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In Silico Methods for Predicting Drug Toxicity de Emilio Benfenati

In Silico Methods for Predicting Drug Toxicity: Methods in Molecular Biology, cartea 2425

Editat de Emilio Benfenati
en Limba Engleză Paperback – mar 2022
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.
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Specificații

ISBN-13: 9781071619629
ISBN-10: 1071619624
Pagini: 680
Ilustrații: XIV, 680 p. 244 illus., 215 illus. in color.
Dimensiuni: 178 x 254 mm
Greutate: 1.19 kg
Ediția:2nd ed. 2022
Editura: Springer Us
Colecția Humana
Seria Methods in Molecular Biology

Locul publicării:New York, NY, United States

Cuprins

QSAR Methods.- PBPK Modeling to Simulate the Fate of Compounds in Living Organisms.- Pharmacokinetic Tools and Applications.- In Silico Tools and Software to Predict ADMET of New Drug Candidates.- Development of In Silico Methods for Toxicity Prediction in Collaboration between Academia and the Pharmaceutical Industry.- Emerging Bioinformatics Methods and Resources in Drug Toxicology.- In Silico Prediction of Chemically-Induced Mutagenicity: A Weight of Evidence Approach Integrating Information from QSAR Models and Read-Across Predictions.- In Silico Methods for Chromosome Damage.- In Silico Methods for Carcinogenicity Assessment.- In Silico Models for Developmental Toxicity.- 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.- In Silico Models for Predicting Acute Systemic Toxicity.- In Silico Models for Skin Sensitization and Irritation.- In Silico Models for Hepatotoxicity.- Machine Learning Models for Predicting Liver Toxicity.- Implementation of In Silico Toxicology Protocols in Leadscope.- Use of Lhasa Limited Products for the In Silico Prediction of Drug Toxicity.- Using VEGAHUB within a Weight-of-Evidence Strategy.- MultiCASE Platform for In Silico Toxicology.- Adverse Outcome Pathways as Versatile Tools in Liver Toxicity Testing.- The Use of In Silico Methods for the Regulatory Toxicological Assessment of Pharmaceutical Impurities.- Computational Modeling of Mixture Toxicity.- In Silico Methods for Ecological Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives.- Increasing the Value of Data within a Large Pharmaceutical Company through In Silico Models.

Textul de pe ultima copertă

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.

Caracteristici

Includes cutting-edge techniques Provides step-by-step detail essential for reproducible results Contains key implementation advice from the experts