Prediction of soil ecotoxicity against Folsomia candida using acute and chronic endpoints

Soil invertebrates serve as great biological indicators of soil quality. However, there are very few in silico models developed so far on the soil toxicity of chemicals against soil invertebrates due to paucity of data. In this study, three available soil ecotoxicity data (pLC50, pLOEL and pNOEL) against the soil invertebrate Folsomia candida were collected from the ECOTOX database (cfpub.epa.gov/ecotox) and subjected to quantitative structure-activity relationship (QSAR) analysis using 2D descriptors. The collected data for each endpoint were initially curated and used to develop a partial least squares (PLS) regression model based on the features selected through a genetic algorithm followed by the best subset selection. Both internal and external validation metrics of the models' predictions are well-balanced and within the acceptable range as per the Organization for the Economic Cooperation and Development (OECD) criteria. From the developed models, it has been found that molecular weight and presence of phosphate group, electron donor groups, and polyhalogen substitution have a significant impact on the soil ecotoxicity. The soil ecotoxicological risk assessment of organic chemicals can therefore be prioritized by these features. With the availability of additional data in the future, the models may be further refined for more precise predictions.