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A Review of Quantitative Structure-Activity Relationship Modelling Approaches to Predict the Toxicity of Mixtures

Belfield, SJ, Firman, JW, Enoch, SJ, Madden, JC, Erik Tollefsen, K and Cronin, MTD (2022) A Review of Quantitative Structure-Activity Relationship Modelling Approaches to Predict the Toxicity of Mixtures. Computational Toxicology. p. 100251. ISSN 2468-1113

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Open Access URL: https://doi.org/10.1016/j.comtox.2022.100251 (Published version)


Exposure to chemicals generally occurs in the form of mixtures. However, the great majority of the toxicity data, upon which chemical safety decisions are based, relates only to single compounds. It is currently unfeasible to test a fully representative proportion of mixtures for potential harmful effects, and as such in silico modelling provides a practical solution to inform safety assessment. Traditional methodologies for deriving estimation of mixture effect, exemplified by principles such as concentration addition (CA) and independent action (IA), are limited as regards the scope of chemical combinations to which they can reliably be applied. Development of appropriate quantitative structure-activity relationships (QSARs) has been forwarded as a solution to the shortcomings present within these techniques – allowing for the potential formulation of versatile predictive tools capable of capturing the activities of a full contingent of possible mixtures. This review addresses the current state-of-the-art as regards application of QSAR towards mixture toxicity, discussing the challenges inherent in the task, whilst considering the strengths and limitations of existing approaches. Forty studies are examined within – through reference to several characteristic elements including the nature of the chemicals and endpoints modelled, the form of descriptors adopted, and the principles behind the statistical techniques employed. Recommendations are in turn provided for practices which may assist in further advancing the field, most notably with regards to ensuring confidence in the acquired predictions.

Item Type: Article
Subjects: R Medicine > RM Therapeutics. Pharmacology
Divisions: Pharmacy & Biomolecular Sciences
Publisher: Elsevier BV
SWORD Depositor: A Symplectic
Date Deposited: 09 Nov 2022 11:28
Last Modified: 23 Nov 2022 09:45
DOI or ID number: 10.1016/j.comtox.2022.100251
URI: https://researchonline.ljmu.ac.uk/id/eprint/18083
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