Nonlinear Mixed-Effects and Full Bayesian Population Pharmacokinetic Analysis of Ceftolozane-Tazobactam in Critically Ill Patients

1.Pharmacokinetic studies in critically ill patients are often constrained by small sample sizes, limiting the strength and generalizability of conclusions drawn solely from observed data. Bayesian inference offers a powerful strategy to address this challenge by incorporating prior knowledge. In this study, we evaluated two model-based approaches for characterizing the population pharmacokinetics of ceftolozane and tazobactam in critically ill patients, comparing nonlinear mixed-effects modeling with Bayesian hierarchical analyses. The Bayesian methods incorporated literature-derived prior information. The data was collected from 13 critically ill patients receiving 3.0 g of ceftolozane combined with tazobactam (2:1) via intravenous infusion. Pharmacokinetic modeling was performed using NONMEM and Stan software with the Torsten extension. Model diagnostics and graphical analyses were conducted in RStudio with relevant packages. In the absence of prior information, a one-compartment model with a limited set of parameters describing inter-individual variability adequately characterized the pharmacokinetics of ceftolozane and tazobactam. When prior information was incorporated, a two-compartment model became feasible and yielded a characterization of parameter variability and correlations that was more consistent with published literature. The application of Bayesian inference ensured alignment with existing literature on ceftolozane and tazobactam pharmacokinetics and mitigated some systematic biases observed in the data-driven approaches. Moreover, the Bayesian approach enables direct decision-making by incorporating uncertainty into the analysis, as demonstrated by probability of target attainment analysis. Collectively, these results underscore the utility of Bayesian methods in pharmacokinetic modeling for critically ill patients, offering a robust framework for optimizing dosing strategies in data-limited settings.