Development of Population and Bayesian Models for Applied Use in Patients Receiving Cefepime

Background and Objective Understanding pharmacokinetic disposition of cefepime, a β-lactam antibiotic, is crucial for developing regimens to achieve optimal exposure and improved clinical outcomes. This study sought to develop and evaluate a unified population pharmacokinetic model in both pediatric...

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Bibliographic Details
Published in:Clinical pharmacokinetics 2020-08, Vol.59 (8), p.1027-1036
Main Authors: Liu, Jiajun, Neely, Michael, Lipman, Jeffrey, Sime, Fekade, Roberts, Jason A., Kiel, Patrick J., Avedissian, Sean N., Rhodes, Nathaniel J., Scheetz, Marc H.
Format: Article
Language:English
Subjects:
Adult
Adults
Anti-Bacterial Agents - pharmacokinetics
Antibiotics
Bacterial infections
Bayes Theorem
Bias
Cefepime - pharmacokinetics
Child
Clinical outcomes
Creatinine
Drug dosages
Humans
Internal Medicine
Medicine
Medicine & Public Health
Neutropenia
Nosocomial infections
Original
Original Research Article
Patients
Pediatrics
Pharmacodynamics
Pharmacokinetics
Pharmacology/Toxicology
Pharmacotherapy
Population
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Summary:Background and Objective Understanding pharmacokinetic disposition of cefepime, a β-lactam antibiotic, is crucial for developing regimens to achieve optimal exposure and improved clinical outcomes. This study sought to develop and evaluate a unified population pharmacokinetic model in both pediatric and adult patients receiving cefepime treatment. Methods Multiple physiologically relevant models were fit to pediatric and adult subject data. To evaluate the final model performance, a withheld group of 12 pediatric patients and two separate adult populations were assessed. Results Seventy subjects with a total of 604 cefepime concentrations were included in this study. All adults ( n  = 34) on average weighed 82.7 kg and displayed a mean creatinine clearance of 106.7 mL/min. All pediatric subjects ( n  = 36) had mean weight and creatinine clearance of 16.0 kg and 195.6 mL/min, respectively. A covariate-adjusted two-compartment model described the observed concentrations well (population model R 2 , 87.0%; Bayesian model R 2 , 96.5%). In the evaluation subsets, the model performed similarly well (population R 2 , 84.0%; Bayesian R 2 , 90.2%). Conclusion The identified model serves well for population dosing and as a Bayesian prior for precision dosing.
ISSN:0312-5963
1179-1926
DOI:10.1007/s40262-020-00873-3