Abstract
Background: The traditional Chinese medicine Huangqi decoction (HQD) consists of Radix Astragali and Radix Glycyrrhizae in a ratio of 6 : 1, which has been used for the treatment of liver fibrosis. In this study, we tried to elucidate its action of mechanism (MoA) via a combination of metabolomics data, network pharmacology and molecular docking methods. Methods: Firstly, we collected prototype components and metabolic products after administration of HQD from a publication. With known and predicted targets, compound-target interactions were obtained. Then, the global compound-liver fibrosis target bipartite network and the HQD-liver fibrosis protein-protein interaction network were constructed, separately. KEGG pathway analysis was applied to further understand the mechanisms related to the target proteins of HQD. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets. Finally, after taking concentration of prototype compounds and metabolites of HQD after administration into consideration, the critical compound-liver fibrosis target bipartite network was constructed.Results: We collected 68 components, including 17 prototype components and 51 metabolic products after administration of HQD, and 540 compound-target interactions were obtained between the 68 components and 95 targets. Combining network analysis and molecular docking, as well as concentration of prototype compounds and metabolites of HQD, our final results demonstrated that eight compounds (three prototype compounds and five metabolites) and eight targets (CDK1, MMP9, PPARD, PPARG, PTGS2, SERPINE1, TP53, and HIF1A) might contribute to the effects of HQD on liver fibrosis by reducing fibrogenesis and stimulate degradation, which through p53 signaling pathway, PPAR signaling pathway, HIF-1 signaling pathway, IL-17 signaling pathway, and TNF signaling pathway.Conclusions: Our results would shed light on the complicated MoA of traditional Chinese medicine and help to attract attention to the therapeutic effects of metabolites of original components in Chinese herbs through computational methods.