Antidiabetic Potency of Flavonoids Using a Systematic Computer-Aided Drug Design Platform

Abstract

Diabetic mellitus (DM) is a chronic metabolic disorder, with type 2 diabetes (T2DM) being the most prevalent type globally. Despite the availability of several target-specific drugs, the prevalence rate has remained uncontrollable, prompting a systematic exploration of plant secondary metabolites or phytochemicals for mainstream use. Among all natural resources, citrus fruits like oranges, lemons, grapefruits and limes are rich sources of flavonoids and get more attention due to their higher antioxidant, anti-inflammatory and immunomodulatory effects. Additionally, researchers have employed various strategies to locate the most bioactive and drug-able flavonoids from these herbal extracts for use in managing diabetes. Therefore, the present study selected nine citrus-fruit-derived flavonoids and tested their antidiabetic potency using four target enzymes: α-amylase, AKT Serine/Threonine Kinase 1 (AKT1), dipeptidyl peptidase-4 (DPP-IV), and glucose transporter 1 (GLU1) through molecular docking studies. In addition, we have predicted the physiochemical profile, toxicity, bioavailability, lead-likeness, drug-likeness, and lethal dose of flavonoids, along with five standard antidiabetic drugs, to select the most potential candidates. We used AutoDock 4.2 for the docking study, BIOVID-Discovery Studio for the protein-ligand interaction study, SwissADME, ProTox 3.0 and Molsot tools to predict the drug-likeness profile. Individual and average docking scores indicated that naringin (-11.2 and -10.40 kcal/mol) was the most potent flavonoid, and glimepiride (-11.1 and -10.1 kcal/mol) against AKT1 had the most potential among the five antidiabetic drugs. Naringin had non-toxic profiles, a positive drug-likeness score, and ideal physicochemical profiles, which suggested that it might be the best candidate for further testing. To sum up, the computer-aided drug design platform is an important part of the current drug discovery module to accelerate phyto-based drug discovery within limited time and resources.