基于网络药理学探讨痰咳净方防治新型冠状病毒肺炎的可行性  

Potential mechanism analysis of respiratory system treated by Tankejing based on network pharmacology

作　　者：谢静 田野 刘杨 罗学军 缪兴龙[1,2] 李瑞明 陈雅红[3] 周水平 何毅 王成 XIE Jing;TIAN Ye;LIU Yang;LUO Xuejun;MIAO Xinglong;LI Ruiming;CHEN Yahong;ZHOU Shuiping;HE Yi;WANG Cheng(Development Center of Modern Chinese Medicine,Research Institute of Tasly Holding Group Co.Ltd.,Tianjin 300410,China;State Key Laboratory of Critical Technology in Innovative Chinese Medicine,Tasly Pharmaceutical Group Co.Ltd.,Tianjin 300410,China;Tianjin Tasly Pharmaceutical Commercial Co.,Ltd.,Tianjin 300410,China;Shandong Provincial Chest Hospital,Jinan 250013,China)

机构地区：[1]天士力控股集团有限公司研究院现代中药开发中心,天津300410 [2]天士力医药集团股份有限公司创新中药关键技术国家重点实验室,天津300410 [3]天津天士力医药商业有限公司,天津300410 [4]山东省胸科医院,山东济南250013

出　　处：《药物评价研究》2020年第4期591-600,共10页Drug Evaluation Research

基　　金：国家科技重大专项:中医药优势领域的创新中药关键技术开发研究(2017ZX09301005);山东省重点研发计划:新型冠状病毒潜在药物临床评价研究(2020SFXGFY07)。

摘　　要：目的采用网络药理学方法探讨痰咳净方治疗呼吸系统的潜在作用机制,并对其防治新型冠状病毒肺炎(COVID-19)的可能性进行评估。方法通过TCMSP、Swiss Target Prediction、OMIM等在线数据库收集痰咳净方有效化学成分、成分作用靶点及相关疾病信息。采用Cytoscape 3.7.1构建化合物-靶点网络和靶点-疾病网络。将上述靶点用String 10.0数据库进行蛋白质-蛋白质相互作用(PPI)网络的构建并筛选出核心靶点,最后利用DAVID 6.8数据库进行基因本体(GO)功能富集分析和KEGG通路富集分析,预测其作用机制。结果共筛选出49个主要活性成分,与呼吸系统相关靶点85个,针对疾病19种。PPI网络发现关联度较高的靶点有AKT1、IL-6、MAPK1、PTGS2、VEGFA、TNF、EGFR、STAT3、CCND1、MMP9、JUN、IL-10、ESR1等。基因本体(GO)条目1 793个,其中生物过程相关的条目1 555个,分子功能相关的条目134个,细胞组成相关的条目104个。靶点对应的信号通路为137条,涉及炎症性通路、病毒相关通路、免疫调节性通路、信号转导性通路、肿瘤相关通路及内分泌代谢通路等。结论研究初步证明痰咳净方通过作用于ADCY2、AKT1、CASP8、CCND1、TNF、CASP8、IFNG、IL-4、IL-6等靶点调节人巨细胞病毒感染通路、IL-17信号通路、FoxO信号通路、HIF-1信号通路、人T细胞白血病病毒感染、TNF信号通路等多条信号通路,来发挥对COVID-19的防治作用。Objective To study the potential mechanism of Tankejing in the treatment of respiratory system based on network pharmacology, and to evaluate the possibility of Tankejing in the prevention and treatment of COVID-19 pneumonia. Methods The effective chemical components, targets and related diseases of Tankejing were collected through online databases such as TCMSP,Swiss Target Prediction and OMIM. The relationship of Compound-Target network and Target-Disease network was established base on the Cytoscape 3.7.1. The above targets were constructed using String10.0 database for the PPI network and screen out the key targets. Finally, using the DAVID 6.8 database to perform GO enrichment analysis and KEGG pathway enrichment analysis and to predict the action mechanism of Tankejing. Results There were 49 active components of Tankejing with 85 targets sites related to respiratory system, 94 of directly acting on disease. The PPI network found high correlation targets including AKT1, IL6, MAPK1,PTGS2, VEGFA,TNF, EGFR, STAT3, CCND1, MMP9, JUN, IL10, ESR1, etc. There were 1,793 entries in the gene ontology(GO),including 1, 555 entries related to biological processes, 134 entries related to molecular functions, and 104 entries related to cell composition. There were 137 signaling pathways corresponding to the target, involving inflammatory pathways, virus-related pathways, immunoregulatory pathways, signal transduction pathways, tumor-related pathways, endocrine and metabolic pathways,etc. Conclusion This study preliminarily proves that Tankejing can play a role in the prevention and treatment of COVID-19 by acting on adcy2, AKT1, CASP8, CCND1, TNF, CASP8, IFNG, IL4, IL6 and other targets to regulate human cytomegalovirus infection pathway, IL-17 signaling pathway, FOXO signaling pathway, HIF-1 signaling pathway, human T cell leukemia virus 1 infection, TNF signaling pathway and other signaling pathways.

关 键 词：痰咳净 网络药理学 呼吸系统疾病 分子机制 新型冠状病毒肺炎 咖啡因 木犀草素 豆甾醇 龙脑香醇酮 槲皮素 甘草酸 

分 类 号：R285.5[医药卫生—中药学]