Literature DB >> 17048977

Sirolimus population pharmacokinetic/pharmacogenetic analysis and bayesian modelling in kidney transplant recipients.

Nassim Djebli1, Annick Rousseau, Guillaume Hoizey, Jean-Philippe Rerolle, Olivier Toupance, Yann Le Meur, Pierre Marquet.   

Abstract

OBJECTIVES: The objectives of the present study were: (i) to analyse the population pharmacokinetics of sirolimus in renal transplant recipients co-administered mycophenolate mofetil, but no calcineurin inhibitor over the first 3 months post-transplantation and study the influence of different potential covariates, including genetic polymorphisms of cytochrome P450 (CYP) metabolic enzymes and active transporters, on pharmacokinetic parameters; and (ii) to develop a Bayesian estimator able to reliably estimate the individual pharmacokinetic parameters and exposure indices in this population.
METHODS: Twenty-two adult renal transplant patients treated with sirolimus participated in this study. Ninety concentration-time profiles (938 sirolimus whole blood samples) were collected at days 7 and 14, and months 1 and 3 post-transplantation. The population pharmacokinetic study was conducted using the nonlinear mixed effects model software, NONMEM, and validated using both the bootstrap and the cross-validation approaches. Finally, a Bayesian estimator based on a limited sampling strategy was built using the post hoc option.
RESULTS: A two-compartment open model with first-order elimination and Erlang's distribution (to describe the absorption phase) best fitted the data. The mean pharmacokinetic parameter estimates were 5.25 h(-1), 218L and 292L for the transfer rate constant, the apparent volume of the central and peripheral compartments, respectively. The CYP3A5*1/*3 polymorphism significantly influenced the apparent oral clearance: mean oral clearance = 14.1 L/h for CYP3A5 non expressers (CYP3A5*3/*3 genotype) versus 28.3 L/h for CYP3A5 expressers (CYP3A5*1/*3 and *1/*1 genotypes). The standard errors of all the parameter estimates were <15%. Maximum a posteriori Bayesian forecasting allowed accurate prediction of sirolimus area under the concentration-time curve from 0 to 24 hours using a combination of only three sampling times (0, 1 and 3 hours post-dose), with a non-significant bias of -2.1% (range -22.2% to +25.9%), and a good precision (root mean square error = 10.3%). This combination is also easy to implement in clinical practice.
CONCLUSION: This study presents an accurate population pharmacokinetic model showing the significant influence of the CYP3A5*1/*3 polymorphism on sirolimus apparent oral clearance, and a Bayesian estimator accurately predicting sirolimus pharmacokinetics in patients co-administered mycophenolate mofetil, but no calcineurin inhibitor.

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Year:  2006        PMID: 17048977     DOI: 10.2165/00003088-200645110-00007

Source DB:  PubMed          Journal:  Clin Pharmacokinet        ISSN: 0312-5963            Impact factor:   6.447


  28 in total

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2.  Maximum a posteriori bayesian estimation of mycophenolic acid pharmacokinetics in renal transplant recipients at different postgrafting periods.

Authors:  Aurélie Prémaud; Yannick Le Meur; Jean Debord; Jean-Christophe Szelag; Annick Rousseau; Guillaume Hoizey; Olivier Toupance; Pierre Marquet
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3.  Tacrolimus pharmacogenetics: the CYP3A5*1 allele predicts low dose-normalized tacrolimus blood concentrations in whites and South Asians.

Authors:  Iain A M Macphee; Salim Fredericks; Maha Mohamed; Michelle Moreton; Nicholas D Carter; Atholl Johnston; Lawrence Goldberg; David W Holt
Journal:  Transplantation       Date:  2005-02-27       Impact factor: 4.939

4.  Therapeutic drug monitoring of sirolimus: correlations with efficacy and toxicity.

Authors:  B D Kahan; K L Napoli; P A Kelly; J Podbielski; I Hussein; D L Urbauer; S H Katz; C T Van Buren
Journal:  Clin Transplant       Date:  2000-04       Impact factor: 2.863

5.  Sirolimus-induced thrombocytopenia and leukopenia in renal transplant recipients: risk factors, incidence, progression, and management.

Authors:  J C Hong; B D Kahan
Journal:  Transplantation       Date:  2000-05-27       Impact factor: 4.939

6.  The importance of modeling interoccasion variability in population pharmacokinetic analyses.

Authors:  M O Karlsson; L B Sheiner
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7.  Pharmacokinetics of sirolimus in stable renal transplant patients after multiple oral dose administration.

Authors:  J J Zimmerman; B D Kahan
Journal:  J Clin Pharmacol       Date:  1997-05       Impact factor: 3.126

8.  Influence of CYP3A5 and MDR1 (ABCB1) polymorphisms on the pharmacokinetics of tacrolimus in renal transplant recipients.

Authors:  Norihiko Tsuchiya; Shigeru Satoh; Hitoshi Tada; Zhenhua Li; Chikara Ohyama; Kazunari Sato; Toshio Suzuki; Tomonori Habuchi; Tetsuro Kato
Journal:  Transplantation       Date:  2004-10-27       Impact factor: 4.939

9.  Distribution of sirolimus in rat tissue.

Authors:  K L Napoli; M E Wang; S M Stepkowski; B D Kahan
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Authors:  A Rousseau; F Léger; Y Le Meur; F Saint-Marcoux; G Paintaud; M Buchler; P Marquet
Journal:  Ther Drug Monit       Date:  2004-02       Impact factor: 3.681

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  19 in total

1.  Population pharmacokinetics of sirolimus in de novo Chinese adult renal transplant patients.

Authors:  Zheng Jiao; Xiao-jin Shi; Zhong-dong Li; Ming-kang Zhong
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Review 2.  Pharmacokinetic optimization of immunosuppressive therapy in thoracic transplantation: part II.

Authors:  Caroline Monchaud; Pierre Marquet
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Review 3.  Pharmacokinetic optimization of immunosuppressive therapy in thoracic transplantation: part I.

Authors:  Caroline Monchaud; Pierre Marquet
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4.  Prediction tacrolimus blood levels based on the Bayesian method in adult kidney transplant patients.

Authors:  Marie Antignac; Christine Fernandez; Benoît Barrou; Mariona Roca; Jean-Louis Favrat; Saïk Urien; Robert Farinotti
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2011-02-24       Impact factor: 2.441

5.  Bayesian estimation of mycophenolate mofetil in lung transplantation, using a population pharmacokinetic model developed in kidney and lung transplant recipients.

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

6.  Kinetic nomograms assist individualization of drug regimens.

Authors:  Hafedh Marouani; Anastasios Zografidis; Athanassios Iliadis
Journal:  Clin Pharmacokinet       Date:  2011-12-01       Impact factor: 6.447

7.  Population pharmacokinetics and Bayesian estimator of mycophenolic acid in children with idiopathic nephrotic syndrome.

Authors:  Wei Zhao; Valéry Elie; Véronique Baudouin; Albert Bensman; Jean Luc André; Karine Brochard; Françoise Broux; Mathilde Cailliez; Chantal Loirat; Evelyne Jacqz-Aigrain
Journal:  Br J Clin Pharmacol       Date:  2010-04       Impact factor: 4.335

8.  Tacrolimus population pharmacokinetic-pharmacogenetic analysis and Bayesian estimation in renal transplant recipients.

Authors:  Khaled Benkali; Aurelie Prémaud; Nicolas Picard; Jean-Philippe Rérolle; Olivier Toupance; Guillaume Hoizey; Alain Turcant; Florence Villemain; Yannick Le Meur; Pierre Marquet; Annick Rousseau
Journal:  Clin Pharmacokinet       Date:  2009       Impact factor: 6.447

Review 9.  Pharmacogenomics: a new paradigm to personalize treatments in nephrology patients.

Authors:  G Zaza; S Granata; F Sallustio; G Grandaliano; F P Schena
Journal:  Clin Exp Immunol       Date:  2009-11-24       Impact factor: 4.330

10.  Population pharmacokinetics of sirolimus in pediatric patients with neurofibromatosis type 1.

Authors:  Jeffrey R Scott; Joshua D Courter; Shannon N Saldaña; Brigitte C Widemann; Michael Fisher; Brian Weiss; John Perentesis; Alexander A Vinks
Journal:  Ther Drug Monit       Date:  2013-06       Impact factor: 3.681

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