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.

List of contents

PREFACE ix
 
CONTRIBUTORS xi
 
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 3
Laszlo Urban, Jean-Pierre Valentin, Kenneth I Kaitin, and Jianling Wang
 
2 Polypharmacology and Adverse Bioactivity Profiles Predict Potential Toxicity and Drug-related ADRs 23
Teresa Kaserer, Veronika Temml, and Daniela Schuster
 
II INTELLIGENT INTEGRATION AND EXTRAPOLATION OF ADMET DATA
 
3 ADMET Diagnosis Models 49
Bernard Faller, Suzanne Skolnik, and Jianling Wang
 
4 PATH (Probe ADME and Test Hypotheses): A Useful Approach Enabling Hypothesis-driven ADME Optimization 63
Leslie Bell, Suzanne Skolnik, and Dallas Bednarczyk
 
5 PK-MATRIX--A Permeability: Intrinsic Clearance System for Prediction, Classification, and Profiling of Pharmacokinetics and Drug-drug Interactions 89
Urban Fagerholm
 
6 Maximizing the Power of a Local Model for ADMET-property Prediction 103
Sebastien Ronseaux, Jeremy Beck, and Clayton Springer
 
7 Chemoinformatic and Chemogenomic Approach to ADMET 125
Virginie Y. Martiny, Ilza Pajeva, Michael Wiese, Andrew M. Davis, and Maria A. Miteva
 
8 Multiparameter Optimization of ADMET for Drug Design 145
Matthew D. Segall and Edmund J. Champness
 
9 PBPK: Integrating In Vitro and In Silico Data in Physiologically Based Models 167
Hannah M. Jones and Neil Parrott
 
10 Emerging Full Mechanistic Physiologically Based Modeling 189
Kiyohiko Sugano
 
11 Pharmacokinetic/Pharmacodynamic Modeling in Drug Discovery: A Translational Tool to Optimize Discovery Compounds Toward the Ideal Target-specific Profile 211
Patricia Schroeder
 
III ASSESSMENT AND MITIGATION OF CRITICAL CLINICALLY RELEVANT ADMET RISKS IN DRUG DISCOVERY AND DEVELOPMENT
 
12 In Vitro-In Silico Tools to Predict Pharmacokinetics of Poorly Soluble Drug Compounds 235
Christian Wagner and Jennifer B. Dressman
 
13 Evaluation of the Collective Impact of Passive Permeability and Active Transport on In Vivo Blood-brain Barrier and Gastrointestinal Drug Absorption 263
Donna A. Volpe, Hong Shen, and Praveen V. Balimane
 
14 Integrated Assessment of Drug Clearance and Cross-Species Scalability 291
Kevin Beaumont, James R. Gosset, and Chris E. Keefer
 
15 Practical Anticipation of Human Efficacious Doses and Pharmacokinetics using Preclinical In Vitro and In Vivo Data 319
Tycho Heimbach, Rakesh Gollen, and Handan He
 
16 Management and Mitigation of Human Drug-drug Interaction Risks in the Drug Discovery and
Development Phases 353
Heidi J. Einolf and Imad Hanna
 
17 Integrated Assessment and Clinical Translation of In Vitro Off-target Safety Pharmacology Risks 397
Patrick Y. Muller and Christian F. Trendelenburg
 
18 Integrated Risk Assessment of Cardiovascular Safety in Drug Discovery 407
G¨ul Erdemli and Ruth L. Martin
 
19 Drug-induced Hepatotoxicity: Advances in Preclinical Predictive Strategies and Tools 433
Donna M. Dambach
 
20 Carcinogenicity and Teratogenicity Assessment 467
Hans-J¨org Martus, David Beckman, and Lutz Mueller
 
21 Nephrotoxicity: Development of Biomarkers for Preclinical and Clinical Application 491
Frank Dieterle and Estelle Marrer
 
IV SUCCESS STORIES AND LESSONS LEARNED
 
22 Early Intervention with Formulation Strategies for Multidimensional Problems to Optimize for Success 507
Stephanie Dodd, Christina Capacci-Daniel, Christopher Towler, Riccardo Panicucci, and Keith Hoffmaster
 
23 Cytochrome P450-mediated Drug Interaction and Cardiovascular Safety: The Seldane to Allegra
Transformation 523
F. Peter Guengerich
 
24 Clinical Tox

About the author

Jianling Wang is the Cambridge Head of Discovery ADME at Novartis Institutes for BioMedical Research. He has published over 40 journal papers, reviews, and book chapters and lectured at over 30 scientific conferences and courses.
Laszlo Urban is the Executive Director for Preclinical Safety Profiling at Novartis Institutes for BioMedical Research. He has over 10 years of experience in academia and 20 years in the pharmaceutical industry. Among Dr. Urban's publications are over 120 peer-reviewed scientific papers, 3 books including Hit and Lead Profiling: Identification and Optimization of Drug-like Molecules (Wiley, 2009).

Summary

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.

Report

"In conclusion, this volume fulfills its promise of being a very useful tool for guidance and diagnosis on ADMET matters, and I would recommend it to any scientist in the field." ( ChemMedChem , 1 June 2015)

Product details

• Authors: Laszlo Urban, J Wang, Jianlin Wang, Jianling Wang, Jianling (New Mexico State University Wang, Jianling Urban Wang
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 30.05.2014
• EAN: 9781118299920
• ISBN: 978-1-118-29992-0
• No. of pages: 624
• Subject: Natural sciences, medicine, IT, technology > Chemistry > Theoretical chemistry