Optimization of Ganciclovir and Valganciclovir Starting Dose in Children by Machine Learning.

IF 4.6 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Clinical Pharmacokinetics Pub Date : 2024-04-01 Epub Date: 2024-03-16 DOI:10.1007/s40262-024-01362-7
Laure Ponthier, Julie Autmizguine, Benedicte Franck, Anders Åsberg, Philippe Ovetchkine, Alexandre Destere, Pierre Marquet, Marc Labriffe, Jean-Baptiste Woillard
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引用次数: 0

Abstract

Background and objectives: Ganciclovir (GCV) and valganciclovir (VGCV) show large interindividual pharmacokinetic variability, particularly in children. The objectives of this study were (1) to develop machine learning (ML) algorithms trained on simulated pharmacokinetics profiles obtained by Monte Carlo simulations to estimate the best ganciclovir or valganciclovir starting dose in children and (2) to compare its performances on real-world profiles to previously published equation derived from literature population pharmacokinetic (POPPK) models achieving about 20% of profiles within the target.

Materials and methods: The pharmacokinetic parameters of four literature POPPK models in addition to the World Health Organization (WHO) growth curve for children were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles. ML algorithms were developed and benchmarked to predict the probability to reach the steady-state, area-under-the-curve target (AUC0-24 within 40-60 mg × h/L) based on demographic characteristics only. The best ML algorithm was then used to calculate the starting dose maximizing the target attainment. Performances were evaluated for ML and literature formula in a test set and in an external set of 32 and 31 actual patients (GCV and VGCV, respectively).

Results: A combination of Xgboost, neural network, and random forest algorithms yielded the best performances and highest target attainment in the test set (36.8% for GCV and 35.3% for the VGCV). In actual patients, the best GCV ML starting dose yielded the highest target attainment rate (25.8%) and performed equally for VGCV with the Franck model formula (35.3% for both).

Conclusion: The ML algorithms exhibit good performances in comparison with previously validated models and should be evaluated prospectively.

Abstract Image

通过机器学习优化儿童更昔洛韦和缬更昔洛韦的起始剂量
背景和目的:更昔洛韦 (Ganciclovir, GCV) 和缬更昔洛韦 (Valganciclovir, VGCV) 显示出很大的个体间药代动力学变异性,尤其是在儿童中。本研究的目标是:(1) 在蒙特卡罗模拟获得的模拟药代动力学曲线上开发经过训练的机器学习(ML)算法,以估计儿童最佳更昔洛韦或缬更昔洛韦起始剂量;(2) 将其在真实世界曲线上的表现与之前发表的从文献群体药代动力学(POPPK)模型中得出的方程进行比较,结果显示约 20% 的曲线在目标范围内:在 mrgsolve R 软件包中使用了四种文献 POPPK 模型的药代动力学参数以及世界卫生组织(WHO)的儿童生长曲线,模拟了 10,800 份药代动力学曲线。我们开发了 ML 算法并对其进行了基准测试,以便仅根据人口统计学特征预测达到稳态、曲线下面积目标值(AUC0-24 在 40-60 mg × h/L 范围内)的概率。然后使用最佳 ML 算法计算起始剂量,最大限度地实现目标。在测试集和由 32 名和 31 名实际患者(分别为 GCV 和 VGCV)组成的外部集中,对 ML 算法和文献公式的性能进行了评估:在测试集中,Xgboost、神经网络和随机森林算法的组合产生了最佳性能和最高的目标达成率(GCV 为 36.8%,VGCV 为 35.3%)。在实际患者中,最佳 GCV ML 起始剂量的达标率最高(25.8%),VGCV 与 Franck 模型公式的达标率相当(均为 35.3%):结论:与之前的验证模型相比,ML 算法表现良好,应进行前瞻性评估。
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来源期刊
CiteScore
8.80
自引率
4.40%
发文量
86
审稿时长
6-12 weeks
期刊介绍: Clinical Pharmacokinetics promotes the continuing development of clinical pharmacokinetics and pharmacodynamics for the improvement of drug therapy, and for furthering postgraduate education in clinical pharmacology and therapeutics. Pharmacokinetics, the study of drug disposition in the body, is an integral part of drug development and rational use. Knowledge and application of pharmacokinetic principles leads to accelerated drug development, cost effective drug use and a reduced frequency of adverse effects and drug interactions.
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