In Silico Methods for Predicting Drug Toxicity

Emilio Benfenati

ISBN 10: 1071619594 / ISBN 13: 9781071619599

Edité par Springer US, Springer US, 2022

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9781071619599

Détails bibliographiques

• Titre: In Silico Methods for Predicting Drug Toxicity
• Éditeur: Springer US, Springer US
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 1071619594
• ISBN à 13 chiffres: 9781071619599
• Reliure: Buch
• Édition: 2ème Édition
• État de l'article: Neu

A propos de ce titre

Synopsis

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

Frédéric Y. Bois, Cleo Tebby, and Céline 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