Literature DB >> 35188647

Computational Modeling of Mixture Toxicity.

Mainak Chatterjee1, Kunal Roy2.   

Abstract

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.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Computational; Mixture; QSAR; Toxicity

Mesh:

Substances:

Year:  2022        PMID: 35188647     DOI: 10.1007/978-1-0716-1960-5_22

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  34 in total

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Review 3.  Mixture toxicity and its modeling by quantitative structure-activity relationships.

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Authors:  A Kortenkamp; R Altenburger
Journal:  Sci Total Environ       Date:  1998-09-29       Impact factor: 7.963

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Journal:  Pharmacol Rev       Date:  1995-06       Impact factor: 25.468

8.  Measurement and modeling of the toxicity of binary mixtures in the nematode caenorhabditis elegans--a test of independent action.

Authors:  Heather L Martin; Claus Svendsen; Lindsay J Lister; Jose L Gomez-Eyles; David J Spurgeon
Journal:  Environ Toxicol Chem       Date:  2009-01       Impact factor: 3.742

Review 9.  Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling.

Authors:  R S Yang; R S Thomas; D L Gustafson; J Campain; S A Benjamin; H J Verhaar; M M Mumtaz
Journal:  Environ Health Perspect       Date:  1998-12       Impact factor: 9.031

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Authors:  Ken Sexton; Dale Hattis
Journal:  Environ Health Perspect       Date:  2007-01-24       Impact factor: 9.031

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