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应用机器学习模型来预测儿童使用有限采样策略的甘西克洛维尔和瓦尔甘西克洛维尔暴露

Laure Ponthier^1,2, Bénédicte Franck³, Julie Autmizguine^4,5,6

¹Pharmacology and Transplantation, INSERM U1248, Université de Limoges, Limoges, France.

Antimicrobial agents and chemotherapy

|August 28, 2024

概括

机器学习使用有限的血液样本准确地估计了儿童度-时间曲线 (AUC) 下的甘西克洛维尔面积. 这种新的方法改善了静脉注射甘西克洛维尔和口服瓦尔甘西克洛维尔的治疗药物监测.

科学领域:

药理学药理学是指药理学的学科.
计算生物学计算生物学
儿科医学儿科医学

背景情况:

甘西克洛维尔的药理动力学在儿童中显示出显著的变化,影响治疗疗效.
目前的治疗药物监测依赖于度-时间 (AUC) 估计下的面积.
机器学习 (ML) 为传统的AUC估计方法提供了一个有希望的替代方案.

研究的目的:

开发和验证基于ML的有限采样策略 (LSS),用于儿童的甘西克洛维尔AUC0-24估计.
评估ML算法在静脉注射甘西克洛维尔和口服瓦尔甘西克洛维尔后预测甘西克洛维尔AUC的性能.
建立一个临床相关的方法,用于针对儿科患者的AUC向甘西克洛维尔治疗药物监测.

主要方法:

模拟了10800个药理动力学概况,使用人群药理动力学模型和世卫组织生长曲线.
训练了各种ML算法来预测AUC0-24使用两个或三个后剂量血液样本.
在模拟测试集和真实患者的外部数据集上验证了ML模型的性能.

主要成果:

在Xgboost算法中,口服瓦尔根西克洛维尔的2小时和6小时的LSS和静脉注射甘西克洛维尔的0小时和2小时的LSS实现了最佳性能.
在测试组中,最好的模型显示了较低的相对平均预测误差 (rMPE) 和相对根平均平方误差 (rRMSE).
外部验证显示了可接受的性能,valganciclovir的rMPE为0.2%,rRMSE为16.5%,静脉注射ganciclovir的rMPE为-9.7%和rRMSE为17.2%.

结论:

基于ML的LSS仅使用两个血液样本可以在儿童中提供临床相关的个人甘西克洛维尔AUC估计值.
这种方法简化和增强了儿童护理中针对AUC的甘西克洛维尔治疗药物监测的实施.
开发的Xgboost模型为优化儿童甘西克洛维尔剂量提供了一个实用的工具.

关键词:

在Xgboost中使用.人工智能的人工智能是人工智能.孩子们的孩子们的孩子们的孩子们.甘西克洛维尔 (ganciclovir) 是一种药物.机器学习是机器学习.药物动力学药物动力学药学指标药学指标药学指标药学指标移植移植移植移植瓦尔根西克洛维尔 (Valganciclovir) 是一种药物.

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