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An assessment of mutagenicity of chemical substances by (quantitative) structure–activity relationship
Currently, there are more than 100,000 industrial chemicals substances produced and present in our living environments. Some of them may have adverse effects on human health. Given the rapid expansion in the number of industrial chemicals, international organizations and regulatory authorities have...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330942/ https://www.ncbi.nlm.nih.gov/pubmed/32626544 http://dx.doi.org/10.1186/s41021-020-00163-1 |
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author | Honma, Masamitsu |
author_facet | Honma, Masamitsu |
author_sort | Honma, Masamitsu |
collection | PubMed |
description | Currently, there are more than 100,000 industrial chemicals substances produced and present in our living environments. Some of them may have adverse effects on human health. Given the rapid expansion in the number of industrial chemicals, international organizations and regulatory authorities have expressed the need for effective screening tools to promptly and accurately identify chemical substances with potential adverse effects without conducting actual toxicological studies. (Quantitative) Structure–Activity Relationship ((Q)SAR) is a promising approach to predict the potential adverse effects of a chemical on the basis of its chemical structure. Significant effort has been devoted to the development of (Q) SAR models for predicting Ames mutagenicity, among other toxicological endpoints, owing to the significant amount of the necessary Ames test data that have already been accumulated. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline for the assessment and control of mutagenic impurities in pharmaceuticals was established in 2014. It is the first international guideline that addresses the use of (Q) SAR instead of actual toxicological studies for human health assessment. Therefore, (Q) SAR for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. This review introduces the advantages and features of (Q)SAR. Several (Q) SAR tools for predicting Ames mutagenicity and approaches to improve (Q) SAR models are also reviewed. Finally, I mention the future of (Q) SAR and other advanced in silico technology in genetic toxicology. |
format | Online Article Text |
id | pubmed-7330942 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73309422020-07-02 An assessment of mutagenicity of chemical substances by (quantitative) structure–activity relationship Honma, Masamitsu Genes Environ Review Currently, there are more than 100,000 industrial chemicals substances produced and present in our living environments. Some of them may have adverse effects on human health. Given the rapid expansion in the number of industrial chemicals, international organizations and regulatory authorities have expressed the need for effective screening tools to promptly and accurately identify chemical substances with potential adverse effects without conducting actual toxicological studies. (Quantitative) Structure–Activity Relationship ((Q)SAR) is a promising approach to predict the potential adverse effects of a chemical on the basis of its chemical structure. Significant effort has been devoted to the development of (Q) SAR models for predicting Ames mutagenicity, among other toxicological endpoints, owing to the significant amount of the necessary Ames test data that have already been accumulated. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline for the assessment and control of mutagenic impurities in pharmaceuticals was established in 2014. It is the first international guideline that addresses the use of (Q) SAR instead of actual toxicological studies for human health assessment. Therefore, (Q) SAR for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. This review introduces the advantages and features of (Q)SAR. Several (Q) SAR tools for predicting Ames mutagenicity and approaches to improve (Q) SAR models are also reviewed. Finally, I mention the future of (Q) SAR and other advanced in silico technology in genetic toxicology. BioMed Central 2020-07-02 /pmc/articles/PMC7330942/ /pubmed/32626544 http://dx.doi.org/10.1186/s41021-020-00163-1 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Honma, Masamitsu An assessment of mutagenicity of chemical substances by (quantitative) structure–activity relationship |
title | An assessment of mutagenicity of chemical substances by (quantitative) structure–activity relationship |
title_full | An assessment of mutagenicity of chemical substances by (quantitative) structure–activity relationship |
title_fullStr | An assessment of mutagenicity of chemical substances by (quantitative) structure–activity relationship |
title_full_unstemmed | An assessment of mutagenicity of chemical substances by (quantitative) structure–activity relationship |
title_short | An assessment of mutagenicity of chemical substances by (quantitative) structure–activity relationship |
title_sort | assessment of mutagenicity of chemical substances by (quantitative) structure–activity relationship |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330942/ https://www.ncbi.nlm.nih.gov/pubmed/32626544 http://dx.doi.org/10.1186/s41021-020-00163-1 |
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