# A limited sampling strategy for estimating busulfan exposure in pediatric hematopoietic stem cell transplantation

## TL;DR

This study develops a limited sampling strategy to estimate busulfan exposure in children undergoing stem cell transplants, improving drug monitoring accuracy.

## Contribution

A novel limited sampling strategy using multiple linear regression is proposed for accurate busulfan AUC prediction in pediatric patients.

## Key findings

- The four-point model (60, 135, 240, and 360 min) showed the highest accuracy with an adjusted r² of 0.965.
- Models with 2–4 sampling points outperformed single-point models in predicting busulfan AUC0-360.
- External validation confirmed the predictive accuracy of the four-point model in additional cases.

## Abstract

Busulfan (Bu) is the foundation of conditioning regimens for pediatric hematopoietic stem cell transplantation (HSCT). Evidence indicates that the efficacy and side effects of Bu are intimately tied to the area under its concentration-time curve (AUC). Given its cytotoxic nature and a small therapeutic index, coupled with marked inter-individual pharmacokinetic variability, Bu requires therapeutic drug monitoring to facilitate individualized therapy. However, research investigating the relationship between Bu exposure and clinical outcomes among the Chinese population remains scarce. This study aimed to develop a limited sampling strategy (LSS) for estimating Bu exposure in pediatric HSCT recipients using multiple linear regression (MLR) analysis to predict the AUC0-360.

We enrolled 26 pediatric patients who underwent Bu-based conditioning for HSCT. Blood samples were collected at 11 time points after Bu infusion. Pharmacokinetic parameters were calculated using non-compartmental methods. MLR models were developed using 1–4 sampling points to predict the AUC0-360. Model accuracy was assessed using the Jackknife and Bootstrap methods, with consistency evaluated via intraclass correlation coefficient (ICC) and Bland–Altman (BA) analyses.

The mean ± standard deviation (SD) for AUC0-t, mean residence time 0-t, clearance, and volume of distribution were 845.54 ± 111.03 μmol min/L, 181.37 ± 10.55 min, 0.23 ± 0.04 L/h/kg, and 0.73 ± 0.15 L/kg, respectively. Models with 2–4 sampling points showed improved prediction accuracy compared to single-point models. The four-point model (60, 135, 240 and 360 min) demonstrated the highest accuracy with an adjusted r
2 of 0.965. Internal validation confirmed the models’ stability and accuracy, with the four-point model exhibiting the best performance. External validation using three additional cases supported the predictive accuracy of the model.

The LSS model developed in this study accurately predicts the Bu AUC0-360 with 2–4 sampling points, offering a practical and clinically valuable tool for therapeutic drug monitoring in pediatric HSCT recipients. The four-point model was found to be the most accurate and is recommended for clinical applications.