Bayesian forecasting for intravenous tobramycin dosing in adults with cystic fibrosis using one versus two serum concentrations in a dosing interval.

BACKGROUND Intravenous tobramycin treatment requires therapeutic drug monitoring (TDM) to ensure safety and efficacy when used for prolonged treatment, as in infective exacerbations of cystic fibrosis (CF). The 24-h area under the concentration-time curve (AUC24) is widely used to guide dosing; however, there remains variability in practice around methods for its estimation. OBJECTIVES To determine the potential for a sparse sampling strategy using a single post-infusion tobramycin concentration and Bayesian forecasting to assess the AUC24 in routine practice. METHODS Adults with CF receiving once daily tobramycin had paired concentrations measured 2 h (c1) and 6 h (c2) following the end of infusion as routine monitoring. AUC24 exposures were estimated using Tucuxi, a Bayesian forecasting application that incorporates a validated population pharmacokinetic model. Simulations were performed to estimate AUC24 using the full dataset using c1 and c2, compared to estimates using depleted datasets (c1 or c2 only), with and without concentration data from earlier in the course. The agreement between each simulation condition and the reference was assessed graphically and numerically using the median difference ([INCREMENT]) AUC24 and (relative) root mean square error (rRMSE) as measures of bias and accuracy, respectively. RESULTS A total of 55 patients contributed 512 concentrations from 95 tobramycin courses and 256 TDM episodes. Single concentration methods performed well, with median [INCREMENT]AUC24 <2 mg.h.l-1 and rRMSE of <15% for sequential c1 and c2 conditions. CONCLUSIONS Bayesian forecasting implemented in Tucuxi, using single post-infusion concentrations taken 2-6 h following tobramycin administration, yield similar exposure estimates to more intensive (two-sample) methods, and are suitable for routine TDM practice.