External Evaluation of Population Pharmacokinetic Models to Inform Precision Dosing of Meropenem in Critically Ill Patients - Yang, Nan; Wang, Jing; Xie, Yueliang; Ding, Junjie; Wu, Cuifang; Liu, Jingjing; Pei, Qi (Frontiers in Pharmacology, 2022)
Routine clinical meropenem therapeutic drug monitoring data can be applied in model-informed precision dosing. The current study aimed to evaluate the adequacy and predictive capabilities of the published models with routine meropenem data, and identify the dosing adaptations using a priori and Bayesian estimation. 14 meropenem models for the external evaluation, carried out on an independent cohort of 134 patients with 205 meropenem concentrations, were encoded in NONMEM 7.3. The performance was determined using: (1) prediction-based and simulation-based diagnostics, and (2) predicted meropenem concentrations by a priori prediction using patient covariates only and Bayesian forecasting using previous observations. The clinical implication was assessed according to the required dose adaptations using the meropenem concentrations. All assessments were stratified based on the patients with or without continuous renal replacement therapy. Though none of the model passed all tests, the model by Muro et al. showed least bias. Bayesian forecasting could improve the predictability over an a priori approach, with relative bias of -11.63%–68.89% and -302.96%–36.46%, and relative root mean squared error of 34.99%–110.11% and 90.17%–240.81%, respectively. A dosing change was required in 40.00% to 68.97% of the meropenem observations results after Bayesian forecasting. In summary, the published models couldn’t adequately describe the meropenem pharmacokinetics of our center. Though the selection of an initial meropenem dose with a priori prediction is challenging, the further model-based analysis combining therapeutic drug monitoring could be utilized in the clinical practice of meropenem therapy.