Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy

Abstract

Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (ML) algorithms represent an interesting alternative to Maximum-a-Posteriori Bayesian-estimators for AUC estimation. The goal of our study was to develop and validate an ML-based limited sampling strategy (LSS) approach to determine ganciclovir AUC_0-24 after administration of either intravenous ganciclovir or oral valganciclovir in children. Pharmacokinetic parameters from four published population pharmacokinetic models, in addition to the World Health Organization growth curve for children, were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles of children. Different ML algorithms were trained to predict AUC_0-24 based on different combinations of two or three samples. Performances were evaluated in a simulated test set and in an external data set of real patients. The best estimation performances in the test set were obtained with the Xgboost algorithm using a 2 and 6 hours post dose LSS for oral valganciclovir (relative mean prediction error [rMPE] = 0.4% and relative root mean square error [rRMSE] = 5.7%) and 0 and 2 hours post dose LSS for intravenous ganciclovir (rMPE = 0.9% and rRMSE = 12.4%). In the external data set, the performance based on these two sample LSS was acceptable: rMPE = 0.2% and rRMSE = 16.5% for valganciclovir and rMPE = -9.7% and rRMSE = 17.2% for intravenous ganciclovir. The Xgboost algorithm developed resulted in a clinically relevant individual estimation using only two blood samples. This will improve the implementation of AUC-targeted ganciclovir therapeutic drug monitoring in children.

Keywords: Xgboost; artificial intelligence; children; ganciclovir; machine learning; pharmacokinetics; pharmacometrics; transplantation; valganciclovir.

Fig 1

Scatter plot of AUC0-24s estimated using the Xgboost algorithm based on two points at 2 and 6 hours vs reference trapezoidal AUC0-24 in the test set (A) and in the external data set (C) for VGCV, and corresponding Bland–Altman plots (B) and (D). Difference is the difference between the Reference and the Xgboost AUC0-24s, and the mean is the average of both.

Fig 2

Scatter plot of AUC0-24s estimated using the Xgboost algorithm based on two points at 1 and 6 hours vs reference trapezoidal AUC0-24 in the test set (A) and in the external data set (C) for GCV, and corresponding Bland–Altman plots (B) and (D). Difference is the difference between the Reference and the Xgboost AUC0-24s, and the mean is the average of both.

Fig 3

Variable importance plot obtained by random permutation for VGCV. The score obtained quantifies the impact of each variable on the model’s predictions. Out_X corresponds to the concentration sampled at time X, Clcreat is the creatinine clearance calculated using the modified Schwartz formula, Amt is the dose, BSA is the body surface area calculated using the Mostellers formula.

Fig 4

Variable importance plot obtained by random permutation for GCV. The score obtained quantifies the impact of each variable on the model’s predictions. Out_X corresponds to the concentration sampled at time X, Clcreat is the creatinine clearance calculated using the modified Schwartz formula, Amt is the dose.

Fig 5

Scatter plot of AUC0-24s estimated using the Xgboost algorithm based on two points at 2 and 6 hours vs reference trapezoidal AUC0-24 in the test set for VGCV, and corresponding Bland–Altman plots for different classes of age (<5, 5–10, and >10 years old). The difference is the difference between the Reference and the Xgboost AUC0-24s, and the mean is the average of both.

Fig 6

Scatter plot of AUC0-24s estimated using the Xgboost algorithm based on two points at 1 and 6 hours vs reference trapezoidal AUC0-24 in the test set for GCV, and corresponding Bland–Altman plots for different classes of age (<5, 5–10, and >10 years old). The difference is the difference between the Reference and the Xgboost AUC0-24s, and the mean is the average of both.