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Daily intravenous (i.v.) busulfan is increasingly being used in hematopoietic cell transplantation (HCT) conditioning regimens. Intravenous busulfan doses administered at the traditional frequency of every 6 hours can be targeted ((T)Bu) to a patient-specific concentration at steady state (C(ss)) using therapeutic drug monitoring (TDM). In this report, we describe our experiences with TDM of daily i.v. busulfan in an adult population, with the specific aims of (1) evaluating covariates associated with busulfan clearance, and (2) assessing the feasibility of TDM for outpatient administration of daily (T)Bu with pharmacokinetic sampling over 6 hours. A retrospective pharmacokinetic analysis was conducted in 87 adults receiving daily (T)Bu as part of cyclophosphamide followed by (T)BU (CY/(T)BU), fludarabine monophosphate (fludarabine) followed by (T)BU, or (T)BU concurrent with fludarabine conditioning. The desired C(ss) was achieved in 85% of patients receiving daily i.v. busulfan. Busulfan clearance was not associated with sex or age, but was associated with the day of dosing and conditioning regimen (P = .0016). In patients receiving CY/(T)BU, no differences in clearance were found between dosing days (P.36); however, clearance decreased significantly in patients receiving fludarabine-based regimens (P = .0016). Busulfan clearance and C(ss) estimates from pharmacokinetic sampling over 8, 11, or 24 hours were comparable (P.4). However, pharmacokinetic modeling of individual patient concentration-time data over 6 hours could not reliably estimate busulfan clearance or C(ss).

Kinetics-based dose targeting is often conducted in hematopoietic cell transplant (HCT) patients conditioned with intravenous (IV) or oral busulfan to lower rates of rejection, nonrelapse mortality, and relapse. Using the candidate gene approach, the authors evaluated whether busulfan clearance was associated with polymorphisms in the genes regulating the predominant metabolizing enzymes involved in busulfan conjugation, specifically glutathione S-transferase (GST) isoenzymes A1 (GSTA1) and M1 (GSTM1). Busulfan clearance was estimated after the morning dose on days 1, 2, and 3; each patient's average clearance was used for analyses. The average (± standard deviation) busulfan clearance was 3.2 ± 0.56 mL/min/kg in the separate population of 95 patients who received oral busulfan and 103 ± 24 ml/min/m(2) in the 57 patients who received IV busulfan. Oral busulfan clearance was associated with GSTA1 (P = .008) but not GSTM1 (P = .57) genotypes. However, among the GSTA1 haplotypes (ie, *A*A, *A*B, *B*B), there was significant overlap in the observed oral busulfan clearance and similar rates of achieving the target busulfan exposure. Clearance of IV busulfan was not associated with GSTA1 (P = .21) or GSTM1 (P = .99). These data suggest that personalizing either IV or oral busulfan dosing cannot be simplified on the basis of GSTA1 or GSTM1 genotype.

Therapeutic drug monitoring of daily intravenous (IV) busulfan currently requires hospital admission. Population pharmacokinetic modeling and determination of an optimal pharmacokinetic sampling schedule over 6 hours could allow for personalizing these busulfan doses in the outpatient clinic. A retrospective evaluation of daily IV busulfan pharmacokinetics was conducted in 37 adults. SPK and NONMEM software were used to estimate the population pharmacokinetic parameters. Subsequent to model building, the area under the concentration-time curve (AUC) was computed using NONMEM. A 1-compartment model best fit the data. The optimal 6-hour outpatient sampling schedule was constructed using a simulation approach that sought to minimize scaled mean squared error for the clearance and volume parameters for each simulated individual. The best sampling times were 2.75, 3, 3.25, 5.5, 5.75, and 6 hours from the start of a 3-hour infusion. With these sampling times, the maximum a posteriori (MAP) Bayesian estimation was superior to maximum likelihood estimation with more samples. An individual patient's busulfan AUC and pharmacokinetic parameters may be accurately estimated with an outpatient sampling schedule that is used in conjunction with MAP Bayesian estimation, with a parameter prior based on population pharmacokinetic modeling. Prospective validation of this approach is needed.