Elucidation of molecular mechanisms involved in tadpole toxicity employing QSTR and q-RASAR approach.

Tadpoles, as early developmental stages of frogs, are vital indicators of toxicity and environmental health in ecosystems exposed to harmful organic compounds from industrial and runoff sources. Evaluating each compound individually is challenging, necessitating the use of in silico methods like Quantitative Structure Toxicity-Relationship (QSTR) and Quantitative Read-Across Structure-Activity Relationship (q-RASAR). Utilizing the comprehensive US EPA's ECOTOX database, which includes acute LC toxicity and chronic endpoints, we extracted crucial data such as study types, exposure routes, and chemical categories. Regression-based QSTR and q-RASAR models were developed from this dataset, emphasizing key chemical descriptors. Lipophilicity and unsaturation were significant for predicting acute toxicity, while electrophilicity, nucleophilicity, and molecular branching were crucial for chronic toxicity predictions. Additionally, q-RASAR models integrated with the "intelligent consensus" algorithm were employed to enhance predictive accuracy. The performance of these models was rigorously compared across various approaches. These refined models not only predict the toxicity of untested compounds but also reveal underlying structural influences. Validation through comparison with existing literature affirmed the relevance and robustness of our approach in ecotoxicology.