properties. For example, a target requiring very large hydrophobic compounds may result in molecules that are metabolized primarily by CYP3A4, while molecules with a small polar surface area are needed to cross the blood–brain barrier and may be more likely metabolized by CYP2D6 and CYP2C9. Although these are rather general if not overly simplistic observations, the physicochemical needs for a target can obviously heavily influence


LIST

OF

PROPERTIES MEASURED

TABLE 4 OR CALCULATED

FOR THE

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‘‘COMPOUND CV’’

In vivo pharmacokinetics—in multiple species Bioavailability Chemical structure and purity Absorption (Caco2 or Pampa) Distribution CNS penetration Excretion mass‐balance logP (measured and calculated) Solubility (measured and predicted) Plasma protein binding Blood cell binding In vivo toxicity (enzyme induction, maximal tolerated dose, projected clinical dose) In vitro toxicity (measured and predicted) AMES, hERG, cytotoxicity Metabolism (measured and predicted) in vitro microsomes, hepatocytes, intrinsic clearance in multiple species, human‐specific metabolites In vitro inhibition of P450s Induction of CYP3A4—PXR EZux transporter role

the types of metabolism and other ADME/Tox issues that need to be considered. The importance of a particular project may also drive the acceptable values for each of the properties. While in the early stages of drug discovery, computational approaches act as a filter to remove unacceptable molecules from compound libraries for screening without further experimentation. The judicious use of computational models later, such as those for metabolism, will almost certainly be followed by in vitro or in vivo verification. This will provide further data to update the models and point out limitations in the structural space that is covered computationally.

8. Newer Technologies Drug metabolism is already following genomic and proteomic technologies by shrinking the scale of in vitro experiments. This may represent one of the future techniques already under development, where drug‐metabolizing enzymes, whole cells, or intricate organ architectures are immobilized on or in chip‐like devices. Already there have been glimpses of these eVorts initially focused on toxicology applications. One recent study encapsulated recombinant expressed P450s in sol–gel and nanoliter volumes were spotted on a

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microscope slide. This MetaChip was also stamped with an MCF‐7 cell monolayer which enabled the live/dead assessment of the cells after treatment with drugs that were activated by the immobilized enzyme [227]. The development of other micro and nanotechnologies that may be applicable to understanding drug metabolism or toxicology assessment as they are developed into comprehensive lab‐on‐chip methods has also been reviewed [228]. Rat hepatocytes have been cultured for several weeks in alginate sponges which may represent a useful matrix for 3D culture as cell functions were maintained in the spheroids that formed [229], and more recent reports indicate that phase I and II enzymes remain high over 7 days [230]. Microfabricated bioreactors have been created from silicon wafers and used to culture rat hepatocytes for a similar period of time which similarly formed spheroids [231, 232]. EVorts to physically mimic a PB/PK model have used a microscale cell culture analog fabricated from silicon. This system originally contained etched areas for liver, lung, and other tissues [223] and was expanded to contain an area for fat [233, 234]. A built in oxygen sensor allowed real time consumption determinations. This system was used to evaluate naphthalene toxicity mediated by metabolism with rat lung and human HepG2 cells, using GSH depletion, hydrogen peroxide formation, and MTS assays for cytotoxicity [233]. The addition of a third cell type 3T3‐L1 adipocytes in the fat chamber mimicked bioaccumulation [234]. The integration of multiple cell types in a diVerent plate‐based in vitro system has also been proposed and used with cells from five major humans and breast cancer cells [235]. One could imagine perhaps more complex fabricated systems that would scale this integrated organ culture down still further. Another bioreactor design uses polydimethylsiloxane layers with structures for oxygen supply and has been used to culture HepG2 cells for up to 12 days [236]. Although individual xenobiotic enzymes were not assessed directly in any of these latter systems, they appear to have some promise for predicting animal and human response to molecules. This miniaturization and sensitivity down to the single cell or molecule interaction level may allow many more experiments in higher formats, for example, screening very large compound libraries on a chip or other such formats. The mobilization of the technologies could enable their use in locations outside of the laboratory environment etc., facilitating the development of metabolic biosensors as well as an adjunct for organ replacement. Some of these biosensors and bioreactors will allow us to probe metabolic reactions at a new level of detail. There has been some speculations on the development of ADME/Tox biosensors at least from the theoretical aspects without any experimental demonstrations as yet [237]. This is in contrast to the development of advanced electrochemical oxygen sensors for monitoring cancer cell growth and their interactions with toxins [238]. It will certainly be of interest


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to see how diVerent technologies can be brought together to minimize the costs of bioreactor fabrication and enable the construction of new in vitro methods for evaluating metabolism. We envisage that companies such as Hurel Corp (http://www.hurelcorp.com/) may stimulate other bioentrepreneurs to develop technologies that use precious animal and human cells sparingly to obtain important PK/PD knowledge for drug discovery. The scale of in vivo human dosing of test molecules can also be decreased using techniques such as microdosing that take advantage of the ultrasensitive accelerator mass spectrometry method capable of measuring drug and metabolites in the low pictogram to femtogram range. This method uses either 100 mg or 100th of the pharmacological dose of a drug and requires a minimal toxicology package and smaller sample sizes for analysis. The advantages include less radioactive material disposed of and faster studies that are cheaper than a full phase I trial [239]. Microdosing has subsequently been called phase 0. Microdosing has been used to show linear pharmacokinetics for a nucleoside analog in dogs [240] and a human absolute bioavailability study was performed with administration of an intravenous microdose of Nelfinavir simultaneously with a conventional pharmacological oral dose of the same drug [241]. The latter provided information on the gastrointestinal absorption vs first‐pass metabolic turnover. Microdosing may be one way that promising molecules could make it to human testing faster, although there are just a few examples of companies that have used the approach to expedite development, one example is Speedel Pharma and their renin inhibitor program [242]. Historically, drug metabolism research in the industry has paid most attention to the role of polymorphic or major inducible enzymes. So CYP3A4, CYP2D6, and CYP2C9 are well studied. In contrast other P450s are less well characterized. We have seen periodic interest in some of these other enzymes such as CYP2B6 [121] and more recently CYP2C8 [243]. At one level we can learn from these studies that compile databases of substrates or inhibitors for a particular enzyme as they allow us to understand the physicochemical characteristics required for binding to these enzymes. In addition, they provide datasets that are likely suitable for more complex computational models as described earlier. For example, using a published set of 209 molecules that were tested as inhibitors of CYP2C8, we are able to generate a recursive partitioning model with 2D descriptors using commercially available software that produces interpretable models (Fig. 1) that can be used to screen large databases of molecules to select those for testing as potential CYP2C8 inhibitors. When half of this set is used as a test set and half to build a model, we can generate statistically significant ranking of the molecules with this admittedly noisy data (Spearman rho 0.49, p < 0.0001) that is in line with similar models generated with much larger datasets for CYP3A4 and CYP2D6 published previously [122]. In addition, the CYP2C8 inhibitor

156 Predicted percentage of control CYP2C8 activity

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120 100 80 60 40 R 2 = 0.822 20 0 0

20

40

60

80

100

120

Observed percentage of control CYP2C8 activity FIG. 1. Observed vs predicted plot for a CYP2C8 inhibitor recursive partitioning QSAR model generated using ChemTree (GoldenHelix, Bozeman, MT) with 2D molecular descriptors [97, 210, 211] and previously published in vitro data for 209 molecules published by Pfizer [243].

model can be used to find known CYP2C8 substrates that were seeded into a database of approximately 6500 known drugs and other xenobiotics. The enrichment that is observed is considerably better than random (Fig. 2), indicating that even relatively noisy screening data for these enzymes can assist in prioritizing compounds to identify substrates. For example, the first three substrates were ranked and identified five times faster than random selection. Perhaps more importantly this example suggests that 2D molecular descriptors have some value in model generation for these enzymes. While not intended as models for predicting the site of metabolism, they can be used as filters for database screening. Certainly as larger in vitro datasets continue to be generated, we will fill in the holes of our understanding of some of the less studied P450s, perhaps revealing important features that have not been realized by crystal structures. The present goal of targeting metabolism of a compound by multiple P450s may be steered away from or toward some of these minor enzymes, and computational models could be useful in this capacity.

9. Conclusions We have briefly reviewed the in silico, in vitro, and in vivo eVorts to predict human drug metabolism. The future is likely to focus on newer technologies for miniaturization and analytical methods for drug metabolism that will


157

Number of known CYP2C8 substrates identified

9 8 7 6 5 4 3

Random

2

CYP2C8 inhibitor model Perfect model

1 0 0

2000

4000

6000

Number of molecules FIG. 2. Enrichment curve obtained after using the CYP2C8 inhibitor model described in Fig. 1 to select eight molecules that are metabolized by CYP2C8 [repaglinide, estradiol 17‐ glucuronide, retinoic acid, amiodarone, troglitazone, tazarotenic acid, dimethylsulphaphenazole (DMZ), and rosiglitazone], not in the model but seeded in a database of approximately 6500 known drugs. The ideal rate of finding the molecules is indicated by the perfect model, and the actual CYP2C8 inhibitor model performs markedly better than random at prioritizing or ‘‘cherry picking’’ the eight molecules out of this database of over 6000 xenobiotics.

develop alongside computational methods for each of the enzymes. One possibility is that the computational approaches are more closely integrated with the miniaturization technologies for in vitro metabolism, where the in vitro data is automatically integrated into computational tools for their further refinement. Alternatively, computational methods may also control automated machines performing the in vitro methods, dictating which molecules may be tested based on the predicted outcomes [244]. At present, we can see that a combination of the diVerent techniques described is essential as computational methods allow us to quickly prioritize the large number of compounds to take further for more expensive in vitro and in vivo experiments. The generation of a compound CV using predicted and experimentally determined properties represents a means to fully characterize multiple molecules for comparative purposes prior to selection and further processing. Multidimensional approaches will be necessary as previously proposed with computational ADME/Tox data [245, 246]. Drug metabolism related properties are hence an important omnipresent component that cannot be omitted. Predictive metabolism methods may also have a role for screening compounds that are in the environment with initiatives such as REACH. These could ultimately help in the prioritization of in vivo studies that utilize animals.

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With all of the methods described, the focus has been primarily on small drug‐like molecules and we have not considered drug metabolism of protein‐ based biopharmaceuticals that are of considerable importance to the industry and patients. Similarly, we are not aware of the application of computational methods for this area as yet. This may represent a field for future study and review as companies invest increasingly more in the development of these large complex molecules. To date there has only been very limited discussion of drug metabolism related to antisense oligonucleotides developed up to 2000 [247] and proteolysis or other modification of polypeptides, for example [248–254]. These types of large molecules are atypical in the routes of elimination and the enzymes involved in their metabolism when compared with smaller molecules that have been more extensively studied. It is apparent that few of the computational tools for metabolism prediction provide the major integrated functions needed to assist in drug discovery and development to minimize attrition or aid in decision making. In addition, these tools as a whole only consider a narrow component of the drug discovery and development process. Similarly, the diVerent in vitro methods for human drug metabolism themselves have implicit limitations. As recently suggested, the availability of computational approaches and models which span virtually all aspects of pharmaceutical research and development that could be integrated is becoming increasingly important [255]. Drug metabolism represents one area in which the development of computational models over the last 30 or more years has paralleled the in vitro and analytical methods and is likely to continue. The consequences of this are that we are still learning a great deal about human drug metabolism, the characteristics of enzymes, and the reactions they catalyze as well as their selectivity or promiscuity toward the wide array of xenobiotics. This is very important for the development of future molecules with therapeutic uses that are tolerated safely by as large a population as possible. ACKNOWLEDGMENTS Sean Ekins acknowledges helpful discussions with Dr. Maggie AZ Hupcey. The development of MetaDrug™ was supported by a National Institutes of Health Grant 2‐R44‐GM069124–02 ‘‘In Silico Assessment of Drug Metabolism and Toxicity.’’

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A SUMMARY ANALYSIS OF DOWN SYNDROME MARKERS IN THE LATE FIRST TRIMESTER Glenn E. Palomaki, Geralyn M. Lambert‐Messerlian, and Jacob A. Canick Department of Pathology and Laboratory Medicine, Women and Infants Hospital, Brown University, Providence, Rhode Island

1. Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Patients/Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Dimeric Inhibin‐A: Sample Collection and Available Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Dimeric Inhibin‐A Assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Dimeric Inhibin‐A Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6. Modeling Screening Performance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. New Dimeric Inhibin‐A Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Dimeric Inhibin‐A Measurements in the Literature . . . . . . . . . . . . . . . . . . . . . . 4.3. Pregnancy‐Associated Plasma Protein‐A Measurements in the Literature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Human Chorionic Gonadotropin Measurements in the Literature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Free ‐Subunit of hCG Measurements in the Literature . . . . . . . . . . . . . . . . . 4.6. Nuchal Translucency Thickness Measurements in the Literature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7. Correlation Coefficients and Truncation Limits . . . . . . . . . . . . . . . . . . . . . . . . . 4.8. Modeling Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Abstract Prenatal screening for Down syndrome in the late first trimester involves the measurement of maternal serum markers and the fetal ultrasound 177 0065-2423/07 $35.00 DOI: 10.1016/S0065-2423(06)43006-7


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marker, nuchal translucency (NT). In addition to the established first trimester maternal serum markers, pregnancy‐associated plasma protein‐A (PAPP‐A), and the free ‐subunit of hCG (free ), studies have indicated that the known second trimester markers, total hCG and inhibin‐A (DIA), are also useful in screening in the late first trimester. In this chapter, we review the existing literature on first trimester biochemical and ultrasound markers for Down syndrome, develop week‐specific marker parameters, and combine the results via modeling in a comprehensive overview of screening performance. All first trimester markers vary in their usefulness during the 11 through 13 completed week‐screening window and the literature is reasonably consistent. NT is the best single marker for Down syndrome in the first trimester of pregnancy (weighted summary detection rate is 60% at a constant 5% false positive rate). The two best maternal serum markers, taken individually, are PAPP‐A and free (weighted summary detection rates of 36% and 37%, respectively). Combining maternal age, NT and PAPP‐A, with either free , total hCG, or DIA gives similar screening performance (weighted summary detection rates of 83%, 81%, and 81%, respectively). When both free and DIA, or total hCG and DIA are combined with maternal age, NT and PAPP‐A, weighted summary detection rates are 85% and 83%, respectively.

2. Introduction The aim of this chapter is to review the existing literature on selected first trimester biochemical and ultrasound markers for Down syndrome, to summarize the individual marker findings by gestational age, incorporate the results via modeling to a comprehensive overview of the screening performance of marker combinations, and place these findings into the context of current and future prenatal screening strategies. In the United States, second trimester maternal serum screening for Down syndrome has been, and continues to be, the standard of care. Measurements of maternal serum dimeric inhibin‐A (DIA) in the second trimester are now being routinely performed in combination with three other second trimester serum markers (the ‘‘quadruple test’’). As part of the ongoing search for more eVective screening, ultrasound and biochemical markers in the first trimester of pregnancy are also being investigated and, in some places, used routinely. Pilot trials that utilize combinations of nuchal translucency (NT) and biochemistry in the first trimester have shown it to be at least as eVective as second trimester ‘‘quadruple’’ testing. Integrated screening (the combination of both first and second trimester markers) has the highest detection for a given false positive rate for any existing Down syndrome screening protocol [1].

FIRST TRIMESTER DOWN SYNDROME SCREENING

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The current study aims to evaluate only the first trimester performance of various combinations of serum and ultrasound markers. It is limited to the four serum markers that have been used clinically or for which extensive data are available. These markers include: pregnancy‐associated plasma protein‐A (PAPP‐A), human chorionic gonadotropin (hCG), the free ‐subunit of hCG (free ), and DIA. Some new data for DIA measurements is also included in this literature review. Although extensive data do exist for first trimester measurements of ‐fetoprotein (AFP) and unconjugated estriol (uE3), there is less interest in the first trimester measurement of these markers. We also chose not to include information on more recently reported serum markers (e.g., ADAM‐12 and invasive trophoblast antigen) for which limited data are available. The screening performance of the selected first trimester biochemical markers vary by gestational age. Recently, it has become clear that the performance of ultrasound measurements of NT thickness also varies [2, 3]. Because of this week‐to‐week variability, individual studies often will not have suYcient numbers of observations to derive reliable population parameters for both Down syndrome and unaVected pregnancies. For that reason, the current study summarizes the literature on first trimester marker levels by week of gestation, using consensus estimates to reduce overall variability. A similar methodology has been used previously to analyze PAPP‐A results [4]. Multivariate modeling is then utilized to estimate Down syndrome screening performance for combinations of these markers by completed week of gestation. These detection and false positive rates can be used by screening programs to help choose which combination(s) of markers might be best suited to their needs. In addition, the population parameters and limits (logarithmic means, standard deviations, pairwise correlation coeYcients, and truncation limits) can be used by screening programs to assign reliable patient‐specific Down syndrome risk that take gestational age into account. In addition, they can help form the basis of future inquiries into more refined analyses of marker combinations such as integrated protocols (including sequential and contingent) and protocols including repeated measures.

3. Patients/Methods 3.1. DIMERIC INHIBIN‐A: SAMPLE COLLECTION AVAILABLE INFORMATION

AND

This summary includes some new data on DIA and Down syndrome. It is based on a nested case control sample set derived from an NIH‐sponsored trial evaluating first trimester ultrasound and serum markers [5]. Briefly,

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maternal serum samples were collected prior to chorionic villus sampling or early amniocentesis. The main indication for the diagnostic testing was advanced maternal age. None of the women were referred because of abnormal NT or biochemical measurements. After informed consent, the women provided demographic and pregnancy‐related information and agreed to have the karyotype provided to the study center. All pregnancies were ultrasound dated by either crown rump length or biparietal diameter measurements. Serum samples were collected, stored in a refrigerator, and express mailed to the central laboratory in Maine. After receipt, the samples were immediately aliquoted and all but one aliquot was frozen at 20 C. Measurements of AFP, uE3, hCG, free , and PAPP‐A were measured in the remaining fresh sera. Given the observational nature of the study, we achieved a complete ascertainment in the first trimester of all Down syndrome pregnancies. The resulting measurements provide an unbiased estimate of the median level in Down syndrome pregnancies without the need for any adjustments due to biases in ascertainment. Soon after study completion, samples from 61 Down syndrome pregnancies were each matched with five unaVected pregnancies on gestational age, mother’s race/ethnicity, and time in freezer. In February 2002, aliquots from 51 cases and 271 controls were thawed and tested for DIA without knowledge of the associated karyotype. The missing case and control samples had been exhausted. 3.2. DIMERIC INHIBIN‐A ASSAY DIA was measured at Women and Infants Hospital, Providence, Rhode Island using a kit from Diagnostic Systems Laboratory (Webster, TX) without knowledge of whether the sample was a case or control. The assay was run according to the manufacturers’ recommendations. Results were expressed in picogram per milliliters over the range of 5–500 pg/mL. An in‐house control (a maternal serum pool) with an average value of 215 pg/mL had a long‐term coeYcient of variation of less than 12%. 3.3. DIMERIC INHIBIN‐A STATISTICAL ANALYSIS DIA measurements were converted to multiples of the median (MoM) using day‐specific medians derived from the unaVected pregnancies. The average gestational age for each completed week was regressed against the log of the median value for each completed week, and the results algebraically converted to a day‐specific equation. The resulting DIA MoM levels were then corrected for maternal weight. A probability plot was used to derive population parameters for both case and control pregnancies, after


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logarithmic transformation. Separate pairwise correlation coeYcients were computed after the exclusion of results outside of three standard deviations. 3.4. LITERATURE REVIEW The literature through December 2004 was searched for articles reporting maternal serum measurements in the first trimester of pregnancy. The search criteria included the keywords first trimester, serum, Down syndrome, along with the analyte of interest (e.g., inhibin, NT, hCG). The reference lists from retrieved publications were also searched for additional references. If week‐ specific median levels in case and control pregnancies were not explicitly reported, those data were estimated from accompanying figures. Studies were not included in the literature review if they did not contain week‐specific median levels, they only contained data outside of 9–14 weeks’ gestation, they contained results that were duplicated in a subsequent report; or for miscellaneous other reasons included (e.g., use of a nonspecific assay). Analytes included in the review are DIA, hCG, the free , PAPP‐A, and NT thickness. Studies were classified as case/control if they appeared to be derived from a cohort study, but with analytes measured on frozen stored samples. Studies were classified as a cohort study if the analytes appeared to be measured on fresh samples as part of routine practice. Both of these types of studies might be subject to an ascertainment bias that could overestimate the analyte levels in Down syndrome pregnancies by not identifying the ‘‘missed’’ cases. Missed cases, those that were either not identified at birth or were spontaneously lost between the time of screening and delivery, would tend to have less abnormal results. However, these biases have been estimated to have only a small impact on the median serum analyte level (H Cuckle, Down’s Screening News, 2004) and we have not accounted for it. 3.5. DATA ANALYSIS Median analyte measurements in Down syndrome pregnancies were obtained for 9 through 14 completed gestational weeks from all identified published reports, along with the numbers of samples. An overall median for each week was computed for each of the analytes (weighted by the number of observations for each study). The resulting values were then subjected to regression analysis to obtain ‘‘smoothed’’ estimates. Although weeks 10 through 13 were of prime interest, weeks 9 and 14 were also included to stabilize the regression analysis. The confidence interval of a consensus estimate was derived using pooled standard deviations, after trimming and other adjustments. The population standard deviations (after a logarithmic transformation) were also collected for both Down syndrome and unaVected

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pregnancies from each of the studies for all of the analytes. A preliminary pooled variance, weighted by the number of samples, was computed and used to identify outliers via the F‐test. Each reported variance was compared to the pooled estimate during a single pass and those found to be significantly high (or low) were removed, resulting in a trimmed pooled variance. Pooled variances are provided both before and after trimming to determine the impact of trimming. Since the pooled variance for Down syndrome pregnancies includes a component due to the varying median levels by week, we adjusted the variance downward to take this into account [4]. This was done by creating a large population of individual MoM results (via a Monte Carlo simulation) that matched the median value each week, represented the relative proportions of cases observed at each week, and used a candidate log standard deviation that was smaller than the observed standard deviation in the meta‐analysis. The overall standard deviation for the simulated data was then computed and compared to the observed standard deviation. If the simulated one was too large, the candidate standard deviation was slightly reduced and the process repeated until the computed and observed standard deviations were identical. The candidate standard deviation was then taken as the corrected one. This reduction in variance requires that the correlation coeYcients also be modified using the known relationship among covariance, correlation, and the standard deviations for pairs of markers. Correlation coeYcients were not subjected to a trimming algorithm due to the relatively small number of estimates available. Modeling results using this reduced variance in Down syndrome pregnancies will more accurately predict performance when screening programs actually use gestational week‐specific parameters like those reported in the current study. 3.6. MODELING SCREENING PERFORMANCE Estimating Down syndrome detection and false positive rates for various combinations of markers is based on a model employing overlapping multivariate Gaussian distributions. These distributions are described by logarithmic means and standard deviations for both unaVected and Down syndrome pregnancies and pairwise correlation coeYcients. A Monte Carlo simulation is used to generate several hundred thousand random pregnancies from both groups. The maternal ages associated with the unaVected pregnancies correspond to the distribution reported for livebirths in the United States in 2000 [6]. The age‐associated risk for Down syndrome [7] was used to generate the expected maternal age distribution for aVected pregnancies. If analyte levels were above (or below) specified truncation limits, the risk was derived by assuming the value was at the limit. A first trimester Down syndrome risk was then assigned to each of the simulated combinations using an a priori risk,


183

the term risk [7] divided by 0.57 [8] to account for the 43% rate of spontaneous fetal loss from the late first trimester to term. This risk is multiplied by the likelihood ratio derived from the overlapping Gaussian distributions to create the final risk. The proportion of unaVected pregnancies with risks at or above a selected Down syndrome risk cutoV level is the false positive rate. The proportion of Down syndrome pregnancies at or above the same risk cutoV level is the detection rate. Care needs to be taken when using summary means and standard deviations based on the literature as the resulting models may be unstable. In order to avoid problems, the simulation utilized very large numbers of observations that would have exposed any systematic problems. In addition, results from the modeling performed in the present study can be compared to those already published to further confirm the reliability of the individual Down syndrome risks and screening performance.

4. Results 4.1. NEW DIMERIC INHIBIN‐A RESULTS The first trimester maternal serum DIA measurements in the 271 unaVected pregnancies fitted a log‐linear regression well (median DIA ¼ 10[0.0077(days) þ 2.12735]), with a 12% decrease per week. These day‐specific median levels were then used to convert all DIA results to MoM. The relationship between maternal weight and MoM level in the unaVected pregnancies was fitted using a published algorithm [9]. The resulting equation [expected DIA ¼ 0.1894 þ 150.159 1/(weight in pounds)] was used to adjust all DIA MoM results for maternal weight. Figure 1 shows the weight‐ adjusted DIA MoM levels in case and control pregnancies by gestational age. The data from unaVected pregnancies were consistent with a constant variance and, therefore, the 95th centile was estimated by drawing a horizontal line at 2.55 MoM. Five percent of the unaVected DIA MoM levels fall above this line. Prior to 12 weeks’ gestation, no DIA measurements in Down syndrome pregnancies are above this level. At 12 weeks’ gestation and later, 10 of 29 (34%) cases are above the 95th centile. After logarithmic transformation, the means and standard deviations over the entire gestational age range were computed to be 0.000; 0.2215 and 0.1826; 0.2441 for unaVected and Down syndrome pregnancies, respectively. 4.2. DIMERIC INHIBIN‐A MEASUREMENTS

IN THE

LITERATURE

In addition to the new data shown in the preceding section, eight other studies were identified that reported DIA measurements in first trimester

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Dimeric inhibin-A (MoM)

8 5 3 2 1 0.7 0.5 0.3 9

10

11

12

13

14

15

Gestational age (weeks) FIG. 1. First trimester maternal serum dimeric inhibin‐A (DIA) measurements in multiples of the median (MoM) for unaVected and Down syndrome pregnancies. The horizontal axis displays the gestational age in decimal weeks based on ultrasound measurements. The vertical logarithmic axis shows the weight‐adjusted DIA MoM levels. Large open circles represent the 51 maternal serum samples from pregnancies aVected with Down syndrome. The 271 small open circles represent observations from the matched controls. The thick horizontal line is drawn at the median MoM level of 1.00 while the thin horizontal line is drawn at the 95th centile (2.55 MoM).

Down syndrome pregnancies [10–17]. Two were removed from consideration because they employed an assay that was not specific for DIA [16, 17]. Results from the remaining studies and the present dataset are shown in the first seven rows of Table 1. Each column corresponds to a completed week of gestation and includes the median DIA MoM level and the number of Down syndrome pregnancies on which it is based. For example, at nine completed weeks of gestation, the current study finds a median of 1.20 MoM, based on one sample. The week‐specific observed medians for all seven studies are also shown. For example, at 9 weeks’ gestation, the overall median is 1.26 MoM, based on a total of 17 observations. As gestation increases, the median DIA level in Down syndrome pregnancies also tends to increase, resulting in improved screening performance at later weeks. Figure 2 displays the consensus week‐specific DIA MoM levels from the seven studies with approximate confidence intervals. A logarithmic quadratic model fitted the data well. This diVers from the linear model reported by SURUSS [13]. The diVerence is likely due to two factors. First, the number of samples in the current study is much larger and the corresponding confidence interval of the median for a given week of gestation is tighter. Also, the regression model in the current study is based on weeks 9 through 14 while SURUSS limited observations to


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TABLE 1 FIRST TRIMESTER MATERNAL SERUM DIMERIC INHIBIN‐A (DIA) MEASUREMENTS IN DOWN SYNDROME PREGNANCIES BY GESTATIONAL WEEK: RESULTS FROM THE CURRENT STUDY AND THE LITERATURE Observed median MoM dimeric inhibin‐A levels in Down syndrome pregnancies (number) Completed week of gestation References

9

10

11

12

13

14

Current study [13] [12] [11] [14] [10] [15] All studies‐observed All studies‐regresseda

1.20 (1)

1.07 (10) 1.14 (10)

1.36 (15) 1.12 (19) 1.04 (11) 1.58 (27) 0.98 (13) 0.62 (1) 2.47 (9) 1.33 (95) 1.26

1.64(10) 1.62 (30) 1.30 (26) 1.27 (31)

1.97 (8) 2.44 (25) 1.67 (8) 1.49 (15) 1.49 (16) 1.69 (4) 3.32 (7) 1.93 (83) 1.93

2.77 (7) 2.78 (12)

1.33 (13) 1.01 (3) 1.26 (17)

1.74 (3) 1.12 (35) 1.34 (2) 1.15 (60) 1.19

1.52 (7) 1.42 (104) 1.48

3.38 (2) 2.83 (21)

a Median dimeric inhibin‐A MoM in Down syndrome pregnancies (within the range of 10 weeks, 0 days through 13 weeks, 6 days) fitted the following equation: 10(0.0004694373 days days 0.068414569 days þ 2.56787907) . Regressed values were calculated using the midpoint of the gestational age period (i.e., 12 weeks is 12 weeks þ 3 days).

10 through 13 weeks. The last row in Table 1 shows the regressed medians for 10 through 13 completed weeks, along with the equation of the curve shown in Fig. 2. Table 2 shows data from these same seven studies, including the range of gestational ages, the type of study, the number of observations, and logarithmic standard deviations for both unaVected and Down syndrome pregnancies. Although 2073 unaVected pregnancies were included in the seven studies, only five of the reports contained an estimate for the logarithmic standard deviation. One of these studies [12] has a significantly smaller variance than the pooled estimate (F ¼ 1.4, p < 0.05) and is removed, resulting in a slightly broader standard deviation in unaVected pregnancies of 0.2363 (untrimmed estimate was 0.2268). This diVerence is small compared to the overall range of remaining standard deviations (from 0.2191 to 0.2500) indicating that the impact on modeling due to trimming will be less than would be found by using the range of reported standard deviations. A similar approach is used to estimate the standard deviation in the 380 Down syndrome pregnancies. The pooled standard deviation is 0.2792, and none of the estimates is identified as being an outlier. When the variation in median levels by gestational age is taken into account, the adjusted standard deviation is reduced to 0.2597 (a reduction in variance of 13%).

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FIG. 2. The relationship between median dimeric inhibin (DIA) MoM measurements in Down syndrome pregnancies and gestational age. Gestational age in completed weeks is shown on the horizontal axis vs the median DIA MoM levels (open circles) and 95% confidence interval (vertical lines) on the vertical logarithmic scale. The estimates are based on data from Table 1. The solid line indicates a fitted logarithmic quadratic equation, weighted by the square root of the numbers of Down syndrome samples at each week. The focus of the analysis is on weeks 10 through 13, but data at 9 and 14 weeks’ gestation were used in the regression analysis to increase robustness.

4.3. PREGNANCY‐ASSOCIATED PLASMA PROTEIN‐A MEASUREMENTS IN THE LITERATURE Thirteen published studies were identified that reported PAPP‐A levels by gestational week in first trimester Down syndrome pregnancies [5, 13, 14, 18–27]. Those results are summarized in Table 3. As gestation advances, the median MoM PAPP‐A level in Down syndrome pregnancies increases, resulting in reduced performance later in the first trimester. Figure 3 displays the consensus week‐specific PAPP‐A MoM levels (on a logarithm scale) using the combined data from the 13 studies with approximate confidence intervals. A linear regression model fitted the data well. The last row in Table 3 shows the regressed medians for 10 through 13 completed weeks, along with the equation of the line shown in Fig. 3. Table 4 shows additional data from the 13 studies. Eleven reported logarithmic standard deviations, based on 18,111 unaVected pregnancies. Three of these studies were trimmed and not included in determining the pooled standard deviation of 0.2392 in unaVected pregnancies. Among the 1129 Down syndrome pregnancies, 1 study was trimmed. Accounting for the varying mean levels


187

TABLE 2 FIRST TRIMESTER MATERNAL SERUM DIMERIC INHIBIN‐A (DIA) MEASUREMENTS: STANDARD DEVIATIONS IN DOWN SYNDROME AND UNAFFECTED PREGNANCIES

References Current study [13] [12] [11] [14] [10] [15] All studies‐ observedb All studies‐ outliers removed All studies‐ adjustedc

Gestational age range (weeks)

UnaVected pregnancies

Down syndrome

Type of study

Number

SD (log10)

Number

SD (log10)

9–14

Case/control

271

0.2215

51

0.2441

10–13 10–13 10–13 8–14 9–14 11–13 8–14

Case/control Case/control Case/control Case/control Case/control Case/control

420 493 800 383 206 89 2073

0.2191 0.1936a 0.2500 NR NR 0.2240 0.2268

96 45 76 77 12 23 380

0.2343 0.3485 0.3334 0.2431 0.3000 0.2300 0.2792

1580

0.2363

380

0.2597

1580

0.2363

380

0.2597

a

Excluded from observed evaluation as two‐sided F‐test revealed significant diVerence from the all studies‐observed. b Numbers of pregnancies are for studies reporting a standard deviation. The standard deviation is the weighted pooled estimate. c The standard deviation for Down syndrome pregnancies has been reduced to account for the varying median levels at diVerent gestational weeks as described in Section 2. NR ¼ not reported.

by week results in an adjusted standard deviations of 0.2861 (a reduction in variance of 11%). 4.4. HUMAN CHORIONIC GONADOTROPIN MEASUREMENTS IN THE LITERATURE Twelve studies reported median hCG levels (Table 5) by gestational week in first trimester Down syndrome pregnancies [5, 13, 14, 26, 28–35]. As gestation advances, the median MoM hCG levels in Down syndrome pregnancies also increase resulting in improved performance at later weeks. Figure 4 displays the consensus week‐specific hCG MoM levels from the 12 studies with approximate confidence intervals. A logarithmic quadratic regression fitted the data well. The last row in Table 5 shows the regressed medians for 10 through 13 completed weeks, along with the equation of the curve shown in Fig. 4. Table 6 shows information about the standard

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TABLE 3 FIRST TRIMESTER MATERNAL SERUM PREGNANCY‐ASSOCIATED PLASMA PROTEIN‐A (PAPP‐A) MEASUREMENTS IN DOWN SYNDROME PREGNANCIES BY GESTATIONAL WEEK: RESULTS FROM THE LITERATURE Observed median MoM PAPP‐A levels in Down syndrome pregnancies (number) Completed week of gestation References [13] [18, 28] [19] [5] [20] [21] [14] [22] [23] [24] [25] [26] [27] All studies‐ observed All studies‐ regresseda

9

0.37 (21) 0.54 (1) 0.46 (6) 0.36 (13) 0.45 (3) 0.27 (11) 0.30 (1) 0.50 (1) 0.36 (57)

10

11

12

13

14

0.42 (10) 0.42 (47) 0.28 (1) 0.31 (10) 0.78 (7) 0.88 (1) 0.44 (35) 0.36 (2) 0.31 (9) 0.24 (5) 0.48 (6) 0.30 (6) 0.23 (3) 0.40 (142)

0.38 (19) 0.48 (115) 0.35 (5) 0.37 (15) 1.06 (2) 0.16 (4) 0.33 (13) 0.65 (2) 0.75 (11) 0.27 (3) 0.27 (2) 0.30 (4) 0.20 (4) 0.43 (199)

0.44 (30) 0.54 (192) 0.75 (4) 0.62 (10) 1.04 (5) 0.30 (6)

0.60 (24) 0.62 (114) 0.82 (4) 0.39 (8)

0.56 (12) 0.71 (29)

0.75 (5) 0.93 (7) 1.49 (1) 0.21 (2) 2.00 (2) 2.12 (1) 0.55 (265)

0.60 (177)

0.40

0.46

0.51

0.58

0.41 (5) 0.59 (16) 0.78 (4) 0.58 (2)

0.68 (7)

1.00 (3)

0.40 (9) 0.40 (9) 0.59 (69)

a Median PAPP‐A MoM in Down syndrome pregnancies (within the range of 9 weeks, 0 days through 13 weeks, 6 days) fitted the following equation: 10((0.0073192308 days) 0.92744997). Regressed values were calculated using the midpoint of the gestational age period (i.e., 12 weeks is 12 weeks þ 3 days).

deviations in both unaVected and Down syndrome pregnancies. One study [34] was removed, resulting in a trimmed standard deviation of 0.2019 in unaVected pregnancies; four of the Down syndrome studies were trimmed (two with overly broad variances and two with overly tight variances). The adjusted standard deviation in Down syndrome pregnancies is 0.2095 (a reduction in variance of 14%). 4.5. FREE ‐SUBUNIT

OF

hCG MEASUREMENTS

IN THE

LITERATURE

Twelve studies reported free levels (Table 7) in first trimester Down syndrome pregnancies [5, 11, 13, 14, 19, 21, 25, 26, 28, 29, 34, 36]. As gestation advances, the median MoM free level in Down syndrome pregnancies increases, resulting in improved performance in later weeks. Figure 5 displays the consensus free MoM levels fitted by a logarithmic quadratic regression.


189

FIG. 3. The relationship between median pregnancy‐associated plasma protein‐A (PAPP‐A) MoM measurements in Down syndrome pregnancies and gestational age. Gestational age in completed weeks is shown on the horizontal axis vs the median PAPP‐A MoM levels (open circles) and 95% confidence intervals (vertical lines) on the vertical logarithmic scale. The estimates are based on data from Table 3. The solid horizontal line indicates PAPP‐A MoM values for controls. The solid line drawn through the open circles indicates a fitted linear equation, weighted by the square root of the numbers of Down syndrome samples at each week. The focus of the analysis is on weeks 10 through 13, but data at 9 and 14 weeks’ gestation were used in the regression analysis to increase robustness.

Table 8 shows data dealing with standard deviations from these same 12 studies. Based on the seven studies remaining after trimming, the standard deviation in unaVected pregnancies is 0.2386. All 12 studies reported a standard deviation for Down syndrome pregnancies, and 4 of these were trimmed (because of high or low estimated variance), leaving 904 observations. After accounting for varying median levels by gestation, the adjusted standard deviation in Down syndrome pregnancies is 0.2888 (a reduction in variance of 14%). 4.6. NUCHAL TRANSLUCENCY THICKNESS MEASUREMENTS IN THE LITERATURE Eight published studies were identified [5, 13, 19, 37–41]. One study reported only 11 Down syndrome pregnancies and was not included [37]. Another was considered to have unreliable NT measurements due to nonstandard methods of measurements [5]. Two studies were excluded because it was not possible to estimate gestational age‐specific standard deviations in unaVected pregnancies [19, 40]—a confirmed finding in several large series. Two studies were not considered [38, 39] because they appeared to be

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TABLE 4 FIRST TRIMESTER PREGNANCY‐ASSOCIATED PLASMA PROTEIN‐A (PAPP‐A) MEASUREMENTS: STANDARD DEVIATIONS IN DOWN SYNDROME AND UNAFFECTED PREGNANCIES

References [13] [18, 28] [19] [5] [20] [21] [14] [22] [23] [24] [25] [26] [27] All studies‐ observedb All studies‐outliers removed All studies‐ adjustedc

Gestational age range (weeks) 10–13 9–14 10–13 9–14 7–12 10–13 9–13 9–14 10–13 9–12 9–12 8–14 9–14 8–14

UnaVected pregnancies Type of study Number Case/control Case/control Cohort Case/control Case/control Cohort Case/control Case/control Case/control Case/control Case/control Case/control Cohort

Down syndrome

SD (log10)

Number

SD (log10)

420 14,607 1454 265 306 1119 383 31 210 280 89 258 66 18,111

0.2495 0.2361 0.2500 0.2880 0.3359a NR 0.2659 0.2054 0.3729a 0.2975a 0.2217 NR 0.2530 0.2441

95 724 13 52 20 16 77 19 42 20 11 23 17 1129

0.2802 0.2822 0.2500 0.3344 0.3035 0.3777 0.3471 0.2537 0.4473a 0.3269 0.2142 0.3156 0.4012 0.3030

17,315

0.2392

1087

0.2959

17,315

0.2392

1087

0.2861

a

Excluded from adjusted evaluation as two‐sided F‐test revealed significant diVerence from the all studies‐observed. b Numbers of pregnancies for studies reporting a standard deviation. The standard deviation is the weighted pooled estimate. c The standard deviation for Down syndrome pregnancies has been reduced to account for the varying median levels by gestational age. NR ¼ not reported.

included in a larger summary study by those authors [41]. Two studies remained for analysis [13, 41]. Results from these studies are shown in Table 9. Median NT MoM levels from one study [41] have been adjusted downward by 14%, due to an ascertainment bias reported by the authors [42]. As gestation increases, the median NT level in Down syndrome pregnancies decreases, resulting in poorer separation between aVected and unaVected pregnancies in later weeks. Figure 6 displays the week‐specific NT MoM levels separately for the two studies, with approximate confidence intervals. Table 10 shows the reported logarithmic standard deviations by gestational week for unaVected pregnancies. One of the studies [41] did not report a standard deviation at 10 weeks’ and included only 21 observations at 14 weeks’ gestation. The other study [13] did not report a standard deviation


191

TABLE 5 FIRST TRIMESTER MATERNAL SERUM HUMAN CHORIONIC GONADOTROPIN (hCG) MEASUREMENTS IN DOWN SYNDROME PREGNANCIES BY GESTATIONAL WEEK: RESULTS FROM THE LITERATURE Observed median MoM hCG levels in Down syndrome pregnancies (number) Completed week of gestation References

9

[13] [18, 28] [35] [34] [5] [14] [29] [26] [30] [31] [32] [33] All studies‐ observed All studies‐ regresseda

1.36 (3)

1.67 (1) 1.25 (13) 0.30 (1) 1.03 (11) 0.31 (1) 1.16 (2) 1.08 (32)

10

11

12

13

14

0.96 (10) 1.07 (15) 1.22 (6) 1.06 (22) 1.08 (10) 1.05 (35) 1.24 (14) 1.35 (4)

1.45 (30) 1.41 (148) 1.24 (7) 1.75 (16) 1.79 (10)

2.07 (25) 1.69 (73)

2.54 (12) 2.28 (14)

1.16 (9) 1.06 (7) 1.16 (8) 1.09 (140)

1.24 (19) 1.15 (68) 1.25 (11) 1.28 (20) 1.50 (15) 1.19 (13) 1.44 (17) 1.35 (4) 1.44 (3) 1.38 (3) 0.91 (2) 1.53 (6) 1.26 (181)

1.13

1.26

1.45

2.50 (8) 3.30 (1) 1.79 (3) 2.60 (1) 0.51 (1) 1.08 (1) 1.48 (226)

1.66 (5) 1.85 (8) 1.50 (16) 1.36 (2)

1.70 (7)

1.80 (9) 1.73 (5)

1.73 (134)

2.13 (42)

1.74

a Using median hCG MoM ¼ 10(0.00014959334 days days 0.016016425 days þ 0.4236247) in the range of 9 weeks, 0 days through 13 weeks, 6 days. Regressed values were calculated using the midpoint of the gestational age period (i.e., 12 weeks is 12 weeks þ 3 days).

at 14 weeks’, and reported that the standard deviation was constant at 12 and 13 weeks’ gestation. The weighted summary standard deviations for 10 through 14 weeks’ are shown in the last column and range from 0.1732 at 10 weeks’ to 0.1199 at 14 weeks’ gestation. Both of the studies reported a single standard deviation for Down syndrome pregnancies over the range of 11 through 13 weeks’ gestation. The observed pooled standard deviation is 0.2354 and, after accounting for varying mean NT levels over those weeks, the adjusted estimate is 0.2317 (a reduction in variance of 3%). 4.7. CORRELATION COEFFICIENTS

AND

TRUNCATION LIMITS

Pairwise correlation coeYcients for unaVected and Down syndrome pregnancies are shown in Tables 11 and 12, respectively. At the bottom are the consensus correlation coeYcients weighted by the number of observations.

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FIG. 4. The relationship between median human chorionic gonadotropin (hCG) MoM measurements in Down syndrome pregnancies and gestational age. Gestational age in completed weeks is shown on the horizontal axis vs the median hCG MoM levels (open circles) and 95% confidence intervals (vertical lines) on the vertical logarithmic scale. The estimates are based on data from Table 5. The solid line indicates a fitted logarithmic quadratic equation, weighted by the square root of the numbers of Down syndrome samples at each week. The focus of the analysis is on weeks 10 through 13, but data at 9 and 14 weeks’ gestation were used in the regression analysis to increase robustness.

Below each of the consensus correlation coeYcients for Down syndrome (Table 12) is the corresponding value, adjusted for the previously reported reduction in variance. For some of the analytes, the correlation coeYcients vary widely, especially in Down syndrome pregnancies. Much of this is likely due to the small number of samples, but it is also possible that studies handled outlying values diVerently. Some of the studies included in the preceding analyses reported truncation limits that are used to help ensure reliable risk estimates and modeling results. Since many did not provide this information, we employed the following process to define such limits. In general, the upper (and lower) truncation limits can be preliminarily defined as the logarithmic mean plus 2.5 standard deviations of the lower (or upper) distribution. This makes the assumption that the original data fitted the Gaussian parameters well between the 1st and 99th centiles. Univariate likelihood ratios are then examined to verify that the ratios monotonically increase (or decrease). This will not be the case if the standard deviations in unaVected and Down syndrome pregnancies diVer greatly. In such circumstances, risk estimates near the extremes can be inappropriate. Such a problem was found at smaller NT measurements where the likelihood ratios


193

TABLE 6 FIRST TRIMESTER MATERNAL SERUM HUMAN CHORIONIC GONADOTROPIN (hCG) MEASUREMENTS: STANDARD DEVIATIONS IN DOWN SYNDROME AND UNAFFECTED PREGNANCIES

References [13] [18, 28] [35] [34] [5] [14] [29] [26] [30] [31] [32] [33] All studies‐ observedb All studies‐ outliers removed All studies‐ adjustedc

Gestational age range (weeks) 10–13 9–14 9–11 10–13 9–14 8–14 10–13 9–14 11–13 9–13 9–12 9–12 8–14


Down syndrome

Type of study

Number

SD (log10)

Number

SD (log10)

Case/control Case/control Case/control Case/control Case/control Case/control Case/control Case/control Case/control Case/control Case/control Case/control

420 1277 115 400 260 383 394 258 261 1348 55 112 4642

0.1950 0.2179 0.1800 0.1697a 0.1986 NR 0.2156 NR 0.1876 0.1904 0.1702 0.1892 0.1993

84 321 23 63 51 77 41 19 11 24 11 17 742

0.2276 0.2238 0.1500a 0.2158 0.1867 0.2437 0.1893 0.3938a 0.0664a 0.2429 0.3062 0.1174a 0.2263

4242

0.2019

672

0.2251

4242

0.2019

672

0.2095

a


inappropriately increase. A higher truncation limit is required. The final lower and upper truncation limits selected for use are DIA (0.4–4.0 MoM), PAPP‐A (0.2–2.7 MoM), hCG (0.4–3.2 MoM), free (0.4–4.0 MoM), and NT (0.8–2.2 MoM). Figure 7 shows the univariate likelihood ratios for the five first trimester markers (based on the 12‐week parameters), after the truncation limits are applied. The likelihood ratios for NT measurements are also shown without truncation limits (dotted line) to show what the impact on assigning individual Down syndrome risk estimates might be if truncation limits are not set. In the rare instance that an NT MoM of 0.20 is reported, the likelihood ratio would indicate an increase in Down syndrome risk of 55‐fold. This is not biologically plausible and demonstrates the need

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TABLE 7 FIRST TRIMESTER MATERNAL SERUM FREE ‐SUBUNIT OF hCG (FREE ) MEASUREMENTS IN DOWN SYNDROME PREGNANCIES BY GESTATIONAL WEEK: RESULTS FROM THE LITERATURE Observed median free levels in Down syndrome pregnancies (number) Completed week of gestation References [13] [18, 28] [34] [19] [5] [11] [21] [14] [29] [26] [25] [36] All studies‐observed All studies‐regresseda

9

1.55 (17)

3.97 (1)

2.33 (13) 0.20 (1) 1.11 (1) 1.26 (2) 1.71 (35)

10

11

12

13

14

1.94 (10) 1.74 (50) 1.74 (22) 0.82 (3) 1.45 (10) 2.07 (3) 1.34 (1) 1.50 (35) 1.64 (16) 2.40 (5) 2.08 (6) 2.46 (3) 1.69 (164) 1.72

1.61 (19) 1.91 (117) 1.64 (20) 1.98 (4) 2.71 (15) 1.61 (27) 1.25 (5) 1.42 (13) 2.28 (12) 1.15 (4) 2.27 (2) 1.62 (3) 1.82 (241) 1.82

2.22 (30) 2.05 (187) 1.85 (16) 1.71 (4) 2.07 (10) 1.76 (31) 1.43 (8)

2.50 (24) 2.16 (126) 2.71 (5) 2.14 (4) 1.91 (8) 2.20 (15) 1.52 (5) 2.56 (16) 1.83 (2)

4.00 (11) 2.22 (46)

2.23 (11) 3.80 (1) 0.87 (2) 0.70 (1) 1.99 (301) 1.98

2.17 (205) 2.21

2.79 (7)

2.52 (64)

a

Using median free MoM ¼ 10(0.000123949 days days 0.0155533 days þ 0.710629) within the range of 9 weeks, 0 days through 13 weeks, 6 days. Regressed values were calculated using the midpoint of the gestational age period (i.e., 12 weeks is 12 weeks þ 3 days).

for truncation limits. Alternatively, were an NT MoM of 5.0 be reported, the likelihood ratio would be 120,000 and the pregnancy might be assigned an unreasonably high risk estimate (100:1), regardless of the biochemical testing results. 4.8. MODELING RESULTS The regressed week‐specific logarithmic means, standard deviations, correlation coeYcients, and truncation limits were then combined with a standard maternal age distribution in a multivariate Gaussian model. Table 13 shows estimated Down syndrome detection rates at three false positive rates at 11 through 13 weeks’ gestation for each marker alone, for combinations of maternal age and two or three biochemical markers, and these same combinations with NT measurements. The last three columns show a summary estimate of screening performance, assuming that 25% of samples are collected at 11 and 13 weeks’ gestation, and the remaining 50% are collected at 12 weeks’ gestation. The best single marker is NT measurements with a


195

FIG. 5. The relationship between median free ‐subunit of human chorionic gonadotropin (free ) MoM measurements in Down syndrome pregnancies and gestational age. Gestational age in completed weeks is shown on the horizontal axis vs the median free MoM levels (open circles) and 95% confidence interval (vertical lines) on the vertical logarithmic scale. The estimates are based on data from Table 7. The solid line indicates a fitted logarithmic quadratic equation, weighted by the square root of the numbers of Down syndrome samples at each week. The focus of the analysis is on weeks 10 through 13, but data at 9 and 14 weeks’ gestation were used in the regression analysis to increase robustness.

summary estimate of 60% detection at a 5% false positive rate (ranging from 63% at 11 weeks’ to 56% at 13 weeks’ gestation). Among the combinations of maternal age and biochemistry without NT measurements, the highest detection of 69% is found using PAPP‐A, free , and DIA measurements (ranging from 68% to 71%). The next best two combinations are PAPP‐A and free , and PAPP‐A, hCG and DIA at 66% and 62%, respectively. When NT measurements are included, the two combinations utilizing three biochemical markers can detect about 85% of Down syndrome pregnancies; a rate that is reasonably constant at 11 through 13 weeks’ gestation. Table 14 shows the estimated false positive rates at three detection rates for the same combinations of biochemical and ultrasound markers. When NT measurements are included with maternal age, PAPP‐A, free , and DIA measurements, it is possible to detect 85% of Down syndrome pregnancies, with false positive rates as low as 4.9%. If hCG is substituted for free , the false positive rate increases to 6.1%. If DIA is removed from these two combinations, the false positive rates are even higher at 7.1% and 7.5%, respectively. Table 15 shows the weighted detection and false positive rates at first trimester Down syndrome risk cutoV levels of 1:100, 1:150, 1:200, and 1:250

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TABLE 8 FIRST TRIMESTER MATERNAL SERUM FREE ‐SUBUNIT OF hCG (FREE ) MEASUREMENTS: STANDARD DEVIATIONS IN DOWN SYNDROME AND UNAFFECTED PREGNANCIES

References [13] [18, 28] [34] [19] [5] [11] [21] [14] [29] [26] [25] [36] All studies‐ observedb All studies‐ outliers removed All studies‐ adjustedc

Gestational age range (weeks) 9–14 9–14 10–13 10–13 9–14 10–13 10–13 8–14 10–13 9–14 8–12 9–12 8–14


Down syndrome

Type of study

Number

SD (log10)

Number

SD (log10)

Case/control Case/control Case/control Case/control Case/control Case/control Case/control Case/control Case/control Case/control Case/control Case/control

508 31,711 400 1454 265 800 >40 383 394 258 89 666 36,004

0.2651 0.2362 0.2157 0.2600 0.2680 0.2694 NR 0.2833a 0.3045a NR 0.2573 NR 0.2399

95 543 63 13 52 76 19 77 41 21 11 9 1020

0.2569 0.3027 0.2322a 0.5300a 0.2610 0.2630 0.1384a 0.2870 0.3091 0.4270a 0.2571 0.2753 0.2950

35,277

0.2386

904

0.2922b

35,277

0.2386

904

0.2888b

a


(approximately equal to the age‐associated risk of an average 38‐, 36‐, 35‐, and 34‐year‐old woman, respectively) for multiple marker combinations. At the risk cutoV level of 1:200, detection rates range from 69% to 75%, with associated false positive rates from 9.3% to 7.7%. With the addition of NT measurements, the detection rates increase by 9–12% points and become more consistent for the various combinations (81–84%) at lower false positive rates (4.3–5.1%). The odds of being aVected given a positive result (OAPR) range from 1:16 to 1:21. The screening performance estimate generated using the composite literature‐based parameters are close to those that are also provided by SURUSS [13], further confirming that the results are reliable.

197


TABLE 9 FIRST TRIMESTER NUCHAL TRANSLUCENCY MEASUREMENTS IN DOWN SYNDROME PREGNANCIES GESTATIONAL WEEK: RESULTS FROM THE LITERATURE

BY

Observed median NT MoM in Down syndrome pregnancies (number) Completed week of gestation References [13] [41]a All studies‐observed All studies‐regressedb

9

10

11

12

13

2.02 (3)

2.10 (22) 2.25 (116) 2.23 (138) 2.18

1.66 (27) 1.89 (231) 1.86 (258) 1.92

1.76 (21) 1.73 (81) 1.74 (102) 1.69

14

a

After adjusting downward by 14% due to bias of ascertainment [42]. Using median NT MoM ¼ 10(0.0078109497 days þ 0.96338874) within the range of 11 weeks, 0 days through 13 weeks, 6 days. Regressed values were calculated using the midpoint of the gestational age period (i.e., 12 weeks is 12 weeks þ 3 days). b

FIG. 6. The relationship between median nuchal translucency thickness (NT) MoM measurements in Down syndrome pregnancies and gestational age. Gestational age in completed weeks is shown on the horizontal axis vs the median NT MoM levels (circles) and 95% confidence interval (vertical lines) on the vertical logarithmic scale. The estimates are from two publications summarized in Table 9 (closed circles indicate data from Spencer et al. [41] and open circles from Wald et al. [13]). The solid line indicates a fitted linear equation, weighted by the square root of the numbers of Down syndrome samples at each week. The regression analysis only used data from 11 through 13 weeks’ gestation because too few observations were available at earlier, and later, weeks.

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TABLE 10 FIRST TRIMESTER NUCHAL TRANSLUCENCY MEASUREMENTS: STANDARD DEVIATIONS SYNDROME AND UNAFFECTED PREGNANCIES

IN

DOWN

All studies

Type of study

[13]

[18, 28]

Cohort

Cohort

Observed

Adjusted

UnaVected pregnancies (N) 10 Weeks 0.1732 (3379) 11 Weeks 0.1439 (9051) 12 Weeks 0.1329 (17,346) 13 Weeks 14 Weeksa NR

NR 0.1613 (36,204) 0.1324 (62,081) 0.1117 (24,969) 0.1199 (21)

0.1732 (3379) 0.1578 (45,255) 0.1325 (70,754) 0.1172 (33,642) 0.1199 (21)

0.1732 (3379) 0.1578 (45,255) 0.1325 (70,754) 0.1172 (33,642) 0.1199 (21)

Down syndrome pregnancies (N) 11–13 Weeksb 0.2313 (73)

0.2350 (428)

0.2354 (501)

0.2317 (501)

a

The standard deviation has been derived from the regression equation contained in the study. The standard deviation for Down syndrome pregnancies has been reduced to account for the varying median levels by gestational age. NR ¼ not reported. b

5. Discussion Measurements of DIA are most commonly used in the second trimester as an addition to the standard ‘‘triple’’ test (maternal age in combination with AFP, uE3, and hCG measurements) to form the ‘‘quadruple’’ test. In the first trimester, DIA measurements are not as useful. Measurements from the 22 samples collected from Down syndrome pregnancies prior to 12 weeks’ gestation are all below the 95th centile of unaVected pregnancies. After 12 weeks’ gestation, the performance is much improved with 34% above the 95th centile. However, by the second trimester, the detection rate will be as high as 50%. By combining the information provided in the literature, there were suYcient data at each week to derive weekly observed median MoM levels in Down syndrome pregnancies that could be regressed to provide even more robust estimates. It is theoretically possible to provide day‐specific median MoM estimates in Down syndrome pregnancies for the five markers studied, but the increment of performance improvement is unlikely to justify the added complexity. Overall, there were 380 Down syndrome cases reported for DIA, 501 for NT, 742 for hCG, 1020 for free , and 1129 for PAPP‐A. All showed consistent patterns of either linear or quadratic changes (on a logarithmic scale) over the gestational ages of 9 through 14 weeks. In no

CORRELATION COEFFICIENTS

FOR

TABLE 11 SELECTED FIRST TRIMESTER BIOCHEMICAL AND ULTRASOUND MARKERS RESULTS FROM THE LITERATURE

IN

UNAFFECTED PREGNANCIES:

Correlation coeYcient betweena DIA Study Current study [13] [12] [34] [19] [11] [10] [14] [29] [48] [49] [47] [38] All studies (observed) Studies reporting a

N 3169 420 493 400 1450 800 438 383 394 959 320 227 947 10,400

hCG 0.672 0.577

Free

PAPP‐A

0.542 0.496 0.242

Free

hCG

0.210 0.238 0.076

NT 0.078

Free 0.752 0.718

PAPP‐A 0.170 0.220

NT 0.074

PAPP‐A

PAPP‐A NT

NT

0.154 0.140

0.038

0.050

0.190

0.042

0.097

0.626

0.270

0.386 0.150 0.141 0.010

0.010

0.214

0.618 4027

0.455 5320

0.197 4082

Reported coeYcients rounded to three significant digits.

0.078 420

0.766 3989

0.184 4548

0.033 814

0.168 0.086 0.160 0.154 6916

–0.057 0.040 3211

0.000 0.042 2817

CORRELATION COEFFICIENTS

FOR

TABLE 12 SELECTED FIRST TRIMESTER BIOCHEMICAL AND ULTRASOUND MARKERS RESULTS FROM THE LITERATURE

IN

DOWN SYNDROME PREGNANCIES:

Correlation coeYcient betweena DIA Study

N

Current study [13] [12] [34] [19] [11] [10] [14] [29] [48] [49] [47] [38] All studies (observed) Number of samples All studies (adjusted)

48 96 45 63 13 76 12 77 41 130 21 52 210

a

884

hCG 0.768 0.397

Free

hCG

Free

PAPP‐A

NT

0.542 0.283 0.385

0.031 0.112 0.424

0.129

Free

PAPP‐A

0.647 0.505

0.121 0.128

PAPP‐A

NT

PAPP‐A

NT

NT

0.082

0.038 0.069

0.108

0.151

0.240

0.210

0.570

0.452

0.390

0.308 0.230 0.065 0.153

0.151

0.427

0.511 156 0.590

0.349 277 0.380

0.150 189 0.167

Reported coeYcients reported to three significant digits.

0.129 96 0.141

0.522 207 0.567

0.269 274 0.299

0.103 137 0.112

0.574 0.223 0.216 0.088 517 0.092

0.000 0.019 360 0.020

0.089 0.081 319 0.085


201

FIG. 7. A comparison of univariate likelihood ratios vs analyte level for the five first trimester markers studied. The logarithmic horizontal axis shows the analyte result in multiples of the median (MoM) for any of the five markers (NT ¼ nuchal translucency thickness, hCG ¼ human chorionic gonadotropin, free ¼ the free ‐subunit of hCG, DIA ¼ dimeric inhibin‐A, and PAPP‐A ¼ pregnancy‐associated plasma protein‐A). The labeled curves show the corresponding likelihood ratios for Down syndrome on the logarithmic vertical axis. The horizontal lines at the upper (and lower) analyte results indicate the levels at which the upper (or lower) truncation limit was reached. The dotted line indicates the likelihood ratios for NT measurements that would be assigned if truncation limits were not used. For example, if the NT MoM levels were 0.2 or 5 MoM, the corresponding likelihood ratios without truncation limits would be 55 and 120,000, respectively.

instance did one (or more) of the studies have data that were systematically diVerent from the others to an extent that would contradict the observed consensus estimate. This would not have been true for NT measurements, if an adjustment had not been made. The largest dataset available for NT measurements was collected by one group in England [41]. The reported MoM levels were consistently higher than those reported by another group in England [13]. However, a reanalysis of the larger dataset identified a bias that could be responsible for a 14% overestimate of the medians [42]. Once this was taken into account, week‐specific median MoM levels in Down syndrome pregnancies between the two studies became reasonably consistent (Fig. 6). As an example of the importance of having access to a large number observations, consider the number of Down syndrome samples reported in SURUSS [13] and in our meta‐analysis for first trimester hCG. This is an important issue because replacing free ‐hCG measurement with intact or total hCG measurements is under active and intense debate at this time. SURUSS covered weeks 10 through 13, with 3, 22, 27, and 21 cases of

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TABLE 13 EXPECTED DOWN SYNDROME DETECTION RATES AT THREE FALSE POSITIVE RATES FOR VARIOUS COMBINATIONS OF BIOCHEMICAL AND ULTRASOUND MARKERS IN THE FIRST TRIMESTER AND AT SELECTED WEEKS OF GESTATION

NT only PAPP‐A only Free only DIA only hCG only Maternal age PAPP‐A and free PAPP‐A and hCG PAPP‐A and DIA PAPP‐A, free , and DIA PAPP‐A, hCG, and DIA Maternal age, NT PAPP‐A and free PAPP‐A and hCG PAPP‐A and DIA PAPP‐A, free , and DIA PAPP‐A, hCG, and DIA

11 Weeks’ gestation (%)



1113 Weeks’ combined gestation (%)a

1

3

5

1

3

5

1

3

5

1

3

5

45 22 15 4 4

57 35 26 9 9

63 42 32 13 13

46 18 19 7 7

56 29 30 15 15

61 36 37 20 21

42 13 23 15 14

51 23 36 27 25

56 29 43 35 33

45 18 19 8 8

55 29 30 16 16

60 36 37 22 22

45 35 35 45 36

60 51 50 60 52

67 58 58 68 60

44 35 35 45 37

59 51 50 60 53

66 59 58 68 61

43 37 39 48 42

58 54 54 64 55

66 63 62 71 67

44 36 36 46 38

59 52 51 61 53

66 60 59 69 62

68 62 62 69 64

80 76 75 81 77

85 82 81 85 83

68 64 63 70 66

79 76 76 81 78

83 81 81 85 83

66 64 65 71 68

76 76 76 81 79

81 81 81 85 84

68 64 63 70 66

79 76 77 82 78

83 81 81 85 83

a Assuming 25% of the women are tested at 11 and at 13 weeks’ gestation, and the remaining 50% at 12 weeks.

Down syndrome at each week, for a total of 73 cases. Our meta‐analysis contains data from 9 through 14 gestational weeks, with 32, 140, 181, 225, 134, and 42 cases at each week, for a total of 742 cases. This has allowed us to show that the association between gestational age and median hCG level in Down syndrome pregnancies is not log‐linear, as assumed in SURUSS, but is a curve. This single finding has important implications for screening performance at 10 and 11 gestational weeks where earlier studies may have underestimated the usefulness of hCG measurements. The reported standard deviations in unaVected and Down syndrome pregnancies were formally checked to identify outlying values. Some reported standard deviations from individuals were significantly broader (or tighter) than the overall consensus and were trimmed. However, this trimming process did not have an important impact on any of the consensus estimates for the standard deviations in unaVected or Down syndrome pregnancies for any of the five markers examined. Since the pooled estimate of the standard

TABLE 14 EXPECTED DOWN SYNDROME FALSE POSITIVE RATES AT THREE DETECTION RATES FOR VARIOUS COMBINATIONS OF BIOCHEMICAL MARKERS IN THE FIRST TRIMESTER AND AT SELECTED WEEKS OF GESTATION 11 Weeks’ gestation (%) 65 NT only PAPP‐A only Free only DIA only hCG only Maternal age PAPP‐A and free PAPP‐A and hCG PAPP‐A and DIA PAPP‐A, free , and DIA PAPP‐A, hCG, and DIA Maternal age, NT PAPP‐A and free PAPP‐A and hCG PAPP‐A and DIA PAPP‐A, free , and DIA PAPP‐A, hCG, and DIA a

75

12 Weeks’ gestation (%) 85

65

5.7 17 27 50 46

12 27 39 62 58

27 43 56 76 72

7.2 22 22 38 34

4.4 7.1 7.7 4.2 17

8.6 13 14 8.3 12

18 23 26 17 23

0.8 1.3 1.3 0.7 1.1

1.9 2.8 2.9 1.7 2.5

5.3 6.8 7.2 4.8 6.3

75

13 Weeks’ gestation (%) 85

17 34 34 51 46

37 51 50 66 61

4.7 7.0 7.9 4.2 6.3

9.2 12 14 8.3 12

19 22 26 17 21

0.7 1.1 1.2 0.6 0.9

2.0 2.7 2.9 1.7 2.3

6.1 7.2 7.7 5.0 6.3

65 12 30 16 22 21

75

AND

ULTRASOUND

11–13 Weeks’ combined gestation (%)a 85

65

27 43 26 32 31

54 60 42 47 45

8.0 23 22 37 34

4.8 5.8 5.9 3.3 4.5

9.2 10 11 6.6 8.3

19 19 21 14 16

0.9 1.1 1.0 0.5 0.8

2.6 2.8 2.6 1.6 2.0

7.6 7.3 7.3 4.8 5.6

Assuming 25% of the women are tested at 11 and at 13 weeks gestation, and the remaining 50% at 12 weeks.

75

85

18 34 33 49 45

39 51 50 64 60

4.7 6.7 7.4 4.0 8.5

9.1 12 13 7.9 11

19 22 25 16 20

0.8 1.2 1.2 0.6 0.9

2.1 2.8 2.8 1.7 2.3

6.3 7.1 7.5 4.9 6.1

TABLE 15 MODELED DOWN SYNDROME DETECTION RATE (DR), FALSE POSITIVE RATE (FPR) AND ODDS OF BEING AFFECTED GIVEN AT SELECTED FIRST TRIMESTER RISK CUTOFF LEVELS

A

POSITIVE RESULT (OAPR)

First trimester Down syndrome risk cutoff levela 1:100

Maternal age PAPP‐A and free PAPP‐A and hCG PAPP‐A and DIA PAPP‐A, free , and DIA PAPP‐A, hCG, and DIA Maternal age, NT PAPP‐A and free PAPP‐A and hCG PAPP‐A and DIA PAPP‐A, free , and DIA PAPP‐A, hCG, and DIA a

1:150

1:200

1:250

DR

FPR

OAPRb

DR

FPR

OAPR

DR

FPR

OAPR

DR

FPR

OAPR

63 59 57 65 61

4.0 4.7 4.5 3.9 4.5

1:21 1:27 1:26 1:20 1:25

69 66 64 71 68

6.0 7.3 6.9 5.8 6.9

1:29 1:36 1:36 1:28 1:34

73 72 69 75 72

8.0 9.7 9.3 7.7 9.2

1:37 1:45 1:45 1:34 1:42

76 75 73 78 76

9.9 12 12 9.5 12

1:43 1:53 1:53 1:41 1:50

75 74 74 77 76

2.2 2.6 2.5 2.1 2.4

1:10 1:12 1:11 1:9 1:11

79 79 78 81 80

3.3 3.8 3.7 3.1 3.6

1:14 1:16 1:16 1:13 1:15

82 82 81 84 82

4.3 5.1 4.9 4.1 4.7

1:18 1:21 1:20 1:16 1:19

84 84 83 85 84

5.4 6.2 6.0 5.0 5.8

1:21 1:25 1:24 1:20 1:23

1:100, 1:150, 1:200, and 1:250 correspond approximately to the a priori age‐associated risk of a 38‐, 37‐, 35‐, and 34‐year old, respectively. Based on a prevalence of 1 in 333 using USA 2000 maternal age distribution, a priori maternal age risk from Hecht [7] and a Down syndrome fetal loss rate from late first trimester to term [8]. b


205

deviation in Down syndrome pregnancies contains a known source of variability (the varying median levels by week), we modeled the associated reduction in variance. These reductions range from 11% to 14% for the biochemical markers, but the reduction is only 4% for NT measurements. Reducing the variances will have the eVect of improving screening performance. Fewer studies reported pairwise correlation coeYcients. These coeYcients are important when modeling screening performance when multiple markers are used. In general, NT measurements are not highly correlated with any of the biochemical markers, in either Down syndrome or unaVected pregnancies. The highest correlations were found between hCG and free measurements. DIA measurements had relatively high correlations with both hCG and free measurements. PAPP‐A measurements tended to have low correlations with the other biochemical markers. The current study does not address whether Down syndrome screening should be in the first trimester, the second trimester or the results from both trimester integrated into a single interpretation in the second trimester. Other variants on integrated screening, such as sequential [43], contingent [44], or repeated measures [45], have also been suggested as ways to optimize the screening process and other summary analyses have addressed these [46]. One recent analysis contained summary information for most of the markers we examined [46], and where comparisons are possible, their estimates of parameters and performance are in‐line with ours. The present study concentrates only on the markers that may be used in a stand‐alone first trimester screening test. Although Tables 13–15 show the modeled screening performance for each marker alone and for various combinations of biochemical markers without NT measurements, they are mainly for purposes of comparison. If first trimester screening is to be performed, it should include maternal age, NT measurements, and a combination of biochemical markers [46]. Given the limited information about NT measurements at 10 and 14 weeks’ gestation, the current report limits modeling to between 11 and 13 weeks’ gestation. In addition to providing performance at 11, 12, and 13 weeks’ gestation, a weighted summary estimate is computed allowing easier comparison. Ultrasound measurement of NT is the best single marker for Down syndrome in the first trimester of pregnancy. At a 5% false positive rate, the weighted summary detection is 60%, ranging from a high of 63% at 11 weeks’ to a low of 56% at 13 weeks’ gestation (Table 13). The two best single biochemical markers are PAPP‐A and free measurements, with a weighted summary detection of 36% and 37%, respectively. Detection rates for PAPP‐A measurements diminish from a high of 42% at 11 weeks’ to a low of 29% at 13 weeks’ gestation. In contrast, detection rates for free measurements

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increase from 32% to 43% over the same gestational age range. DIA and hCG measurements increase from about 13% to 33%. Although performance at a single week may be better than any weighted combination of weeks, we have chosen to use the weighted combination because it is unlikely that the screening window can be compressed into a single week. The best overall weighted combination of first trimester markers includes NT measurements and maternal serum PAPP‐A, free , and DIA measurements. At a 5% false positive rate, the weighted summary detection is 85%, which is consistent across 11–13 weeks. Performance is only slightly lower if DIA is removed (weighted summary detection 83%, range 81–85%). All other combinations that include NT and PAPP‐A measurements show similar or only slightly lower detection rates, indicating that the choice of the third or fourth marker is a less important decision and has little impact on performance. If the focus shifts to the false positive rates at selected detection rates (Table 14), the choice is clearer. At a fixed detection rate of 85%, the best combination is NT, PAPP‐A, free , and DIA measurements with a false positive rate of 4.9%. The next best two combinations are NT, PAPP‐A and free , and NT, PAPP‐A, hCG and DIA; both having a false positive rate of approximately 6.2%. The other two combinations of NT, PAPP‐A and DIA (or hCG) have false positive rates of about 7.3%. It is likely that some screening programs will be satisfied with detection rates lower than 80–85% because of the reduced false positive rates. The tradeoV between lower detection and false positive rates, and higher detection and false positive rates can be seen in Table 15. At a high risk cutoV level of 1:100 (about the first trimester Down syndrome risk of a 38‐year‐old woman), false positive rates for marker combinations that include NT measurements are all under 3% (2.1–2.6%), with detection rates between 74% and 77%. If a lower risk cutoV level of 1:200 (about the first trimester Down syndrome risk of a 35‐year‐old woman), the false positive rates are about twice as high (between 4.1% and 5.1%), with a gain in detection of about 7% points (range 6–8 points). These diVerences in performances are also reflected in the OAPR decreasing by nearly 50% (from about 1:9 to about 1:21). These high odds are due to both the relatively high screening performance and the high rate of Down syndrome occurring in the late first trimester. This study provides Down syndrome detection and false positive rates based on gestational week‐specific parameters for all markers, including DIA, hCG, free , PAPP‐A, and NT. These parameters are based on a large published experience and are derived using a statistical approach that appears to provide reliable parameters for computing Down syndrome risk and associated performance estimates for a variety of marker combinations.


207

The specific combination of choice will depend not only on Down syndrome screening performance but also on the costs of testing, patents and licensing, and marker availability (e.g., availability of NT measurements). Although the current study does not address integrated testing directly (combining first and second trimester markers together for a single interpretation) [1], the parameters for at least some of these markers reported here will be useful in creating more reliable integrated risks and expected program performance. They can also be useful when modeling alternative screening protocols such as sequential, contingent screening, or repeated measures. ACKNOWLEDGMENTS We thank Louis M. Neveux, Senior Medical Statistician, Women and Infants Hospital for his statistical analyses, Klaus Steinort for his help in identifying and extracting information from the literature, and Drs. James E. Haddow and George J. Knight, Women and Infants Hospital for their helpful comments on early drafts of this chapter. Diagnostic System Laboratories (Webster, TX) provided reagents to measure dimeric inhibin‐A.

REFERENCES [1] Wald NJ, Watt HC, Hackshaw AK. Integrated screening for Down’s syndrome on the basis of tests performed during the first and second trimesters. N Engl J Med 1999; 341:461–467. [2] Wald N, Rodeck C, Hackshaw A, Rudnicka A. Correlations between nuchal translucency and serum markers in SURUSS. Prenat Diagn 2004; 24:835–836. [3] Wald NJ, Rodeck C, Hackshaw AK, Rudnicka A. SURUSS in perspective. BJOG 2004; 111:521–531. [4] Cuckle HS, van Lith JM. Appropriate biochemical parameters in first‐trimester screening for Down syndrome. Prenat Diagn 1999; 19:505–512. [5] Haddow JE, Palomaki GE, Knight GJ, Williams J, Miller WA, Johnson A. Screening of maternal serum for fetal Down’s syndrome in the first trimester. N Engl J Med 1998; 338:955–961. [6] CDC: Vital and health statistics 2000‐natality data set, Series 21, No 14. [Database on CD‐ ROM]. Hyattsville, MD: Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2002. [7] Hecht CA, Hook EB. Rates of Down syndrome at livebirth by one‐year maternal age intervals in studies with apparent close to complete ascertainment in populations of European origin: A proposed revised rate schedule for use in genetic and prenatal screening. Am J Med Genet 1996; 62:376–385. [8] Morris JK, Wald NJ, Watt HC. Fetal loss in Down syndrome pregnancies. Prenat Diagn 1999; 19:142–145. [9] Neveux LM, Palomaki GE, Larrivee DA, Knight GJ, Haddow JE. Refinements in managing maternal weight adjustment for interpreting prenatal screening results. Prenat Diagn 1996; 16:1115–1119.

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[10] Aitken DA, Wallace EM, Crossley JA, Swanston IA, van Pareren Y, van Maarle M, et al. Dimeric inhibin A as a marker for Down’s syndrome in early pregnancy. N Engl J Med 1996; 334:1231–1236. [11] Noble PL, Wallace EM, Snijders RJ, Groome NP, Nicolaides KH. Maternal serum inhibin‐A and free beta‐hCG concentrations in trisomy 21 pregnancies at 10 to 14 weeks of gestation. Br J Obstet Gynaecol 1997; 104:367–371. [12] Spencer K, Liao AW, Ong CY, Geerts L, Nicolaides KH. Maternal serum levels of dimeric inhibin A in pregnancies aVected by trisomy 21 in the first trimester. Prenat Diagn 2001; 21:441–444. [13] Wald NJ, Rodeck C, Hackshaw AK, Walters J, Chitty L, Mackinson AM. First and second trimester antenatal screening for Down’s syndrome: The results of the Serum, Urine and Ultrasound Screening Study (SURUSS). J Med Screen 2003; 10:56–104. [14] Wald NJ, George L, Smith D, Densem JW, Petterson K. Serum screening for Down’s syndrome between 8 and 14 weeks of pregnancy. International Prenatal Screening Research Group. Br J Obstet Gynaecol 1996; 103:407–412. [15] Wallace EM, Grant VE, Swanston IA, Groome NP. Evaluation of maternal serum dimeric inhibin A as a first‐trimester marker of Down’s syndrome. Prenat Diagn 1995; 15:359–362. [16] Van Lith JM, Mantingh A, Pratt JJ. First‐trimester maternal serum immunoreactive inhibin in chromosomally normal and abnormal pregnancies. Dutch Working Party on Prenatal Diagnosis. Obstet Gynecol 1994; 83:661–664. [17] Wallace EM, Harkness LM, Burns S, Liston WA. Evaluation of maternal serum immunoreactive inhibin as a first trimester marker of Down’s syndrome. Clin Endocrinol (Oxf) 1994; 41:483–486. [18] Spencer K, Talbot JA, Abushoufa RA. Maternal serum hyperglycosylated human chorionic gonadotrophin (HhCG) in the first trimester of pregnancies aVected by Down syndrome, using a sialic acid‐specific lectin immunoassay. Prenat Diagn 2002; 22:656–662. [19] De Biasio P, Siccardi M, Volpe G, Famularo L, Santi F, Canini S. First‐trimester screening for Down syndrome using nuchal translucency measurement with free beta‐hCG and PAPP‐A between 10 and 13 weeks of pregnancy—the combined test. Prenat Diagn 1999; 19:360–363. [20] Qin QP, Nguyen TH, Christiansen M, Larsen SO, Norgaard‐Pedersen B. Time‐resolved immunofluorometric assay of pregnancy‐associated plasma protein A in maternal serum screening for Down’s syndrome in first trimester of pregnancy. Clin Chim Acta 1996; 254:113–129. [21] Casals E, Fortuny A, Grudzinskas JG, Suzuki Y, Teisner B, Comas C, et al. First‐trimester biochemical screening for Down syndrome with the use of PAPP‐A, AFP, and beta‐hCG. Prenat Diagn 1996; 16:405–410. [22] Bersinger NA, Marguerat P, Pescia G, Schneider H. Pregnancy‐associated plasma protein A (PAPP‐A): Measurement by highly sensitive and specific enzyme immunoassay, importance of first‐trimester serum determinations, and stability studies. Reprod Fertil Dev 1995; 7:1419–1423. [23] Bersinger NA, Brizot ML, Johnson A, Snijders RJ, Abbott J, Schneider H, et al. First trimester maternal serum pregnancy‐associated plasma protein A and pregnancy‐specific beta 1‐glycoprotein in fetal trisomies. Br J Obstet Gynaecol 1994; 101:970–974. [24] Van Lith JM, Grudzinskas JG, Mantingh A, Shrimanker K, Suzuki Y, Macintosh MCM. First trimester maternal serum PAPP‐A levels in pregnancies with chromosomally normal and abnormal fetuses. Rijksuniversiteit Groningen, The Netherlands, 1994, published doctoral thesis (ISBN 90–9007280–2).


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ESTROGEN HYDROXYLATION IN OSTEOPOROSIS Nicola Napoli and Reina Armamento‐Villareal Division of Bone and Mineral Diseases, Washington University School of Medicine, St. Louis, Missouri

1. 2. 3. 4. 5. 6.

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pathways and Products of Estrogen Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Factors Influencing Estrogen Hydroxylation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of Estrogen Hydroxylation in Bone Density and Osteoporosis. . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

211 178 179 179 180 180 222

1. Abstract Estrogen is metabolized predominantly via two competing pathways, the 2‐hydroxyl (nonestrogenic) and the 16‐hydroxyl (estrogenic) pathways. Studies have indicated that these pathways are important determinants of bone mineral density (BMD) in postmenopausal women. Women with predominant metabolism through the 2‐hydroxyl pathway have accelerated postmenopausal bone loss and lower BMD compared to those with predominant 16‐hydroxylation who are protected from bone loss. Increased 2‐hydroxylation has been observed in women with a positive family history of osteoporosis suggesting that the increased risk of osteoporosis in those with family history may, in part, be related to inherited diVerences in estrogen metabolism. Polymorphisms in the cytochrome P450 (CYP450) enzymes that metabolize estrogen are believed to result in alteration in the activity of these enzymes leading to diVerences in estrogen hydroxylation. It is the resulting ‘‘estrogen tone’’ generated from the variable accumulation of metabolic products with divergent estrogenic activity that has been hypothesized to modify the risks for hormone‐dependent disorders associated with these polymorphisms, for example, osteoporosis. In support of this notion is the finding of lower BMD in women with the A allele for the C4887A polymorphism of the 211 0065-2423/07 $35.00 DOI: 10.1016/S0065-2423(06)43007-9


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CYP1A1 gene, who are found to have accelerated rate of estrogen hydroxylation. These findings may have broader clinical significance as recent data indicate that women with predominance of the 2‐hydroxyl pathway appear to have better BMD response to estrogen/hormone replacement therapy (ERT/ HRT) compared to those with predominant 16‐hydroxylation. It is likely that individual responses to ERT/HRT may vary according to patterns of estrogen hydroxylation, in turn a result of varying activity of the diVerent CYP450 enzyme variants, thus, allowing the future possibility of identifying responders by genetic and/or metabolic profiling.

2. Introduction The crucial role of estrogen in the regulation of skeletal health is demonstrated by the rapid bone loss that follows after menopause [1] and by the preventative eVect of estrogen replacement therapy (ERT) on bone loss [2] and fractures [3, 4]. It is well recognized that adequate estrogen exposure is vital to both the acquisition and the maintenance of bone mass once a peak has been achieved [5, 6], the role of postmenopausal estrogen, however, remains controversial [7–9]. Studies correlating estrogen levels with bone loss and fractures have yielded conflicting results suggesting that serum estrogen concentrations may not accurately reflect the total estrogen activity. Such discrepancies may be explained, in part, by the presence of metabolites deriving from the catabolism of estradiol (E2) and estrone (E1) with varying estrogenic activity which may contribute to the overall estrogen ‘‘tone’’ ultimately aVecting the risks of hormone‐related disorders including osteoporosis. While most of the studies investigating the impact of estrogen metabolism on human diseases have focused on estrogen‐dependent malignancies, mainly breast cancer, recent studies also suggest that it is important in skeletal health as well [10–12]. In this chapter, we review the relevance of estrogen metabolism in bone health and in the physiopathology of postmenopausal osteoporosis.

3. Pathways and Products of Estrogen Metabolism Since ovarian activity is minimal after menopause, most circulating estrogen in postmenopausal women is derived—just as in men—from the aromatization of androstenedione to E1, which can be reversibly oxidized to E2. Although other relatively minor oxidative mechanisms exist [13], the major metabolic pathway of E1 is irreversible hydroxylation at either the C‐16 (active) or the C‐2 (inactive) position [13, 14] (Fig. 1). The products of these two competitive

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ESTROGEN HYDROXYLATION IN OSTEOPOROSIS

OH

O

OI

OH HO

HO

O

OH

HO

HO Estradiol (E2)

Estriol (E3)

16a-Hydroxyestrone (16aOHE1)

O

O Estrone (E1)

CH3O

HO

HO

HO 2-Hydroxyestrone (2OHE1)

2-Methoxyestrone (2MeOE1)

FIG. 1. Major pathways of estradiol metabolism: oxidation of estradiol at C‐17 by 17‐ dehydrogenase leads to the formation of estrone. Estrone is 16‐hydroxylated to 16‐ hydroxyestrone, which can be reduced to estriol. Estrone can also be 2‐hydroxylated to form 2‐hydroxyestrone, which can be converted to 2‐methoxyestrone. Adapted from Martucci et al. [81].

pathways are essentially opposite in their biological properties. The C‐16 hydroxylation leads to the formation of 16‐hydroxyestrone (16OHE1) and estriol (E3), which retain proestrogenic activity [15], whereas the 2‐hydroxylation results in formation of 2‐hydroxyestrone (2OHE1) and 2‐methoxyestrone (2MeOE1) that are devoid of estrogenic activity or even antiestrogenic in some systems [16]. Because these two pathways are mutually exclusive, the dominance of one pathway over the other would shift the estrogen balance toward either a proestrogenic or a nonestrogenic state [17]. Consequently, one would envision that the final products of E1 hydroxylation and the reciprocal activity of the two catabolic pathways may condition risk for hormone‐related diseases. Results from in vitro and animal studies are in agreement with the proposed biological properties of these metabolites. For instance, when administered to ovariectomized (OVX) rats, 16OHE1 prevented ovariectomy‐induced bone loss, suppressed bone resorption, and reduced cholesterol levels to the same degree as 17‐estradiol [18]. Likewise, treatment of human fetal osteoblastic cells with 16OHE1 decreased osteocalcin secretion, induced alkaline phosphatase gene expression and activity, and stimulated the expression of early response gene, transforming growth factor‐ (TGF‐), an index of estrogenic activity [19]. Additionally, these studies also suggest that these metabolites may have tissue‐selective activity. An example is the observation of an equivalent eVect on bone density between 16OHE1 and 17‐estradiol

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when administered to OVX rats, whereas the eVect of 16OHE1 on the uterus and mammary gland were significantly less than that of 17‐estradiol [18]. It has been reported that the estrogenic potency of 16OHE1 is augmented by its low aYnity to sex hormone‐binding globulin (SHBG) [15] that makes it readily available in the circulation to bind covalently to estrogen receptors (ER) producing estrogenic eVects [20]. By comparison, none of estrogenic changes observed with 16OHE1 were observed with 2OHE1 administration [18]. Previous studies have shown that 2OHE1 is not only devoid of estrogenic property but may serve as natural antiestrogens. In animal studies, administration of 2OHE1 to OVX rats did not produce estrogenic eVects on bone, uterine, and mammary gland tissues [18]. Although it binds to ER, it does not stimulate transactivation of the TGF‐ [21]. It also disappears rapidly from the blood, faster than other natural steroids. It is a weak ligand for ER, and may inhibit multiple signal transduction pathways important for cell growth in cardiovascular and estrogen‐responsive tissues [22, 23]. It inhibits the proliferation of ER positive breast cancer (MCF‐7) cell lines [24, 25] suggesting that it, may in fact, has antiestrogenic properties and also stimulates the synthesis of SHBG, thus reducing the amount of available free sex steroids to their target organs [26]. Meanwhile some investigators claimed that it may have partial estrogen agonistic activity in vitro [27, 28] when administered in supraphysiologic doses to breast cancer cell lines [28]. In humans, 2OHE1 is considered as the ‘‘good estrogen’’ because of the apparent protective eVect of this metabolite in malignancies as breast cancer [29]. Unfortunately, women with increased 2‐hydroxylation are the ones at risk for osteoporosis [11]. 2‐Methoxyestrogens, 2MeOE1 and 2‐methoxyestradiol (2MeOE2), are produced by O‐methylation of 2OHE1 and/or 2‐hydroxyestradiol [30]. These metabolites have no further estrogenic eVect but have been found to have antitumor activity. 2MeOE2 is a potent inhibitor of angiogenesis and has been shown to disrupt microtubules in tumor cells [31–34]. It induces apoptosis of osteosarcoma cells but has no eVect on normal osteoblastic cells [35]. It is also hypothesized as the metabolite that inhibits growth of breast cancer cells and may be responsible for the low risk of cancers among women who have increased 2‐hydroxylation [36]. It is currently under investigation in clinical trials as treatment for multiple myeloma, and possibly has the potential for therapeutic use in malignancies as osteosarcoma and melanoma [37, 38]. E3 results from further metabolism of 16OHE1 [13, 14]. It has estrogen agonistic activity [39–42], but because of its short half‐life and the low aYnity to the estrogen receptor, that activity is much lower than that of E2 [43]. However, it also has antiestrogenic eVects and interferes with E2 binding and receptor dimerization when administered in pharmacological doses [44, 45].


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A dose tenfold higher than that of E2 resulted in reduction of E2‐dependent transcription by 85%, and E2 binding by 50% [45]. Urine excretion of E3 was found to vary between races; it was much lower in Asians compared to Caucasians and was at some point hypothesized to account for diVerences in the risk of breast cancer across races [46, 47]. Further studies, however, showed no diVerence in E3 production and clearance rate between healthy women and those with breast cancer [48]. It was investigated for its potential clinical use in the relief of postmenopausal symptoms with good results. [49–51]. Results from studies on bone mineral density (BMD), on the other hand, are not consistent. Although a study in postmenopausal women treated with 2 mg of oral E3 for 24 months resulted in prevention of significant bone loss in the lumbar spine comparable to that achieved by conjugated estrogen [52], another study using the same dose of oral E3 was unable to prevent significant bone loss in both lumbar spine and proximal femur [53]. Since these metabolites coexist in the body, it is hypothesized that they contribute to the overall estrogenic environment. Because 2‐hydroxylated compounds, 2OHE1 and 2MeOE1, are inactive or estrogen antagonists [29, 54], whereas 16‐hydroxylated estrogens, 16OHE1 and E3, are agonists [18, 54], the rate of either metabolic pathway or the dominance of one pathway over another (reflected by 2OHE1/16OHE1 ratio) is more predictive of the risk for hormone‐related disorders. So far from our own experience, it seems that both pathway preference and the rate of estrogen metabolism are important determinants of estrogen exposure as will be discussed in more detail later.

4. Factors Influencing Estrogen Hydroxylation There are several factors that have been found to modulate estrogen metabolism. FoodstuVs containing high levels of phytochemicals and indole 3‐carbinol found in vegetables like cabbage, cauliflower, Brussels sprouts, broccoli, and kale, increased urinary excretion of 2OHE1 relative to 16OHE1, through induction of CYP450 2‐hydroxylase activity [55]. Exposure to polycyclic aromatic hydrocarbons from cigarette smoking also causes an increase in 2‐hydroxylation and this may partly explain the higher risk for osteoporosis in smokers [56]. Medications such as phenobarbital [57] and thyroid hormones may induce 2‐hydroxylation [58], while cimetidine may reduce overall rate of estrogen metabolism [59]. Use of oral contraceptives [60] and increased body weight may significantly lower 2OHE1/ 16OHE1 ratio [61]. The lower ratio in overweight individuals is mostly related to a decrease in C‐2 hydroxylation, without a change in 16‐ hydroxylation [61]. This proestrogenic ratio created by excess body weight

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may add to the already highly estrogenic state generated by increased aromatization of adrenal androgens in the adipocytes and the decreased SHBG production in obese women. The resulting hyperestrogenic environment is one of the mechanisms thought to be responsible for why obese women are at an increased risk for breast cancer but protected from osteoporosis [62, 63]. Increased physical activity may also push estrogen metabolism toward the inactive pathway and may account for the lower risk for breast cancer reported in women who exercise regularly [64, 65]. Even calcium intake may modulate estrogen metabolism. In a study of 175 postmenopausal women, we found that increasing calcium intake was associated with proportionate increases in the absolute levels of most urinary metabolites (with the exception of urinary E3) and total metabolites [66]. Moreover, dividing the average daily calcium intake into quartiles showed that levels of metabolites increase with increasing quartiles of calcium intake, with the highest quartile having the highest levels of these metabolites (Fig. 2). This

1st Quartile 2nd Quartile 3rd Quartile 4th Quartile

35

30

p-value < 0.06

ng/mg cr

25

20

15

p-value < 0.06

10 p-value < 0.05 5

0 2MeOE1

16aOHE1

2OHE1

Total metabolites

FIG. 2. Levels of urinary metabolites stratified according to quartiles of average daily calcium intake (milligrams per day). Each group represents the diVerent quartiles of 2OHE1, 2MeOE1, 16OHE1, and total metabolites, from the lowest to the highest (left to right). Quartile values for calcium intake: quartile 1, less than 500; quartile 2, 501–1000; quartile 3, 1001–1500; and quartile 4, greater than 1500. Adapted from Napoli et al. [66]. Copyright 2005, The Endocrine Society.


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relationship was not seen with 2OHE1/16OHE1 and 2MeOE1/16OHE1 ratios. Interestingly, women whose calcium intake came only from supplements had lower total urinary metabolite levels than women whose calcium intake came predominantly from dietary sources with or without supplements. The average calcium intake between those with only dietary calcium sources and those taking calcium supplements only were comparable, whereas those with both dietary and supplemental calcium intake, as expected, had a higher average calcium intake [66]. Given that estrogen metabolism can be modulated by common environmental factors, agents that may push estrogen metabolism in a particular direction may represent as promising tools to manipulate estrogen metabolism and alter the risk of diseases.

5. Role of Estrogen Hydroxylation in Bone Density and Osteoporosis The importance of estrogen metabolism in the pathogenesis of hormone‐ related diseases has come primarily from studies on breast cancer. For example, a lower circulating 2OHE1/16OHE1 ratio is associated with an increased risk of breast cancer [67–70], whereas high serum 16OHE1 is a strong predictor of breast cancer risk [67]. In vitro studies showed that breast tissues obtained from women with breast cancer have increased 16‐hydroxylation. Furthermore, the addition of E2 to MCF‐7 cell lines upregulates 16‐hydroxylation with 2‐hydroxylation unchanged. The ability of estrogen hydroxylation to alter hormonal status even among women in the reproductive age is likewise suggested by observation of frequent menstrual irregularities in young women with high 2‐hydroxylated estrogens [71, 72]. What’s more, estrogen metabolism may also be an important regulator of male hormonal status as well since high levels of 16OHE1 and low 2OHE1/ 16OHE1 ratio were associated with an increased risk of prostate cancer [73]. Finally, studies in the last decade have also indicated that the oxidative metabolism of estrogens may be an important determinant of bone density in the postmenopausal period, and more importantly, that it may modulate BMD response to estrogen/hormone replacement therapy (ERT/HRT). The succeeding discussion will examine available data investigating the role of estrogen hydroxylation in BMD and osteoporosis. We previously reported that the ratio of 2OHE1 to 16OHE1, is an important determinant of bone loss in the postmenopausal period [11]. In a previous study of 71 untreated, early postmenopausal women (age 47–59), whom we followed for 1 year, those in the lowest quartile of urinary 2OHE1/ 16OHE1 ratio (A, with high predictivity for impaired function of cytochrome P450 2D6 in white subjects. Clin Pharmacol Ther 2004; 76:128–138. [136] Denson J, Wu Y, Yang W, Zhang J. Inter‐individual variation of several cytochrome P450 2D6 splice variants in human liver. Biochem Biophys Res Commun 2005; 330:498–504. [137] Pai HV, Kommaddi RP, Chinta SJ, Mori T, Boyd MR, Ravindranath V. A frameshift mutation and alternate splicing in human brain generate a functional form of the pseudogene cytochrome P450 2D7 that demethylates codeine to morphine. J Biol Chem 2004; 279:27383–27389. [138] Zhang F, Deng H, Kemp R, Singh H, Gopal VR, Falck JR., et al. Decreased levels of cytochrome P450 2E1‐derived eicosanoids sensitize renal arteries to constrictor agonists in spontaneously hypertensive rats. Hypertension 2005; 45:103–108. [139] Borlak J, Walles M, Levsen K, Thum T. Verapamil: Metabolism in cultures of primary human coronary arterial endothelial cells. Drug Metab Dispos 2003; 31:888–891.

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INDEX A

Atherosclerosis, 11, 23 Atherosclerotic lesions, 13 Atorvastatin, 244

Abdominal aortic aneurysm, 10 Acenocoumarol, 251 Acidobacteria, 99 Actinobacteria, 99 Activins activin A after brain injury, in animals after acute excitotoxic injury, 120 after cerebral stroke, 121–122 after hypoxin/ischemic injury, 120–121 after mechanical brain injury, 121 after meningitis, 123 after seizure, 122–123 activin A and neuroprotection, 123–124 activin A‐binding proteins after brain injury, 122 biochemistry of, 119 human studies, 125–127 Adhesion proteins, 232 ADME‐AP database, 145 ADME properties, 134 ADME/Tox biosensors, 154 ADME/Tox data, 145, 150, 157 Agarose gel electrophoresis, 68 ALMOND software, 141 Alphaproteobacteria, 99 2‐Amino‐A, 84 Amlodipine, 244 Amphitropic proteins, 16 Analyze/StripMiner software, 150 Angiographic restenoses, risk of, 11 Angiotensin receptor antagonists, 251 Antidiabetic sulfonylureas, oral, 252 ApcMin mice, 61 ApexTM, 151 Apoptosis, 61–62 Arachidonic acid, 60 ARB, 88 ARCH915, 98 Archaea, 99 Associated adenocarcinoma, 66

B Bacteria, 90 Bacterial lipopolysaccharides, 5 Bacterial targets oligonucleotide probes, 98–100 quenched probes, 100–101 Balloon angioplasty, 10 Barrett’s esophagus, 66 Basic fibroblast growth factor (bFGF), 62–63, 102, 123 Bcl‐2 protein expression, 61 Belgian men and women, 30 Betaproteobacteria, 99 Bezoar, 36 Bile pigments, biological eVects of, 25 Bilirubin albumin‐bound, 21 anticomplement action of, 21 antiproliferative eVects of, 31 biological eVects eVect of elevated levels on oxidative stress, 23 human studies. See Human studies, of bilirubin immunological eVects of, 22 immunomodulatory eVects of, 23 delta, 21 ditaurate, 20 factors aVecting serum concentration of, 34 IX , 24 low serum concentrations of, 32 meso, 20 other eVects of, 21–22 protective eVects of elevated levels, 27–28 as risk factor for coronary heart disease, 28–29 281

282

INDEX

Bilirubin (cont.) therapeutic agents aVecting, 35–37 treatment of, 21 UDP‐glucuronosyltransferase, 23 Biliverdin reductase (BVR), 3, 35, 38 anti‐inflammatory eVects of, 23 biological eVects of, 18–19 cytoprotective eVects of, 16–17 and reduction of central methenyl bridge, 5 Bladder cancer, 67 ‐Blockers, 251 Blood glucose levels, 15 Bone mineral density (BMD), 215, 219 Brachyspira, 99 Breast cancer, 66–67 British Regional Heart Study (BRHS), 26 Bronchopulmonary dysplasia, 31 Brucella, 99 Burkholderia cepacia, 100 C Ca2þ chelator, 64 Calcium supplements, role of, 217 Candida spp., 99 Carbon monoxide biological eVects of, 14 cytoprotective eVects of, 13, 15–16 hemodynamic eVects of, 13, 15–16 therapeutic agents aVecting, 35–37 Carboxymethyllysine, 34 Carcinoembryonic antigen (CEA), detection of, 69 Cardiac allograft survival, 9 Cardiovascular disease (CVD), 3, 26, 28, 37, 231 Carotid balloon injury, 13 Caspase‐8, 13 Caucasian women, 31 Caveats, 150–151 CD14, 232 CD44 expression, 63 CD95/Fas ligand‐mediated apoptosis, 13 Central nervous system (CNS), 118 Cerebral computerized tomography, 125 Cerebral ultrasound, 125 Cerebrospinal fluid (CSF), 123 Cerivastatin, 244 cGMP concentrations, 13

ChemScore value, 144 Chinese women, 31 Chlamydiae, 99 Chronic allograft nephropathy, 11 Chronic aortic graft rejection, 13 Chronic pulmonary emphysema, 10 Cloe PKTM, 150 ClustalW, 88 Colorectal cancer (CRC), 30, 60 CoMFA software, 141 Complementary DNA (cDNA), 68 Compound Curriculum Vitae (CV), 152 Computational pharmacophore models, 142–144 Confocal z‐stack analysis, 121 CO‐releasing molecules (CORMs), 15 CO‐releasing molecules (CORMs)‐3, 16 Coronary artery disease (CAD), 231 Coronary heart disease (CHD), 3, 11, 26, 34, 37 and levels of bilirubin, 27 odds ratio of, 26 COX‐2 inhibitors, 35, 59 NS398, 62, 64 C‐5 propynyl pyrimidines, 84 C‐reactive protein, 231 Crenarchaeota, 90, 99 CSF assessment, 125–126 Curcumin, 35 Cy3, 91 Cy5, 91 Cyanobacteria, 99 Cyclin A, 13 Cyclin D1, 13 Cyclooxygenase‐2 in carcinogenesis, roles of angiogenesis, 62–63 apoptosis, 61–62 immune modulation, 64–65 invasiveness, 63–64 mutagenesis, 65 proliferation, 60–61 expression and clinicopathological factors bladder cancer, 67 breast cancer, 66–67 colorectal cancer, 65–66 esophageal cancer, 66 gastric cancer, 66 lung cancer, 67 prostate cancer, 67–68 fecal assay of, 68–70

INDEX CYPs, 233, 243–247 genes CYP2A6, 234–235 CYP3A4, 238–239 CYP3A5, 238–239 CYP4A11, 239–240 CYP2B1/2, 235 CYP2B6, 234–235 CYP4B1, 239–240 CYP2C8, 236 CYP2C9, 236 CYP2D6, 236–237 CYP2E1, 237 CYP4F2, 239–240 CYP4F11, 239–240 CYP4F12, 239–240 CYP2J2, 237–238 CYP3A4 autoactivators, 143 CYP1A1 C4887A polymorphism, 219–220 CYP3A enzymes, 142 CYP1A1 gene, 219 CYP1A2 ligands, 141 CYP2B6 autoactivators, 143 CYP2C3/5 enzyme, 144 CYP2C9 ligands, 141 CYP2D6 inhibition, 144 CYP450 enzymes, 219 CYP450 genes, 219 CYP protein models, 145 Cytochrome P450 enzymes, 232–233 and environmental risk factors for cardiovascular disease obesity, 248–249 smoking, 247–248 gene variants and drug interactions in cardiovascular disease anticoagulants, 251–252 antidiabetic drugs, 252 antihypertensives, 251 lipid lowering drugs, 249–251 gene variants interacting with vascular homeostasis, 245–247 regulation of enzymic activity epigenetic factors, 242–244 genetic factors, 234–241 post‐translational modification, 241–242 Cytochrome P450 reductase, 35 Cytophagales, 99 Cytotoxic T‐cells (CTL), 64

283 D

Dabsyl probe, 95–96 Dendritic cells (DCs), 64 Dentate gyrus neurons, 121 Dextran sulfate, 10 Diabetes, 11 Diabetes mellitus, 252 Diclofenac, 35 Dimeric inhibin‐A (DIA). See Down syndrome screening, in first trimester analysis DISCO software, 141 DiverseSolutions, 142 Dopamine, 35 Doppler velocimetry recordings, 125 Down syndrome screening, in first trimester analysis patients and methods data analysis, 181–182 dimeric inhibin A assay, 180 dimeric inhibin A statistical analysis, 180–181 dimerin inhibin A, sample and information collection, 179–180 literature review, 181 model screening performance, 182–183 result analysis correlation coeYcients and truncation limits, 191–194 correlation coeYcients for ultrasound markers in down syndrome pregnancies, 200 correlation coeYcients for ultrasound markers in unaVected pregnancy, 199 dimeric inhibin A results, 183 expected down syndrome detection rates in the first trimester analysis, 202 expected down syndrome positive rates in the first trimester analysis, 203 free ‐subunit of hCG measurements, 188–189 human chronic gonadotropin measurements, 187–188 modeled down syndrome detection rates, false positive rates and OAPR in the first trimester risk cutoV levels, 204 modeling results, 194–198

284

INDEX

Down syndrome screening, in first trimester analysis (cont.) nuchal translucency thickness measurements, 189–191 pregnancy associated plasma protein A measurements, 186–187 3D‐QSAR software, 141 DrugBank, 146 Drug metabolism databases, 145–146 Drug‐metabolizing enzymes, 133 Dual energy X‐ray absorptiometry (DEXA) scans, 219 Dual‐label immunocytochemistry, 121 Dukes’ staging, 68 E EGFR tyrosine kinase inhibitor, 61 Electroencephalography, 125–126 EMBL databases, 88 Endothelial cell‐derived hyporelaxation factor (EDHF) synthase, 233 Enterobacteriaceae, 99 Enterococci, 92 Enterococcus spp., 99 Enterococcus faecalis, 99 Environmental pollutants, 244 EP4‐EGFR‐PI3K‐Akt pathway, 64 Epidermal growth factor receptor (EGFR), 61 ERK1/ERK2 kinase pathways, 13 ERK2/JNK1 signaling pathways, 63 Escherichia coli, 87, 99 16S rRNA, 89–90, 100–101 Estrogen/hormone replacement therapy (ERT/HRT), 220–222 Estrogen hydroxylation factors influencing, 215–217 pathways and products of, 212–215 role in bone density, 217–222 role in osteoporosis, 217–222 Estrogen metabolism, 212–215 Estrogen receptors (ER), 214 Ethanol, 35 EUB338, 98 EUROASPIRE surveys, of coronary risk factors, 232 European ribosomal RNA database, 88 Euryarchaeota, 90, 99

F Fecal COX‐2 assay, 60, 68–70 Fecal occult blood test (FOBT), 70 Fibrates, 250–251 Fibrobacter, 99 Firmicutes, 99 FISH probes. See Fluorescence in situ hybridization Flavonoids, 243 Flow cytometry, 93 Fluorescein, 98 Fluorescence in situ hybridization applications of bacterial targets, 98–101 human cell targets, 101–102 online RNA sequence databases, 88 online tools for probe design, 82 principles of base and backbone modifications, 84–87 hybridization activity and specificity, 82–84 targets, 87–88 target site accessibility, 88–90 types of probes in molecular beacons, 93–95 oligonucleotide probes, 90–93 quenched autoligation probes, 95–98 Fluorescence microscopy, 93 Fluorescence resonance energy transfer (FRET), 95 Fluvastatin, 244 Follistatin, 119 mRNA, 122 ‐related gene (FLRG), 119 Formamide, 93 Free E2 index (FEI), 219 Free heme, 4 FRET–QUAL probes, 102 F‐test, 182 G Gammaproteobacteria, 99 Gastric cancer, 66 Gastric ulcers, 10 GastroPlusTM, 150 Gaussian distribution functions, 182–183 GenBank databases, 88 Gilbert’s polymorphism, 31

INDEX Gilbert syndrome (GS), 24, 28, 32 Gilbert UGT1A1*28 genotype, women with, 31 Glucose, 15 tolerance tests, 15 GOLPE software, 141 Grapefruit juice, 244 G‐50T carriers, 246 H Haemophilus influenzae, 100 HDL‐cholesterol, 26 Helicobacter pylori, 99 Helper probes, 89 Heme catabolism, 4–5 Heme oxygenase gene transfer, 5, 9–10 HO‐1 gene promoter polymorphism, in the pathogenesis of oxidative stress‐ mediated conditions, 10–13 induction, 5, 9–10 upregulation, 5, 9–10 Hemin inhalation, 36 Hemoglobin, 4 Hepatitis B‐induced hyperbilirubinemia, 32 Hepatocyte incubations, 137 High density lipoproteins (HDL), 11 HL‐60 cells, 102 HO‐2, 5 HO‐1 activity, 5 biological eVects of, 6–9 cytoprotective eVects of, 10 expressions in Watanabe heritable hyperlipidemic rabbits, 9 expressions in LDL‐receptor knockout mice, 9 gene polymorphism, 10–13 overexpression of, 9 therapeutic agents aVecting, 35–37 HO‐1/biliverdin/bilirubin system, 22 HO‐1 mRNA, 9 HSP 32 protein, 5 Human drug metabolism, methods for predicting computational metabolism methods caveats, 150–151 2D and 3D‐QSAR, 140–144 databases, 145–146 electronic models, 144

285

expert systems, 146–148 homology models, 144–145 hybrid methods, 148–150 metabolism prediction applications, 151–152 from high‐throughput assays, 137–138 human enzymes involved in metabolism, 135–136 integration of drug metabolism and data interpretation, 152–153 other technologies, 153–156 in vitro techniques, 134–137 in vivo from in vitro techniques, 138–139 Human studies, of bilirubin and cancer, 30–31 and CHD and mortality, 28 prospective studies, 26–27 protective eVects of elevated levels of, 27–28 retrospective studies, 26 as risk predictor for CHD, 28–29 and markers of oxidative stress and other markers of oxidative stress, 34 urinary biopyrrins, 33–34 and other oxidative stress‐mediated disease, 31–32 and peripheral vascular disease, 29–30 and serum/plasma antioxidant capacity, 24 Hydrogen peroxide, 5, 21 Hyperbilirubinemic Gunn rats, 23 Hypercholesterolemia, 11 Hypothalamus–pituitary–gonadal axis, 119 Hypoxia‐inducible factor 1, 13 Hypoxic/ischemic encephalopathy (HIE), 125–126 Hypoxic/ischemic injury, 120–121 HYTHER, 84 I Idiopathic pulmonary fibrosis, 32 IL‐10, 10 Indole 3‐carbinol, 215 Inducible nitric oxide synthase (iNOS), 243 Inflammatory bowel disease, 9 Intraventricular hemorrhage, 31 Invasiveness, 63–64

286

INDEX

I/R‐isolated rat heart model, 23 I/R lung injury, 15 J Jak‐Stat pathways, 16 JNK‐signaling pathway, 13 JUND mRNAs, 102 K Kainate, 122 Karen ethnic groups, 12 Kernel‐PLS (K‐PLS) algorithm, 150 Klebsiella pneumoniae, 99 Kohonen maps, 142 Korean women, 218 L Labeled oligonucleotides, 91 Lactobacillus reuteri, 100 Lactococci, 92 L alleles, 11–12 L class homozygotes, 11 LC/MS detection, 137 LDL‐receptor knockout mice, 9 Legionella pneumophila, 99 Liver cirrhosis, 9 Locked nucleic acid (LNA), 84 Longer GT repeats, patients with, 11 Long‐Evans hooded rats, 123 Lovastatin, 244 Low‐density lipoprotein (LDL), 5 Lung cancer, 67 M Magnetic resonance imaging, 125 MAPkinase‐dependent pathway, 62 MCF‐7 breast cancer cells, 16 Menstrual irregularities, in young women, 217 Messenger RNAs (mRNAs), 87 METATM, 145–146 MetabolExpertTM, 145–146, 151 MetaDrugTM database, 149, 151 MetaSite, 149 METEORTM, 145–146, 151 Methanosarcina acetivorans, 100

2‐Methoxyestrogens, 214 mFOLD, 88–89 Microdosing technique, 155 Middle cerebral artery occlusion (MCAO), 121 p38 Mitogen‐activated protein kinase (MAPK)‐signaling transduction pathway, 13 MK801, 121–122 Molecular beacons (MBs), 93–94 Monte Carlo simulations, 182 Mouse cardiac allografts, 5 Multiples of the median (MoM) analysis, 180, 182–183 Mycobacteria, 99 Mycobacterium tuberculosis, 100 N NADPH‐cytochrome P450 reductase, 5, 35 NADPH oxidase, 21 N‐dealkylation rates, 142 Necrotizing enterocolitis, 31 Nelfinavir, 155 Neonatal jaundice, 32 NF‐B, 13 activation of, 22 Nicotine, 15 Nitric oxide, 35 Nitrospira, 99 N‐methyl‐D‐aspartate (NMDA) receptor antagonist, 121–123 Nonsmall cell lung cancer (NSCLC), 67 Nonsteroidal anti‐inflammatory drugs (NSAIDs), 60–61, 63–64 Nuclear receptors, 240–241 O Obesity, 248–249 2OHE1/16OHE1 ratio, 215 Oligonucleotide probes, 89–93, 98–100 standard protocols, 92–93 Oriental traditional medicines, 36 2´‐O‐methyl RNA, 84, 86 Osteoporosis, 217 Ovariectomized (OVX) rats, 213 Ox gallstones, 36 Oxidative stress, 3, 5

INDEX P Parkinson’s disease, 15 Partial least squares (PLS) regression models, 142 Pentylenetetrazol, 122 Peptide nucleic acid (PNA), 85 Peripheral vascular disease (PVD), 29–30 Peroxidase (POX) activity, 65 P‐glycoprotein (P‐gp), 150 Phenprocoumon, 251 PI3K/Akt pathway, 16 P450 inhibition, 134, 137, 148 fluorometric assay of, 138 PipelinePilot, 151 Pituitary follicle‐stimulating hormone (FSH), 119 Planctomycetes, 99 Plasminogen activator inhibitor‐1 (PAI‐1), 124 Plasmodium falciparum, 12 Platelet‐derived growth factor‐B, 13 Platelet‐derived growth factor (PDGF), 62 Polyphenolic chalcones, 35 PPAR activators, 35 ProbeBase databases, 88 PROBE Design Tool (PDT), 89 Probucol, 35 Proinflammatory prostaglandins (PGs), 60 7‐Propynyl‐8‐aza‐7‐deazapurines, 84 Prostate cancer, 67 Protein carbonyls, 20 Protein kinase phosphorylation, 17 Proton magnetic resonance spectroscopy, 125 Pseudomonas areruginosa, 99–100 Q QikProp, 142 QMPRPlusTM, 150 Quantitative structure activity relationships (QSAR), 140, 148–150 algorithm methods, 141 Quantitative structure metabolism relationships (QSMR), 140, 148–150 Quenched autoligation (QUAL) probes, 95–98, 100–101 Quenched probes, 81

287 R

Rapamycin, 35 Rat intestinal epithelial (RIE) cells, 61 Rat orthotopic transplantation, 9 Reactive oxygen species (ROS), 3, 233 Receiver–operator characteristics (ROC) curve analysis, 126 Resveratrol, 35 Reverse transcriptase‐polymerase chain reaction (RT‐PCR), 121 Rheumatoid arthritis, 22, 32 Ribonuclease protection assay, 121–122 Ribosomal Database Project (RDP), 88 Ribosomal RNA (rRNA), 87 Ribosomes, 101 2´‐fluoro RNA, 84, 86 RNA interference (RNAi), 86 Rosolic acid, 36 Rosuvastatin, 244 S S alleles, 11–12 Salmonella enterica, 101 SC‐58125, 61 SDS, 92 SERM, 220 Serum, Urine and Ultrasound Screening Study (SURUSS). See Down syndrome screening, in first trimester analysis Serum pentosidine, 34 Sex hormone‐binding globulin (SHBG), 214 S9 fraction incubations, 137 Short (GT)n repeats, patients with, 10 Sickle cell anemia, 24 SimCYPTM, 150 Simvastatin, 244 Singapore Chinese women, 219 SpORCalc, 146 5S subunit databases, 88 Standard FISH protocols, 92–93 Staphylococcus aureus, 99–100 Statins, 244, 249–250 Stenotrophomonas maltophilia, 100 Streptococci, 92 Streptococcus spp., 99 Streptococcus pneumoniae, 123 SULT1A3, 141 Systemic lupus erythematosus, 22

288

INDEX T

T cell proliferation, 22 Tetramethylrhodamine (TAMRA), 91 Texas Red, 91 Thiazolidinediones, 252 Threonine to asparagine, at codon 461 (Thr461Asn), 219 Thrombotic markers, 232 Thromboxane (TX) A2, 60 Tiered screens, application of, 134 Tissue inhibitors of MMPs (TIMPs), 63 Tissue metabolism simulator (TIMES), 148 Tocotrienols, 244 Transcription factor AP‐1, 13 Transforming growth factor‐ (TGF‐ ), 118, 213 Trinitrobenzene sulfonic acid‐induced colitis model, 10 Triton X, 92 Trolox. See Vitamin E analogue Tumor necrosis factors (TNFs), 232 Tween, 92 Type 2 diabetes, 11

Ultraviolet light, 5 Unconjugated bilirubin (UCB) concentrations, 3 UNIV1392, 98 Urinary biopyrrins, 33–34 Urinary N‐telopeptide (NTx), 219 V Vascular cell adhesion molecule (VCAM) signaling, 22 ‐1‐mediated leukocyte recruitment, 22 Vascular endothelial growth factor (VEGF), 13, 62–63 Vascular smooth muscle cells (VSMC), 12 Verrucomicrobiales, 99 Vitamin A, oxidation of, 17 Vitamin E analogue, 20 von Willebrand factor, 231 W Warfarin, 251 Watanabe heritable hyperlipidemic rabbits, 9 Wegener granulomatosis, 22, 32

U X Ubiquinol‐10, 24 UDPGT 1A1, 143 UDPGT 1A4, 143 UGT1A6 enzymes, 141 UGT1A9 enzymes, 141

Xenobiotics, 132 biotransformation pathways of, 134 metabolism of, 133 X‐ray crystallography, 89

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Advances in Clinical Chemistry, Volume 43

Advances in Heterocyclic Chemistry, Volume 43

Advances in Organometallic Chemistry, Volume 43

Advances in Physical Organic Chemistry, Volume 43

Advances in Protein Chemistry, Volume 43

Advances in Clinical Chemistry Volume 7

Advances in Clinical Chemistry, Volume 33

Advances in Clinical Chemistry Volume 18

Advances in Clinical Chemistry Volume 8

Advances in Clinical Chemistry Volume 44

Advances in Clinical Chemistry, Volume 49

Advances in Clinical Chemistry Volume 12

Advances in Genetics Volume 43

Advances in Clinical Chemistry Volume 24

Advances in Clinical Chemistry Volume 41

Advances in Clinical Chemistry Volume 16

Advances in Clinical Chemistry, Volume 47

Advances in Clinical Chemistry Volume 15

Advances in Clinical Chemistry Volume 2

Advances in Clinical Chemistry, Volume 54

Advances in Clinical Chemistry Volume 10

Advances in Clinical Chemistry Volume 38

Advances in Clinical Chemistry Volume 14

Advances in Clinical Chemistry, Volume 48

Advances in Clinical Chemistry, Volume 33

Advances in Clinical Chemistry Volume 5

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Advances in Clinical Chemistry Volume 29

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