Chemical pollution is an important concern nowadays because the continuous expansion of chemical compounds in versatile fields is affecting human health and environmental safety. It is a proven fact that different classes of chemicals occur in mixtures in the environment. Due to their improper use and disposal, pharmaceuticals and pesticides outweigh other classes of chemicals. As mixtures of bioactive compounds, they can produce additive effects or non-additive interactions like synergism and antagonism depending on the nature of their constituent chemicals. But there is no universally accepted effective way to assess the environmental risk posed by mixtures of pharmaceuticals and pesticides. An investigation into the toxic effect of mixtures of pharmaceuticals and pesticides was conducted in this study to develop a quantitative structure-activity relationship (QSAR) model using additive mixture descriptors. Toxicity data (pEC50 value) of 198 binary and multi-component mixtures against marine bacterium Aliivibrio fischeri has been used in this study for modelling. The partial least squares (PLS) regression has been utilized to model the structural features against toxicity. The model has been rigorously validated and proved to be a robust and extremely predictive one. To counter the chances of overestimation of validation metrics, three cross-validations (mixtures out, compounds out, and everything out) have been applied and results were satisfactory. A chemical similarity-based read-across prediction has also been carried out to justify the effectiveness of selected structural features in this study. The use of simple 2-dimensional descriptors makes prediction much easier, and also makes the hypothesis easily interpretable. The developed QSAR model is more robust with better predictivity than the previously published works to the best of our knowledge. In summary, QSAR has been found to be an effective alternative approach for predicting toxicity of mixtures containing pharmaceuticals and pesticides. In addition, the use of mixture descriptors is proved to be an effective technique for modelling mixtures.

Application of 2D-QSAR and read-across techniques to predict aquatic toxicity of binary and multicomponent pharmaceutical mixtures against Aliivibrio fischeri

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.