ABSTRACT
Possible mechanisms by which Polygonati rhizoma opposes atherosclerosis (AS) were identified by network pharmacology and molecular docking analyses. The Traditional Chinese Medicine Database (TCMD) and the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) were utilized to identify the likely active components of Polygonati rhizoma. The potential targets set of Polygonati rhizoma were predicted with the PharmaDB database and the Swiss TargetPrediction database. The targets set for AS was retrieved by OMIM, DisGeNET and NCBI Gene database. We used the STRING platform to construct a protein-protein interaction network of the intersectional targets and performed visual analysis in Cytoscape. The key targets of Polygonati rhizoma in AS were searched by network topology and the resulting GO and KEGG enrichment was analyzed by Clue GO. In addition, the key targets were verified by molecular docking in Discovery Studio 4.0. A total of 45 active ingredients and 51 potential targets were obtained in the treatment of AS. The results of the topology analysis included five key targets: serum albumin, mitogen-activated protein kinase 3, mitogen-activated protein kinase 1, proto-oncogene tyrosine-protein kinase Src and matrix metalloproteinase-9. The 131 GO items showed that the biological process mainly involved the steroid receptor, cell response to steroid stimulation, the phosphatidylinositol-3 kinase signal pathway, and others. The KEGG pathway analysis included 37 pathways, which were closely related to peroxisome proliferation activated receptor signaling pathway, platelet activation pathway, vascular endothelial growth factor pathway, hypoxia inducible factor pathway and adhesion connection pathway. The results of molecular docking proved that the combined activity of the components with potential key targets is excellent. This study proposes mechanisms by which Polygonati rhizoma might act to reverse or minimize AS and provides a scientific basis for clinical research on Polygonati rhizoma.
ABSTRACT
With the widespread use of traditional Chinese medicine(TCM) and the integration of TCM and western medicine, drug-drug interaction(DDI) is considered as a major cause of therapeutic failures and side effects. Cytochrome P450 enzymes(CYPs) are responsible for large number of drug metabolism. CYP3 A4 and CYP2 D6, two important CYP isoforms, are responsible for about 80% drug metabolism of CYPs super family. The inhibition of CYPs is likely to be the most common factor leading to adverse DDI. Therefore, it is of great significance to predict potential CYP3 A4 and CYP2 D6 inhibitors to prevent the DDI. A fast and low-cost me-thod for calculating and predicting CYP inhibiting components was established in this paper, namely support vector machine(SVM) and molecular docking technology which are used to predict and screen drugs. Firstly, 12 qualitative models of two targets were established by using SVM, and the optimal model was selected to predict the compounds in traditional Chinese medicine database(TCMD). Then, molecular docking technology was used to establish docking model. By analyzing the key amino acids involved in drug-target interactions and combining with SVM model, potential inhibitors of CYP3 A4 and CYP2 D6 were found. From the computational results, astin D and epiberberine exhibited inhibition effect on CYP3 A4 and CYP2 D6, respectively. Astin D was only found in astins family from Aster tataricus, while epiberberine was considered to be the active constituent of Coptidis Rhizoma. Therefore, for the risk of DDI, extra attention should be paid to the source of these potential inhibitors, Asteris Radix et Rhizoma and Coptidis Rhizoma. This computational method provides technical support for discovering potential natural inhibitors of CYPs from Chinese herbs by using SVM and molecular docking model, and it is also helpful to recognize the CYPs-mediated DDI existing in TCM, providing research ideas for further pharmacovigilance of integrated therapy.