1. Executive Summary
2. Introduction
2.1 Toxicity and Safety Testing
2.2 Adverse Drug Reactions
2.3 Computational Toxicology
2.4 Future Trends
2.5 References
Volume I - In Vitro Experimental Approaches
3. Human Liver Microsomes to Predict Potentially Toxic Drug-Drug Interactions
Part I: High-Throughput In Vitro Assays Based on LC/MS Analysis
3.1 Moving DMPK Studies to Drug Discovery
3.2 Inhibition of Cytochrome P450-Mediated Metabolism
3.3 Increase in Throughput Capacity in Enzyme-Inhibition Studies
3.4 Use of Inhibition Data to Screen and Rescue NCEs
3.5 Higher-Throughput LC/MS/MS Analysis
3.6 Progress in Inhibition Assay Throughput
3.7 Questions & Answers
Part II: Increasing the Speed of LC/MS Sample Throughput
3.8 High-Throughput Use of Analytical Methods
3.9 In Vitro Assays Supported by LC/MS Analysis
3.10 Increased Sample Throughput
3.11 Increased Throughput via Parallel HPLC/Tandem Mass Spectrometry
3.12 Metabolite Identification
3.13 Dramatic Improvement in Screening Capability
3.14 Questions & Answers
4. Hepatocytes as a Model for Preclinical Evaluation of Drug Metabolism and Toxicity
4.1 The Role of Metabolism and Toxicity Studies in Preclinical Drug Development
4.2 High-Throughput Screens and In Vitro Model Systems
4.3 The Use of Hepatocytes for Enzyme-Induction Studies
4.4 The Use of Hepatocytes in Mechanistic Cytotoxicity Studies
4.5 Cytotoxicity Induced by Mitochondrial Stress
4.6 Cytotoxicity Induced by Apoptotic Activation
4.7 The Use of Mechanistic Data for Selecting Lead Compounds
4.8 Looking Ahead: The Emergence of Toxicogenomics
4.9 Questions & Answers
5. Liver-Slice Models for the Prediction of Metabolic Drug-Drug Interactions
5.1 The Importance of Predicting Drug-Drug Interactions
5.2 Cytochrome P450 Enzymes in Drug Metabolism
5.3 In Vitro Systems for Evaluating Drug Interactions
5.4 The Similarity Between Liver Slice Assays and In Vivo Studies
5.5 Case Study: Zileuton and Theophylline
Metabolic Inhibition vs. Hepatocytotoxicity
5.6 Case Study: AZT Phase II Metabolism
5.7 Liver Microsome Assay System
Mechanism-Based Inhibitors
5.8 Physiological Models - A Rational Approach to Interaction Studies
5.9 Questions & Answers
5.10 References
6. Optimizing Human Cell Assay Systems for ADME/Tox Evaluation
6.1 Predicting Clinical Success
6.2 Use of Intestinal Cells to Test for Bioavailability
6.3 Use of Liver Systems to Test for Metabolism and Toxicity
6.4 Human Hepatocyte High-Throughput Screening to Test Metabolic Stability
6.5 Human Hepatocyte High-Throughput Screening to Test for Hepatotoxicity
6.6 Human Hepatocyte High-Throughput Screening to Test for Drug-Drug Interaction Potential
6.7 The Current Drug Testing Paradigm
6.8 New Experimental Models and Assays for More Chemical Structures
6.9 References
7. New Analytical and In Vitro Techniques for High-Throughput Predictive ADME Studies
7.1 New Models to Speed Drug-Candidate Selection
7.2 Meeting the Data-Handling Demands of Increased Numbers of NCEs
7.3 Cassette Techniques to Reduce Sample Numbers
7.4 Automation of Testing and Analysis
7.5 Speeding the Analysis
7.6 Summary of Approaches for Accelerating Development of Hits to Leads
7.7 References
8. High-Throughput Microchip Assays to Develop ADME/Tox Drug-Screening Assays
8.1 Miniature Assay Chips for Automated Screening
8.2 Miniaturized Enzyme-Inhibition Assays
8.3 Cell-Based Assays on a Microchip
8.4 Continuous-Flow Enzyme Assays
8.5 Continuous-Flow Cell Assays
8.6 Human Serum Albumin Binding Assays Using Microchips
8.7 Summary of Microfluidic Chip Capabilities
9. Use of Combinatorial Chemical Libraries for ADME/Tox Profiling in Drug-Lead Identification and Optimization
9.1 The Need to Optimize the 'Hit-to-Lead' Process
9.2 Process Development
9.3 Compound-Screening Libraries
9.4 ADME/Tox Profiling in Drug Discovery
9.5 Summary of ArQule's Lead-Development Programs
9.6 Questions & Answers
Volume II - Pharmacogenomic Approaches
10. Bringing Pharmacogenomics to Drug Development
Part I: Molecular Tools to Measure Variations in Drug Response
10.1 Genetic Variation in Drug Response
10.2 Benefits from Using Pharmacogenomics in Drug Development
10.3 The Use of Pharmacogenomics in ADME Testing
10.4 The Use of Pharmacogenomics in Dosing Studies
10.5 The Use of Pharmacogenomics to Focus Clinical Trials
10.6 The Use of Pharmacogenomics to Detect Genetic Variance in Drug Response
10.7 Prospective Pharmacogenomic Studies
10.8 Clinical Trial Design and Study Size
10.9 Pharmacogenomics: Opportunities and Challenges
10.10 Questions & Answers
Part II: Expression Profiling to Study Drug Metabolism and Toxicity
10.11 The Emergence of Pharmacogenetics: History of PPGx
10.12 Variability in Drug Metabolism
10.13 Predicting and Preventing Adverse Drug Reactions
10.14 Genotyping Assays to Assess Drug Metabolism
10.15 Genotyping Assays to Assess a Drug's Efficacy
10.16 Expression Profiling of Metabolism and Toxicity Genes Using DNA Microarrays
10.17 Case Studies of Clinical Trials Using Pharmacogenetics
10.18 Issues Associated with Pharmacogenetic Testing
10.19 Regulatory Requirements for Genetic Testing
10.20 The Future of Pharmacogenomic Testing
10.21 Questions & Answers
11. Expression Profiling in Human Hepatocytes to Assess Drug Toxicity and Interactions
11.1 Metabolic Enzyme Induction as a Predictor of Drug Metabolism
11.2 Tests Using Human Tissue to Reduce Drug-Failure Rate
11.3 The Tissue Issue
11.4 Freshly Derived Human Hepatocytes
11.5 Induction of Cytochrome P450 Enzymes
11.6 The RT-PCR Assay
11.7 Case Study: Effect of the Glitazones on Genes for CYP450 Enzymes
11.8 Hepatotoxic Effects of the Glitazones
11.9 Gene Assays for Frank Toxicity
11.10 Advantages of Fresh Hepatocytes Over Cryopreserved Hepatocytes
11.11 Advantages of Human-Hepatocyte-Based Toxicity Studies
11.12 Questions & Answers
11.13 References
12. Differential Gene Expression Technology to Profile Drug-Induced Toxicity
12.1 Addressing the Need for Predictive Toxicology
12.2 Differential Gene Expression Profiling: The Process
12.3 Pharmacogenomics and Gene Identification
12.4 Expression Pharmacogenomics in Cardiotoxicity
12.5 Genes Regulated by the Fenfluramines
12.6 Gene Profiling of Human Tissues
12.7 Troglitazone Hepatotoxicity and Gene Expression
12.8 Drug-Response Genes as Markers in Predictive Toxicology
12.9 Acknowledgments
12.10 References
13. Gene Expression Analysis for Accurate Quantification of Toxicity Targets
13.1 Gene Expression to Evaluate Toxicity Risks
13.2 Fluorescent Signals to Identify Gene Expression
13.3 Creation of Universal Assays
13.4 Normalization of Gene Expression Against an Endogenous Control
13.5 Reagent Panels and Fixed-Menu Assay Cards
13.6 Applications of Gene Expression Analysis in Toxicology
13.7 Gene Expression Analysis for Clinical Toxicology and Predictive Toxicology
13.8 Questions & Answers
14. Expression Profiling to Identify Molecular Mechanisms of Drug Treatment/Toxicity
14.1 Transcriptional and Proteomic Profiling
14.2 Creating High-Throughput Profiling Assays
14.3 The Use of Expression Profiling to Accelerate Drug Discovery
14.4 Expression Profiling to Reveal Compounds' Effects on Cellular Response
14.5 The Role of Expression Profiling in Toxicity Testing
14.6 Looking Forward to a Pharmacological Reference Database
14.7 Questions & Answers
15. Gene Expression Analysis and Database Integration for Predictive Toxicology
15.1 Toxicity Assessment Using Gene Response Profiling
15.2 Profiles of mRNA Expression
15.3 Integrating Data from Different Platforms
15.4 Objectives of Profiling Gene Responses to Toxic Compounds
15.5 Identifying Gene Expression Patterns in Response to Toxins
Stratification of Responses
15.6 Using Gene Expression Patterns to Predict Human Toxicity
15.7 Identifying Gene Response to Toxic Compounds: Dual Capabilities
15.8 Questions & Answers
Volume III - Computational and Database Approaches
16. The Trend Toward e-R&D: Using In Silico Approaches in Predictive Toxicology
16.1 Toxicity Screening: A Bottleneck in Pharmaceutical R&D
16.2 Moving Toward Pharmaceutical e-R&D
16.3 Integrating ADME, PK and Toxicity Testing Into e-R&D
16.4 Defining the Tools for Toxicity Evaluation
16.5 Developing Chemistry/Toxicity-Based Informatics Software
16.6 Elements of the e-Tox System
16.7 The Coming In Silico Revolution
16.8 Questions & Answers
17. In Silico Systems to Integrate Toxicoinformatics in Drug Discovery and Development
17.1 Definition of In Silico Toxicology
17.2 Components of In Silico Systems
17.3 Generation and Use of STR Data
17.4 Genetic Safety Assessment
Genetic Toxicology Information Sources
17.5 Use of a Predictive-Toxicology System at Pfizer
17.6 Development Opportunities for In Silico Toxicology
17.7 Questions & Answers
17.8 References
18. Computational Toxicity Assessment in Early Drug Discovery and Development
18.1 Decision-Support Software for Drug Discovery and Development
18.2 Rule-Based Approaches
18.3 The QSAR Approach
18.4 Defining Quantitative Structure-Toxicity Relationships
18.5 Optimizing the QSAR Model
18.6 Database Integration in Decision Support
18.7 Calculation and Information Integration to Optimize Therapeutic Index
18.8 Toxicity Assessment in Virtual Chemical Libraries
18.9 Testing In Silico to Predict Toxicity
18.10 Summary of Virtual Modeling Capabilities
18.11 Comment & Response
19. Development of Chemical-Profiling Software for Early Lead Selection
19.1 Organizing the Output of High-Throughput Screening
19.2 Phylogenetic-Like Grouping of Chemical Substructures
19.3 Analyzing Toxicologic Structural Alerts
19.4 Correlating Chemical Substructures with Compound Activity
19.5 Identifying Consensus Substructures as Mutagenic Alerts
19.6 Identifying False-Positives and False-Negatives
19.7 Integrating Toxicophore Substructure Analysis in Early Lead Selection
19.8 Questions & Answers
19.9 References
20. Using FDA Databases and Computational Models in Regulatory and R&D Decision-Making
20.1 The FDA-CDER as a Unique Source of Scientific Information
20.2 Mission of the Regulatory Research & Analysis Staff (RRAS)
20.4 The Carcinogenicity Database
20.5 Collaborative R&D Agreement with Multicase
20.6 Interpretation of the Virtual Study
20.7 Validation of the Predictive Model
20.8 Computational Toxicology Applications
20.9 FDA-RRAS Long-Term Objectives
21. A Consortium Approach to Building a Toxicity Database from Proprietary Compounds
21.1 Approaches to Predictive Toxicology
21.2 Predictive Toxicology Databases
Public Databases
Proprietary Databases
21.3 Plans for A Shared Industry Database for Toxicity
21.4 The Ideal Database for Predictive Toxicology
21.5 Globalization and International Harmonization
21.6 Approaches to Assembling and Sharing the Toxicity Database
21.7 The High Production Volume Chemical Program
21.8 The IUCLID Candidate Database at the ECB
21.9 An Accelerated Approach to Predictive Toxicology
21.10 Questions & Answers
22. Computational Pharmacokinetics for Drug Discovery
22.1 Interfacing High-Throughput Pharmacokinetics (HTPkS) and PK-Informatics
22.2 Discovery and Selection of Small-Molecule Drugs
22.3 Gains from Early Pharmacokinetic Studies
22.4 In Vitro Pharmacokinetic Screening
22.5 Virtual, or In Silico, Pharmacokinetic Screening
22.6 In Vitro ADME Screening
22.7 In Silico Prediction of Pharmacokinetics - The iDEA Model
22.8 A Consortium Approach - The iDEA Consortium
22.9 The iDEA Model - Benefits of Simulation
22.10 Questions & Answers
