Is One Sample Enough? β-Lactam Target Attainment and Penetration into Epithelial Lining Fluid Based on Multiple Bronchoalveolar Lavage Sampling Time Points in a Swine Pneumonia Model.

Abstract

Describing the disposition of antimicrobial agents at the site of infection is crucial to guide optimal dosing for investigational agents. For antibiotics in development for the treatment of nosocomial pneumonia, concentrations in the epithelial lining fluid (ELF) of the lung are frequently determined from a bronchoscopy at a single time point. The influence of profiles constructed from a single ELF concentration point for each subject has never been reported. This study compares the pharmacokinetics of two β-lactams, ceftolozane and piperacillin, among different ELF sampling approaches using simulated human regimens in a swine pneumonia model. Plasma and ELF concentration-time profiles were characterized in two-compartment models by the use of robustly sampled ELF concentrations and by the random selection of one or two ELF concentrations from each swine. A 5,000-subject Monte Carlo simulation was performed for each model to define the ELF penetration, as described by the ratio of the area under the concentration curve (AUC) for ELF to the AUC for free drug in plasma (AUCELF/fAUCplasma) and the probability of target attainment (PTA). Given the intersubject variability of the ELF penetrations observed, differences between the models developed using robust numbers of ELF samples versus one or two ELF samples per swine were minimal for both drugs (maximum dispersion < 20%). Using a threshold exposure target of 60% of the time that the free drug concentration remains above the MIC target, the ceftolozane and piperacillin regimens achieved PTAs of ≥90% at MICs of up to 4 and 1 μg/ml, respectively, among the different ELF sampling strategies. These models suggest that the ELF models constructed with concentrations from sparse ELF sampling time points result in exposure estimates similar to those constructed from robustly sampled ELF profiles.