QSAR prediction of hepatic metabolic clearence of organic drug-like compounds

Abstract

Hepatic metabolic clearance is one of the key pharmacokinetic parameters determining the bioavailability, safety and therapeutic efficacy of drug candidates. Accurate prediction of this parameter is essential in modern pharmaceutical and biotechnological research, as it enables the identification of compounds with optimal metabolic stability at the early stages of drug discovery. Traditional experimental determination of hepatic clearance is time-consuming, resource-intensive and requires the use of biological materials. Therefore, the development of computational approaches such as Quantitative Structure–Activity Relationship (QSAR) modeling provides a powerful alternative that integrates chemical informatics, molecular design and pharmacokinetic analysis. In the context of pharmaceutical biotechnology, QSAR-based prediction supports the rational design of new bioactive molecules, minimizes experimental workload and enhances the efficiency of bioprocesses involved in drug development and optimization. This approach aligns with global trends toward sustainable, data-driven and cost-effective drug discovery.