Network pharmacology and UPLC‐MS/MS‐based study of active ingredients in Jiu Wei decoction

Abstract

Jiu Wei decoction (JWD) is a traditional Chinese medicine (TCM) applied to control rheumatoid arthritis (RA). However, the active ingredients of JWD are still unclear. In this study, bioinformatics and ultra-high-performance liquid chromatography coupled with mass spectrometry (UPLC-MS/MS) were used to uncover the material basis and mechanism of JWD against RA. Ingredients and potential targets of herbs in JWD were collected from online databases. Then, these targets and RA related targets were overlapped, and the overlapped targets were subjected to further PPI (protein-protein interaction, PPI) analysis. Drug-component-target network and molecular docking were used to predict the potential active compounds. UPLC-MS/MS method was applied to detect and quantify these compounds in JWD alcohol extract. A total of 90 constituents and 520 targets were collected from JWD and 95 potential targets were overlapped by comparing these targets with RA-related targets. PPI network analysis indicated that TNF-α, IL-6, AKT1 and VEGFA were the main targets of JWD to RA, and the pharmacological mechanism of these targets might be related to anti-inflammation, pro-apoptosis, and inhibition of angiogenesis. Drug-component-target network and molecular docking results showed that quercetin, wogonin, baicalein, ferulic acid, kaempferol and berberine might be the potential compounds of JWD to RA. Through UPLC-MS/MS analysis, four compounds including quercetin, wogonin, baicalein, and berberine were detected and quantified. Moreover, among the four ingredients, quercetin, wogonin, berberine and baicalein accounted for 15.85%, 25.84%, 15.01%, and 43.3%, respectively. Collectively, our study proved that bioinformatic analysis combined with UPLC-MS/MS is a promising method to explore the material basis of anti-RA in JWD and the method is also applicable to other TCMs. The effect of the active compounds still needs further experimental verification in the disease cell model.