中国药物警戒 ›› 2023, Vol. 20 ›› Issue (10): 1129-1133.
DOI: 10.19803/j.1672-8629.20230224

• 法规与管理研究 • 上一篇    下一篇

人工智能在美国药物警戒中的应用现状及启示

王涛1, 郑明节1, 刘红亮1, 王青2, 沈传勇1,*   

  1. 1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;
    2清华大学药物警戒信息技术与数据科学创新中心,北京 100084
  • 收稿日期:2023-04-12 出版日期:2023-10-15 发布日期:2023-10-16
  • 通讯作者: *沈传勇,男,博士,副教授,药品与医疗器械警戒技术和方法研究。E-mail: shenchuanyong@cdr-adr.org.cn
  • 作者简介:王涛,男,博士,研究员,药品上市后安全性评价。
  • 基金资助:
    国家科技重大专项重大新药创制（2017ZX091010 01-001-003）; 国家自然科学基金资助项目（72274065）

Current applications of artificial intelligence in pharmacovigilance in the USA and implications

WANG Tao1, ZHENG Mingjie1, LIU Hongliang1, WANG Qing2, SHEN Chuanyong1,*   

  1. 1Center for Drug Reevaluation, NMPA/ NMPA Key Laboratory for Research and Evaluation of Pharmacovigilance, Beijing 100076, China;
    2Pharmacovigilance Information Technology and Data Science Innovation Center of Tsinghua University, Beijing 100084, China
  • Received:2023-04-12 Online:2023-10-15 Published:2023-10-16

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摘要： 目的 论述人工智能在美国药物警戒中的应用现状,以期为我国的药品上市后监管工作提供借鉴。方法 通过查阅文献,从药品上市后监管视角,系统介绍人工智能相关定义和性能指标,美国食品药品监督管理局（FDA）应用人工智能的背景,FDA在人工智能的实践进展和考量,以及在药物警戒工作中的挑战等。结果 近10余年,FDA已广泛探索人工智能在药物警戒中的应用,现阶段FDA主要专注于人工智能处理和评价提交至FDA不良事件报告系统中的个例安全性报告（ICSRs）,并在ICSRs关键信息提取和分析、ICSRs关联性评价等方面取得了主要进展。结论 借鉴FDA经验,建议我国加快引入人工智能技术、加强相关人员培训和人才培养与引进、建立人工智能社会技术与管理体系、促进人工智能领域国际领域交流和合作。

关键词: 人工智能, 机器学习, 药物警戒, 不良事件, 个例安全性报告

Abstract: Objective To study the current applications of artificial intelligence in pharmacovigilance in the United States in order to provide reference for the supervision of China's postmarketing drugs. Methods Based on literature review, from the perspective of drug regulation, this article outlined the concepts and performance indicators related to artificial intelligence, the background of application, FDA's experience and challenges to pharmacovigilance from the perspective of monitoring of postmarketing drugs. Results In the past decade, FDA was extensively exploring the applications of artificial intelligence in pharmacovigilance. Currently, FDA focused on the applications of artificial intelligence in handling and evaluating individual safety reports submitted to the FDA adverse event reporting system, and major progress had been made in the retrieval and analysis of key information about ICSRs, as well as in causality assessment for ICSRs. Conclusion Based on the experience of FDA, it is recommended that we accelerate the introduction of artificial intelligence technologies, strengthen training of professionals, establish a social technology and management system for artificial intelligence and promote international exchanges and cooperation in this field.

Key words: artificial intelligence (AI), machine learning (ML), pharmacovigilance, adverse events (AE), individual case safety reports (ICSRs)

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