中国药学(英文版) ›› 2024, Vol. 33 ›› Issue (4): 339-351.DOI: 10.5246/jcps.2024.04.026
刘芳琳1,#, 张春娟1,#, 沈秋跃2,#, 周昔程1, 赵越3, 富冯峰2,*(), 刘芳3,*()
收稿日期:
2023-10-24
修回日期:
2023-11-28
接受日期:
2023-12-08
出版日期:
2024-04-30
发布日期:
2024-04-30
通讯作者:
富冯峰, 刘芳
Fanglin Liu1,#, Chunjuan Zhang1,#, Qiuyue Shen2,#, Xicheng Zhou1, Yue Zhao3, Fengfeng Fu2,*(), Fang Liu3,*()
Received:
2023-10-24
Revised:
2023-11-28
Accepted:
2023-12-08
Online:
2024-04-30
Published:
2024-04-30
Contact:
Fengfeng Fu, Fang Liu
About author:
# Fanglin Liu, Chunjuan Zhang and Qiuyue Shen contributed equally to this work.
Supported by:
摘要:
参芪扶正注射液(SFI)是一种由黄芪(黄芪, HQ)和党参(党参, DS)组成的传统中药注射液, 我们通过网络药理学和分子对接分析研究了SFI治疗慢性阻塞性肺病(COPD)的潜在机制和分子靶点。首先, 我们在多个数据库中检索和筛选了SFI的潜在生物活性化合物和相应的COPD相关靶基因。随后, 使用Cytoscape 3.9.1和STRING分析并构建了蛋白质-蛋白质相互作用(PPI)网络, 以获得关键靶基因。随后, 为了确定与SFI相关的潜在信号通路, 研究人员进行了GO和KEGG富集研究, 并进行了分子对接分析以验证结果。通过网络药理学分析, 共获得65种活性成分(DS 35种, HQ 30种)和233个关键靶点。其中, 前五大生物活性成分(槲皮素、芹菜素、大豆异黄酮、山柰酚和木犀草素)和前五大靶点(TP53、AKT1、EP300、MAPK1和JUN)在分子对接分析中表现出良好的亲和力和稳定的结合力, 结合能≤ –5.0 kcal/mol。这些研究结果表明, SFI可通过多种化合物和多种途径对慢性阻塞性肺病发挥治疗作用。此外, 本研究还为SFI作为慢性阻塞性肺疾病的辅助治疗药物进一步应用于临床提供了药理学依据和参考。
Supporting:
刘芳琳, 张春娟, 沈秋跃, 周昔程, 赵越, 富冯峰, 刘芳. 基于网络药理学和分子对接技术探讨参芪扶正注射液治疗慢性阻塞性肺疾病的潜在药理学机制[J]. 中国药学(英文版), 2024, 33(4): 339-351.
Fanglin Liu, Chunjuan Zhang, Qiuyue Shen, Xicheng Zhou, Yue Zhao, Fengfeng Fu, Fang Liu. Unraveling the pharmacological mechanisms of Shenqi Fuzheng injection in chronic obstructive pulmonary disease: a network pharmacology and molecular docking approach[J]. Journal of Chinese Pharmaceutical Sciences, 2024, 33(4): 339-351.
Figure 1. Venn diagram of the targets of SFI and the targets of COPD (yellow, red, and blue nodes represent HQ, DS, and COPD protein targets, respectively).
Figure 2. SFI-compound-target network (red and yellow nodes are the active ingredients of SFI, while the blue nodes in the middle are the potential targets for COPD).
Figure 3. The PPI network of the SFI and COPD putative targets. Nodes represent proteins. The key nodes with higher degrees (above 50) were surrounded and arranged in circles according to the degree values. The size and opacity of the nodes indicate the degree values, with the bigger and darker nodes representing higher degree values. The colors from red to blue represent the degree of binding between proteins. Edges represent PPIs. The thickness and opacity of the edges indicate the value of the combined score, with the wider and darker edges indicating a higher combined score.
Figure 4. The top 20 items of BP, top 10 items of CC, and top 10 items of MF in the GO enrichment analysis (P < 0.001). The bar colors correspond to different pathway functions (red, BP; blue, CC; and green, MF).
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