一种基于药物相似性的贝叶斯方法,用于早期检测药物不良事件
Yi Shi1, Yuedi Yang1, Ruoqi Liu2
1Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN, USA.
Drug safety
|April 22, 2025
概括
使用药物相似性的新贝叶斯方法加速药物不良事件 (ADE) 检测并控制错误阳性. 与现有方法相比,这种方法可以改善现实数据中的早期信号识别.
科学领域:
- 药物监督 药物监督 药物监督
- 计算毒理学计算毒理学
- 生物统计学 生物统计学
背景情况:
- 生物化学药物相似性有助于预测药物不良事件 (ADEs),并增强现实世界的数据挖掘信号.
- 现有的方法需要改进假阳性控制和检测时间评估.
研究的目的:
- 测试一种基于药物相似性的贝叶斯法,用于在假阳性对照中早期检测ADE.
- 用现实数据和模拟来评估方法的性能与参考方法相比.
主要方法:
- 采用贝叶斯的方法,在这种方法中,较少使用的药物的先前ADE概率是从生物化学上相似的,更频繁的药物中获得的.
- 用于信号检测和假阳性控制的零假设的后置概率.
- 使用美国FDA的不良事件报告系统 (FAERS) 和模拟研究的评估方法.
主要成果:
- 经过测试的贝叶斯方法在FAERS数据中比参考方法快2年 (5年 vs. ≥7年) 实现了ADE检测.
- 该方法在模拟中显示出更高的AUC值 (0.57-0.79对比0.32-0.71) 和更好的检测概率 (0.31-0.60对比0.11-0.41).
- 在高通量ADE挖矿中,在不同药物相似性类型中证明有效的假阳性控制和可比性能.
结论:
- 基于药物相似性的贝叶斯方法显示了加速ADE检测的潜力.
- 这种方法可以更好地控制药物监督中错误阳性率.
- 这些发现支持这种方法对早期识别药物不良事件的有用性.
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