Background of study: Luteolin and its glycoside derivatives from natural sources possess potent anti-inflammatory and antioxidant properties, yet their multi-target mechanisms remain incompletely characterized.
Aims and scope of paper: This study aimed to systematically elucidate the multi-target pharmacological mechanisms of three luteolin glycoside compounds (Luteolin 7-apiosyl(1→6)glucoside, Luteolin 7-sambubioside, and Luteolin 7-primeveroside) extracted via NaDES technology using integrated network pharmacology approaches, focusing on identification of core therapeutic targets and pathways modulating inflammatory and oxidative stress responses.
Method: Comprehensive network pharmacology analysis was conducted through: (1) target prediction via TargetNet and Swiss Target Prediction platforms; (2) protein-protein interaction (PPI) network construction using STRING database; (3) disease-associated gene identification through GeneCards; (4) topological centrality analysis using Cytoscape with CytoNCA plugin. Hub proteins were prioritized based on degree, betweenness, and closeness centrality measures.
Result: Network analysis identified 40 predicted targets with 5-6 intersection genes per compound. Venn diagram analysis revealed TNF (Tumor Necrosis Factor-alpha) as the critical hub protein (degree centrality 3.0, betweenness centrality 7.0), establishing a TNF-XDH-CYP1A2-ALOX15 integrated regulatory axis. Luteolin 7-apiosyl(1→6)glucoside and Luteolin 7-primeveroside demonstrated highest potency with 6 intersection targets, while Luteolin 7-sambubioside exhibited selective oxidative stress pathway.
Conclusion: Network pharmacology analysis successfully elucidated the integrated TNF-XDH-CYP1A2-ALOX15 axis as a core mechanism through which luteolin glycosides coordinately modulate inflammatory and oxidative stress pathways. These findings provide mechanistic validation for therapeutic potential in chronic inflammatory diseases including inflammatory bowel disease, type 2 diabetes, and cardiovascular disorders. Future experimental validation is essential to translate these predictions into clinical therapeutic applications.