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通过机器学习优化儿童的甘西克洛维尔和瓦尔甘西克洛维尔初始剂量

Laure Ponthier^1,2, Julie Autmizguine^3,4,5, Benedicte Franck^6,7

¹Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France.

Clinical pharmacokinetics

|March 16, 2024

概括

机器学习模型可以优化儿童的甘西克洛维 (GCV) 和瓦尔甘西克洛维 (VGCV) 起始剂量. 与现有方法相比,这些算法有望改善药理动力学目标的实现.

科学领域:

药理动力学和药理动力学
机器学习在医学中的应用
儿科药理学儿科药理学

背景情况:

甘西克洛维尔 (GCV) 和瓦尔甘西克洛维尔 (VGCV) 具有显著的个体间药理动力学变异性,特别是在儿科患者群体中.
准确的剂量对于治疗疗效和尽量减少儿童中毒性至关重要.

研究的目的:

开发和验证机器学习 (ML) 算法,以优化儿童的GCV和VGCV起始剂量.
为了比较ML衍生剂量策略的性能与已建立的人口药理动力学 (POPPK) 模型.

主要方法:

使用POPPK模型和WHO生长曲线的模拟药理学概况 (n=10,800) 通过蒙特卡洛模拟生成.
基于人口统计数据,ML算法 (Xgboost,神经网络,随机森林) 被训练来预测目标实现 (AUC0-24在40-60 mg*h/L范围内).
在接受GCV或VGCV的儿科患者的测试组和外部验证组中评估了性能.

主要成果:

组合ML模型在测试组中实现了最高的目标 (36.8%的GCV,35.3%的VGCV).
在实际患者中,ML-derived GCV起始剂量显示达到最高的目标 (25.8%).
对于VGCV,ML方法的性能与弗兰克模型公式 (35.3%) 相比.

结论:

ML算法在优化儿科GCV和VGCV剂量方面表现强.
这些ML模型为实现治疗目标的现有方法提供了潜在的改进.
对这些基于ML的剂量策略进行前性评估是有必要的.

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