Prediction of Targets of Curculigoside A in Osteoporosis and Rheumatoid Arthritis Using Network Pharmacology and Experimental Verification

PURPOSE: Network pharmacology is considered to be the next-generation drug development model that uses bioinformatics to predict and identify multiple drug targets and interactions in diseases. Here, network pharmacology was used to investigate the mechanism by which Curculigoside A (CA) acts in rheumatoid arthritis (RA) and osteoporosis. METHODS: First, TCMSP and SwissADME were applied to predict the druggability of CA. Then, potential targets were identified from overlapping data in SwissTarget and TargetNet, and targets were analyzed using Genemania and DAVID6.8 to obtain information about the GO and KEGG pathways. Ultimately, the drug-target-pathway network was identified after using Cytoscape 3.0 for visualization. Besides, qPCR was used to validate the predicted five major genes targets (EGFR, MAP2K1, MMP2, FGFR1, and MCL1). RESULTS: The results of TCMSP and SwissADME demonstrated that CA exhibits good druggability; 26 potential protein targets were classified by SwissTarget and TargetNet. The results of Genemania and DAVID6.8 indicated that CA probably caused anti-osteoporosis and anti-RA effects by regulating some biological pathways, especially nitrogen metabolism, estrogen signaling pathway, Rap1 signaling pathway, and PI3K/Akt signaling pathway. Besides, the result of Cytoscape 3.0 showed that the 26 targets participate in osteoporosis and RA-related pathways, metabolism, and other physiological processes. In vitro induced inflammation cell model experiments, the qPCR results showed that CA pretreatment significantly decreased the expression of EGFR, MAP2K1, MMP2, FGFR1, and MCL1 genes. CONCLUSION: These results suggested that network pharmacology may provide possible mechanism of how CA exerts therapeutic effects in osteoporosis and RA.