Clinical Pharmacokinetics

, Volume 48, Issue 12, pp 805–816 | Cite as

Tacrolimus Population Pharmacokinetic-Pharmacogenetic Analysis and Bayesian Estimation in Renal Transplant Recipients

  • Khaled Benkali
  • Aurelie Prémaud
  • Nicolas Picard
  • Jean-Philippe Rérolle
  • Olivier Toupance
  • Guillaume Hoizey
  • Alain Turcant
  • Florence Villemain
  • Yannick Le Meur
  • Pierre Marquet
  • Annick RousseauEmail author
Original Research Article


Objectives: The aims of this study were (i) to investigate the population pharmacokinetics of tacrolimus in renal transplant recipients, including the influence of biological and pharmacogenetic covariates; and (ii) to develop a Bayesian estimator able to reliably estimate the individual pharmacokinetic parameters and inter-dose area under the blood concentration-time curve (AUC) from 0 to 12 hours (AUC12) in renal transplant patients.

Methods: Full pharmacokinetic profiles were obtained from 32 renal transplant patients at weeks 1 and 2, and at months 1, 3 and 6 post-transplantation. The population pharmacokinetic analysis was performed using the nonlinear mixed-effect modelling software NONMEM® version VI. Patients’ genotypes were characterized by allelic discrimination for PXR −25385C>T genes.

Results: Tacrolimus pharmacokinetics were well described by a two-compartment model combined with an Erlang distribution to describe the absorption phase, with low additive and proportional residual errors of 1.6 ng/mL and 9%, respectively. Both the haematocrit and PXR −25385C>T single nucleotide polymorphism (SNP) were identified as significant covariates for apparent oral clearance (CL/F) of tacrolimus, which allowed improvement of prediction accuracy. Specifically, CL/F decreased gradually with the number of mutated alleles for the PXR −25385C>T SNP and was inversely proportional to the haematocrit value. However, clinical criteria of relevance, mainly the decrease in interindividual variability and residual error, led us to retain only the haematocrit in the final model. Maximum a posteriori Bayesian forecasting allowed accurate prediction of the tacrolimus AUC12 using only three sampling times (at 0 hour [predose] and at 1 and 3 hours postdose) in addition to the haematocrit value, with a nonsignificant mean AUC bias of 2% and good precision (relative mean square error = 11%).

Conclusion: Population pharmacokinetic analysis of tacrolimus in renal transplant recipients showed a significant influence of the haematocrit on its CL/F and led to the development of a Bayesian estimator compatible with clinical practice and able to accurately predict tacrolimus individual pharmacokinetic parameters and the AUC12.


Tacrolimus Bayesian Estimator Population Pharmacokinetic Model Population Pharmacokinetic Analysis Objective Function Value 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was funded by Roche and Astellas. Khaled Benkali received a PhD grant from Conseil Régional du Limousin. We gratefully thank François-Ludovic Sauvage, Clinical Chemist, and Hélène Roussel, Clinical Research Assistant, for their excellent technical assistance. We also thank the Limoges University Hospital for their support. The authors have no other conflicts of interest that are directly relevant to the contents of this study.


  1. 1.
    Venkataramanan R, Swaminathan A, Prasad T, et al. Clinical pharmacokinetics of tacrolimus. Clin Pharmacokinet 1995 Dec; 29(6): 404–30PubMedCrossRefGoogle Scholar
  2. 2.
    Tada H, Satoh S, Iinuma M, et al. Chronopharmacokinetics of tacrolimus in kidney transplant recipients: occurrence of acute rejection. J Clin Pharmacol 2003 Aug; 43(8): 859–65PubMedCrossRefGoogle Scholar
  3. 3.
    Wong KM, Shek CC, Chau KF, et al. Abbreviated tacrolimus area-under-the-curve monitoring for renal transplant recipients. Am J Kidney Dis 2000 Apr; 35(4): 660–6PubMedCrossRefGoogle Scholar
  4. 4.
    Hebert MF. Contributions of hepatic and intestinal metabolism and P-glycoprotein to cyclosporine and tacrolimus oral drug delivery. Adv Drug Deliv Rev 1997 Sep 15; 27(2–3): 201–14PubMedCrossRefGoogle Scholar
  5. 5.
    Saeki T, Ueda K, Tanigawara Y, et al. Human P-glycoprotein transports cyclosporin A and FK506. J Biol Chem 1993 Mar 25; 268(9): 6077–80PubMedGoogle Scholar
  6. 6.
    Hesselink DA, van Schaik RH, van der Heiden I, et al. Genetic polymorphisms of the CYP3A4, CYP3A5, and MDR-1 genes and pharmacokinetics of the calcineurin inhibitors cyclosporine and tacrolimus. Clin Pharmacol Ther 2003 Sep; 74(3): 245–54PubMedCrossRefGoogle Scholar
  7. 7.
    Uesugi M, Masuda S, Katsura T, et al. Effect of intestinal CYP3A5 on postoperative tacrolimus trough levels in living-donor liver transplant recipients. Pharmacogenet Genomics 2006 Feb; 16(2): 119–27PubMedCrossRefGoogle Scholar
  8. 8.
    Fredericks S, Moreton M, Reboux S, et al. Multidrug resistance gene-1 (MDR-1) haplotypes have a minor influence on tacrolimus dose requirements. Transplantation 2006 Sep 15; 82(5): 705–8PubMedCrossRefGoogle Scholar
  9. 9.
    Bertilsson G, Heidrich J, Svensson K, et al. Identification of a human nuclear receptor defines a new signaling pathway for CYP3A induction. Proc Natl Acad Sci U S A 1998 Oct 13; 95(21): 12208–13PubMedCrossRefGoogle Scholar
  10. 10.
    Moore JT, Kliewer SA. Use of the nuclear receptor PXR to predict drug interactions. Toxicology 2000 Nov 16; 153(1–3): 1–10PubMedCrossRefGoogle Scholar
  11. 11.
    Pascussi JM, Drocourt L, Gerbal-Chaloin S, et al. Dual effect of dexamethasone on CYP3A4 gene expression in human hepatocytes: sequential role of glucocorticoid receptor and pregnane X receptor. Eur J Biochem 2001 Dec; 268(24): 6346–58PubMedCrossRefGoogle Scholar
  12. 12.
    Lamba J, Lamba V, Strom S, et al. Novel single nucleotide polymorphisms in the promoter and intron 1 of human pregnane X receptor/NR1I2 and their association with CYP3A4 expression. Drug Metab Dispos 2008 Jan; 36(1): 169–81PubMedCrossRefGoogle Scholar
  13. 13.
    Zhang J, Kuehl P, Green ED, et al. The human pregnane X receptor: genomic structure and identification and functional characterization of natural allelic variants. Pharmacogenetics 2001 Oct; 11(7): 555–72PubMedCrossRefGoogle Scholar
  14. 14.
    Miura M, Satoh S, Inoue K, et al. Influence of CYP3A5, ABCB1 and NR1I2 polymorphisms on prednisolone pharmacokinetics in renal transplant recipients. Steroids 2008 Oct; 73(11): 1052–9PubMedCrossRefGoogle Scholar
  15. 15.
    Fukudo M, Yano I, Yoshimura A, et al. Impact of MDR1 and CYP3A5 on the oral clearance of tacrolimus and tacrolimus-related renal dysfunction in adult living-donor liver transplant patients. Pharmacogenet Genomics 2008 May; 18(5): 413–23PubMedCrossRefGoogle Scholar
  16. 16.
    Li D, Lu W, Zhu JY, et al. Population pharmacokinetics of tacrolimus and CYP3A5, MDR1 and IL-10 polymorphisms in adult liver transplant patients. J Clin Pharm Ther 2007 Oct; 32(5): 505–15PubMedCrossRefGoogle Scholar
  17. 17.
    Le Meur Y, Djebli N, Szelag JC, et al. CYP3A5*3 influences sirolimus oral clearance in de novo and stable renal transplant recipients. Clin Pharmacol Ther 2006 Jul; 80(1): 51–60PubMedCrossRefGoogle Scholar
  18. 18.
    Boekmann AJ, Sheiner LB, Beal SL. NONMEM user’s guide, part V: introductory guide. San Francisco (CA): NONMEM Project Group, University of California, 1992Google Scholar
  19. 19.
    Saint-Marcoux F, Knoop C, Debord J, et al. Pharmacokinetic study of tacrolimus in cystic fibrosis and non-cystic fibrosis lung transplant patients and design of Bayesian estimators using limited sampling strategies. Clin Pharmacokinet 2005; 44(12): 1317–28PubMedCrossRefGoogle Scholar
  20. 20.
    Etienne MC, Chatelut E, Pivot X, et al. Co-variables influencing 5-fluorouracil clearance during continuous venous infusion: a NONMEM analysis. Eur J Cancer 1998 Jan; 34(1): 92–7PubMedCrossRefGoogle Scholar
  21. 21.
    Parke J, Holford NH, Charles BG. A procedure for generating bootstrap samples for the validation of nonlinear mixed-effects population models. Comput Methods Programs Biomed 1999 Apr; 59(1): 19–29PubMedCrossRefGoogle Scholar
  22. 22.
    Ishibashi T, Yano Y, Oguma T. Population pharmacokinetics of platinum after nedaplatin administration and model validation in adult patients. Br J Clin Pharmacol 2003 Aug; 56(2): 205–13PubMedCrossRefGoogle Scholar
  23. 23.
    Premaud A, Le MY, Debord J, et al. Maximum a posteriori Bayesian estimation of mycophenolic acid pharmacokinetics in renal transplant recipients at different postgrafting periods. Ther Drug Monit 2005 Jun; 27(3): 354–61PubMedCrossRefGoogle Scholar
  24. 24.
    Sheiner LB, Beal SL. Some suggestions for measuring predictive performance. J Pharmacokinet Biopharm 1981 Aug; 9(4): 503–12PubMedGoogle Scholar
  25. 25.
    Scholten EM, Cremers SC, Schoemaker RC, et al. AUC-guided dosing of tacrolimus prevents progressive systemic overexposure in renal transplant recipients. Kidney Int 2005 Jun; 67(6): 2440–7PubMedCrossRefGoogle Scholar
  26. 26.
    Rousseau A, Leger F, Le Meur Y, et al. Population pharmacokinetic modeling of oral cyclosporin using NONMEM: comparison of absorption pharmacokinetic models and design of a Bayesian estimator. Ther Drug Monit 2004 Feb; 26(1): 23–30PubMedCrossRefGoogle Scholar
  27. 27.
    Djebli N, Rousseau A, Hoizey G, et al. Sirolimus population pharmacokinetic/pharmacogenetic analysis and Bayesian modelling in kidney transplant recipients. Clin Pharmacokinet 2006; 45(11): 1135–48PubMedCrossRefGoogle Scholar
  28. 28.
    Staatz CE, Willis C, Taylor PJ, et al. Population pharmacokinetics of tacrolimus in adult kidney transplant recipients. Clin Pharmacol Ther 2002 Dec; 72(6): 660–9PubMedCrossRefGoogle Scholar
  29. 29.
    Antignac M, Barrou B, Farinotti R, et al. Population pharmacokinetics and bioavailability of tacrolimus in kidney transplant patients. Br J Clin Pharmacol 2007 Dec; 64(6): 750–7PubMedGoogle Scholar
  30. 30.
    Tunblad K, Lindbom L, McFadyen L, et al. The use of clinical irrelevance criteria in covariate model building with application to dofetilide pharmacokinetic data. J Pharmacokinet Pharmacodyn 2008 Oct; 35(5): 503–26PubMedCrossRefGoogle Scholar
  31. 31.
    Undre NA, Schafer A. Factors affecting the pharmacokinetics of tacrolimus in the first year after renal transplantation. European Tacrolimus Multicentre Renal Study Group. Transplant Proc 1998 Jun; 30(4): 1261–3PubMedCrossRefGoogle Scholar
  32. 32.
    Andrews WS, Sommerauer J, Conlin C, et al. Comparison of cyclosporine- vs tacrolimus-based immunosuppression in pediatric liver transplantation. Transplant Proc 1996 Apr; 28(2): 897–8PubMedGoogle Scholar
  33. 33.
    Thervet E, Anglicheau D, King B, et al. Impact of cytochrome p450 3A5 genetic polymorphism on tacrolimus doses and concentration-to-dose ratio in renal transplant recipients. Transplantation 2003 Oct 27; 76(8): 1233–5PubMedCrossRefGoogle Scholar

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© Adis Data Information BV 2009

Authors and Affiliations

  • Khaled Benkali
    • 1
  • Aurelie Prémaud
    • 1
  • Nicolas Picard
    • 1
  • Jean-Philippe Rérolle
    • 2
  • Olivier Toupance
    • 3
  • Guillaume Hoizey
    • 4
  • Alain Turcant
    • 5
  • Florence Villemain
    • 6
  • Yannick Le Meur
    • 1
    • 7
  • Pierre Marquet
    • 1
  • Annick Rousseau
    • 1
    • 8
    Email author
  1. 1.INSERM U850University of LimogesLimogesFrance
  2. 2.Department of Nephrology-TransplantationCHU LimogesFrance
  3. 3.Department of Nephrology-TransplantationUniversity HospitalReimsFrance
  4. 4.Department of PharmacologyUniversity HospitalReimsFrance
  5. 5.Department of PharmacologyUniversity HospitalAngersFrance
  6. 6.Department of Nephrology-TransplantationUniversity HospitalAngersFrance
  7. 7.Department of Nephrology-TransplantationUniversity HospitalBrestFrance
  8. 8.Faculty of Pharmacy, Laboratory of BiophysicsUniversity of LimogesLimogesFrance

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