| Literature DB >> 29321678 |
Jianling Liu1, Jingjing Liu1, Fengxia Shen1, Zonghui Qin1, Meng Jiang1, Jinglin Zhu1, Zhenzhong Wang2, Jun Zhou2, Yingxue Fu3, Xuetong Chen3, Chao Huang3, Wei Xiao4, Chunli Zheng5, Yonghua Wang6.
Abstract
Traditional Chinese medicine (TCM) follows the principle of formulae, in which the pharmacological activity of a single herb can be enhanced or potentiated by addition of other herbs. Nevertheless, the involved synergy mechanisms in formulae remain unknown. Here, a systems-based method is proposed and applied to three representative Chinese medicines in compound saffron formula (CSF): two animal spices (Moschus, Beaver Castoreum), and one herb Crocus sativus which exert synergistic effects for cardiovascular diseases (CVDs). From the formula, 42 ingredients and 66 corresponding targets are acquired based on the ADME evaluation and target fishing model. The network relationships between the compounds and targets are assembled with CVDs pathways to elucidate the synergistic therapeutic effects between the spices and the herbs. The results show that different compounds of the three medicines show similar curative activity in CVDs. Additionally, the active compounds from them shared CVDs-relevant targets (multiple compounds-one target), or functional diversity targets but with clinical relevance (multiple compounds-multiple targets-one disease). Moreover, the targets of them are largely enriched in the same CVDs pathways (multiple targets-one pathway). These results elucidate why animal spices and herbs can have pharmacologically synergistic effects on CVDs, which provides a new way for drug discovery.Entities:
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Year: 2018 PMID: 29321678 PMCID: PMC5762866 DOI: 10.1038/s41598-017-18764-2
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Figure 1The detailed process introduction of the systems pharmacology research method.
Figure 2Gene Ontology (GO) analysis of potential targets. The y-axis shows significantly enriched ‘Biological Process’ (BP) categories in GO associated with the targets, the x-axis shows the enrichment scores of these terms (P Value ≤ 0.05).
Figure 3Compound-target network. Blue nodes represent potential drug targets, 670 purple, green and red nodes remark drug ingredients of Crocus sativus (purple), 671 Moschus (green) and Beaver castoreum (red), respectively. And each edge represents the interaction between them. Node size is proportional to its degree.
Figure 4Target-disease-disease category network. Green nodes represent potential drug targets, red nodes remark the CVDs-related diseases, blue nodes represent disease category, each edge represents the interaction between them. Node size is proportional to its degree.
Figure 5CVDs pathway. Distribution of protein targets of herbs on the compressed ‘CVDs pathway’. Three pathways (colorless rectangle) form the compressed CVDs pathway. Light green rectangles remark targets on the CVDs pathway. Light blue rectangles represent targets of active compounds. Brown rectangles remark 683 therapeutic module. Arrows indicate activation, T-arrows indicate inhibition.