《中国药典》2020年版中药制剂关键信息挖掘
Key information mining of traditional Chinese medicine preparations in Chinese Pharmacopoeia 2020 edition
严鹏应 1张喜武2
作者信息
- 1. 黑龙江中医药大学药学院,黑龙江 哈尔滨 150040
- 2. 黑龙江中医药大学,黑龙江 哈尔滨 150040
- 折叠
摘要
目的 旨在通过对《中国药典》2020 年版(简称《中国药典》)收录的中药制剂的关键信息进行系统分析,为中药制剂的研发提供重要参考.方法 以《中国药典》收录的1 607 个中药制剂作为研究对象,利用《中国药典》网页版、蒲标网及《中国药典》电子版检索相关信息.采用Microsoft Excel 2019 软件构建《中国药典》成方制剂和单味制剂数据库,系统分析其处方、制法、鉴别、检查、含量测定等研究内容的信息.结果 通过对《中国药典》收录的中药制剂的系统分析,总结了中药制剂的内在规律,明确其关键信息.这些关键信息包括处方组成、制备方法、鉴别特征、质量检查和含量测定等方面的内容.结论 通过对《中国药典》中药制剂的系统分析,不仅为中医药研究提供了丰富而准确的数据支持,更为中医药高层次人才的培养和科研能力的提升提供了重要支撑.
Abstract
Objective To systematically analyze the key information of traditional Chinese medicine(TCM)formulations included in the Chinese Pharmacopoeia 2020 edition,and to provide important references for the development of TCM formulations.Methods The study selected 1 607 TCM formulations listed in the Chinese Pharmacopoeia as research subjects.Information was retrieved using the web version of the Chinese Pharmacopoeia,Pubo Network,and the electronic version of the Chinese Pharmacopoeia.Microsoft Excel 2019 was used to build a database for both compound and single-herb TCM formulations,and a systematic analysis was conducted on the information related to prescriptions,preparation methods,identification,testing,and content determination.Results Through the systematic analysis of TCM formulations listed in the Chinese Pharmacopoeia,this study summarized the intrinsic patterns and identified key information.The key information included prescription composition,preparation methods,identification characteristics,quality testing,and content determination.Conclusion The systematic analysis of TCM formulations in the Chinese Pharmacopoeia not only provides rich and accurate data support for TCM research but also offers significant support for the cultivation of high-level TCM talents and the enhancement of their research capabilities.
关键词
《中国药典》/中药制剂/处方规律/数据挖掘/新药开发Key words
Chinese Pharmacopoeia/traditional Chinese medicine preparation/prescription rule/data mining/new drug development引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据