In Silico Approaches to Predict Drug Metabolism: Pharmacophore Mapping and Virtual Screening of Heterocyclic Antidiabetic Agents.

Abstract

Introduction: An emerging impetus has driven the development of various small-molecule compounds for the management of type 2 diabetes. With the advent of novel heterocyclic derivatives, an expansive field of pharmacological endeavors has opened up to stimulate Glucokinase (GK) activation. Recent evidence has validated GK's legitimacy as a potential target for pharmaceutical intervention in diabetes. Glucokinase, an enzyme critical to maintaining blood glucose equilibrium, malfunctions in individuals with type 2 diabetes. A key component of this innovative approach is the use of heterocyclic derivatives to stimulate the GK enzyme, thereby serving as powerful pharmaceutical agents to improve type 2 diabetes management substantially.

Methods: Maybridge's digital archive contained 53,000 compounds, known for their efficacy, which were subjected to thorough examination. From this extensive repository, 422 compounds with an indole core were identified. The structures generated by ChemBioDraw Ultra through this method were meticulously docked using AutoDock Vina 1.5.6. Online log P predictions were also made possible by the Swiss ADME algorithm. PKCSM software was used to assess the potential toxicity of the leading compounds.

Results: Auto Dock Vina 1.5.6 was used to dock 422 indole derivatives. Compounds with the greatest binding affinity to glucokinase (GK) were found in the majority of the compounds. Based on the superior binding capabilities of the top eight molecules compared to Dorzagliatin (the standard drug) and MRK (the co-crystallized ligand). Following the enhancement of their pharmacokinetic profiles and compliance with Lipinski's rule of five, these eight candidates were further evaluated using ADMET analysis. As a result, AH249 displayed the greatest binding affinity with -11.5 kcal/mol. In contrast to the standard drugs Dorzagliatin (GKA) and MRK (cocrystallized ligand), AH249 showed no skin sensitization, AMES toxicity, or hepatotoxicity using PKCSM.

Discussion: This research highlights the importance of in silico techniques in discovering antidiabetic medicines, as AH249 outperformed 421 other indole derivatives in terms of glucokinase affinity. According to SwissADME results, both compounds, AH249 and AH102, are drug-like. Analysis through molecular docking revealed interactions that can be tested and verified using experiments.

Conclusion: There are 422 selected lead compounds containing an indole base in the 53,000 compound database that demonstrated the best activation of glucokinase. According to the findings of this computational drug design analysis, the most promising drug candidates deserve consideration for advancement to the in vitro stage, particularly AH249, which exhibits the greatest binding affinity, a favorable pharmacokinetic profile, and negligible toxicity. For a better understanding of the therapeutic implications of the drug, particularly within the field of type 2 diabetes, more thorough investigation and evaluation are essential, especially the use of in-vivo models such as Streptozotocin- induced diabetic rats.