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中国临床药理学与治疗学 ›› 2025, Vol. 30 ›› Issue (11): 1541-1549.doi: 10.12092/j.issn.1009-2501.2025.11.012

• 综述与讲座 • 上一篇    下一篇

抗体药物的药物警戒:基于贝叶斯网络的分析与实践

曹尚,饶玉清,董成龙,李紫薇,阚红卫   

  1. 国家药品监督管理局药品审评检查长三角分中心,上海  200120
  • 收稿日期:2025-02-14 修回日期:2025-03-19 出版日期:2025-11-26 发布日期:2025-12-04
  • 通讯作者: 阚红卫,男,博士,研究员,主要从事药品技术审评工作。 E-mail: kanhw@ydcdei.org.cn
  • 作者简介:曹尚,男,博士,主管医师,主要从事药品技术审评工作。 E-mail: jsyzgps@sina.com
  • 基金资助:
    长三角区药品领域创新与高质量发展监管科学研究资助课题(24692122700)

Pharmacovigilance of antibody drugs: Bayesian network practice

CAO Shang, RAO Yuqing, DONG Chenglong, LI Ziwei, KAN Hongwei   

  1. Yangtze River Delta Center for Drug Evaluation and Inspection of National Medical Products Administration, Shanghai 201210, China 
  • Received:2025-02-14 Revised:2025-03-19 Online:2025-11-26 Published:2025-12-04

摘要:

药物警戒是确保药物安全性的重要环节,抗体药物这类复杂的大分子生物药物而言,其不良反应的监测与管理更具挑战性。贝叶斯网络作为一种基于概率推断的因果分析工具,在应对复杂数据关系和不确定性方面具有显著优势。本文结合抗体药物的特点,从高靶点特异性、不良反应模式和免疫原性等角度出发,探索如何利用贝叶斯网络整合多源数据,初步探讨在变量缺失和复杂交互条件下对不良事件的因果推断与风险预测的可行性,为抗体药物警戒研究提供参考和方法学基础。

关键词: 贝叶斯网络, 药物警戒, 抗体药物, 因果推断, 风险预测

Abstract:

Pharmacovigilance is a critical measure to ensure drug safety, particularly challenging in the context of antibody drugs-complex macromolecular biologics, which due to their intricate adverse reaction monitoring and management needs. As a causal analysis tool grounded in probabilistic inference, Bayesian networks offer significant advantages in handling complex data relationships and uncertainties. Focusing on the characteristics of antibody drugs, including high target specificity, patterns of adverse reactions, and immunogenicity, this paper explores how to integrate multi-source data using Bayesian networks. Furthermore, it provides a preliminary investigation into the feasibility of causal inference and risk prediction for adverse events under conditions of variable missingness and complex interactions. The study aims to offer both a reference and methodological foundation for pharmacovigilance research on antibody drugs.

Key words: Bayesian network, pharmacovigilance, antibody drugs, causal inference, risk prediction

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