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      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

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          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 m 2 (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.

          Electronic supplementary material

          The online version of this article (10.1007/s40262-020-00859-1) contains supplementary material, which is available to authorized users.

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          Most cited references42

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          Prediction of Creatinine Clearance from Serum Creatinine

          A formula has been developed to predict creatinine clearance (C cr ) from serum creatinine (S cr ) in adult males: Ccr = (140 – age) (wt kg)/72 × S cr (mg/100ml) (15% less in females). Derivation included the relationship found between age and 24-hour creatinine excretion/kg in 249 patients aged 18–92. Values for C cr were predicted by this formula and four other methods and the results compared with the means of two 24-hour C cr’s measured in 236 patients. The above formula gave a correlation coefficient between predicted and mean measured Ccr·s of 0.83; on average, the difference between predicted and mean measured values was no greater than that between paired clearances. Factors for age and body weight must be included for reasonable prediction.
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            Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models.

            Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.
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              Alternatives to the Median Absolute Deviation

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                Author and article information

                Contributors
                f.develde@erasmusmc.nl
                Journal
                Clin Pharmacokinet
                Clin Pharmacokinet
                Clinical Pharmacokinetics
                Springer International Publishing (Cham )
                0312-5963
                1179-1926
                20 January 2020
                20 January 2020
                2020
                : 59
                : 7
                : 885-898
                Affiliations
                [1 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Medical Microbiology and Infectious Diseases, , Erasmus University Medical Center, ; Rotterdam, The Netherlands
                [2 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Hospital Pharmacy, , Erasmus University Medical Center, ; Rotterdam, The Netherlands
                [3 ]Children’s Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA USA
                [4 ]GRID grid.150338.c, ISNI 0000 0001 0721 9812, Division of Infectious Diseases, , Geneva University Hospitals, Faculty of Medicine, ; Geneva, Switzerland
                [5 ]GRID grid.150338.c, ISNI 0000 0001 0721 9812, Infection Control Program, , Geneva University Hospitals, Faculty of Medicine, ; Geneva, Switzerland
                Author information
                http://orcid.org/0000-0003-4581-9688
                Article
                859
                10.1007/s40262-020-00859-1
                7329758
                31956969
                0e506163-4180-4064-b34a-eeebea335e81
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

                History
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100010767, Innovative Medicines Initiative;
                Award ID: 115523
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100006389, Université de Genève;
                Award ID: PRD 09-II-025
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100011201, FP7 Science in Society;
                Award ID: Health-F3-2011-278348
                Award Recipient :
                Categories
                Original Research Article
                Custom metadata
                © Springer Nature Switzerland AG 2020

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