Revealing the potential bioactive components and mechanism of Qianhua Gout Capsules in the treatment of gouty arthritis through network pharmacology, molecular docking and pharmacodynamic study strategies.

Recent clinical studies have confirmed the effectiveness of Qianhua Gout Capsules (QGC) in the treatment of gouty arthritis (GA). However, the specific regulatory targets and mechanisms of action of QGC are still unclear. To address this gap, we utilized network pharmacology, molecular docking, and pharmacodynamic approaches to investigate the bioactive components and associated mechanisms of QGC in the treatment of GA. By employing UPLC-Q Exactive-MS, we identified the compounds present in QGC, with active ingredients defined as those with oral bioavailability ≥30 % and drug similarity ≥0.18. Subsequently, the targets of these active compounds were determined using the TCMSP database, while GA-related targets were identified from DisGeNET, GeneCards, TTD, OMIM, and DrugBank databases. Further analysis including PPI analysis, GO analysis, and KEGG pathway enrichment was conducted on the targets. Validation of the predicted results was performed using a GA rat model, evaluating pathological changes, inflammatory markers, and pathway protein expression. Our results revealed a total of 130 components, 44 active components, 16 potential shared targets, GO-enriched terms, and 47 signaling pathways related to disease targets. Key active ingredients included quercetin, kaempferol, β-sitosterol, luteolin, and wogonin. The PPI analysis highlighted five targets (PPARG, IL-6, MMP-9, IL-1β, CXCL-8) with the highest connectivity, predominantly enriched in the IL-17 signaling pathway. Molecular docking experiments demonstrated strong binding of CXCL8, IL-1β, IL-6, MMP9, and PPARG targets with the top five active compounds. Furthermore, animal experiments confirmed the efficacy of QGC in treating GA in rats, showing reductions in TNF-α, IL-6, and MDA levels, and increases in SOD levels in serum. In synovial tissues, QGC treatment upregulated CXCL8 and PPARG expression, while downregulating IL-1β, MMP9, and IL-6 expression. In conclusion, this study applied a network pharmacology approach to uncover the composition of QGC, predict its pharmacological interactions, and demonstrate its