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, 2425）（Springer protocols）

Humana Press, c2022

2nd ed

• : [hardback]

Note

Includes bibliographical references and index

Description and Table of Contents

Description

This fully updated book explores all-new and revised protocols involving the use of in silico models, particularly with regard to pharmaceuticals. Divided into five sections, the volume covers the modeling of pharmaceuticals in the body, toxicity data for modeling purposes, in silico models for multiple endpoints, a number of platforms for evaluating pharmaceuticals, as well as an exploration of challenges, both scientific and sociological. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and implementation advice necessary for successful results. Authoritative and comprehensive, In Silico Methods for Predicting Drug Toxicity, Second Edition aims to guide the reader through the correct procedures needed to harness in silico models, a field which now touches a wide variety of research specialties.

Table of Contents

1. QSAR Methods Giuseppina Gini Part I: Modeling a Pharmaceutical in the Body 2. PBPK Modeling to Simulate the Fate of Compounds in Living Organisms Frederic Y. Bois, Cleo Tebby, and Celine Brochot 3. Pharmacokinetic Tools and Applications Judith C. Madden and Courtney V. Thompson 4. In Silico Tools and Software to Predict ADMET of New Drug Candidates Supratik Kar, Kunal Roy, and Jerzy Leszczynski Part II: Exploiting Toxicity Data for Modeling Purposes 5. Development of In Silico Methods for Toxicity Prediction in Collaboration between Academia and the Pharmaceutical Industry Manuel Pastor, Ferran Sanz, and Frank Bringezu 6. Emerging Bioinformatics Methods and Resources in Drug Toxicology Karine Audouze and Olivier Taboureau Part III: The Use of In Silico Models for the Specific Endpoints 7. In Silico Prediction of Chemically-Induced Mutagenicity: A Weight of Evidence Approach Integrating Information from QSAR Models and Read-Across Predictions Enrico Mombelli, Giuseppa Raitano, and Emilio Benfenati 8. In Silico Methods for Chromosome Damage Diego Baderna, Ilse Van Overmeire, Giovanna J. Lavado, Domenico Gadaleta, and Birgit Mertens 9. In Silico Methods for Carcinogenicity Assessment Azadi Golbamaki, Emilio Benfenati, and Alessandra Roncaglioni 10. In Silico Models for Developmental Toxicity Marco Marzo, Alessandra Roncaglioni, Sunil Kulkarni, Tara S. Barton-Maclaren, and Emilio Benfenati 11. 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, Domenico Gadaleta, and Emilio Benfenati 12. In Silico Models for Predicting Acute Systemic Toxicity Ivanka Tsakovska, Antonia Diukendjieva, and Andrew P. Worth 13. In Silico Models for Skin Sensitization and Irritation Gianluca Selvestrel, Federica Robino, and Matteo Zanotti Russo 14. In Silico Models for Hepatotoxicity Claire Ellison, Mark Hewitt, and Katarzyna Przybylak 15. Machine Learning Models for Predicting Liver Toxicity Jie Liu, Wenjing Guo, Sugunadevi Sakkiah, Zuowei Ji, Gokhan Yavas, Wen Zou, Minjun Chen, Weida Tong, Tucker A Patterson, and Huixiao Hong Part IV: Platforms for Evaluating Pharmaceuticals 16. Implementation of In Silico Toxicology Protocols in Leadscope Kevin Cross, Candice Johnson, and Glenn J. Myatt 17. Use of Lhasa Limited Products for the In Silico Prediction of Drug Toxicity David J. Ponting, Michael J. Burns, Robert S. Foster, Rachel Hemingway, Grace Kocks, Donna S. Macmillan, Andrew L. Shannon-Little, Rachael E. Tennant, Jessica R. Tidmarsh, and David J. Yeo 18. Using VEGAHUB within a Weight-of-Evidence Strategy Serena Manganelli, Alessio Gamba, Erika Colombo, and Emilio Benfenati 19. MultiCASE Platform for In Silico Toxicology Suman K. Chakravarti and Roustem D. Saiakhov Part V: The Scientific and Society Challenges 20. Adverse Outcome Pathways as Versatile Tools in Liver Toxicity Testing Emma Gustafson, Eva Gijbels, Bruna dos Santos Rodrigues, Vania Vilas-Boas, and Mathieu Vinken 21. The Use of In Silico Methods for the Regulatory Toxicological Assessment of Pharmaceutical Impurities Simona Kovarich and Claudia Ileana Cappelli 22. Computational Modeling of Mixture Toxicity Mainak Chatterjee and Kunal Roy 23. In Silico Methods for Ecological Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives Maria Chiara Astuto, Matteo R. Di Nicola, Jose V. Tarazona, Yann Devos, Djien A.K. Liem, George E.N. Kass, Maria Bastaki, Reinhilde Schoonjans, Angelo Maggiore, Sandrine Charles, Aude Ratier, Christelle Lopes, Ophelia Gestin, Tobin Robinson, Anthony Williams, Nynke Kramer, Edoardo Carnesecchi, and Jean-Lou C.M. Dorne 24. Increasing the Value of Data within a Large Pharmaceutical Company through In Silico Models Alessandro Brigo, Doha Naga, and Wolfgang Muster

by "Nielsen BookData"

Details

• NCID: BD0090437X
• ISBN:
  • 9781071619599
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: New York
• Pages/Volumes: xiv, 680 p.
• Size: 27 cm
• Classification:
  • DC23 : 615.190113
• Subject Headings:
  • LCSH : Drug development -- Computer simulation
  • LCSH : Drugs -- Testing -- Computer simulation
• Parent Bibliography ID:
  • BA10794673
  • BA88873632