QSAR modeling of chronic rat toxicity of diverse organic chemicals

Abstract

Chronic toxicity is one of the most important toxicological endpoints related to human health. Since experimental tests are costly and difficult, in silico methods are crucial to assessing the chronic toxicity of compounds. There are only very few QSAR studies available on chronic toxicity prediction. This study aimed to develop a QSAR model using 650 diverse and complex compounds based on the lowest observed adverse effect level (LOAEL), which was determined in rats by orally exposing them to these compounds. We have extracted important descriptors from a pool of 868 descriptors using stepwise regression and a genetic algorithm. We validated the developed partial least squares (PLS) model statistically, and the results demonstrate the model's reliability, robustness, and predictive ability (R2 = 0.60, Q2(LOO) = 0.58, Q2F1 = 0.56, and Q2F2 = 0.56). Our validated models were also used to assess the chronic toxicity of 11,300 pharmaceuticals present in the DrugBank database. It has been found that hydrophobicity, electronegativity, lipophilicity, bulkiness, complex chemical structure, bridgehead atoms, and phosphate group play a crucial role in chronic toxicity. Therefore, these markers can be used to synthesize safe, and eco-friendly organic chemicals.