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In Silico Methods for Predicting Drug Toxicity (Methods in Molecular Biology)

SKU: 9781493980932

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In Silico Methods for Predicting Drug Toxicity (Methods in Molecular Biology), Emilio Benfenati, 9781493980932

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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. 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 Cline 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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