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Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures
Industrial advances have led to generation of multi-component chemicals, materials and pharmaceuticals which are directly or indirectly affecting the environment. Although toxicity data are available for individual chemicals, generally there is no toxicity data of chemical mixtures. Most importantly...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468900/ https://www.ncbi.nlm.nih.gov/pubmed/30893892 http://dx.doi.org/10.3390/toxics7010015 |
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author | Kar, Supratik Leszczynski, Jerzy |
author_facet | Kar, Supratik Leszczynski, Jerzy |
author_sort | Kar, Supratik |
collection | PubMed |
description | Industrial advances have led to generation of multi-component chemicals, materials and pharmaceuticals which are directly or indirectly affecting the environment. Although toxicity data are available for individual chemicals, generally there is no toxicity data of chemical mixtures. Most importantly, the nature of toxicity of these studied mixtures is completely different to the single components, which makes the toxicity evaluation of mixtures more critical and challenging. Interactions of individual chemicals in a mixture can result in multifaceted and considerable deviations in the apparent properties of its ingredients. It results in synergistic or antagonistic effects as opposed to the ideal case of additive behavior i.e., concentration addition (CA) and independent action (IA). The CA and IA are leading models for the assessment of joint activity supported by pharmacology literature. Animal models for toxicity testing are time- and money-consuming as well as unethical. Thus, computational approaches are already proven efficient alternatives for assessing the toxicity of chemicals by regulatory authorities followed by industries. In silico methods are capable of predicting toxicity, prioritizing chemicals, identifying risk and assessing, followed by managing, the risk. In many cases, the mechanism behind the toxicity from species to species can be understood by in silico methods. Until today most of the computational approaches have been employed for single chemical’s toxicity. Thus, only a handful of works in the literature and methods are available for a mixture’s toxicity prediction employing computational or in silico approaches. Therefore, the present review explains the importance of evaluation of a mixture’s toxicity, the role of computational approaches to assess the toxicity, followed by types of in silico methods. Additionally, successful application of in silico tools in a mixture’s toxicity predictions is explained in detail. Finally, future avenues towards the role and application of computational approaches in a mixture’s toxicity are discussed. |
format | Online Article Text |
id | pubmed-6468900 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64689002019-04-22 Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures Kar, Supratik Leszczynski, Jerzy Toxics Review Industrial advances have led to generation of multi-component chemicals, materials and pharmaceuticals which are directly or indirectly affecting the environment. Although toxicity data are available for individual chemicals, generally there is no toxicity data of chemical mixtures. Most importantly, the nature of toxicity of these studied mixtures is completely different to the single components, which makes the toxicity evaluation of mixtures more critical and challenging. Interactions of individual chemicals in a mixture can result in multifaceted and considerable deviations in the apparent properties of its ingredients. It results in synergistic or antagonistic effects as opposed to the ideal case of additive behavior i.e., concentration addition (CA) and independent action (IA). The CA and IA are leading models for the assessment of joint activity supported by pharmacology literature. Animal models for toxicity testing are time- and money-consuming as well as unethical. Thus, computational approaches are already proven efficient alternatives for assessing the toxicity of chemicals by regulatory authorities followed by industries. In silico methods are capable of predicting toxicity, prioritizing chemicals, identifying risk and assessing, followed by managing, the risk. In many cases, the mechanism behind the toxicity from species to species can be understood by in silico methods. Until today most of the computational approaches have been employed for single chemical’s toxicity. Thus, only a handful of works in the literature and methods are available for a mixture’s toxicity prediction employing computational or in silico approaches. Therefore, the present review explains the importance of evaluation of a mixture’s toxicity, the role of computational approaches to assess the toxicity, followed by types of in silico methods. Additionally, successful application of in silico tools in a mixture’s toxicity predictions is explained in detail. Finally, future avenues towards the role and application of computational approaches in a mixture’s toxicity are discussed. MDPI 2019-03-19 /pmc/articles/PMC6468900/ /pubmed/30893892 http://dx.doi.org/10.3390/toxics7010015 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Kar, Supratik Leszczynski, Jerzy Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures |
title | Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures |
title_full | Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures |
title_fullStr | Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures |
title_full_unstemmed | Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures |
title_short | Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures |
title_sort | exploration of computational approaches to predict the toxicity of chemical mixtures |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468900/ https://www.ncbi.nlm.nih.gov/pubmed/30893892 http://dx.doi.org/10.3390/toxics7010015 |
work_keys_str_mv | AT karsupratik explorationofcomputationalapproachestopredictthetoxicityofchemicalmixtures AT leszczynskijerzy explorationofcomputationalapproachestopredictthetoxicityofchemicalmixtures |