RESUMO
Objective:To study the mechanism of Wulingsan (WLS) in the treatment of rheumatoid arthritis (RA) by network pharmacology. Method:The active components of WLS were screened on traditional Chinese medicine systems pharmacology(TCMSP) platform, and the targets were predicted in DragBank database. The "component-target" network was constructed by Cytoscape 3.2.1 software. Disease targets were searched in TTD, DrugBank and DisGenet databases. The Venn diagram was built to extract the target of WLS in the treatment of RA, and the gene oesthetics(GO) function annotation and Kyoto Encyclopedin of Genes and Genomes(KEGG) signal pathway enrichment analysis were performed by cluego plugin. The TCM-component-target-pathway network of WLS was constructed, and the network feature analysis was made by Network Analyzer. Result:Totally 52 components and 297 potential targets in WLS and 1 845 targets relating to RA were excavated, and 49 common targets of WLS-RA were obtained. The common targets were mainly enriched in 322 biological processes and 31 signaling pathways. Conclusion:WLS may regulate targets, such as prostaglandin epoxide synthase 2 (PTGS2), transforming growth factor-β1 (TGF-β1), cysteine aspartate protein-3 (Caspase-3), transcription factor p65 (RELA), progesterone receptor (PGR), and adjust cancer-related pathways, tumor necrosis factor(TNF) signaling pathways, interleukin-17(IL-17) signaling pathways, nuclear factor-κB(NF-κB) signaling pathways, Th17 cell differentiation, so as to inhibit the inflammatory response, regulate immune function and adjust apoptosis to treat rheumatoid arthritis.
RESUMO
Objective:Computer network pharmacology technology was used to screen the main active ingredients of Tripterygium hypoglaucum radix-Leonurus japonicus herba for the treatment of rheumatoid arthritis(RA), predict the targets of the active ingredients, establish a pharmaceutical ingredient-active ingredient-target network, and further explore the potential mechanism of Tripterygium hypoglaucum radix-Leonurus japonicus herba for the treatment of RA. Method:RA disease targets were collected through DisGeNET, TTD, and Drugbank databases, the potential active components of Tripterygium hypoglaucum radix and Leonurus japonicus herba and their corresponding targets were obtained from the Chinese Medicine System Pharmacology Analysis Platform (TCMSP); common targets for drugs and diseases were screened by using the ImageGP platform; a common target interaction (PPI) network model was constructed by using the String database, a "drug-active ingredient-key target" network was constructed by using Cytoscape software, a protein interaction network was constructed by using the String database, gene function (GO) analysis and pathway enrichment analysis based on the Kyoto Gene and Genomic Encyclopedia (KEGG) were performed by using the ClueGO plug-in. Result:Through screening, 9 active pharmaceutical ingredients were obtained, involving a total of 235 targets, and 7 active ingredients were related to the disease targets. 24 common targets for Tripterygium hypoglaucum radix Leonurus japonicus herba-disease were obtained. The common targets were mainly enriched in 278 biological processes and 141 signaling pathways to play a role in the treatment of RA. Conclusion:The therapeutic effect of Tripterygium hypoglaucum radix Leonurus japonicus herba on RA reflects the characteristics of multi-component-multi-target-multi-channel of traditional Chinese medicine, and provides a scientific basis for explaining its mechanism and clinical application of RA.