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      A Population Pharmacokinetic Model to Predict the Individual Starting Dose of Tacrolimus Following Pediatric Renal Transplantation

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          Abstract

          Background

          Multiple clinical, demographic, and genetic factors affect the pharmacokinetics of tacrolimus in children, yet in daily practice, a uniform body-weight based starting dose is used. It can take weeks to reach the target tacrolimus pre-dose concentration.

          Objectives

          The objectives of this study were to determine the pharmacokinetics of tacrolimus immediately after kidney transplantation and to find relevant parameters for dose individualization using a population pharmacokinetic analysis.

          Methods

          A total of 722 blood samples were collected from 46 children treated with tacrolimus over the first 6 weeks after renal transplantation. Non-linear mixed-effects modeling (NONMEM ®) was used to develop a population pharmacokinetic model and perform a covariate analysis. Simulations were performed to determine the optimal starting dose and to develop dosing guidelines.

          Results

          The data were accurately described by a two-compartment model with allometric scaling for bodyweight. Mean tacrolimus apparent clearance was 50.5 L/h, with an inter-patient variability of 25%. Higher bodyweight, lower estimated glomerular filtration rate, and higher hematocrit levels resulted in lower total tacrolimus clearance. Cytochrome P450 3A5 expressers and recipients who received a kidney from a deceased donor had a significantly higher tacrolimus clearance. The model was successfully externally validated. In total, these covariates explained 41% of the variability in clearance. From the significant covariates, the cytochrome P450 3A5 genotype, bodyweight, and donor type were useful to adjust the starting dose to reach the target pre-dose concentration. Dosing guidelines range from 0.27 to 1.33 mg/kg/day.

          Conclusion

          During the first 6 weeks after transplantation, the tacrolimus weight-normalized starting dose should be higher in pediatric kidney transplant recipients with a lower bodyweight, those who express the cytochrome P450 3A5 genotype, and those who receive a kidney from a deceased donor.

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          Most cited references47

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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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            Long-term renal allograft survival in the United States: a critical reappraisal.

            Renal allograft survival has increased tremendously over past decades; this has been mostly attributed to improvements in first-year survival. This report describes the evolution of renal allograft survival in the United States where a total of 252 910 patients received a single-organ kidney transplant between 1989 and 2009. Half-lives were obtained from the Kaplan-Meier and Cox models. Graft half-life for deceased-donor transplants was 6.6 years in 1989, increased to 8 years in 1995, then after the year 2000 further increased to 8.8 years by 2005. More significant improvements were made in higher risk transplants like ECD recipients where the half-lives increased from 3 years in 1989 to 6.4 years in 2005. In low-risk populations like living-donor-recipients half-life did not change with 11.4 years in 1989 and 11.9 years in 2005. First-year attrition rates show dramatic improvements across all subgroups; however, attrition rates beyond the first year show only small improvements and are somewhat more evident in black recipients. The significant progress that has occurred over the last two decades in renal transplantation is mostly driven by improvements in short-term graft survival but long-term attrition is slowly improving and could lead to bigger advances in the future. ©2010 The Authors Journal compilation©2010 The American Society of Transplantation and the American Society of Transplant Surgeons.
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              Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP3A5 Genotype and Tacrolimus Dosing.

              Tacrolimus is the mainstay immunosuppressant drug used after solid organ and hematopoietic stem cell transplantation. Individuals who express CYP3A5 (extensive and intermediate metabolizers) generally have decreased dose-adjusted trough concentrations of tacrolimus as compared with those who are CYP3A5 nonexpressers (poor metabolizers), possibly delaying achievement of target blood concentrations. We summarize evidence from the published literature supporting this association and provide dosing recommendations for tacrolimus based on CYP3A5 genotype when known (updates at www.pharmgkb.org).
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                Author and article information

                Contributors
                +31 10 703 3202 , l.andrews@erasmusmc.nl
                Journal
                Clin Pharmacokinet
                Clin Pharmacokinet
                Clinical Pharmacokinetics
                Springer International Publishing (Cham )
                0312-5963
                1179-1926
                5 July 2017
                5 July 2017
                2018
                : 57
                : 4
                : 475-489
                Affiliations
                [1 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Hospital Pharmacy, Erasmus Medical Center, , University Medical Center Rotterdam, ; P. O. Box 2040, 3000 CA Rotterdam, The Netherlands
                [2 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Internal Medicine, Erasmus Medical Center, , University Medical Center Rotterdam, ; Rotterdam, The Netherlands
                [3 ]GRID grid.461578.9, Department of Pediatric Nephrology, Radboud University Medical Centre, , Amalia Children’s Hospital, ; Nijmegen, The Netherlands
                [4 ]ISNI 0000000122931605, GRID grid.5590.9, Department of Hospital Pharmacy, , Radboud University, ; Nijmegen, The Netherlands
                [5 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Clinical Chemistry, Erasmus Medical Center, , University Medical Center Rotterdam, ; Rotterdam, The Netherlands
                [6 ]ISNI 0000000122931605, GRID grid.5590.9, Department of Pharmacology and Toxicology, , Radboud University, ; Nijmegen, The Netherlands
                [7 ]GRID grid.416135.4, Department of Pediatric Nephrology, Erasmus Medical Center, , Sophia Children’s Hospital, ; Rotterdam, The Netherlands
                Author information
                http://orcid.org/0000-0001-8271-3685
                Article
                567
                10.1007/s40262-017-0567-8
                5856873
                28681225
                eb1b09e4-e631-4610-ba7b-32c11a5bcbeb
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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                © Springer International Publishing AG, part of Springer Nature 2018

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