一个机器学习算法来预测Daptomycin的初始剂量
Florence Rivals1, Sylvain Goutelle2,3,4, Cyrielle Codde5,6
1Service de Pharmacologie, Toxicologie et Pharmacovigilance, CHU Limoges, France.
Clinical pharmacokinetics
|July 31, 2024
概括
机器学习算法通过预测最佳起始剂量来改善达普托米辛的剂量. 与传统的基于体重的剂量相比,这种方法可以提高目标的实现率,特别是在肥胖患者中.
科学领域:
- 药理学 药理学是指药理学的学科.
- 机器学习 机器学习
- 临床药理动力学 临床药理动力学
背景情况:
- 达普托米辛的剂量通常取决于体重,这可能导致肥胖个体过度暴露.
- 药理动力学/药理动力学 (PK/PD) 目标对于达普素的疗效 (AUC/CMI>666) 和安全性 (C0 <24.3 mg/L) 是至关重要的.
- 之前的研究利用蒙特卡洛模拟来开发机器学习 (ML) 算法,用于预测达普素初始剂量.
研究的目的:
- 开发和评估一种基于ML的新型方法,用于达普素剂量达到目标的概率.
- 通过最大限度地提高所需的PK/PD标,同时最大限度地降低毒性,优化达普托米辛的初始剂量.
- 将ML算法的性能与传统基于体重的剂量策略进行比较.
主要方法:
- 在mrgsolve R包中实现了Dvorchik daptomycin模型,模拟了4950个药理动力学概况.
- 四个ML算法被训练并进行了基准测试;选择了最佳算法以代确定daptomycin剂量.
- 用模拟和外部患者数据库评估了ML算法的预测性能,并将其与人口药理动力学模型进行比较.
主要成果:
- Xgboost ML算法表现出强大的预测性能 (ROC AUC在训练中为0.762,在测试组中为0.761).
- 达普托米辛剂量的关键预测因素包括剂量,肌素清除率,体重和性别.
- 与基于体重的剂量相比,ML指导的剂量在真实患者中显著提高了7.9% (p=0.029) 的目标达到率.
结论:
- 开发的ML算法有效地提高了达普托米辛的目标达到,而不是标准的基于体重的剂量.
- 创建了一个用户友好的Shiny应用程序,以方便计算最佳的达普托米辛起始剂量.
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