Recent Advances of Computational Modeling for Predicting Drug Metabolism: A Perspective

Abstract

Background: Absorption, Distribution, Metabolism, Excretion (ADME) properties along with drug induced adverse effects are the major reasons for the late stage failure of drug candidates as well as the cause for the expensive withdrawal of many approved drugs from the market. Considering the adverse effects of drugs, metabolism factor has great importance in medicinal chemistry and clinical pharmacology because it influences the deactivation, activation, detoxification and toxification of drugs.

Methods: Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and metabolism followed by adverse effects, as they serve the integration of information on several levels to enhance the reliability of outcomes.

Results and discussion: In silico profiling of drug metabolism can help progress only those molecules along the discovery chain that is less likely to fail later in the drug discovery process. This positively impacts the very high costs of drug discovery and development. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true influence on drug discovery at different levels. If applied in a scientifically consequential way, computational tools may improve the capability to identify and evaluate potential drug molecules considering pharmacokinetic properties of drugs.

Conclusion: Herein, current trends in computational modeling for predicting drug metabolism are reviewed highlighting new computational tools for drug metabolism prediction followed by reporting large and integrated databases of approved drugs associated with diverse metabolism issues.

Keywords: ADMET; ADR; In silico; QSAR; SoM; docking; expert systems; metabolism.