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In Silico Methods for Predicting Drug Toxicity

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Cover of 'In Silico Methods for Predicting Drug Toxicity'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 QSAR Methods.
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    Chapter 2 In Silico 3D Modeling of Binding Activities.
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    Chapter 3 Modeling Pharmacokinetics.
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    Chapter 4 Modeling ADMET.
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    Chapter 5 In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their Results.
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    Chapter 6 In Silico Methods for Carcinogenicity Assessment.
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    Chapter 7 VirtualToxLab: Exploring the Toxic Potential of Rejuvenating Substances Found in Traditional Medicines.
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    Chapter 8 In Silico Model for Developmental Toxicity: How to Use QSAR Models and Interpret Their Results.
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    Chapter 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.
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    Chapter 10 In Silico Models for Acute Systemic Toxicity.
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    Chapter 11 In Silico Models for Hepatotoxicity.
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    Chapter 12 In Silico Models for Ecotoxicity of Pharmaceuticals.
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    Chapter 13 Use of Read-Across Tools.
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    Chapter 14 Adverse Outcome Pathways as Tools to Assess Drug-Induced Toxicity.
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    Chapter 15 A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure.
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    Chapter 16 In Silico Study of In Vitro GPCR Assays by QSAR Modeling.
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    Chapter 17 Taking Advantage of Databases.
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    Chapter 18 QSAR Models at the US FDA/NCTR.
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    Chapter 19 A Round Trip from Medicinal Chemistry to Predictive Toxicology.
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    Chapter 20 The Use of In Silico Models Within a Large Pharmaceutical Company.
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    Chapter 21 The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities.
Attention for Chapter 19: A Round Trip from Medicinal Chemistry to Predictive Toxicology.
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Chapter title
A Round Trip from Medicinal Chemistry to Predictive Toxicology.
Chapter number 19
Book title
In Silico Methods for Predicting Drug Toxicity
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3609-0_19
Pubmed ID
Book ISBNs
978-1-4939-3607-6, 978-1-4939-3609-0
Authors

Giuseppe Felice Mangiatordi, Angelo Carotti, Ettore Novellino, Orazio Nicolotti

Editors

Emilio Benfenati

Abstract

Predictive toxicology is a new emerging multifaceted research field aimed at protecting human health and environment from risks posed by chemicals. Such issue is of extreme public relevance and requires a multidisciplinary approach where the experience in medicinal chemistry is of utmost importance. Herein, we will survey some basic recommendations to gather good data and then will review three recent case studies to show how strategies of ligand- and structure-based molecular design, widely applied in medicinal chemistry, can be adapted to meet the more restrictive scientific and regulatory goals of predictive toxicology. In particular, we will report: Docking-based classification models to predict the estrogenic potentials of chemicals. Predicting the bioconcentration factor using biokinetics descriptors. Modeling oral sub-chronic toxicity using a customized k-nearest neighbors (k-NN) approach.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Librarian 1 20%
Student > Ph. D. Student 1 20%
Professor > Associate Professor 1 20%
Researcher 1 20%
Unknown 1 20%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 1 20%
Unknown 4 80%