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      Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling

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          The aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models.


          Data were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting.


          Pharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60 kg was estimated to be 16.1 l h −1 [95% CI 12.6, 18.0 l h −1]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An E max model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of −1.2% (95% CI −3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range.


          A theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.

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          Most cited references 43

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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.

                Author and article information

                Br J Clin Pharmacol
                Br J Clin Pharmacol
                British Journal of Clinical Pharmacology
                John Wiley & Sons Ltd (Oxford, UK )
                September 2014
                20 February 2014
                : 78
                : 3
                : 509-523
                [1 ]Department of Transplant Medicine, Oslo University Hospital Rikshospitalet Oslo, Norway
                [2 ]Institute of Clinical Medicine, University of Oslo Oslo, Norway
                [3 ]Department of Pharmacology and Clinical Pharmacology, University of Auckland Auckland, New Zealand
                [4 ]School of Pharmacy, University of Queensland Brisbane, Australia
                [5 ]Australian Centre of Pharmacometrics Brisbane, Australia
                [6 ]Department of Clinical Pharmacology, Aarhus University Hospital Aarhus, Denmark
                [7 ]Department of Pharmacology, Oslo University Hospital Oslo, Norway
                [8 ]School of Pharmacy, University of Oslo Oslo, Norway
                [9 ]Department of Medical Biochemistry, Oslo University Hospital Oslo, Norway
                Author notes
                Ms Elisabet Størset MSc, Department of Transplant Medicine, Oslo University Hospital Rikshospitalet, Postbox 4950 Nydalen, Oslo 0424, Norway., Tel.: +47 2307 0000, Fax: +47 2307 3865, E-mail: elisabet.storset@
                © 2014 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of The British Pharmacological Society.

                This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 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.



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