Predicting Cross-Reactivity from Computational Studies for Pre-evaluation of Specific Hepatic Glycogen Phosphorylase Inhibitors

Abstract

Abstract. Over the past decade, only very few drugs made it to the market this bottleneck is mainly attributed due to their failures at the pre-clinical and clinical stages of drug development pipeline. Several bioinformatics tools and molecular docking approaches have enormously contributed to the rational design of cost-effective, efficacious and novel lead molecules. Research over past few decades resulted in the development of virtual screening methods, where several million compounds from various databases can be screened in limited time very effectively. Majority of the investigations performed so far focuses only on a single target ruling out the possibility that the same ligand can bind to an offtarget with a high-sequence similarity (especially isozymes) leading to unwanted effects. Glycogen phosphorylases (GP) are phosphorylase enzymes that are implicated in several metabolic disorders such as type-2 diabetes and also other disease like cancer and coronary disease. GP are present both in muscle and hepatic tissue as isozymes and many inhibitors of GP (GPi) due to lack of specificity cross-react between both these isozymes and lead to delirious sideeffects. Taking together the role and features of GP, the objective of the current work is to develop a strategy in molecular docking approach to pre-evaluate specificity of GPi using in silico derived pentacyclic triterpenes.