Exposure to mycophenolic acid better predicts immunosuppressive efficacy than exposure to calcineurin inhibitors in renal transplant patients

Clinical Pharmacology and Therapeutics

Volumn 96, Issue 4, 2014, Pages 508-515

  Daher Abdi, Z ^a,b   Premaud A ^a,b   Essig, M ^a,b,c   Alain, S ^b,c   Munteanu, E ^c   Garnier, F ^c   Le Meur, Y ^d   Marquet, P ^a,b,c   Rousseau, A ^a,b  

^a INSERM   (France)

^b UNIVERSITY OF LIMOGES   (France)

^c LIMOGES UNIVERSITY HOSPITAL   (France)

^d BREST UNIVERSITY HOSPITAL   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CALCINEURIN INHIBITOR; CYCLOSPORIN; IMMUNOSUPPRESSIVE AGENT; MYCOPHENOLIC ACID; TACROLIMUS;

BIOLOGICAL MODEL; COMPARATIVE STUDY; CYTOMEGALOVIRUS INFECTIONS; DRUG COMBINATION; GRAFT REJECTION; HUMAN; KIDNEY TRANSPLANTATION; MULTICENTER STUDY (TOPIC); RANDOMIZED CONTROLLED TRIAL (TOPIC); STATISTICAL MODEL;

CALCINEURIN INHIBITORS; CYCLOSPORINE; CYTOMEGALOVIRUS INFECTIONS; DRUG THERAPY, COMBINATION; GRAFT REJECTION; HUMANS; IMMUNOSUPPRESSIVE AGENTS; KIDNEY TRANSPLANTATION; LOGISTIC MODELS; MODELS, BIOLOGICAL; MULTICENTER STUDIES AS TOPIC; MYCOPHENOLIC ACID; RANDOMIZED CONTROLLED TRIALS AS TOPIC; TACROLIMUS;

EID: 84921686095     PISSN: 00099236     EISSN: 15326535     Source Type: Journal    
DOI: 10.1038/clpt.2014.140     Document Type: Article

References (50)

1. Levy, G., Thervet, E., Lake, J. & Uchida, K., Consensus on Neoral C(2): Expert Review in Transplantation (CONCERT) Group. Patient management by neoral C(2) monitoring: an international consensus statement. Transplantation 73, S12-S18 (2002).
2. Wallemacq, P. et al. Opportunities to optimize tacrolimus therapy in solid organ transplantation: report of the European consensus conference. Ther. Drug Monit. 31, 139-152 (2009).
3. van Gelder, T. Therapeutic drug monitoring for mycophenolic acid is value for (little) money. Clin. Pharmacol. Ther. 90, 203-204 (2011).
4. Byrne, R., Yost, S.E. & Kaplan, B. Mycophenolate mofetil monitoring: is there evidence that it can improve outcomes? Clin. Pharmacol. Ther. 90, 204-206 (2011).
5. Ekberg, H. et al.; ELITE-Symphony Study. Reduced exposure to calcineurin inhibitors in renal transplantation. N. Engl. J. Med. 357, 2562-2575 (2007).
6. Ekberg, H. et al. Cyclosporine sparing with mycophenolate mofetil, daclizumab and corticosteroids in renal allograft recipients: the CAESAR Study. Am. J. Transplant 7, 560-570 (2007).
7. Frobel, A.K. et al. A time-to-event model for acute rejections in paediatric renal transplant recipients treated with ciclosporin A. Br. J. Clin. Pharmacol. 76, 603-615 (2013).
8. Bouamar, R. et al. Tacrolimus predose concentrations do not predict the risk of acute rejection after renal transplantation: a pooled analysis from three randomized-controlled clinical trials(+). Am. J. Transplant 13, 1253-1261 (2013).
9. van Gelder, T. Mycophenolate blood level monitoring: recent progress. Am. J. Transplant 9, 1495-1499 (2009).
10. Le Meur, Y. et al. Individualized mycophenolate mofetil dosing based on drug exposure significantly improves patient outcomes after renal transplantation. Am. J. Transplant 7, 2496-2503 (2007).
11. van Gelder, T. et al. Comparing mycophenolate mofetil regimens for de novo renal transplant recipients: the fixed-dose concentration-controlled trial. Transplantation 86, 1043-1051 (2008).
12. Le Meur, Y. et al. Early steroid withdrawal and optimization of mycophenolic acid exposure in kidney transplant recipients receiving mycophenolate mofetil. Transplantation 92, 1244-1251 (2011).
13. Sagedal, S. et al. The impact of cytomegalovirus infection and disease on rejection episodes in renal allograft recipients. Am. J. Transplant 2, 850-856 (2002).
14. Reischig, T. Cytomegalovirus-associated renal allograft rejection: new challenges for antiviral preventive strategies. Expert Rev. Anti. Infect. Ther. 8, 903-910 (2010).
15. Reischig, T., Jindra, P., Hes, O., Bouda, M., Kormunda, S. & Treska, V. Effect of cytomegalovirus viremia on subclinical rejection or interstitial fibrosis and tubular atrophy in protocol biopsy at 3 months in renal allograft recipients managed by preemptive therapy or antiviral prophylaxis. Transplantation 87, 436-444 (2009).
16. Smith, J.M. et al. Subclinical viremia increases risk for chronic allograft injury in pediatric renal transplantation. J. Am. Soc. Nephrol. 21, 1579-1586 (2010).
17. Fryd, D.S., Peterson, P.K., Ferguson, R.M., Simmons, R.L., Balfour, H.H. Jr & Najarian, J.S. Cytomegalovirus as a risk factor in renal transplantation. Transplantation 30, 436-439 (1980).
18. Eriksson, B.M. et al. A prospective study of rapid methods of detecting cytomegalovirus in the blood of renal transplant recipients in relation to patient and graft survival. Clin. Transplant. 10, 494-502 (1996).
19. Sagedal, S. et al. Impact of early cytomegalovirus infection and disease on long-term recipient and kidney graft survival. Kidney Int. 66, 329-337 (2004).
20. Le Page, A.K., Jager, M.M., Kotton, C.N., Simoons-Smit, A. & Rawlinson, W.D. International survey of cytomegalovirus management in solid organ transplantation after the publication of consensus guidelines. Transplantation 95, 1455-1460 (2013).
21. Rizopoulos, D. JM: an R package for the joint modeling of longitudinal and time-to-event data. J. Stat. Softw. 35, 1-33 (2010).
22. Tsiatis, A.A. & Davidian, M. Joint modeling of longitudinal and time-to-event data: an overview. Stat. Sin. 14, 809-834 (2004).
23. Yu, M., Law, N.J., Taylor, J.M.G. & Sandler, H.M. Joint longitudinal-survivalcure models and their application to prostate cancer. Stat. Sin. 14, 835-862 (2004).
24. Jacqmin-Gadda, H., Thiébaut, R. & Dartigues, J.F. [Joint modeling of quantitative longitudinal data and censored survival time]. Rev. Epidemiol. Sante. Publique 52, 502-510 (2004).
25. Horrocks, J. & van Den Heuvel, M.J. Prediction of pregnancy: a joint model for longitudinal and binary data. Bayesian. Anal. 4, 523-538 (2009).
26. Li, E., Zhang, D. & Davidian, M. Conditional estimation for generalized linear models when covariates are subject-specific parameters in a mixed model for longitudinal measurements. Biometrics 60, 1-7 (2004).
27. Li, E., Wang, N. & Wang, N.Y. Joint models for a primary endpoint and multiple longitudinal covariate processes. Biometrics 63, 1068-1078 (2007).
28. Pillans, P.I. et al. A retrospective analysis of mycophenolic acid and cyclosporin concentrations with acute rejection in renal transplant recipients. Clin. Biochem. 34, 77-81 (2001).
29. Okamoto, M. et al. Therapeutic drug monitoring of mycophenolic acid in renal transplant recipients. Transplant. Proc. 37, 859-860 (2005).
30. Satoh, S. et al. The influence of mycophenolate mofetil versus azathioprine and mycophenolic acid pharmacokinetics on the incidence of acute rejection and infectious complications after renal transplantation. Transplant. Proc. 37, 1751-1753 (2005).
31. Pawinski, T., Durlik, M., Szlaska, I., Urbanowicz, A., Majchrnak, J. & Gralak, B. Comparison of mycophenolic acid pharmacokinetic parameters in kidney transplant patients within the first 3 months post-transplant. J. Clin. Pharm. Ther. 31, 27-34 (2006).
32. Armstrong, V.W., Shipkova, M., Schütz, E., Weber, L., Tönshoff, B. & Oellerich, M.; German Study Group on MMF Therapy in Pediatric Renal Transplant Recipients. Monitoring of mycophenolic acid in pediatric renal transplant recipients. Transplant. Proc. 33, 1040-1043 (2001).
33. Daher Abdi, Z. et al. Impact of longitudinal exposure to mycophenolic acid on acute rejection in renal-transplant recipients using a joint modeling approach. Pharmacol. Res. 72, 52-60 (2013).
34. Zhou, Z., Shen, J., Hong, Y., Kaul, S., Pfister, M. & Roy, A. Time-varying belatacept exposure and its relationship to efficacy/safety responses in kidneytransplant recipients. Clin. Pharmacol. Ther. 92, 251-257 (2012).
35. Israni, A.K. et al.; DeKAF Genomics Investigators. Tacrolimus trough levels after month 3 as a predictor of acute rejection following kidney transplantation: a lesson learned from DeKAF Genomics. Transpl. Int. 26, 982-989 (2013).
36. Gaston, R.S. et al. Fixed-or controlled-dose mycophenolate mofetil with standard-or reduced-dose calcineurin inhibitors: the Opticept trial. Am. J. Transplant 9, 1607-1619 (2009).
37. Kotton, C.N. et al.; Transplantation Society International CMV Consensus Group. Updated international consensus guidelines on the management of cytomegalovirus in solid-organ transplantation. Transplantation 96, 333-360 (2013).
38. Solez, K. et al. Banff 07 classification of renal allograft pathology: updates and future directions. Am. J. Transplant 8, 753-760 (2008).
39. Sauvage, F.L., Gaulier, J.M., Lachâtre, G. & Marquet, P. A fully automated turbulent-flow liquid chromatography-tandem mass spectrometry technique for monitoring antidepressants in human serum. Ther. Drug Monit. 28, 123-130 (2006).
40. Benkali, K. et al. Tacrolimus population pharmacokinetic-pharmacogenetic analysis and Bayesian estimation in renal transplant recipients. Clin. Pharmacokinet. 48, 805-816 (2009).
41. Prémaud, A. et al. Maximum a posteriori bayesian estimation of mycophenolic acid pharmacokinetics in renal transplant recipients at different postgrafting periods. Ther. Drug Monit. 27, 354-361 (2005).
42. Beal, S.L., Sheiner, L.B., Boeckmann, A.J. & Bauer, R.J. NONMEM User's Guide (1989-2009). (Icon Development Solutions, Ellicott City, MD, 2009).
43. Lindbom, L., Ribbing, J. & Jonsson, E.N. Perl-speaks-NONMEM (PsN)-a Perl module for NONMEM related programming. Comput. Methods Programs Biomed. 75, 85-94 (2004).
44. Jonsson, E.N. & Karlsson, M.O. Xpose-an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput. Methods Programs Biomed. 58, 51-64 (1999).
45. Holford, N. A time to event tutorial for pharmacometricians. CPT Pharmacometrics. Syst. Pharmacol. 2, e43 (2013).
46. Commenges, D. Inference for multi-state models from interval-censored data. Stat. Methods Med. Res. 11, 167-182 (2002).
47. Wilkins, J.J., Karlsson, M.O. & Jonsson, E.N. Patterns and power for the visual predictive checks [abstract]. . Accessed 5 August 2013.
48. Parke, J., Holford, N.H. & Charles, B.G. A procedure for generating bootstrap samples for the validation of nonlinear mixed-effects population models. Comput. Methods Programs Biomed. 59, 19-29 (1999).
49. Lindbom, L., Pihlgren, P., Jonsson, E.N. & Jonsson, N. PsN-Toolkit-a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput. Methods Programs Biomed. 79, 241-257 (2005).
50. Holford, N. Time to Event analysis Diagnostic Plots [slides]. . Accessed 15 August 2013.