Abstract
The research of multicomponent drugs, such as in Chinese Medicine, on both mechanism dissection and drug discovery is challenging, especially the approaches to systematically evaluating the efficacy at a molecular level. Here, we presented a network pharmacology-based approach to evaluating the efficacy of multicomponent drugs by genome-wide transcriptional expression data and applied it to Shenmai injection (SHENMAI), a widely used Chinese Medicine composed of red ginseng (RG) and Radix Ophiopogonis (RO) in clinically treating myocardial ischemia (MI) diseases. The disease network, MI network in this case, was constructed by combining the protein-protein interactions (PPI) involved in the MI enriched pathways. The therapeutic efficacy of SHENMAI, RG, and RO was therefore evaluated by a network parameter, namely, network recovery index (NRI), which quantitatively evaluates the overall recovery rate in MI network. The NRI of SHENMAI, RG, and RO were 0.876, 0.494, and 0.269 respectively, which indicated SHENMAI exerts protective effects and the synergistic effect of RG and RO on treating myocardial ischemia disease. The successful application of SHENMAI implied that the proposed network pharmacology-based approach could help researchers to better evaluate a multicomponent drug on a systematic and molecular level.
Highlights
Chinese Medicine, featured as having “multiple ingredients and multiple targets,” has been widely used to treat complex diseases for decades [1]
The result of this study quantitatively showed that Shenmai injection (SHENMAI) exerts protective effects on treating myocardial ischemia, as it made the biological network recover from disease state to normal state
Pathway enrichment analysis was applied to these genes using the KEGG pathway database, and 27 significant enriched pathways were found by ArrayTrack (P < 0.05)
Summary
Chinese Medicine, featured as having “multiple ingredients and multiple targets,” has been widely used to treat complex diseases for decades [1]. How to evaluate the efficacy of Chinese Medicine on a systematic and molecular level is challenging, especially when treating complex diseases such as cancer [5] and cardiovascular disease [6]. Previous studies demonstrated that microarray could be used to find potential disease biomarkers [8,9,10,11,12,13], which was valuable in prognosis prediction and mechanism explanation. Due to the large number of features and relatively small number of samples in omics data, statistically significant DEGs might not have valuable biological meanings; moderately expressed biomarkers would be overlooked if a high cutoff was set to filter out noise. Most approaches to finding significant biomarkers did not take the multiplex interactions into consideration
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