Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures

• Published in: Water research (Oxford) Vol. 236; p. 119981
• Main Authors: Yang, Yu-Ting, Ni, Hong-Gang
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.06.2023
• Subjects: acute toxicity; aquatic environment; Aquatic toxicology; Combined effect; Computer Simulation; Cosmetic and personal care additives; Daphnia magna; data collection; domain; environmental assessment; methylparaben; Mixtures; pollution; prediction; QSAR models; Quantitative Structure-Activity Relationship; quantitative structure-activity relationships; regression analysis; Risk Assessment; support vector machines; synergism; water; Water Pollutants, Chemical - toxicity; ABD; Mixtures; TCS; DBD; PPCP; LV; SVM; Combined effect; SD; MLR; OECD; CPCA; RDF; PLS; RBF; IA; EC50; MDR; GA; KSD; CA; MOA; CCC; pEC50; CI; Cosmetic and personal care additives; QSAR models; RMSE; LOO; CP; AR; BPA; QSAR; CRC; BZP; NOEC; Aquatic toxicology; MPB; HHCB; S-MLR; EPI
• ISSN: 0043-1354; 1879-2448; 1879-2448
• DOI: 10.1016/j.watres.2023.119981

•The synergistic effect of the five CPCAs is mainly caused by methylparaben.•Optimal model can predict the aquatic toxicity of CPCAs mixtures with any ratios.•A comprehensive parameter was brought out for evaluation of QSAR models.•Mixture toxicity is related to RDF descriptor and autocorrelation descriptors.•Mixture descriptors and the molar fractions are nonlinearly relate in most cases. As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed. [Display omitted]