Computational Modeling of Mixture Toxicity.
Computational
Mixture
QSAR
Toxicity
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
21
2
2022
pubmed:
22
2
2022
medline:
24
2
2022
Statut:
ppublish
Résumé
Environmental pollution has become an inevitable problem and a relevant global issue of the twenty-first century. The fast industrial growth has caused the production and release of various chemical species and multicomponent mixtures to the environment which affect the entire living world adversely. Various industrial regulatory agencies are working in this domain to regulate the production of chemical entities, proper release of chemical wastes, and the risk assessment of the industrial and hazardous chemicals; however, they mostly rely upon the single chemical risk assessment instead of considering the toxicity of multicomponent mixtures. In this era of chemical advances, single chemical exposure is a myth. The entire living world is always being exposed to the environmental chemical mixtures but the scarcity of toxicity data of chemical mixtures is a serious concern. The nature of toxicity of mixtures is entirely different and complex from the individual chemicals because of the interactions (synergism/antagonism) among the mixture components. Various regulatory authorities and the scientific world have come up with a handful of methodologies and guidelines for evaluating the harmful effects of the multicomponent mixtures, though there is no such significant, standard, and reliable approach for the toxicity evaluation of chemical mixtures and their management across diverse fields. Toxicity experimentations on laboratory animals are troublesome, time-consuming, costly, and unethical. Thus, to reduce the animal experimentations, the scientific communities, regulatory agencies, and the industries are now depending upon the already proven computational alternatives. The computational approaches are capable of predicting toxicities, prioritizing chemicals, and their risk assessment. Besides these, the in silico methods are cost-effective, less time-consuming, and easy to understand. It has been found out that most of the in silico toxicity predictions are on single chemicals and till date there are very few computational studies available for chemical mixtures in the scientific literature. Therefore, the current chapter illustrates the importance of determination of toxicity of mixtures, the conventional methods for toxicity evaluation of chemical mixtures, and the role of in silico methods to assess the toxicity, followed by the types of various computational methods used for such purpose. Additionally, few successful applications of computational tools in toxicity prediction of mixtures have been discussed in detail. At the end of this chapter, we have discussed some future perspectives toward the role and applications of in silico techniques for toxicity prediction of mixtures.
Identifiants
pubmed: 35188647
doi: 10.1007/978-1-0716-1960-5_22
doi:
Substances chimiques
Hazardous Substances
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
561-587Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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