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      Population Pharmacokinetics of Metoclopramide in Infants, Children, and Adolescents

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          Abstract

          Metoclopramide is commonly used for gastroesophageal reflux. The aims of the present study were to develop a pediatric population pharmacokinetic (PopPK) model, which was applied to simulate the metoclopramide exposure following dosing used in clinical practice. Opportunistic pharmacokinetic data were collected from pediatric patients receiving enteral or parenteral metoclopramide per standard of care and these data were simultaneously fitted using NONMEM. Allometric scaling with body weight was included a priori in the model. Using the final model, the steady‐state maximum concentrations (C ss,max) and the area under the metoclopramide plasma concentration‐time curve at steady state from 0 to 6 hours (AUC ss,0–6h) were simulated following 0.1 or 0.15 mg/kg orally every 6 hours in virtual patients, and compared with previously reported ranges associated with toxicity or the efficacy for gastroesophageal reflux in infants. A two‐compartment model with first‐order absorption best characterized 87 concentration measurements from 50 patients (median [range] postnatal age of 8.89 years [0.01–19.13]). There were 20 infants (≤ 2 years), 9 children (2 years to age ≤ 12 years), and 21 adolescents (> 12 years). Body weight was the only covariate included in the final model. For > 75% of virtual patients, simulated C ss,max and AUC ss,0–6h estimates were within the range associated with efficacy for gastroesophageal reflux in infants; however, slightly lower exposures were predicted in virtual patients < 2 years. Our study suggests that a metoclopramide enteral dose of 0.1 mg/kg every 6 hours, which was previously recommended for pediatric patients, results in simulated exposure generally within suggested ranges for the treatment of gastroesophageal reflux.

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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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            Perl-speaks-NONMEM (PsN)--a Perl module for NONMEM related programming.

            The NONMEM program is the most widely used nonlinear regression software in population pharmacokinetic/pharmacodynamic (PK/PD) analyses. In this article we describe a programming library, Perl-speaks-NONMEM (PsN), intended for programmers that aim at using the computational capability of NONMEM in external applications. The library is object oriented and written in the programming language Perl. The classes of the library are built around NONMEM's data, model and output files. The specification of the NONMEM model is easily set or changed through the model and data file classes while the output from a model fit is accessed through the output file class. The classes have methods that help the programmer perform common repetitive tasks, e.g. summarising the output from a NONMEM run, setting the initial estimates of a model based on a previous run or truncating values over a certain threshold in the data file. PsN creates a basis for the development of high-level software using NONMEM as the regression tool.
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              A simple estimate of glomerular filtration rate in adolescent boys.

              We reexamined the relationship between creatinine clearance (Ccr) and body habitus in 212 girls and 356 boys, including 181 boys and 69 girls between 13 and 21 years of age. The use of formula Ccr = k L/Pcr, where k = 0.55 for the calculation of GFR, resulted in a significant underestimation of GFR in adolescent boys but was suitable for girls. In 51 adolescent boys the equation Ccr = 0.7 L/Pcr resulted in an accurate estimate of GFR. Regression analysis in 133 boys aged 3 to 21 years showed that the constant k increased gradually and linearly with age (r = 0.35, P less than 0.01). GFR could be better estimated for boys of any age by the linear bivariate equation Ccr = 1.5 (age) + 0.5 (L/Pcr), where age is given in years (r = 0.82, P less than 0.001). This equation yielded slightly better results than did 0.7 L/Pcr in 91 additional clearance studies performed in adolescent boys with native kidneys or functioning renal transplants. The larger value for the constant k (0.7) and the age correction for GFR reflect the greater rate of urinary creatine excretion (and thus muscle mass) per unit of body mass in adolescent boys.
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                Author and article information

                Contributors
                daniel.gonzalez@unc.edu
                Journal
                Clin Transl Sci
                Clin Transl Sci
                10.1111/(ISSN)1752-8062
                CTS
                Clinical and Translational Science
                John Wiley and Sons Inc. (Hoboken )
                1752-8054
                1752-8062
                27 May 2020
                November 2020
                : 13
                : 6 ( doiID: 10.1111/cts.v13.6 )
                : 1189-1198
                Affiliations
                [ 1 ] Division of Pharmacotherapy and Experimental Therapeutics UNC Eshelman School of Pharmacy The University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
                [ 2 ] University of Maryland School of Medicine Baltimore Maryland USA
                [ 3 ] Duke Clinical Research Institute Durham North Carolina USA
                [ 4 ] Department of Pediatrics Duke University School of Medicine Durham North Carolina USA
                [ 5 ] Kosair Charities Pediatric Clinical Research Unit Department of Pediatrics University of Louisville Louisville Kentucky USA
                [ 6 ] Norton Children's Hospital Louisville Kentucky USA
                [ 7 ] Medical University of South Carolina Children's Hospital Charleston South Carolina USA
                [ 8 ] Wesley Medical Center Wichita Kansas USA
                [ 9 ] School of Medicine University of California‐San Diego San Diego California USA
                [ 10 ] The Emmes Company, LLC Rockville Maryland USA
                Author notes
                [*] [* ] Correspondence: Daniel Gonzalez ( daniel.gonzalez@ 123456unc.edu )

                [‡]

                See Acknowledgments for listing of committee members.

                Article
                CTS12803
                10.1111/cts.12803
                7719387
                32324313
                0cd4d73f-cef0-4e16-becd-4c95a7c37c1b
                © 2020 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society of Clinical Pharmacology and Therapeutics.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 19 December 2019
                : 06 April 2020
                Page count
                Figures: 4, Tables: 2, Pages: 10, Words: 7642
                Funding
                Funded by: National Institute of Child Health and Human Development (NICHD) , open-funder-registry 10.13039/100000071;
                Award ID: HHSN275201000003I
                Award ID: K23HD083465
                Award ID: R01HD096435
                Funded by: National Institutes of Health , open-funder-registry 10.13039/100000002;
                Categories
                Article
                Research
                Articles
                Custom metadata
                2.0
                November 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.5 mode:remove_FC converted:06.12.2020

                Medicine
                Medicine

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