Safer and greener chemicals for the aquatic ecosystem: Chemometric modeling of the prolonged and chronic aquatic toxicity of chemicals on Oryzias latipes.

In the modern era, chemicals and their products have been used everywhere like agriculture, healthcare, food, cosmetics, pharmaceuticals, household products, clothing industry, etc. These chemicals find their way to reach the aquatic ecosystem (directly/indirectly) and cause severe chronic and prolonged toxic effects to aquatic species which is also then translated to human beings. Prolonged and chronic toxicity data of many chemicals that are used daily is not available due to high experimentation testing costs, time investment, and the requirement of a large number of animal sacrifices. Thus, in silico approaches (e.g., QSAR (quantitative structure-activity relationship)) are the best alternative for chronic and prolonged toxicity predictions. The present work offers multi-endpoint (five endpoints: chronic_LOEC, prolonged_14D_LC, prolonged_14D_NOEC, prolonged_21D_LC, prolonged_21D_NOEC) QSAR models for addressing the prolonged and chronic aquatic toxicity of chemicals toward fish (O. latipes). The statistical results (R =0.738-0.869, Q =0.712-0.831, Q =0.618-0.731) of the developed models show that they were robust, reliable, reproducible, accurate, and predictive. Some of the features that are responsible for prolonged and chronic toxicity of chemicals towards O. latipes are as follows: the presence of substituted benzene, hydrophobicity, unsaturation, electronegativity, the presence of long-chain fragments, the presence of a greater number of atoms at conjugation, and the presence of halogen atoms. On the other hand, hydrophilicity and graph density descriptors retard the aquatic chronic and prolonged toxicity of chemicals toward O. latipes. The PPDB (pesticide properties database) and experimental and investigational classes of drugs from the DrugBank database were also screened using the developed model. Thus, these multi-endpoint models will be helpful for data-gap filling and provide a broad range of applicability. Therefore, this research will aid in the in silico QSAR (quantitative structure-activity relationship) prediction (non-animal testing) of the prolonged and chronic toxicity of untested and new toxic chemicals/drugs/pesticides, design and development of eco-friendly, novel, and safer chemicals, and help to protect the aquatic ecosystem from exposure to toxic and hazardous chemicals.