中国药物警戒 ›› 2026, Vol. 23 ›› Issue (4): 443-449.
DOI: 10.19803/j.1672-8629.20250655

• 安全与合理用药 • 上一篇    下一篇

基于“文献方剂-医案实践-网络预测”的脱疽内服核心方药研究

常梦丽1, 张丰荣1, 李煜1, 王欢欢2,3, 徐核1, 张毅1, 吴宏伟1, 唐仕欢1,*   

  1. 1中国中医科学院中药研究所,北京 100700;
    2天津市中西医结合急腹症研究所,天津 300100;
    3天津中医药大学中西医结合医院,中西医结合急腹症研究所,天津 300100
  • 收稿日期:2025-09-14 出版日期:2026-04-15 发布日期:2026-04-15
  • 通讯作者: *唐仕欢,男,博士,研究员,中药新药处方筛选技术研究。E-mail: tshuan800@126.com
  • 作者简介:常梦丽,女,在读博士,中药复杂体系解析与新药开发研究。
  • 基金资助:
    中国中医科学院科技创新工程项目（CI2021A03705、CI2023E001TS）; 国家自然科学基金资助项目（81973711、82574737）; 国家重点研发计划（2018YFC1704101）

Core Prescriptions for Oral Gangrene Based on “Literature Prescriptions-Medical Record Practices-Network Prediction”

CHANG Mengli1, ZHANG Fengrong1, LI Yu1, WANG Huanhuan2,3, XU He1, ZHANG Yi1, WU Hongwei1, TANG Shihuan1,*   

  1. 1Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China;
    2Institute of Integrative Medicine for Acute Abdominal Diseases, Tianjin 300100, China;
    3Institute of Integrative Medicine for Acute Abdominal Diseases, Hospital of Integrated Chinese and Western Medicine, Tianjin University of TCM, Tianjin 300100, China
  • Received:2025-09-14 Online:2026-04-15 Published:2026-04-15

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摘要： 目的 分析脱疽内服的验方和验案用药规律并结合网络预测,为脱疽的中医临床遣方用药和中药新药研发提供参考。方法 搜集古今治疗脱疽的验方和验案,利用中医传承计算平台3.0分析药物频数、关联规则以及组方配伍规律等,基于药效预测分析平台（TCMATCOV平台）对聚类得到的核心组方进行药效预测评分,基于文献方剂、医案实践、网络预测综合评价核心方药的推荐研发优先等级。结果 筛选出验方389首,包括药物450味,高频药物组合167对;验案132则,包括药物221味,高频药物组合242对。共有的药对28对（7%）,如甘草-当归,当归-黄芪。核心组方分析各得出验方、验案5个核心组方。TCMATCOV平台分析发现,验方和验案处方的核心组合分值均较高,综合分析得出最具研发价值的验方核心组合（乳香、当归、没药、甘草、金银花、白芷）,可作为基础方进一步研发。结论 以脱疽为例,通过分析和挖掘获得脱疽内服方剂的用药特点和规律,筛选具有研发潜力的药物组合,形成基于“文献方剂-医案实践-网络预测”的核心方药筛选方法,可为临床优势病种的防治和中药新药候选处方筛选提供借鉴。

关键词: 甘草-当归, 当归-黄芪, 脱疽, 处方筛选, 数据挖掘, 验方, 验案

Abstract: Objective To analyze the way ancient and modern proved prescriptions for oral gangrene are used in order to provide data for clinical medication and development of new drugs. Methods Prescriptions, ancient or modern, for the treatment of gangrene were collected before drug frequency, association rules, and compatibility of combination prescriptions were analyzed using the Traditional Chinese Medicine Inheritance Platform System (TCMICS) 3.0. The pharmacodynamic effects of potential prescriptions were predicted based on the TCMATCOV platform. Results A total of 389 ancient proved prescriptions were obtained, involving 450 types of traditional Chinese medicinal materials, and 167 pairs of high-frequency drug combinations, compared with 132, 221 and 242 respectively for modern proved cases. The percentage of common drug pairs was 7% (28), such as Glycyrrhizae Radix et Rhizoma-Angelicae Sinensis Radix and Angelicae Sinensis Radix-Astragali Radix. Five types of core prescriptions were obtained from the ancient proved prescriptions and modern proved cases. The results of the TCMATCOV platform analysis showed that drug combinations of the ancient proved recipes and modern proved cases had high scores. Based on comparisons, drug combination (Olibanum, Angelicae Sinensis Radix, Myrrh, Glycyrrhizae Radix et Rhizoma, Lonicerae Japonicae Flos, Angelicae Dahuricae Radix) from ancient proved prescriptions proved to be the best target of development. Conclusion By taking gangrene as an example, the “literature prescriptions-medical record practices-network prediction” method has been established, which can provide references for the prevention and treatment of clinically dominant diseases and the screening of candidate prescriptions of traditional Chinese medicine.

Key words: Glycyrrhizae Radix et Rhizoma-Angelicae Sinensis Radix, Angelicae Sinensis Radix-Astragali Radix, Gangrene, Prescription Screening, Data Mining, Ancient Proved Prescriptions, Modern Proved Cases

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