RESUMO
AIM: The aim of this investigation was to develop a model-based dosing algorithm for busulfan and identify an optimal sampling scheme for use in routine clinical practice. METHODS: Clinical data from an ongoing study (n = 29) in stem cell transplantation patients were used for the purposes our analysis. A one compartment model was selected as basis for sampling optimization and subsequent evaluation of a suitable dosing algorithm. Internal and external model validation procedures were performed prior to the optimization steps using ED-optimality criteria. Using systemic exposure as parameter of interest, dosing algorithms were considered for individual patients with the scope of minimizing the deviation from target range as determined by AUC(0,6 h). RESULTS: Busulfan exposure after oral administration was best predicted after the inclusion of adjusted ideal body weight and alanine transferase as covariates on clearance. Population parameter estimates were 3.98 h(-1), 48.8 l and 12.3 l h(-1) for the absorption rate constant, volume of distribution and oral clearance, respectively. Inter-occasion variability was used to describe the differences between test dose and treatment. Based on simulation scenarios, a dosing algorithm was identified, which ensures target exposure values are attained after a test dose. Moreover, our findings show that a sparse sampling scheme with five samples per patient is sufficient to characterize the pharmacokinetics of busulfan in individual patients. CONCLUSION: The use of the proposed dosing algorithm in conjunction with a sparse sampling scheme may contribute to considerable improvement in the safety and efficacy profile of patients undergoing treatment for stem cell transplantation.