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Predictive ADMET : Integrated Approaches in Drug Discovery and Development.
By guiding in the application of techniques and tools for predicting ADMET outcomes in drug candidates, Predictive ADMET offers a road map for drug discovery scientists to generate effective and safe drugs for unmet medical needs. Featuring case studies and lessons learned from real drug discovery a...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Wiley,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.