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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

  1. Altmetric Badge
    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.
  22. Altmetric Badge
    Chapter 21 The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities.
Attention for Chapter 18: QSAR Models at the US FDA/NCTR.
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Chapter title
QSAR Models at the US FDA/NCTR.
Chapter number 18
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_18
Pubmed ID
Book ISBNs
978-1-4939-3607-6, 978-1-4939-3609-0
Authors

Huixiao Hong, Minjun Chen, Hui Wen Ng, Weida Tong

Editors

Emilio Benfenati

Abstract

Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 3 19%
Student > Master 3 19%
Other 2 13%
Researcher 2 13%
Student > Ph. D. Student 1 6%
Other 2 13%
Unknown 3 19%
Readers by discipline Count As %
Environmental Science 2 13%
Biochemistry, Genetics and Molecular Biology 2 13%
Computer Science 2 13%
Agricultural and Biological Sciences 2 13%
Medicine and Dentistry 2 13%
Other 2 13%
Unknown 4 25%