挖掘现实世界的大数据以定量描述药物不良反应:混合方法研究研究
Qi-Xuan Yue1,2, Ruo-Fan Ding1, Wei-Hao Chen1,2
1State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
Journal of medical Internet research
|May 3, 2024
概括
本研究引入了一种定量药物不良反应分类系统 (ADReCS) 模型,以评估药物毒性的严重程度和频率. 该模型为改善药物安全和发现提供了从定性到定量评估的范式转变.
科学领域:
- 药监和药物安全 药监和药物安全
- 计算毒理学计算毒理学
- 精准医学是一门精准的医学.
背景情况:
- 药物不良反应 (ADRs) 是临床医学的一个重大问题,影响药物监督和新药发现.
- 目前的药物安全评估往往是定性性的,需要定量方法来提高准确性.
研究的目的:
- 开发一个定量模型来描述药物不良反应 (ADR) 的严重程度.
- 建立一个强大的系统来评估药物安全性和改善药物监督.
主要方法:
- 开发了不良药物反应分类系统 (ADReCS) 严重程度分级模型,使用数百万真实世界的不良事件报告.
- 引入了一个'Severity_score'参数,为五个严重程度级别定义了边界.
- 挖掘药物处方以计算ADR发生率在大型患者群体中.
主要成果:
- ADReCS模型与专家评级 (不良事件常用术语标准) 达到了99.22%的一致性.
- 在129,407对药物-ADR对中,量化对6,277个ADR的严重程度进行了分级.
- 在127,763个药物-ADR对中,计算了6272个不同的ADR的发生率.
结论:
- 这项研究提供了对ADR严重程度和频率的第一个全面的定量评估.
- 为人工智能驱动的药物发现奠定了基础,专注于高疗效和低毒性.
- 标志着临床毒性研究向量化评估的转变.
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