Deciphering the Anti‐Diabetic Potential of Gymnema Sylvestre Using Integrated Computer‐Aided Drug Design and Network Pharmacology

• Published in: Journal of cellular and molecular medicine 2025-01, Vol.29 (1), p.e70349-n/a
• Main Authors: Mayyas, Amal, Al‐Samydai, Ali, Oraibi, Amjad Ibrahim, Debbabi, Nawres, Hassan, Sara S., Al‐Hussainy, Hany Aqeel, Salamatullah, Ahmad Mohammad, Dauelbait, Musaab, Bourhia, Mohammed, Almaary, Khalid S.
• Format: Article
• Language: English
• Subjects: Acetic acid; Acids; AKT1 protein; Antidiabetics; Bioactive compounds; Blood levels; Cell surface; Computer-Aided Design; Diabetes; Diabetes mellitus; Diabetes Mellitus - drug therapy; Diabetes Mellitus - metabolism; Disease; Disease management; Drug Design; Drug development; Glucose; Gymnema; Gymnema Sylvestre; Gymnema sylvestre - chemistry; Heparan sulfate proteoglycans; Herbal medicine; Humans; Hypoglycemic Agents - chemistry; Hypoglycemic Agents - pharmacology; Insulin; Medicine; molecular docking; Molecular Docking Simulation; Molecular dynamics; Molecular Dynamics Simulation; Naturopathy; Network Pharmacology; Ontology; Original; Pharmacology; Phytic acid; Protein Interaction Maps - drug effects; Proteins; Public health; Software
• ISSN: 1582-1838; 1582-4934; 1582-4934
• DOI: 10.1111/jcmm.70349

ABSTRACT This study explores novel therapeutic avenues for diabetes, a global health concern marked by elevated blood glucose levels. We investigated the anti‐diabetic potential of Gymnema Sylvestre's bioactive compounds, including Gymnemic acid I, Stigmasterol, Deacylgymnemic acid, Beta‐Amyrin acetate, Longispinogenin, Gymnemic acid II, Gymnemic acid, Gymnemic acid X, Gymnemaside VI, Phytic acid and Gymnemic acid X. Employing network pharmacology, molecular docking and molecular dynamics (MD), we elucidated the potential mechanism of action. SwissTargetPrediction identified targets for bioactive constituents, while DisGeNET provided diabetes‐related targets. A GeneVenn diagram revealed 397 common potential targets for diabetes management. The protein–protein interaction network, constructed via the STRING database, underwent topological analysis in Cytoscape, identifying AKT1, SRC, TNF, PPARG and IL1B as top targets. Gene ontology analysis using FunRich identified crucial roles of screened targets in integrin family cell surface interactions and glypican pathways for diabetes management. Molecular interactions and binding affinities with the top target, AKT1, were assessed, with Gymnemic acid I displaying the least binding energy (−9.813) with H‐ and non‐H‐bond interactions. Molecular dynamics simulations provided insights into the distinct behaviours of Gymnemic acid I within the protein complex. In conclusion, our study elucidates the potential anti‐diabetic mechanism of Gymnemic acid I, underscoring the need for further in vitro, in vivo and clinical studies to validate our findings.