In Silico Methods for Predicting Drug Toxicity

ISBN-13: 9781493980932 / Angielski / Miękka / 2018 / 534 str.

Emilio Benfenati

Spis treści

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 Céline 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 Smieško 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

Blurb

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.