Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate

Abstract

Background: Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results.

Methods: Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m² (interquartile range 104-124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software.

Results: For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (K_e). The parametric population parameter estimates were K_e 0.637 h^-1 (between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (V_c) 29.6 L (without BSV). The nonparametric values were K_e 0.681 h^-1 (34.0% CV) and V_c 31.1 L (42.6% CV).

Conclusions: Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies.

Fig. 1

Distribution of K_e in the NONMEM and Pmetrics popPK models. NONMEM: normal distribution (mean 0.637 h⁻¹ and SD 0.121 h⁻¹ [CV 19.0%]). Pmetrics: marginal distribution of 16 support points with 11 unique values for K_e (weighted mean 0.681 h⁻¹ and SD 0.232 h⁻¹ [CV 34.0%]). K_e elimination rate constant, popPK population pharmacokinetics, SD standard deviation, CV coefficient of variation

Fig. 2

Goodness-of-fit plots with observed against predicted concentrations of both models. a Goodness-of-fit plots of the final parametric model. The log-transformed concentrations are back-transformed for easier comparison with the untransformed concentrations in Fig. 2b. b Goodness-of-fit plots of the final nonparametric model. Solid line represents the identity (1:1) line, and the dotted line represents the regression line. Conc. concentration

Fig. 3

VPCs of both models. a VPC of the final parametric model. The log-transformed concentrations are back-transformed for easier comparison with the untransformed concentrations in Fig. 3b. b VPC of the final nonparametric model. Circles represent observed concentrations; upper, middle and lower lines represent the 95th, 50th and 5th percentile of observations, respectively; and shaded areas represent the 95% confidence interval of the corresponding percentiles of predictions. VPCs visual predictive checks