医薬品の副作用予測

Background

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Report Format (3 Vols)

• 290+ pages of fully edited transcripts
• 150+ tables and figures
• 170+ weblinks
• Available as printed hardcopy with searchable CD-ROM

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 践it-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痴 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