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A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients
AIMS: The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient. METHODS: Data on tacrolimus exposure were collected for the first 3 months following renal...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6379219/ https://www.ncbi.nlm.nih.gov/pubmed/30552703 http://dx.doi.org/10.1111/bcp.13838 |
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author | Andrews, L. M. Hesselink, D. A. van Schaik, R. H. N. van Gelder, T. de Fijter, J. W. Lloberas, N. Elens, L. Moes, D. J. A. R. de Winter, B. C. M. |
author_facet | Andrews, L. M. Hesselink, D. A. van Schaik, R. H. N. van Gelder, T. de Fijter, J. W. Lloberas, N. Elens, L. Moes, D. J. A. R. de Winter, B. C. M. |
author_sort | Andrews, L. M. |
collection | PubMed |
description | AIMS: The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient. METHODS: Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed‐effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. RESULTS: A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two‐compartment model. The mean absorption rate was 3.6 h(−1), clearance was 23.0 l h(–1) (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: [Formula: see text] Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model. CONCLUSIONS: For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation. |
format | Online Article Text |
id | pubmed-6379219 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63792192019-02-28 A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients Andrews, L. M. Hesselink, D. A. van Schaik, R. H. N. van Gelder, T. de Fijter, J. W. Lloberas, N. Elens, L. Moes, D. J. A. R. de Winter, B. C. M. Br J Clin Pharmacol Original Articles AIMS: The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient. METHODS: Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed‐effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. RESULTS: A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two‐compartment model. The mean absorption rate was 3.6 h(−1), clearance was 23.0 l h(–1) (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: [Formula: see text] Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model. CONCLUSIONS: For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation. John Wiley and Sons Inc. 2019-01-17 2019-03 /pmc/articles/PMC6379219/ /pubmed/30552703 http://dx.doi.org/10.1111/bcp.13838 Text en © 2018 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Original Articles Andrews, L. M. Hesselink, D. A. van Schaik, R. H. N. van Gelder, T. de Fijter, J. W. Lloberas, N. Elens, L. Moes, D. J. A. R. de Winter, B. C. M. A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients |
title | A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients |
title_full | A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients |
title_fullStr | A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients |
title_full_unstemmed | A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients |
title_short | A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients |
title_sort | population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6379219/ https://www.ncbi.nlm.nih.gov/pubmed/30552703 http://dx.doi.org/10.1111/bcp.13838 |
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