Dapagliflozin in Chronic Kidney Disease: Insights from Network Pharmacology and Molecular Docking Simulation

Abstract

Chronic kidney disease (CKD) involves inflammation, oxidative stress, and fibrosis, leading to renal dysfunction. Dapagliflozin, an SGLT2 inhibitor, shows renoprotective effects beyond glucose control, but its precise molecular mechanisms remain unclear. This study utilizes network pharmacology and molecular docking to elucidate its multi-target effects in CKD. Dapagliflozin’s SMILES structure was analyzed for ADMET properties. Potential targets were identified via SwissTargetPrediction, GeneCards, and SEA, and common CKD-related targets were determined. A protein–protein interaction (PPI) network was constructed, and key pathways were identified using GO and KEGG enrichment analyses. Molecular docking was conducted to validate dapagliflozin’s binding affinities with hub proteins. A total of 208 common targets were identified, including EGFR, GSK3β, and IL-6. GO and KEGG analyses highlighted key pathways, such as PI3K-Akt, MAPK, and AGE-RAGE, involved in inflammation, oxidative stress, and metabolic regulation. Molecular docking confirmed strong binding affinities with EGFR (−8.42 kcal/mol), GSK3β (−7.70 kcal/mol), and IL-6 (−6.83 kcal/mol). Dapagliflozin exhibits multi-target therapeutic potential in CKD by modulating inflammation, oxidative stress, and metabolic pathways. This integrative approach enhances the understanding of its mechanisms, supporting future experimental validation and clinical application in CKD management.