Background Arnebia euchroma (A. euchroma) is a traditional Chinese medicine (TCM) used for the treatment of blood diseases including leukemia. In recent years, many studies have been conducted on the anti-tumor effect of shikonin and its derivatives, the major active components of A. euchroma. However, the underlying mechanism of action (MoA) for all the components of A. euchroma on leukemia has not been explored systematically. Methods In this study, we analyzed the MoA of A. euchroma on leukemia via network pharmacology approach. Firstly, the chemical components and their concentrations in A. euchroma as well as leukemia-related targets were collected. Next, we predicted compound-target interactions (CTIs) with our balanced substructure-drug-target network-based inference (bSDTNBI) method. The known and predicted targets of A. euchroma and leukemia-related targets were merged together to construct A. euchroma-leukemia protein-protein interactions (PPIs) network. Then, weighted compound-target bipartite network was constructed according to combination of eight central attributes with concentration information through Cytoscape. Additionally, molecular docking simulation was performed to calculate whether the components and predicted targets have interactions or not. Results A total of 65 components of A. euchroma were obtained and 27 of them with concentration information, which were involved in 157 targets and 779 compound-target interactions (CTIs). Following the calculation of eight central attributes of targets in A. euchroma-leukemia PPI network, 37 targets with all central attributes greater than the median values were selected to construct the weighted compound-target bipartite network and do the KEGG pathway analysis. We found that A. euchroma candidate targets were significantly associated with several apoptosis and inflammation-related biological pathways, such as MAPK signaling, PI3K-Akt signaling, IL-17 signaling, and T cell receptor signaling pathways. Moreover, molecular docking simulation demonstrated that there were eight pairs of predicted CTIs had the strong binding free energy. Conclusions This study deciphered that the efficacy of A. euchroma in the treatment of leukemia might be attributed to 10 targets and 14 components, which were associated with inhibiting leukemia cell survival and inducing apoptosis, relieving inflammatory environment and inhibiting angiogenesis.

Drug-target interaction (DTI) plays a central role in drug discovery. How to predict DTI quickly and accurately is a key issue. Traditional structure-based and ligand-based methods have some inherent deficiencies....

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, considering the concentrations of prototype compounds and metabolites of HQD, the critical compound-liver fibrosis target bipartite network was constructed. Results 68 compounds including 17 prototype components and 51 metabolic products were collected. 540 compound-target interactions were obtained between the 68 compounds and 95 targets. Combining network analysis, molecular docking and concentration of compounds, 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. These interactions would maintain the balance of ECM, reduce liver damage, inhibit hepatocyte apoptosis, and alleviate liver inflammation through five signaling pathways including p53, PPAR, HIF-1, IL-17, and TNF signaling pathway. Conclusions This study provides a new way to understand the MoA of HQD on liver fibrosis by considering the concentrations of components and metabolites, which might be a model for investigation of MoA of other Chinese herbs.

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.

Background: Arnebia euchroma (A. euchroma), a traditional Chinese medicine (TCM) for the treatment of blood diseases. In recent years, more and more researches have been conducted on the anti-tumor effect of shikonin and its derivatives, the major active components of A. euchroma. However, the underlying mechanism of action (MoA) for all the ingredients of A. euchroma on leukemia has not been explored systematically.Methods: In this study, we tried to analyze the MoA of A. euchroma on leukemia via network pharmacology approach. Firstly, the chemical components and their concentrations in A. euchroma as well as leukemia-related targets were collected. Next, we predicted compound-target interactions (CTIs) via our balanced substructure-drug-target network-based inference (bSDTNBI) method. The known and predicted targets of A. euchroma and leukemia-related targets were merged together to construct A. euchroma-leukemia protein-protein interactions (PPIs) network. Then, weighted compound-target bipartite network was constructed according to combination of eight central attributes with concentrations information via Cytoscape. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets.Results: A total of 65 components of A. euchroma were obtained and 27 of them with concentration information, which involved in 157 targets, 779 compound- target interactions (CTIs), including 129 known CTIs (KCTIs) and 650 predicted CTIs (PCTIs). Following the analysis of A. euchroma-leukemia PPI network, 37 targets were used to construct the weighted compound-target bipartite network and further KEGG pathway analysis. We found that A. euchroma candidate targets were significantly associated with several apoptosis and inflammation-related biological pathways, such as MAPK signaling pathway, PI3K-Akt signaling pathway, IL-17 signaling pathway, T cell receptor signaling pathway. Moreover, molecular docking simulation demonstrated that there were 8 pairs of PCTIs had the strong binding free energy.Conclusions: This network pharmacology-based study considered the information of component concentration, in combination with molecular docking, deciphered that the efficacy of A. euchroma in the treatment of leukemia may be mainly attributed to 10 targets and 14 components, which were associated with inhibiting leukemia cell survival and inducing apoptosis, relieving inflammatory environment and inhibiting angiogenesis.