Literature DB >> 19855314

Time of drug administration, CYP3A5 and ABCB1 genotypes, and analytical method influence tacrolimus pharmacokinetics: a population pharmacokinetic study.

Flora Tshinanu Musuamba1, Michel Mourad, Vincent Haufroid, Isabelle Karine Delattre, Roger Karel Verbeeck, Pierre Wallemacq.   

Abstract

Tacrolimus (TAC) pharmacokinetics are characterized by a very high variability that complicates its therapeutic use. The aims of this study were: 1) to identify and model the effect of demographic, clinical, and genetic factors and time of drug administration on TAC pharmacokinetic variability; and 2) to assess the influence of the analytical method by modeling the TAC blood concentrations measured simultaneously by microparticle enzyme immune assay (MEIA) and liquid chromatography-tandem mass spectroscopy. Data from 19 renal transplant candidates were analyzed. A total of 266 blood samples were analyzed for TAC by both techniques. Linear regression and Bland and Altman analyses were performed to compare TAC blood concentrations obtained with MEIA and liquid chromatography-tandem mass spectroscopy. A population pharmacokinetic analysis was performed. As expected, blood concentrations obtained by MEIA were higher than those obtained by liquid chromatography-tandem mass spectroscopy. A two-compartment model with first-order absorption and elimination best fit TAC blood concentrations. An exponential model was used to describe the interindividual and interoccasion variability and a mixed model was retained for the residual variability. A supplementary proportional term was necessary for the residual error in case of TAC blood concentrations determined by MEIA. The following covariates were retained in the final model: time of drug administration on the absorption rate constant and CYP3A5 and ABCB1 genotypes on the TAC apparent clearance. All parameter estimates had reliable values. The final model was found to be stable and generated parameters with good precision. The validation of the final model by bootstrapping (2000 bootstraps), case deletion diagnostics, crossvalidation, and visual predictive check (1000 simulated subjects) gave satisfactory results. This is the first population pharmacokinetic study confirming the chronopharmacokinetics of TAC and showing an effect of ABCB1 genotype and analytical method on TAC pharmacokinetics. These results may be helpful for TAC dose individualization.

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Year:  2009        PMID: 19855314     DOI: 10.1097/FTD.0b013e3181bf8623

Source DB:  PubMed          Journal:  Ther Drug Monit        ISSN: 0163-4356            Impact factor:   3.681


  13 in total

1.  Evaluation of limited sampling methods for estimation of tacrolimus exposure in adult kidney transplant recipients.

Authors:  Katherine A Barraclough; Nicole M Isbel; Carl M Kirkpatrick; Katie J Lee; Paul J Taylor; David W Johnson; Scott B Campbell; Diana R Leary; Christine E Staatz
Journal:  Br J Clin Pharmacol       Date:  2011-02       Impact factor: 4.335

2.  Population pharmacokinetic model and Bayesian estimator for two tacrolimus formulations--twice daily Prograf and once daily Advagraf.

Authors:  Jean-Baptiste Woillard; Brenda C M de Winter; Nassim Kamar; Pierre Marquet; Lionel Rostaing; Annick Rousseau
Journal:  Br J Clin Pharmacol       Date:  2011-03       Impact factor: 4.335

3.  Population pharmacokinetic analysis of tacrolimus in Mexican paediatric renal transplant patients: role of CYP3A5 genotype and formulation.

Authors:  Carlos Orlando Jacobo-Cabral; Pilar García-Roca; Elba Margarita Romero-Tejeda; Herlinda Reyes; Mara Medeiros; Gilberto Castañeda-Hernández; Iñaki F Trocóniz
Journal:  Br J Clin Pharmacol       Date:  2015-06-22       Impact factor: 4.335

4.  Identifying 24 h variation in the pharmacokinetics of levofloxacin: a population pharmacokinetic approach.

Authors:  Laura Kervezee; Jasper Stevens; Willem Birkhoff; Ingrid M C Kamerling; Theo de Boer; Melloney Dröge; Johanna H Meijer; Jacobus Burggraaf
Journal:  Br J Clin Pharmacol       Date:  2015-11-25       Impact factor: 4.335

5.  A New CYP3A5*3 and CYP3A4*22 Cluster Influencing Tacrolimus Target Concentrations: A Population Approach.

Authors:  Franc Andreu; Helena Colom; Laure Elens; Teun van Gelder; Ronald H N van Schaik; Dennis A Hesselink; Oriol Bestard; Joan Torras; Josep M Cruzado; Josep M Grinyó; Nuria Lloberas
Journal:  Clin Pharmacokinet       Date:  2017-08       Impact factor: 6.447

6.  Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach.

Authors:  Tom M Nanga; Thao T P Doan; Pierre Marquet; Flora T Musuamba
Journal:  Br J Clin Pharmacol       Date:  2019-12-17       Impact factor: 4.335

7.  Population pharmacokinetic modelling and design of a Bayesian estimator for therapeutic drug monitoring of tacrolimus in lung transplantation.

Authors:  Caroline Monchaud; Brenda C de Winter; Christiane Knoop; Marc Estenne; Martine Reynaud-Gaubert; Christophe Pison; Marc Stern; Romain Kessler; Romain Guillemain; Pierre Marquet; Annick Rousseau
Journal:  Clin Pharmacokinet       Date:  2012-03-01       Impact factor: 6.447

8.  Multidrug resistance-associated protein 2 (MRP2/ABCC2) haplotypes significantly affect the pharmacokinetics of tacrolimus in kidney transplant recipients.

Authors:  Ken Ogasawara; Shripad D Chitnis; Reginald Y Gohh; Uwe Christians; Fatemeh Akhlaghi
Journal:  Clin Pharmacokinet       Date:  2013-09       Impact factor: 6.447

Review 9.  Population Pharmacokinetics of Tacrolimus in Transplant Recipients: What Did We Learn About Sources of Interindividual Variabilities?

Authors:  Olivia Campagne; Donald E Mager; Kathleen M Tornatore
Journal:  J Clin Pharmacol       Date:  2018-10-29       Impact factor: 3.126

10.  The Population Pharmacokinetic Models of Tacrolimus in Chinese Adult Liver Transplantation Patients.

Authors:  Liqin Zhu; Hao Wang; Xiaoye Sun; Wei Rao; Wei Qu; Yuan Zhang; Liying Sun
Journal:  J Pharm (Cairo)       Date:  2014-02-13
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