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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...

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Autores principales: Kar, Supratik, Leszczynski, Jerzy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
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.
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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
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