Title: In Silico Metabolism Studies in Drug Discovery: Prediction of Metabolic Stability
Volume: 2
Issue: 2
Author(s): Vijay K. Gombar, James J. Alberts, Kenneth C. Cassidy, Brian E. Mattioni and Michael A. Mohutsky
Affiliation:
Keywords:
In silico, ADME, metabolism, metabolic stability, drug discovery, QSAR models, ADME software
Abstract: The strategy to screen compounds solely for pharmacological potency and selectivity in the early stages of drug discovery brought the pharmaceutical industry to face the stark reality of disproportionate attrition later in the development stage due to poor drug disposition characteristics. This attrition contributed to the exorbitant costs of discovering and developing drugs. Considering ADME (Absorption, Distribution, Metabolism, and Excretion) characteristics of compounds early in the discovery process can wisely direct resources to compounds that have greater potential to survive the clinical trial stages of drug development. However, experimental determination of ADME characteristics is not practical for large numbers of compounds. Therefore, focus is being centered on bringing in silico approaches earlier in the discovery process to assess ADME properties solely from molecular structure. Given that metabolism is one of the most important of the ADME properties, in this paper we review a number of metabolism in silico tools and models that have potential applications in drug discovery. We then describe a step-by-step process, as practiced in our laboratories, to construct and deploy reliable in silico metabolic stability and other ADME screens. Additionally, we give examples of the application of our metabolic stability in silico screens in scaffold selection, ADME space enrichment, and rationalizing synthesis and testing of compounds in the drug discovery process. Agreements between the experimental and in silico metabolic stability values ranging from 84% to 100% have convinced many discovery project teams to routinely use these in silico models. Finally, we present our ideas on the successful implementation of in silico models and tools for significant impact on drug discovery and development
