{"product_id":"predictive-admet-isbn-9781118299920","title":"Predictive ADMET","description":"\u003cp\u003eThis book helps readers integrate \u003ci\u003ein silico, in vitro\u003c\/i\u003e, and \u003ci\u003ein vivo\u003c\/i\u003e ADMET (absorption, distribution, metabolism, elimination and toxicity) and PK (pharmacokinetics) data with routine testing applications so that pharmaceutical scientists can diagnose ADMET problems and present appropriate recommendations to move drug discovery programs forward.\u003c\/p\u003e \u003cp\u003eThe book introduces the current clinical practice for drug discovery and development along with the impact on early risk assessment; consolidates the tools and models to intelligently integrate existing \u003ci\u003ein silico, in vitro\u003c\/i\u003e and \u003ci\u003ein vivo\u003c\/i\u003e ADMET data; and demonstrates successful cases and lessons learned from real drug discovery and development. In short, it is a book aimed to provide a practical road map for drug discovery and development scientists to generate efficacious and safe drugs for unmet medical needs.\u003c\/p\u003e \u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eContributors xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Introduction to the Current Scientific, Clinical, and Social Environment of Drug Discovery and Development\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 Current Social, Clinical, and Scientific Environment of Pharmaceutical R\u0026amp;D 3\u003cbr\u003e\u003ci\u003eLaszlo Urban, Jean-Pierre Valentin, Kenneth I Kaitin, and Jianling Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 Polypharmacology and Adverse Bioactivity Profiles Predict Potential Toxicity and Drug-related ADRs 23\u003cbr\u003e\u003ci\u003eTeresa Kaserer, Veronika Temml, and Daniela Schuster\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Intelligent Integration and Extrapolation of Admet Data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3 ADMET Diagnosis Models 49\u003cbr\u003e\u003ci\u003eBernard Faller, Suzanne Skolnik, and Jianling Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 PATH (Probe ADME and Test Hypotheses): A Useful Approach Enabling Hypothesis-driven ADME Optimization 63\u003cbr\u003e\u003ci\u003eLeslie Bell, Suzanne Skolnik, and Dallas Bednarczyk\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 PK-MATRIX—A Permeability: Intrinsic Clearance System for Prediction, Classification, and Profiling of Pharmacokinetics and Drug–drug Interactions 89\u003cbr\u003e\u003ci\u003eUrban Fagerholm\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Maximizing the Power of a Local Model for ADMET-property Prediction 103\u003cbr\u003e\u003ci\u003eSebastien Ronseaux, Jeremy Beck, and Clayton Springer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 Chemoinformatic and Chemogenomic Approach to ADMET 125\u003cbr\u003e\u003ci\u003eVirginie Y. Martiny, Ilza Pajeva, Michael Wiese, Andrew M. Davis, and Maria A. Miteva\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Multiparameter Optimization of ADMET for Drug Design 145\u003cbr\u003e\u003ci\u003eMatthew D. Segall and Edmund J. Champness\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 PBPK: Integrating In Vitro and In Silico Data in Physiologically Based Models 167\u003cbr\u003e\u003ci\u003eHannah M. Jones and Neil Parrott\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Emerging Full Mechanistic Physiologically Based Modeling 189\u003cbr\u003e\u003ci\u003eKiyohiko Sugano\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 Pharmacokinetic\/Pharmacodynamic Modeling in Drug Discovery: A Translational Tool to Optimize Discovery Compounds Toward the Ideal Target-specific Profile 211\u003cbr\u003e\u003ci\u003ePatricia Schroeder\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Assessment and Mitigation of Critical Clinically Relevant Admet Risks in Drug Discovery and Development\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12 In Vitro–In Silico Tools to Predict Pharmacokinetics of Poorly Soluble Drug Compounds 235\u003cbr\u003e\u003ci\u003eChristian Wagner and Jennifer B. Dressman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13 Evaluation of the Collective Impact of Passive Permeability and Active Transport on In Vivo Blood-brain Barrier and Gastrointestinal Drug Absorption 263\u003cbr\u003e\u003ci\u003eDonna A. Volpe, Hong Shen, and Praveen V. Balimane\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 Integrated Assessment of Drug Clearance and Cross-Species Scalability 291\u003cbr\u003e\u003ci\u003eKevin Beaumont, James R. Gosset, and Chris E. Keefer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 Practical Anticipation of Human Efficacious Doses and Pharmacokinetics using Preclinical \u003ci\u003eIn Vitro\u003c\/i\u003e and \u003ci\u003eIn Vivo\u003c\/i\u003e Data 319\u003cbr\u003e\u003ci\u003eTycho Heimbach, Rakesh Gollen, and Handan He\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16 Management and Mitigation of Human Drug–drug Interaction Risks in the Drug Discovery and Development Phases 353\u003cbr\u003e\u003ci\u003eHeidi J. Einolf and Imad Hanna\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17 Integrated Assessment and Clinical Translation of\u003ci\u003e In Vitro \u003c\/i\u003eOff-target Safety Pharmacology Risks 397\u003cbr\u003e\u003ci\u003ePatrick Y. Muller and Christian F. Trendelenburg\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18 Integrated Risk Assessment of Cardiovascular Safety in Drug Discovery 407\u003cbr\u003e\u003ci\u003eGül Erdemli and Ruth L. Martin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19 Drug-induced Hepatotoxicity: Advances in Preclinical Predictive Strategies and Tools 433\u003cbr\u003e\u003ci\u003eDonna M. Dambach\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20 Carcinogenicity and Teratogenicity Assessment 467\u003cbr\u003e\u003ci\u003eHans-Jörg Martus, David Beckman, and Lutz Mueller\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21 Nephrotoxicity: Development of Biomarkers for Preclinical and Clinical Application 491\u003cbr\u003e\u003ci\u003eFrank Dieterle and Estelle Marrer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Success Stories and Lessons Learned\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22 Early Intervention with Formulation Strategies for Multidimensional Problems to Optimize for Success 507\u003cbr\u003e\u003ci\u003eStephanie Dodd, Christina Capacci-Daniel, Christopher Towler, Riccardo Panicucci, and Keith Hoffmaster\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23 Cytochrome P450-mediated Drug Interaction and Cardiovascular Safety: The Seldane to Allegra Transformation 523\u003cbr\u003e\u003ci\u003eF. Peter Guengerich\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24 Clinical Toxicity Profile of VEGF Inhibitors 535\u003cbr\u003e\u003ci\u003eMark P. S. Sie and Ferry A. L. M. Eskens\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25 Cardiomyopathy: Drug Induced and Predisposed 555\u003cbr\u003e\u003ci\u003eShirley A. Aguirre and Eileen R. Blasi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26 Safety Management by Pharmacokinetic Considerations: Ranibizumab (Lucentis) and Bevacizumab (Avastin) 569\u003cbr\u003e\u003ci\u003eNicole H. Siegel and Manju L. Subramanian\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex 583\u003c\/p\u003e \u003cp\u003e“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.”  (\u003ci\u003eChemMedChem\u003c\/i\u003e, 1 June 2015)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJianling Wang\u003c\/b\u003e 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.\u003c\/p\u003e \u003cb\u003eLaszlo Urban\u003c\/b\u003e 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 \u003ci\u003eHit and Lead Profiling: Identification and Optimization of Drug-like Molecules\u003c\/i\u003e (Wiley, 2009).  \u003cp\u003eThis book helps readers integrate \u003ci\u003ein silico, in vitro\u003c\/i\u003e, and \u003ci\u003ein vivo\u003c\/i\u003e ADMET (absorption, distribution, metabolism, elimination and toxicity) and PK (pharmacokinetics) data with routine testing applications so that pharmaceutical scientists can diagnose ADMET problems and present appropriate recommendations to move drug discovery programs forward.\u003c\/p\u003e \u003cp\u003eThe book introduces the current clinical practice for drug discovery and development along with the impact on early risk assessment; consolidates the tools and models to intelligently integrate existing \u003ci\u003ein silico, in vitro\u003c\/i\u003e and \u003ci\u003ein vivo\u003c\/i\u003e ADMET data; and demonstrates successful cases and lessons learned from real drug discovery and development. In short, it is a book aimed to provide a practical road map for drug discovery and development scientists to generate efficacious and safe drugs for unmet medical needs.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989840249061,"sku":"NP9781118299920","price":173.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118299920.jpg?v=1761785642","url":"https:\/\/k12savings.com\/es\/products\/predictive-admet-isbn-9781118299920","provider":"K12savings","version":"1.0","type":"link"}