基于不良反应监测大数据的药品安全风险发现与识别策略

doi:10.19803/j.1672-8629.20230772

中国药物警戒 ›› 2024, Vol. 21 ›› Issue (1): 1-5.
DOI: 10.19803/j.1672-8629.20230772

基于不良反应监测大数据的药品安全风险发现与识别策略

高云娟^1,2, 赵旭^1△, 白天凯^1,2, 柏兆方¹, 王伽伯³, 宋海波^4#, 肖小河^1,*

¹中国人民解放军总医院第五医学中心,全军中医药研究所,北京 100039;
²成都中医药大学药学院,四川成都 611137;
³首都医科大学中医药学院,北京 100069;
⁴国家药品监督管理局药品评价中心,国家药品监督管理局药物警戒研究与评价重点实验室,北京 100076

收稿日期:2023-12-11 出版日期:2024-01-15 发布日期:2024-01-18
通讯作者: ^*肖小河,男,研究员·博导,临床中药学与安全用药。E-mail: pharmacy302xxh@126.com; ^#为共同通信作者。
作者简介:高云娟,女,博士,临床中药学。^Δ为并列第一作者。肖小河,研究员,解放军总医院第五医学中心肝病医学部学术主任,全军中医药研究所所长,专业技术少将军衔; 国家杰出青年科学基金获得者,首批国家岐黄学者,首批国家中医药传承创新团队带头人,首批全军科技创新领军人才; 主要从事临床中药学研究,在中药安全性评价与精准用药以及肝病新药创制等方面取得系列创新成果; 以第一完成人获国家科技进步二等奖2项、省部级一等奖3项; 自主研制系列肝病治疗新药获国家新药证书和临床批文4个; 获国际和中国发明专利授权40项; 以第一或通信作者在《Hepatology》《科学通报》等国内外知名杂志发表论文500余篇,其中SCI收录210 篇,论文总被引25000余篇次,多次入选“全球前2% 顶尖科学家”“全球学者影响力排行榜”“中国高被引学者” 主编《现代中药研究策论》《临床中药创新及实践》等专著6部; 《中国药物警戒》期刊副主编
基金资助:
国家自然科学基金资助项目(81721002、82230118); 国家中医药管理局中医药创新团队及人才支持计划项目(ZYYCXTD-C-202005); 北京市杰出青年科学基金项目(JQ21026)

Discovery and identification strategy for drug safety risks based on big data monitoring of adverse reactions

GAO Yunjuan^1,2, ZHAO Xu^1△, BAI Tiankai^1,2, BAI Zhaofang¹, WANG Jiabo³, SONG Haibo^4#, XIAO Xiaohe^1,*

¹China Military Institute of Chinese Materia, the Fifth Medical Center of Chinese PLA General Hospital, Beijing 100039, China;
²School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu Sichuan 611137, China;
³School of Traditional Chinese Medicine, Capital Medical University, Beijing 100069, China;
⁴Center for Drug Reevaluation, NMPA/ NMPA Key Laboratory for Research and Evaluation of Pharmacovigilance, Beijing 100076, China

Received:2023-12-11 Online:2024-01-15 Published:2024-01-18

摘要/Abstract

摘要： 目的探索如何从国内外海量的中西药不良反应报告中快速发现与精准识别药物安全风险,并进行科学有效地预测与防控。方法以药物性肝损伤数据为例,针对上市后药品安全风险的发现、评价、确证和防控策略进行论述。结果初步探索建立“不良反应监测大数据发现—多模型识别评析—病证毒理学验证”一体化应对策略和方法体系,并成功应用于药物性肝损伤的识别和评析。结论该策略为药物安全性评价领域的持续发展和创新提供了新的视角,也为保障公众安全用药和促进中西药产业的健康发展提供了技术支撑。

关键词: 药品不良反应, 监测, 大数据, 风险发现, 数据分析, 机器学习

Abstract: Objective To explore how to quickly discover and accurately identify drug safety risks from a vast number of adverse reaction reports from domestic and foreign Chinese and Western medicines. The aim is also to make scientific and effective predictions and control measures for these risks. Methods Drug-induced liver injury data was taken as an example, and the process of discovering, evaluating, confirming, and controlling risks associated with drugs were discussed. Results A preliminary exploration was conducted, leading to the establishment of an integrated strategy and method system for “large-scale adverse reaction monitoring and discovery - multi-model recognition and analysis- disease-symptom-toxicology verification.” This system has been successfully applied in identifying and analyzing drug-induced liver injury. Conclusion This strategy offers a fresh perspective for the continuous development and innovation of drug safety evaluation. It also provides technical support for ensuring public safety in medication and promoting the healthy development of the Chinese and Western medicine industry.

Key words: drug adverse reactions, monitoring, big data, risk discovery, data analysis, machine learning

中图分类号:

R954
R994.11

高云娟, 赵旭, 白天凯, 柏兆方, 王伽伯, 宋海波, 肖小河. 基于不良反应监测大数据的药品安全风险发现与识别策略[J]. 中国药物警戒, 2024, 21(1): 1-5.

GAO Yunjuan, ZHAO Xu, BAI Tiankai, BAI Zhaofang, WANG Jiabo, SONG Haibo, XIAO Xiaohe. Discovery and identification strategy for drug safety risks based on big data monitoring of adverse reactions[J]. Chinese Journal of Pharmacovigilance, 2024, 21(1): 1-5.

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基于不良反应监测大数据的药品安全风险发现与识别策略

Discovery and identification strategy for drug safety risks based on big data monitoring of adverse reactions

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