Comparing two-sample log-linear exposure estimation with Bayesian model-informed precision dosing of tobramycin in adult patients with cystic fibrosis

Tobramycin dosing in patients with cystic fibrosis (CF) is challenged by its high pharmacokinetic (PK) variability and narrow therapeutic window. Doses are typically individualized using two-sample log-linear regression (LLR) to quantify the area under the concentration-time curve (AUC). Bayesian model-informed precision dosing (MIPD) may allow dose individualization with fewer samples; however, the relative performance of these methods is unknown. This single-center retrospective analysis included adult patients with CF receiving tobramycin from 2015 to 2022. Tobramycin concentrations were predicted using LLR or Bayesian estimation with two population PK models (Hennig and Alghanem). Then, both methods were used to estimate the AUC for simulated patients. For Bayesian estimation, AUC estimation with flattened priors and limited sampling strategies were also assessed. Predictions were evaluated using normalized root mean square error (nRMSE), mean percent error (MPE), and accuracy. The data set included 70 treatment courses, with 32 not evaluable by LLR due to detection limits or timing issues. Bayesian estimation demonstrated worse accuracy (47.1%-50.7% vs 75.7%), higher MPE (24.2%-32.4% vs -2.4%), and higher nRMSE (35.0%-39.4% vs 24.8%) than LLR for peak concentrations but performed better on troughs (accuracy: 92.0%-92.9% vs 84.6%). Bayesian estimation with flattened priors and a single sample at 4 h was comparable to LLR performance, with better accuracy (42.9%-68.0% vs 41.1% LLR), comparable MPE (-2.3% to -3.7% vs -0.5%) and nRMSE (11.3%-21.6% vs 17.3%). Bayesian estimation with one concentration and flattened priors can match LLR prediction accuracy. However, popPK models must be improved to better estimate peak samples.