Molecular Docking, Pharmacophore Modeling, and ADMET Prediction of Novel Heterocyclic Leads as Glucokinase Activators.

Abstract

A pivotal impetus has driven the development of numerous small molecules aiming to improve therapeutic strategies for type 2 diabetes. Glucokinase (GK) activation has been offered a new realm of therapeutic antidiabetic activity with novel heter-ocyclic derivatives. In the context of antidiabetic drug design, GK is an interesting and newly validated target. A key enzyme needed for blood glucose homeostasis is Glucokinase, which is dysfunctional in individuals with type 2 diabetes. Heterocyclic derivatives are utilized in this innovative approach to activate GK enzymes as medicinal agents that will significantly improve type 2 diabetes management. To address type 2 diabetes, as well as minimize unwanted side effects, this research endeavor aimed to develop activators of glucokinase. A rigorous scrutiny was conducted of the Maybridge online repository, which houses a formidable collection of 53,000 lead compounds. A collection of 125 compounds that contain the thiazolidinedione core was selected from this extensive collection. The struc-tures were generated using ChemDraw 2D, stabilized conformation with ChemBioDraw Ul-tra, and docked using Auto Dock Vina 1.5.6 in this methodology. In addition, log P was pre-dicted online using the Swiss ADME algorithm. The PKCSM software was used to predict the toxicity of the leading compounds. The highest binding affinity was found for AS72 and AS108 to GK receptors. GI absorption and excretion of these compounds were efficient due to Lipinski's Rule of Five compliance. When compared with the standard drugs Dorzagliatin (GKA) and MRK (co-crys-tallized ligand), these substances demonstrated a notable lack of AMES toxicity, skin sensiti-zation, and hepatotoxicity. In recent studies, lead molecules that possess enhanced pharmacokinetic profiles, increased binding affinity, and lower toxicity were developed to act as glucokinase activators.