In silico techniques for the study and prediction of xenobiotic metabolism: a review

Abstract

Knowledge about metabolism is very important to understand the health risks posed by chemicals. The biochemical process of metabolism causes activation, inactivation, toxification, detoxification as well as changes in the physicochemical properties of a chemical. The long time consumption and high costs associated with animal tests and the challenges faced by traditional quantitative structure-activity relationship (QSAR) models in dealing with situations wherein parent chemical structures are less relevant to the ultimate effects have led to the development of in silico techniques for the prediction of xenobiotic metabolism. The strengths and limitations of some of the most commonly used in silico expert systems, and their application in studying metabolism of xenobiotic chemicals, have been reviewed. The in silico metabolism simulators possessed several distinguishing features imparted in part by the nature of knowledge rules (algorithms) encoded within them and in part by the integration of QSAR libraries and computational engines.