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

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 AUC_0-360.

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 AUC_0-360. 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 AUC_0-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 AUC_0-360 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.