中国药物警戒 ›› 2022, Vol. 19 ›› Issue (12): 1344-1351.
DOI: 10.19803/j.1672-8629.20210612

• 基础与临床研究 • 上一篇    下一篇

藏药榜嘎抗流感的网络药理学分析

白思宇1,2, 陈倩雯1, 叶潇1, 冯伟红1, 荣立新2, 李春1,*   

  1. 1中国中医科学院中药研究所,北京 100700;
    2中国中医科学院广安门医院,北京 100053
  • 收稿日期:2022-06-16 出版日期:2022-12-15 发布日期:2022-12-21
  • 通讯作者: *李春,女,研究员,常用中药与民族药的药效物质基础研究。E-mail：cli@icmm.ac.cn
  • 作者简介:白思宇,女,硕士,常用中药与民族药的药效物质基础研究。
  • 基金资助:
    国家重点研发计划 (2017YFC1701900); 中国中医科学院科技创新工程（CI2021A04402）; 中央级公益性科研院所基本科研业务费（ZXKT17039）

Network pharmacology of Tibetan medicine Ponka against influenza

BAI Siyu1,2, CHEN Qianwen1, YE Xiao1, FENG Weihong1, RONG Lixin2, LI Chun1,*   

  1. 1Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China;
    2Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
  • Received:2022-06-16 Online:2022-12-15 Published:2022-12-21

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摘要： 目的 通过网络药理学分析,探讨榜嘎抗流感病毒及抗炎的活性成分与作用机制。方法 从中国知网及Pubmed数据库收集整理榜嘎中1985年1月1日至2021年5月31日分离鉴定的化合物,通过SIB数据库根据ADME参数筛选出具有生物活性的成分。利用SwissTarget Prediction平台预测活性成分的作用靶点。在OMIM及GeneCards数据库中查找流感病毒及炎症的靶点,运用Venny2.1.0工具获取榜嘎与流感病毒及榜嘎与炎症靶点的交集靶点,在STRING数据库中分析蛋白互作关系,并运用Cytoscape3.6.0软件构建成分-疾病-关键靶点网络图以及核心靶点网络图。利用DAVID6.8数据库进行GO功能分析和KEGG通路富集分析。结果 筛选得到活性成分73个,核心化合物17个,作用靶点841个,榜嘎与流感病毒交集靶点94个,与炎症交集靶点142个。GO功能分析得到榜嘎抗流感病毒生物过程181 个,分子功能42个,细胞组成25个;KEGG富集分析获得榜嘎抗流感病毒信号通路84条,主要涉及TNF、丙型肝炎和癌症信号通路。GO功能分析得到榜嘎抗炎生物过程232个、分子功能52个、细胞组成27个,KEGG富集分析获得榜嘎抗炎信号通路86条,主要涉及NF-Kappa B、TNF和 HIF-1信号通路。结论 榜嘎发挥抗流感活性是多成分、多靶点、多通路协同作用的结果。网络药理学分析结果为后续的实验验证提供了思路和依据,也为新型抗流感药物研发奠定了基础。

关键词: 流感, 榜嘎, 抗炎, 抗流感, 网络药理学, 活性成分, 作用机制, 藏药

Abstract: Objective To explore the active components and mechanism of anti-influenza virus and anti-inflammatory activities of Ponka via network pharmacological analysis. Methods The compounds isolated and identified from Ponka between January 1, 1985 and May 31, 2021 were collected and sorted from CNKI and Pubmed databases, while the bioactive components were screened according to ADME parameters via the SIB database. The targets of these active components were predicted using Swiss StargetPrediction. Influenza virus and inflammation targets were found in OMim and GeneCards databases, and the Venny2.1.0 tool was used to obtain the intersection targets of Ponka and influenza virus as well as Ponka and inflammation targets. The protein interactions were analyzed in the String database. Cytoscape3.6.0 software was used to construct the component-disease-key target network and core target network. GO function analysis and KEGG pathway enrichment analysis were performed using David 6.8 database. Results A total of 73 active components, 17 core ingredients, 841 action targets, 94 intersection targets of Ponka and influenza virus, 142 intersection targets of Ponka and anti-inflammation were retrieved. GO functional analysis revealed 181 biological processes, 42 molecular functions and 25 cell components of Ponka against influenza virus. KEGG enrichment analysis obtained 84 anti-influenza virus signaling pathways, mainly involving TNF, Hepatitis C and cancer signaling pathways. Go functional analysis revealed 232 biological processes, 52 molecular functions and 27 cell components of Ponka against inflammation. KEGG enrichment analysis showed 86 anti-inflammatory signaling pathways, mainly involving NF-Kappa B, TNF and HIF-1 signaling pathways. Conclusion The anti-influenza activity of Ponka results from the synergistic action of multiple components by multiple targets and multiple pathways. This finding can offer novel ideas for subsequent experimental verification.

Key words: influenza, Ponka, anti-inflammatory, anti-influenza, network pharmacology, active ingredients, action mechanism

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