First report on retention time prediction of pesticides and veterinary drugs in cow milk using read-across and intelligent consensus prediction: an alternative for hazard assessment employing food-informatics.

Abstract

Milk is one of the primary sources of food. Pesticides and veterinary drugs are reaching directly or indirectly (pesticides containing grass or other cattle foods) into the milk of the cattle, which are serious health concerns to the animals, infants, babies, and humans. So, in-silico approaches like QSPR, read-across, etc., are used as an alternative (reduce time, cost, complex analytical process) for calculating retention time (RT). The present work involves the development of the first multiple PLS-based QSAR models for the estimation of RT of pesticides, veterinary drugs, and related chemical hazards in milk by strictly obeying the OECD principles. Based on the results, the quality of the models is good enough. In the current work, it was observed that lipophilicity, binding property, rotatable bonds, and reactivity are responsible for high RT while hydrophilicity, the presence of primary amines, aqueous solubility, and branching reduce the RT of the compounds. The established models were utilized to screen the PPDB database to justify its real-world application. The present study will be vital in the food-informatics area for the RT data-gap filling and identification of hazardous chemicals in milk. Thus, it will be helpful to maintain a healthier, safer, and eco-friendly ecosystem.