Predictive ADMET : Integrated Approaches in Drug Discovery and Development

Content: Predictive ADMET
 Contents
 Preface
 Contributors
 I Introduction to the current scientific, clinical, and social environment of drug discovery and development
 1 Current Social, Clinical, and Scientific Environment of Pharmaceutical R & D
 1.1 THE CHANGING LANDSCAPE OF EPIDEMIOLOGY AND MEDICAL CARE
 1.2 COST OF DRUG DEVELOPMENT
 1.2.1 Decline in Industry Productivity
 1.2.2 Rise in Safety Issues
 1.2.3 Increasing Regulatory Requirements
 1.3 THE NEW PARADIGM OF ADMEPK ASSESSMENT
 1.3.1 Recent Advancement of ADMEPK Assessment of Drug Candidates in Discovery and Development. 1.3.2 New Challenges and Emerging Fields of ADMEPK Development1.4 INCREASED SAFETY EXPECTATIONS
 1.4.1 Early Awareness of Safety Hazards
 1.4.2 Logistics for In Vitro Safety Profiling
 1.4.3 Relevance and Confidence in Profiling Data
 1.5 TRANSLATIONAL VALUE OF IN VITRO PROFILING DATA
 1.6 SUMMARY
 References
 2 Polypharmacology and Adverse Bioactivity Profiles Predict Potential Toxicity and Drug-related ADRs
 2.1 INTRODUCTION
 2.2 IN VITRO ADMET PROFILING
 2.3 COMPUTATIONAL METHODS PREDICTING ADMET PROPERTIES
 2.3.1 QSAR, QSPR, and Descriptor-based Methods. 2.3.2 Molecular Interaction- and Shape-based Approaches2.3.3 Docking
 2.4 OUTLOOK
 Acknowledgments
 References
 II Intelligent integration and extrapolation of ADMET data
 3 ADMET Diagnosis Models
 3.1 Introduction
 3.2 Solubility Diagnosis
 3.2.1 Lipophilicity and Maximum Solubility Concept
 3.2.2 Estimating the Impact of the Solid State in the Absence of Crystalline Material
 3.2.3 When Is the Maximal Effect of Ionization Reached?
 3.2.4 The Solubility Diagnosis Matrix
 3.2.5 Diagnosis Examples (Molecules in Table)
 3.3 Diagnosing Permeability. 3.3.1 LogP, PSA, Absorption Model, and Polarity-Lipophilicity Line (PLL)3.3.2 The Permeability Diagnosis Matrix (see Table)
 3.3.3 Diagnosis Examples (Molecules in Table)
 3.4 General Strategy to Apply Adme Diagnosis Models
 3.5 Concluding Remarks
 References
 4 PATH (Probe ADME and Test Hypotheses): A Useful Approach Enabling Hypothesis-driven ADME Optimization
 4.1 Introduction
 4.2 Assumptions and Limitations
 4.2.1 In vitro
 4.2.2 In vivo
 4.3 Clearance IVIVC
 4.3.1 Establishing a Baseline for Clearance Correlation Analysis
 4.3.2 Clearance IVIVC Zones. 4.3.3 Trends, Hypotheses, and Strategies for Clearance Interrogation by Zone4.4 Oral Bioavailability (%F) IVIVC
 4.4.1 Establishing a Baseline for %F Correlation Analysis
 4.4.2 %F IVIVC Zones
 4.4.3 Trends, Hypotheses, and Strategies for %F Interrogation by Zone
 4.5 Payoffs for Intelligent Data Integration in Early Drug Discovery
 References
 5 PK-MATRIX-A Permeability: Intrinsic Clearance System for Prediction, Classification, and Profiling of Pharmacokinetics and Drug-Drug Interactions
 5.1 INTRODUCTION
 5.2 SETTING UP THE PK-MATRIX
 5.3 PK-MATRIX DISTRIBUTION/CLASSIFICATION OF DRUGS.