Abstract
Herbal medicines have long been widely used in the treatment of various complex diseases in China. However, the active constituents and therapeutic mechanisms of many herbal medicines remain undefined. Therefore, the identification of the active components and target proteins in these herbal medicines is a formidable task in herbal medicine research. In this study, we proposed a strategy, which integrates network pharmacology with biomedical analysis and surface plasmon resonance (SPR) to predict the active ingredients and potential targets of herbal medicine Sophora flavescens or Kushen in Chinese, and evaluate its anti-fibrosis activity. First, we applied a virtual HTDocking platform to predict the potential targets of Kushen related to liver fibrosis by selecting five crucial protein targets based on network parameters and text mining. Then, we identified nine components in mice plasma after oral administration of Kushen extract and determined the plasma concentration of each compound. Binding affinities between the nine potential active compounds and five target proteins were detected by SPR assays. Finally, we constructed a multi-parameter network model on the basis of three important parameters to tentatively explain the anti-fibrosis mechanism of Kushen. The results not only provide evidence for the therapeutic mechanism of Kushen but also shed new light on the activity-based analysis of other Chinese herbal medicines.