Chemometric modeling of the lowest observed effect level (LOEL) and no observed effect level (NOEL) for rat toxicity†

Humans and other living species of the ecosystem are constantly exposed to a wide range of chemicals of natural as well as synthetic origin. A multitude of compounds exert profound long-term detrimental health effects. The chronic toxicity profile of chemicals is of utmost importance for long-term risk assessment. Experimental testing of the chronic toxicity of compounds is not always a feasible option considering the magnitude of the number of chemicals, resource intensiveness in terms of time, limited availability of experimental data, and associated cost, which therefore necessitates the use of in silico approaches to overcome the associated limitations. In this work, QSAR (quantitative structure–activity relationship) models were developed employing the regression-based PLS method with strict adherence to OECD guidelines. For this study, chronic and sub-chronic toxicity datasets with LOEL (lowest observed effect levels) and NOEL (no observed effect level) as endpoints were used for model development. The validated models are robust, reliable, and predictable. The statistical results of the models are as follows: R²: 0.6–0.71, Q_LOO²: 0.51–0.635, and Q_F1²: 0.52–0.658. From the validated models, it was concluded that lipophilicity, electronegativity, the presence of aromatic ethers or aliphatic oxime groups, the presence of complexity in structures, the state of unsaturation in molecules, and the presence of halogen and heavy atoms (phosphate, sulphur, etc.) are responsible for chronic/sub-chronic toxicity. The QSAR models developed in our study can be utilized for the effective gap-filling of toxicity data sets, categorization, and prioritization of chemicals, along with chronic toxicity prediction of new synthetic compounds. Furthermore, we used 2568 approved drugs from the DrugBank and PPDB databases for screening purposes using the validated models, which further corroborated the developed models based on the available toxicity data.