Abstract
Traditional Chinese medicine Salvia miltiorrhiza (SM) is a novel application and has shown significant clinical efficacy in treating osteoarthritis (OA). However, the molecular mechanisms of its action have not been systematically evaluated. This study explores the mechanisms of SM in the treatment of osteoarthritis using a network pharmacology approach. In this study, the active ingredients and related targets of SM were obtained following an ADME (absorption, distribution, metabolism, excretion) approach and utilizing the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. OA-related targets were obtained through GeneCard, PharmGkb, TTD, OMIM, and DRUGBANK databases. The common targets were obtained using the jvenn online tool. The ingredient-target network and the crucial active ingredients were obtained by Cytoscape. The protein-protein interaction (PPI) network and the key targets of SM in the treatment of OA were obtained by the STRING database and Cytoscape. The GO function and KEGG pathway enrichment cluster of the common targets were obtained by Metascape. Molecular docking was obtained by SwissDock to verify the correlation between the crucial active ingredients and key targets. We identified 59 active ingredients including luteolin, tanshinone IIA, dihydrotanshinquinone, and danshenxinkun D with important biological effects in the treatment of OA. We screened 72 common targets of SM-OA, among which IL-6, AKT1, VEGFA, TNF, TP53, FOS, MAPK1, and CASP3 are the key targets. The GO function and KEGG pathway enrichment cluster of the common targets revealed that SM acts on OA mainly through the PI3K-AKT, IL-17, HIF-1, and TNF signaling pathways and that its function is mainly to regulate metabolism, apoptosis, inflammation, and cell proliferation. Moreover, the molecular docking analysis indicated that the crucial ingredients were tightly bound to the key targets. Overall, our study has preliminarily revealed the molecular mechanisms of SM in the treatment of OA through multi-component, multi-target, and multi-channel network pharmacology approaches.
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