Journal
CHEMOSPHERE
Volume 308, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chemosphere.2022.136463
Keywords
Pharmaceutical mixtures; Pesticides; Mixture toxicity; QSAR; Read -across
Categories
Funding
- All India Council for Technical Education (AICTE) , New Delhi
- SERB, Govt of India [MTR/2019/000008]
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This study developed a quantitative structure-activity relationship (QSAR) model to assess the environmental risk of pharmaceutical and pesticide mixtures. The developed partial least squares (PLS) model was rigorously validated and proved to be reliable and highly predictive.
Different classes of chemicals are present in the environment as mixtures. Among them, pharmaceuticals and pesticides are of major concern due to their improper use and disposal, and subsequent additive and non-additive effects. To assess the environmental risk posed by the mixtures of pharmaceuticals and pesticides, a quantitative structure-activity relationship (QSAR) model has been developed in this study using the pEC50 values of 198 binary and multi-component mixtures against the marine bacterium Aliivibrio fischeri. The developed partial least squares (PLS) model has been rigorously validated and proved to be a robust and extremely predictive one. To address the chances of overestimation of validation metrics, three cross-validation tests (mixtures out, com-pounds out, and everything out) have been applied, and the results were satisfactory. The use of simple 2 -dimen-sional descriptors makes the prediction much quick, and also makes the model easily interpretable. A machine learning-based chemical read-across prediction has also been performed to justify the effectiveness of selected structural features in this study. In a nutshell, this study proves QSAR and chemical read-across as effective alternative approaches for the toxicity prediction of pharmaceutical and pesticide mixtures and also approves the use of mixture descriptors for modelling mixtures successfully.
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