Defining Optimal Sampling Strategies for Cefepime Model-Informed Precision Dosing.

Area of Science:

• Pharmacokinetics and Pharmacodynamics
• Bayesian Pharmacometrics
• Drug Dosing Optimization

Background:

• Population pharmacokinetic (PK) models are crucial for optimizing drug dosing regimens.
• Bayesian estimation enhances the precision of PK predictions.
• Evaluating the impact of sample collection timing on Bayesian prediction accuracy is essential for clinical application.

Purpose of the Study:

• To develop a cefepime population PK model for Bayesian prior use.
• To assess the influence of sample collection time on the accuracy and precision of Bayesian predictions.
• To determine optimal sampling times for cefepime dosing regimens.

Main Methods:

• A 2-compartment population PK model for cefepime was developed using adult and pediatric data.
• Holdout data were used for model evaluation and optimal sample-time analysis.
• Bayesian predictions were assessed based on infusion duration and deviations from optimal sampling times using Pmetrics for R.

Main Results:

• The population PK model demonstrated an acceptable fit to holdout data (R2 = 0.923).
• Mid-interval sampling was optimal for 1-sample designs, while peak and trough sampling were often optimal for 2-sample designs.
• Prediction imprecision increased significantly for 0.5-hour infusions when samples were collected >2 hours from the optimal time.

Conclusions:

• A nonparametric cefepime population PK model can be effectively used as a Bayesian prior.
• Optimal PK sample collection timing is dependent on the specific dosing regimen and infusion duration.
• Deviations from optimal sampling times, particularly for short infusions, can reduce the precision of Bayesian estimates.