Bayesian Estimation of Tobramycin Exposure in Patients with Cystic Fibrosis

Michael A. Barras, David Serisier, Stefanie Hennig, Katrina Jess, Ross L. G. Norris

Fixed tobramycin (mg/kg) dosing is often inappropriate in patients with cystic fibrosis (CF) as pharmacokinetics are highly variable. The area under the concentration-time curve (AUC) is an exposure metric suited to monitoring in this population. Bayesian strategies to estimate AUC have been available for over 20 years but are not standard practice in the clinical setting. To assess their suitability for use in clinical practice three AUC estimation methods using limited sampling were compared to measured true exposure using intensive sampling tobramycin data. Adults prescribed once daily intravenous tobramycin had eight concentrations taken over 24-hours. An estimate of true exposure within one dosing interval was calculated using the trapezoidal method and compared to three alternate estimates determined using a; 1) 2-sample log-linear regression (LLR) method (local hospital practice); 2) Bayesian estimate using 1-concentration (AUC1); 3) Bayesian estimate using 2-concentrations (AUC2). Each method was evaluated against the true measured exposure using a Bland-Altman analysis. Twelve patients were recruited with a median (range) age and weight of 25 (18-36) years and 66.5 (51-76) respectively. There was good agreement between the true exposure using intensive sampling using the traditional trapezoidal method and the three alternate estimates of AUC, with a mean AUC bias less than 10 mg/L.hr in each case; -8.2 (LLR), 3.8 (AUC1) and 1.0 (AUC2). All three methods may be suitable for use in the clinical setting, however a 1-sample Bayesian method may be most useful in ambulatory patients where coordinating blood samples is difficult. Bayesian based and LLR estimation methods of tobramycin AUC are equivalent to true exposure. However, Bayesian methods using 1-concentration may provide a more practical method. Suitably powered, randomised, clinical trials are required to assess patient outcomes.