Comparing the Predictive Performance of Machine Learning Models with a Newly Developed Population Pharmacokinetic Model for Busulfan in Paediatric Patients.

Background

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Objectives

Busulfan, used in conditioning regimens prior to haematopoietic stem cell transplantation, has a narrow therapeutic index; therefore, due to pharmacokinetic variability, therapeutic drug monitoring (TDM) is employed to ensure drug exposure levels associated with optimal clinical outcomes. This study aimed to facilitate dose adjustment and potentially reduce the number of samples required for TDM in clinical practice.

Methods

Population pharmacokinetic (popPK) and machine learning (ML) models (Random Forest and XGBoost) were developed using a local, retrospective, extensive-sampling dataset of 67 children (637 observations, Denmark) and externally evaluated using prediction error (PE) on a TDM dataset from another centre with 171 children (1871 observations, Netherlands). Limited sampling schedules were evaluated for both methods.

Results

A one-compartment model was the best data fit, showing time-varying clearance decreases of 10.4% and 13% on Days 2 and 3, respectively, compared to Day 1. The popPK, Random Forest, and XGBoost models performed well on the external dataset for concentration prediction, with median PE ranging from -4.82 to -7.44%, median absolute PE of approximately 15%, and approximately 80% of data predicted within ±30% of the PE. Sampling strategies at 0, 0.5, 1, 4 h or 0, 1, 2, 4 h post-infusion adequately predicted busulfan plasma concentration and exposure using the popPK model.

Conclusions

A new popPK model was developed to support individualised dose adjustments in children using fewer blood samples. Both Random Forest and XGBoost could predict busulfan's plasma concentration with performance like that of the popPK model (population prediction). However, a larger dataset is needed for ML methods to describe a complete pharmacokinetic profile.