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https://hdl.handle.net/2445/175111
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DC Field | Value | Language |
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dc.contributor.author | Andrews, L. M. | - |
dc.contributor.author | Hesselink, D. A. | - |
dc.contributor.author | Schaik, R. H. N. | - |
dc.contributor.author | Gelder, T. | - |
dc.contributor.author | Fijter, J. W. | - |
dc.contributor.author | Lloberas Blanch, Núria | - |
dc.contributor.author | Elens, L. | - |
dc.contributor.author | Moes, D. J. A. R. | - |
dc.contributor.author | Winter, B. C. M. | - |
dc.date.accessioned | 2021-03-15T12:28:42Z | - |
dc.date.available | 2021-03-15T12:28:42Z | - |
dc.date.issued | 2019-01-17 | - |
dc.identifier.uri | https://hdl.handle.net/2445/175111 | - |
dc.description.abstract | 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. | - |
dc.format.extent | 15 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Wiley | - |
dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/10.1111/bcp.13838 | - |
dc.relation.ispartof | British Journal of Clinical Pharmacology, 2019, vol. 85, num. 3, p. 601-615 | - |
dc.relation.uri | https://doi.org/10.1111/bcp.13838 | - |
dc.rights | cc by-nc (c) Andrews, L. M. et al., 2019 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/es/ | * |
dc.source | Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) | - |
dc.subject.classification | Farmacocinètica | - |
dc.subject.classification | Trasplantament renal | - |
dc.subject.classification | Immunologia | - |
dc.subject.other | Pharmacokinetics | - |
dc.subject.other | Kidney transplantation | - |
dc.subject.other | Immunology | - |
dc.title | A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients | - |
dc.type | info:eu-repo/semantics/article | - |
dc.date.updated | 2021-03-09T13:43:12Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
dc.identifier.pmid | 30552703 | - |
Appears in Collections: | Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) |
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bcp.13838.pdf | 1.4 MB | Adobe PDF | View/Open |
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