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Computational Toxicology

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Cover of 'Computational Toxicology'

Table of Contents

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    Book Overview
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    Chapter 1 Molecular Descriptors for Structure–Activity Applications: A Hands-On Approach
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    Chapter 2 The OECD QSAR Toolbox Starts Its Second Decade
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    Chapter 3 QSAR: What Else?
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    Chapter 4 (Q)SARs as Adaptations to REACH Information Requirements
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    Chapter 5 Machine Learning Methods in Computational Toxicology
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    Chapter 6 Applicability Domain: A Step Toward Confident Predictions and Decidability for QSAR Modeling
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    Chapter 7 Molecular Similarity in Computational Toxicology
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    Chapter 8 Molecular Docking for Predictive Toxicology
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    Chapter 9 Criteria and Application on the Use of Nontesting Methods within a Weight of Evidence Strategy
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    Chapter 10 Characterization and Management of Uncertainties in Toxicological Risk Assessment: Examples from the Opinions of the European Food Safety Authority
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    Chapter 11 Computational Toxicology and Drug Discovery
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    Chapter 12 Approaching Pharmacological Space: Events and Components
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    Chapter 13 Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation
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    Chapter 14 Enalos Suite: New Cheminformatics Platform for Drug Discovery and Computational Toxicology
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    Chapter 15 Ion Channels in Drug Discovery and Safety Pharmacology
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    Chapter 16 Computational Approaches in Multitarget Drug Discovery
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    Chapter 17 Nanoformulations for Drug Delivery: Safety, Toxicity, and Efficacy
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    Chapter 18 Toxicity Potential of Nutraceuticals
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    Chapter 19 Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach
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    Chapter 20 (Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks
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    Chapter 21 Stem Cell-Based Methods to Predict Developmental Chemical Toxicity
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    Chapter 22 Predicting Chemically Induced Skin Sensitization by Using In Chemico / In Vitro Methods
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    Chapter 23 Hepatotoxicity Prediction by Systems Biology Modeling of Disturbed Metabolic Pathways Using Gene Expression Data
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    Chapter 24 Nontest Methods to Predict Acute Toxicity: State of the Art for Applications of In Silico Methods
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    Chapter 25 Predictive Systems Toxicology
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    Chapter 26 Chemoinformatic Approach to Assess Toxicity of Ionic Liquids
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    Chapter 27 Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results
Attention for Chapter 20: (Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks
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Chapter title
(Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks
Chapter number 20
Book title
Computational Toxicology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7899-1_20
Pubmed ID
Book ISBNs
978-1-4939-7898-4, 978-1-4939-7899-1
Authors

Cecilia Bossa, Romualdo Benigni, Olga Tcheremenskaia, Chiara Laura Battistelli

Abstract

Knowledge of the genotoxicity and carcinogenicity potential of chemical substances is one of the key scientific elements able to better protect human health. Genotoxicity assessment is also considered as prescreening of carcinogenicity. The assessment of both endpoints is a fundamental component of national and international legislations, for all types of substances, and has stimulated the development of alternative, nontesting methods. Over the recent decades, much attention has been given to the use and further development of structure-activity relationships-based approaches, to be used in isolation or in combination with in vitro assays for predictive purposes. In this chapter, we briefly introduce the rationale for the main (Q)SAR approaches, and detail the most important regulatory initiatives and frameworks. It appears that the existence and needs of regulatory frameworks stimulate the development of better predictive tools; in turn, this allows the regulators to fine-tune their requirements for an improved defense of human health.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 50%
Researcher 2 33%
Student > Postgraduate 1 17%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 2 33%
Agricultural and Biological Sciences 1 17%
Chemistry 1 17%
Unknown 2 33%