Effects of Baicalin on Gout Based on Network Pharmacology, Molecular Docking, and in vitro Experiments

Chunliu Liu,1– 3,* Wei Liu,1,2,* Hang Lu,1,2 Yihua Fan,4 Aihua Wang1,2 1Department of Rheumatism and Immunity, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 2National Clinical Research Center for Chinese Medicine A...

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Main Authors: Liu C, Liu W, Lu H, Fan Y, Wang A
Format: Article
Language:English
Published: Dove Medical Press 2025-02-01
Series:Journal of Inflammation Research
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Online Access:https://www.dovepress.com/effects-of-baicalin-on-gout-based-on-network-pharmacology-molecular-do-peer-reviewed-fulltext-article-JIR
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Summary:Chunliu Liu,1– 3,* Wei Liu,1,2,* Hang Lu,1,2 Yihua Fan,4 Aihua Wang1,2 1Department of Rheumatism and Immunity, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 2National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China; 3Department of Respiratory Medicine, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, Tianjin, People’s Republic of China; 4Department of Rheumatism and Immunity, Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Chengdu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei Liu, Email fengshiliuwei@163.comPurpose: Baicalin is a flavonoid of Scutellaria baicalensis Georgi. It possesses antipyretic, analgesic, and anti-inflammatory effects. It has great potential to treat gout. A network pharmacology approach, molecular docking and experimental validation were applied to investigate the pharmacological mechanisms of baicalin in treating gout.Methods: The potential targets of baicalin were retrieved from the TCMSP, PharmMapper, STITCH, and Swiss Target Prediction databases. The gout-related targets were retrieved from the DrugBank, TTD, and Genecards databases. Then, the potential targets and signaling pathways were acquired via protein–protein interaction (PPI), as well as the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Subsequently, the key targets were selected to dock with baicalin based on molecular docking. Finally, in vitro experiments were conducted to further validate the predictions.Results: A total of 318 potential targets of baicalin and 752 gout-related targets were screened. TNF, VEGFA, MMP9, PTGS2, and TLR4 might be the hub therapeutic target genes. The TLR4/NF-κB signaling pathway might be the foremost pathway in baicalin against gout. Moreover, molecular docking showed that baicalin combined well with TNF, VEGFA, MMP9, COX-2, and TLR4, respectively. The results of cell experiments suggested that baicalin could reduce the levels of inflammatory cytokines by inhibiting the TLR4/NF-κB signaling pathway in MSU-stimulated THP-1 cells and regulate the expression of these hub targets.Conclusion: These results revealed that baicalin possesses “multitarget, multipathway, multilevel” regulatory effects. From a therapeutic standpoint, baicalin may be a promising anti-inflammatory agent for alleviating gout. Keywords: baicalin, gout, network pharmacology, molecular docking, in vitro experiments
ISSN:1178-7031