Literature DB >> 27979779

In silico prediction of genotoxicity.

Jörg D Wichard1.   

Abstract

The in silico prediction of genotoxicity has made considerable progress during the last years. The main driver for the pharmaceutical industry is the ICH M7 guideline about the assessment of DNA reactive impurities. An important component of this guideline is the use of in silico models as an alternative approach to experimental testing. The in silico prediction of genotoxicity provides an established and accepted method that defines the first step in the assessment of DNA reactive impurities. This was made possible by the growing amount of reliable Ames screening data, the attempts to understand the activity pathways and the subsequent development of computer-based prediction systems. This paper gives an overview of how the in silico prediction of genotoxicity is performed under the ICH M7 guideline.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Expert systems; Genotoxic impurities; Genotoxicity; In silico methods; QSAR

Mesh:

Substances:

Year:  2016        PMID: 27979779     DOI: 10.1016/j.fct.2016.12.013

Source DB:  PubMed          Journal:  Food Chem Toxicol        ISSN: 0278-6915            Impact factor:   6.023


  3 in total

1.  Comparative Analysis of Transcriptional Responses to Genotoxic and Non-Genotoxic Agents in the Blood Cell Model TK6 and the Liver Model HepaRG.

Authors:  Katrin Kreuzer; Heike Sprenger; Albert Braeuning
Journal:  Int J Mol Sci       Date:  2022-03-22       Impact factor: 5.923

2.  The Key Characteristics of Carcinogens: Relationship to the Hallmarks of Cancer, Relevant Biomarkers, and Assays to Measure Them.

Authors:  Martyn T Smith; Kathryn Z Guyton; Nicole Kleinstreuer; Alexandre Borrel; Andres Cardenas; Weihsueh A Chiu; Dean W Felsher; Catherine F Gibbons; William H Goodson; Keith A Houck; Agnes B Kane; Michele A La Merrill; Herve Lebrec; Leroy Lowe; Cliona M McHale; Sheroy Minocherhomji; Linda Rieswijk; Martha S Sandy; Hideko Sone; Amy Wang; Luoping Zhang; Lauren Zeise; Mark Fielden
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2020-03-09       Impact factor: 4.254

Review 3.  In silico prediction of toxicity and its applications for chemicals at work.

Authors:  Kyung-Taek Rim
Journal:  Toxicol Environ Health Sci       Date:  2020-05-14
  3 in total

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