Network pharmacology and molecular docking-based investigation of flavonoids against diabetes-associated inflammation: Mechanistic insights into a multi-target therapeutic approach.

BACKGROUND: Diabetes mellitus is a chronic metabolic disorder accompanied by persistent low-grade inflammation that drives the progression of debilitating diabetic complications. Flavonoids, a structurally diverse class of naturally occurring polyphenolic compounds, have demonstrated broad pharmacological activities encompassing anti-diabetic, anti-inflammatory, and antioxidant effects. Nevertheless, the multi-target molecular mechanisms underlying their therapeutic actions remain incompletely characterized, and the dynamic stability of their interactions with disease-relevant proteins has not been systematically evaluated.

OBJECTIVE: This study aimed to systematically investigate the molecular mechanisms of ten flavonoids, including Isorhamnetin, Fisetin, Diosmetin, Baicalein, Rutin, Luteolin, Apigenin, Taxifolin, Hesperetin, and Baicalin, against diabetes-associated inflammation through an integrated pipeline combining network pharmacology, protein-protein interaction (PPI) network analysis, and molecular docking.

METHODS: Diabetes- and inflammation-related target genes were retrieved from the GeneCards database using relevance score thresholds, and common targets shared among disease signatures and flavonoid compound targets were identified. A PPI network was constructed using the STRING database and subjected to topological analysis in Cytoscape to isolate core targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Molecular docking was conducted using AutoDock Vina.

RESULTS: A total of 111 co-targets were identified from diabetes-related genes (n = 1039), inflammation-related genes (n = 1044), and flavonoid drug targets (n = 193). PPI network analysis revealed 111 nodes and 1261 edges, with an average node degree of 22.7 and a highly significant PPI enrichment p-value < 1.0 × 10⁻¹ ⁶. Topological analysis identified ten core targets: AKT1, TNF, SRC, EGFR, BCL2, ESR1, HIF1-ALPHA, PTGS2 (COX-2), MMP9, and MAPK3. GO enrichment analysis indicated involvement in positive regulation of cell migration, response to oxidative stress, and negative regulation of signal transduction. KEGG analysis revealed enrichment in the HIF-1 and TNF signaling pathways, lipid metabolism and atherosclerosis, and endocrine resistance. The flavonoids exhibited favorable predicted binding affinities across the core targets, with docking scores ranging from -6.30 to -10.80 kcal/mol. Baicalin demonstrated the highest overall affinity, most notably with AKT1 (-10.80 kcal/mol), MAPK3 (-9.50 kcal/mol), and HIF1-ALPHA (-9.30 kcal/mol), while Luteolin showed superior binding to MMP9 (-10.10 kcal/mol).

CONCLUSION: This study provides a comprehensive, multi-tiered mechanistic characterization of ten flavonoids against diabetes-associated inflammation. Baicalin showed the most favorable predicted docking score among the evaluated flavonoid-target pairs, particularly against AKT1. These findings offer a rigorous scientific basis for the rational development of flavonoid-based therapeutic strategies for diabetic complications and establish a reproducible computational framework for prioritizing natural product leads in drug discovery.