Comparative QSAR and q-RASAR modeling for aquatic toxicity of organic chemicals to three trout species: O. Clarkii, S. Namaycush, and S. Fontinalis.

Oncorhynchus clarkii, Salvelinus fontinalis, and Salvelinus namaycush are vital trout species in North America, crucial for maintaining ecological balance, economic stability, and human health. These species thrive in cold, unpolluted waters and are highly vulnerable to contaminants. Given the rapid proliferation of industrial organic chemicals, traditional in vivo toxicity testing methods are inadequate to ensure timely and comprehensive risk assessments. Therefore, we employed in silico tools, namely Quantitative Structure-Activity Relationship (QSAR) and Quantitative Read-Across Structure-Activity Relationship (q-RASAR), to efficiently predict the aquatic toxicity of chemicals. Utilizing acute median lethal concentration (LC) data from the US EPA's ToxValDB, we developed the first-ever species-specific QSAR and q-RASAR models. The q-RASAR models outperformed traditional QSAR models by achieving higher internal and external statistical quality for each species. Key toxicity-determining descriptors included electrotopological state indices, autocorrelation descriptors, and similarity-based RASAR descriptors. For O. clarkii, the presence of chlorine atoms and rotatable bonds significantly influenced toxicity. S. fontinalis toxicity was strongly affected by polarizability, and van der Waals volumes, while S. namaycush showed sensitivity to weak hydrogen bond acceptors and topological complexity. The models predicted the toxicity of 1172 external compounds, identifying the most and least toxic chemicals for each species. This study not only offers the first comprehensive q-RASAR models for predicting trout species-specific toxicity but also provides novel insights into species-specific toxicological modes of action. The results contribute significantly to chemical screening and prioritization in aquatic risk assessments, effectively filling critical data gaps and advancing predictive modeling techniques.