Literature DB >> 30371942

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

Olivia Campagne1,2, Donald E Mager1, Kathleen M Tornatore3.   

Abstract

Tacrolimus, a calcineurin inhibitor, is a common immunosuppressant prescribed after organ transplantation and has notable inter- and intrapatient pharmacokinetic variability. The sources of variability have been investigated using population pharmacokinetic modeling over the last 2 decades. This article provides an updated synopsis on published nonlinear mixed-effects analyses developed for tacrolimus in transplant recipients. The objectives were to establish a detailed overview of the current data and to investigate covariate relationships determined by the models. Sixty-three published analyses were reviewed, and data regarding the study design, modeling approach, and resulting findings were extracted and summarized. Most of the studies investigated tacrolimus pharmacokinetics in adult and pediatric renal and liver transplants after administration of the immediate-release formulation. Model structures largely depended on the study sampling strategy, with ∼50% of studies developing a 1-compartment model using trough concentrations and a 2-compartment model with delayed absorption from intensive sampling. The CYP3A5 genotype, as a covariate, consistently impacted tacrolimus clearance, and dosing adjustments were required to achieve similar drug exposure among patients. Numerous covariates were identified as sources of interindividual variability on tacrolimus pharmacokinetics with limited consistency across these studies, which may be the result of the study designs. Additional analyses are required to further evaluate the potential impact of these covariates and the clinical implementation of these models to guide tacrolimus dosing recommendations. This article may be useful for guiding the design of future population pharmacokinetic studies and provides recommendations for the selection of an existing optimal model to individualize tacrolimus therapy.
© 2018, The American College of Clinical Pharmacology.

Entities:  

Keywords:  covariate; interindividual variabilities; pharmacokinetics; population; tacrolimus; transplantation

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Substances:

Year:  2018        PMID: 30371942      PMCID: PMC7395655          DOI: 10.1002/jcph.1325

Source DB:  PubMed          Journal:  J Clin Pharmacol        ISSN: 0091-2700            Impact factor:   3.126


  90 in total

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Journal:  Eur J Clin Pharmacol       Date:  2012-06-03       Impact factor: 2.953

2.  Population pharmacokinetics and pharmacogenetics of tacrolimus in healthy Chinese volunteers.

Authors:  Ling Xue; Hua Zhang; Sheng Ma; Jian-Zhong Rui; Li-Yan Miao
Journal:  Pharmacology       Date:  2011-11-10       Impact factor: 2.547

3.  Reduced exposure to calcineurin inhibitors in renal transplantation.

Authors:  Henrik Ekberg; Helio Tedesco-Silva; Alper Demirbas; Stefan Vítko; Björn Nashan; Alp Gürkan; Raimund Margreiter; Christian Hugo; Josep M Grinyó; Ulrich Frei; Yves Vanrenterghem; Pierre Daloze; Philip F Halloran
Journal:  N Engl J Med       Date:  2007-12-20       Impact factor: 91.245

4.  Population-based meta-analysis of hydrochlorothiazide pharmacokinetics.

Authors:  Scott A Van Wart; Susan E Shoaf; Suresh Mallikaarjun; Donald E Mager
Journal:  Biopharm Drug Dispos       Date:  2013-10-10       Impact factor: 1.627

5.  Population pharmacokinetics of tacrolimus in full liver transplant patients: modelling of the post-operative clearance.

Authors:  Marie Antignac; Jean Sebastien Hulot; Emmanuel Boleslawski; Laurent Hannoun; Yvan Touitou; Robert Farinotti; Philippe Lechat; Saïk Urien
Journal:  Eur J Clin Pharmacol       Date:  2005-07-01       Impact factor: 2.953

6.  Population pharmacokinetics of tacrolimus in Asian paediatric liver transplant patients.

Authors:  W J Sam; M Aw; S H Quak; S M Lim; B G Charles; S Y Chan; P C Ho
Journal:  Br J Clin Pharmacol       Date:  2000-12       Impact factor: 4.335

7.  The Effect of Weight and CYP3A5 Genotype on the Population Pharmacokinetics of Tacrolimus in Stable Paediatric Renal Transplant Recipients.

Authors:  Agnieszka A Prytuła; Karlien Cransberg; Antonia H M Bouts; Ron H N van Schaik; Huib de Jong; Saskia N de Wildt; Ron A A Mathôt
Journal:  Clin Pharmacokinet       Date:  2016-09       Impact factor: 6.447

8.  Multi-site analytical evaluation of the Abbott ARCHITECT tacrolimus assay.

Authors:  Pierre Wallemacq; Jean-Sebastien Goffinet; Susan O'Morchoe; Thomas Rosiere; Gregory T Maine; Myriam Labalette; Giuseppe Aimo; Diana Dickson; Ed Schmidt; Reinhard Schwinzer; Rainer W Schmid
Journal:  Ther Drug Monit       Date:  2009-04       Impact factor: 3.681

9.  Basic concepts in population modeling, simulation, and model-based drug development.

Authors:  D R Mould; R N Upton
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2012-09-26

10.  Population pharmacokinetics and pharmacogenetics of once daily tacrolimus formulation in stable liver transplant recipients.

Authors:  D J A R Moes; S A S van der Bent; J J Swen; T van der Straaten; A Inderson; E Olofsen; H W Verspaget; H J Guchelaar; J den Hartigh; B van Hoek
Journal:  Eur J Clin Pharmacol       Date:  2015-10-31       Impact factor: 2.953

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  9 in total

1.  Wuzhi Capsule Dosage Affects Tacrolimus Elimination in Adult Kidney Transplant Recipients, as Determined by a Population Pharmacokinetics Analysis.

Authors:  Lizhi Chen; Yunyun Yang; Xuebin Wang; Chenyu Wang; Weiwei Lin; Zheng Jiao; Zhuo Wang
Journal:  Pharmgenomics Pers Med       Date:  2021-09-03

2.  Predictive Performance of Published Tacrolimus Population Pharmacokinetic Models in Thai Kidney Transplant Patients.

Authors:  Janthima Methaneethorn; Manupat Lohitnavy; Kamonwan Onlamai; Nattawut Leelakanok
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2021-11-24       Impact factor: 2.441

3.  Population Pharmacokinetics and Initial Dosage Optimization of Tacrolimus in Pediatric Hematopoietic Stem Cell Transplant Patients.

Authors:  Xiao-Lin Liu; Yan-Ping Guan; Ying Wang; Ke Huang; Fu-Lin Jiang; Jian Wang; Qi-Hong Yu; Kai-Feng Qiu; Min Huang; Jun-Yan Wu; Dun-Hua Zhou; Guo-Ping Zhong; Xiao-Xia Yu
Journal:  Front Pharmacol       Date:  2022-07-06       Impact factor: 5.988

4.  Variation in Tacrolimus Trough Concentrations in Liver Transplant Patients Undergoing Endoscopic Retrograde Cholangiopancreatography: A Retrospective, Observational Study.

Authors:  Rongrong Wang; Weili Wang; Kuifen Ma; Xin Duan; Fangfang Wang; Mingzhu Huang; Wei Zhang; Tingbo Liang
Journal:  Front Pharmacol       Date:  2020-08-19       Impact factor: 5.810

5.  The impact of IL-10 and CYP3A5 gene polymorphisms on dose-adjusted trough blood tacrolimus concentrations in early post-renal transplant recipients.

Authors:  Zhaolin Chen; Xi Cheng; Liwen Zhang; Liqin Tang; Yan Fang; Hongxiao Chen; Lei Zhang; Aizong Shen
Journal:  Pharmacol Rep       Date:  2021-06-05       Impact factor: 3.024

6.  Predicting model-informed precision dosing: A test-case in tacrolimus dose adaptation for kidney transplant recipients.

Authors:  Ruben Faelens; Nicolas Luyckx; Dirk Kuypers; Thomas Bouillon; Pieter Annaert
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2022-02-02

7.  Population pharmacokinetics and initial dose optimization of tacrolimus in children with severe combined immunodeficiency undergoing hematopoietic stem cell transplantation.

Authors:  Xiao Chen; Dongdong Wang; Feng Zheng; Xiaowen Zhai; Hong Xu; Zhiping Li
Journal:  Front Pharmacol       Date:  2022-07-22       Impact factor: 5.988

Review 8.  Inflammation is a major regulator of drug metabolizing enzymes and transporters: Consequences for the personalization of drug treatment.

Authors:  Françoise Stanke-Labesque; Elodie Gautier-Veyret; Stephanie Chhun; Romain Guilhaumou
Journal:  Pharmacol Ther       Date:  2020-07-11       Impact factor: 12.310

9.  The Effect of Renal Impairment on the Pharmacokinetics and Safety of Itacitinib.

Authors:  Nithya Srinivas; April M Barbour; Noam Epstein; Gongfu Zhou; Susan Petusky; Zhinyin Xun; Brad Yuska; Thomas Marbury; Xuejun Chen; Swamy Yeleswaram; Naresh Punwani
Journal:  J Clin Pharmacol       Date:  2020-03-09       Impact factor: 3.126

  9 in total

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