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

ABSTRACT: 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 esti

SUBMITTER: Tong DMH 

PROVIDER: S-EPMC11823644 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature