In silico methods for predicting drug toxicity

Author(s)

    • Benfenati, Emilio

Bibliographic Information

In silico methods for predicting drug toxicity

edited by Emilio Benfenati

(Methods in molecular biology / John M. Walker, series editor, 1425)(Springer protocols)

Humana Press, c2016

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Note

Includes bibliographical references and index

Description and Table of Contents

Description

This detailed volume explores in silico methods for pharmaceutical toxicity by combining the theoretical advanced research with the practical application of the tools. Beginning with a section covering sophisticated models addressing the binding to receptors, pharmacokinetics and adsorption, metabolism, distribution, and excretion, the book continues with chapters delving into models for specific toxicological and ecotoxicological endpoints, as well as broad views of the main initiatives and new perspectives which will very likely improve our way of modelling pharmaceuticals. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that is key for achieving successful research results. Authoritative and practical, In Silico Methods for Predicting Drug Toxicity offers the advantage of incorporating data and knowledge from different fields, such as chemistry, biology, -omics, and pharmacology, to achieve goals in this vital area of research.

Table of Contents

1. QSAR Methods Giuseppina Gini Part I: Modeling a Pharmaceutical in the Human Body 2. In Silico 3D-Modelling of Binding Activities Stefano Moro, Mattia Sturlese, Antonella Ciancetta, and Matteo Floris 3. Modeling Pharmacokinetics Frederic Y. Bois and Celine Brochot 4. Modeling ADMET Jayeeta Ghosh, Michael S. Lawless, Marvin Waldman, Vijay Gombar, and Robert Fraczkiewicz Part II: The Applications of In Silico Models for the Different Endpoints 5. In Silico Prediction of Chemically-Induced Mutagenicity: How to Use QSAR Models and Interpret Their Results Enrico Mombelli, Giuseppa Raitano, and Emilio Benfenati 6. In Silico Methods for Carcinogenicity Assessment Azadi Golbamaki and Emilio Benfenati 7. VirtualToxLab: Exploring the Toxic Potential of Rejuvenating Substances Found in Traditional Medicines Martin Smiesko and Angelo Vedani 8. In Silico Model for Developmental Toxicity: How to Use QSAR Models and Interpret Their Results Marco Marzo, Alessandra Roncaglioni, Sunil Kulkarni, Tara S. Barton-Maclaren, and Emilio Benfenati 9. In Silico Models for Repeated Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs Fabiola Pizzo and Emilio Benfenati 10. In Silico Models for Acute Systemic Toxicity Julien Burton, Andrew P. Worth, Ivanka Tsakovska, and Antonia Diukendjieva 11. In Silico Models for Hepatotoxicity Mark Hewitt and Katarzyna Przybylak 12. In Silico Models for Ecotoxicity of Pharmaceuticals Kunal Roy and Supratik Kar 13. Use of Read-Across Tools Serena Manganelli and Emilio Benfenati Part III: The Scientific and Society Challenges 14. Adverse Outcome Pathways as Tools to Assess Drug-Induced Toxicity Mathieu Vinken 15. A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure Dennie G.A.J. Hebels, Axel Rasche, Ralf Herwig, Gerard J.P. van Westen, Danyel G.J. Jennen, and Jos C.S. Kleinjans 16. In Silico Study of In Vitro GPCR Assays by QSAR Modeling Kamel Mansouri and Richard S. Judson 17. Taking Advantage of Databases Glenn J. Myatt and Donald P. Quigley 18. QSAR Models at the United States FDA/NCTR Huixiao Hong, Minjun Chen, Hui Wen Ng, and Weida Tong 19. A Round Trip from Medicinal Chemistry to Predictive Toxicology Giuseppe Felice Mangiatordi, Angelo Carotti, Ettore Novellino, and Orazio Nicolotti 20. The Use of In Silico Models Within a Large Pharmaceutical Company Alessandro Brigo and Wolfgang Muster 21. The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities Manuela Pavan, Simona Kovarich, Arianna Bassan, Lorenza Broccardo, Chihae Yang, and Elena Fioravanzo

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