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1.遵义医科大学 药学院 国家级药学实验教学示范中心,基础药理教育部重点实验室暨特色民族药 教育部国际合作联合实验室,贵州 遵义 563000
2.遵义医科大学 附属医院,贵州 遵义 563000
Published:20 January 2022,
Published Online:16 November 2021,
Received:01 July 2021,
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安晓玲,王顺刚,秦琳等.2020年版《中国药典》肝病相关中药成方制剂的MIE配伍网络分析[J].中国实验方剂学杂志,2022,28(02):199-207.
AN Xiao-ling,WANG Shun-gang,QIN Lin,et al.Mutual Information Entropy Analysis of Compatibility of Traditional Chinese Patent Medicines Against Liver Diseases in Chinese Pharmacopoeia(2020 Edition)[J].Chinese Journal of Experimental Traditional Medical Formulae,2022,28(02):199-207.
安晓玲,王顺刚,秦琳等.2020年版《中国药典》肝病相关中药成方制剂的MIE配伍网络分析[J].中国实验方剂学杂志,2022,28(02):199-207. DOI: 10.13422/j.cnki.syfjx.20220115.
AN Xiao-ling,WANG Shun-gang,QIN Lin,et al.Mutual Information Entropy Analysis of Compatibility of Traditional Chinese Patent Medicines Against Liver Diseases in Chinese Pharmacopoeia(2020 Edition)[J].Chinese Journal of Experimental Traditional Medical Formulae,2022,28(02):199-207. DOI: 10.13422/j.cnki.syfjx.20220115.
目的
2
对功能主治与“肝病”相关的中药成方制剂进行药材配伍网络分析,揭示药材配伍规律。
方法
2
以2020年版《中华人民共和国药典》(简称《中国药典》)(一部)为数据来源,以“肝”为检索词在中药成方制剂的功能主治项下进行检索。收集方剂信息,对药材、处方、功能主治相同但剂型不同的中药成方制剂进行合并,采用MIE方法对药材的方剂配伍关系进行量化打分,选择MIE值排名前25%的药对构建药材配伍网络。该网络以药材为网络节点,以药对的配伍关系为边。通过网络节点中心性分析发现重要药材、集群分析发现常用药材组合。随后,对药材集群相关方剂的主治疾病进行统计。采用Cytoscape 3.6.1对药材配伍网络进行可视化及拓扑结构分析。
结果
2
2020年版《中国药典》(一部)共收录179个中药成方制剂,涉及428种药材。使用频数较高的药材有当归、白芍、甘草等,联系较强的药对有菟丝子-枸杞、陈皮-香附、墨旱莲-女贞子等。另外,每个网络集群药材对应不同的中药成方制剂及主治疾病,主要与消化疾病有关。
结论
2
研究发现了中医临床治疗“肝病”的重要潜在药对以及常用药材组合,为揭示药材配伍规律提供理论参考,并为中药新药研究进行了方法学探索。
Objective
2
To investigate the compatibility rule of traditional Chinese patent medicines (TCPMs) against liver diseases through network analysis.
Method
2
With “liver” as the search term, TCPMs against liver diseases were retrieved from volume Ⅰ of
Chinese Pharmacopoeia
(2020 edition), and the basic information of them was collected. TCPMs with same Chinese medicinal materials (CMMs), usage, and indications, but different dosage forms, were unified as one formula. Mutual information entropy (MIE) of CMM couples was calculated to quantify the relationship between them, and the top 25% CMM pairs in MIE were used to construct the compatibility network, with CMM as node and the relationship between CMM pairs as the edge. Key CMM and frequently used CMM combinations were identified based on node centrality and cluster analysis, respectively. The indications of TCPMs related to the CMMs in clusters were recorded. Cytoscape 3.6.1 was employed for visualization and topology analysis of the compatibility network.
Result
2
A total of 179 TCPMs, involving 428 CMMs, were retrieved. Angelicae Sinensis Radix, Paeoniae Radix Alba, and Glycyrrhizae Radix et Rhizoma were identified as key CMMs with high frequency, and Cuscutae Semen-Lycii Fructus, Citri Reticulatae Pericarpium-Cyperi Rhizoma, and Ecliptae Herba-Ligustri Lucidi Fructus combinations had high MIE. Furthermore, the CMMs were clustered into ten groups corresponding to different diseases which, however, all belonged to digestive diseases.
Conclusion
2
This study unveils potential CMM pairs and common CMM combinations against liver diseases, which can serve as a reference for revealing compatibility rules of CMMs and research and development of Chinese medicine.
《中华人民共和国药典》肝病中药成方制剂MIE配伍网络
Chinese Pharmacopoeialiver diseasetraditional Chinese patent medicinesmutual information entropycompatibility network
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