Literature DB >> 21902502

Effect of a new functional CYP3A4 polymorphism on calcineurin inhibitors' dose requirements and trough blood levels in stable renal transplant patients.

Laure Elens1, Ron H van Schaik, Nadtha Panin, Martine de Meyer, Pierre Wallemacq, Dominique Lison, Michel Mourad, Vincent Haufroid.   

Abstract

AIMS: CYP3A4 is involved in the oxidative metabolism of many drugs and xenobiotics including the immunosuppressants tacrolimus (Tac) and cyclosporine (CsA). The objective of the study was to assess the potential influence of a new functional SNP in CYP3A4 on the pharmacokinetic parameters assessed by dose requirements and trough blood levels of both calcineurin inhibitors (CNI) in stable renal transplant patients. PATIENTS &
METHODS: A total of 99 stable renal transplant patients receiving either Tac (n = 49) or CsA (n = 50) were genotyped for the CYP3A4 intron 6 C>T (rs35599367) and CYP3A5*3 SNPs. Trough blood levels ([Tac](0) or [CsA](0) in ng/ml), dose-adjusted [Tac](0) or [CsA](0) (ng/ml per mg/kg bodyweight) as well as doses (mg/kg bodyweight) required to achieve target concentrations were compared among patients according to allelic status for CYP3A4 and CYP3A5.
RESULTS: Dose-adjusted concentrations were 2.0- and 1.6-fold higher in T-variant allele carriers for the CYP3A4 intron 6 C>T SNP compared with homozygous CC for Tac and CsA, respectively. When CYP3A4/CYP3A5 genotypes were combined, the difference was even more striking as the so-defined CYP3A poor metabolizer group presented dose-adjusted concentration 1.6- and 4.1-fold higher for Tac, and 1.5- and 2.2-fold higher for CsA than the intermediate metabolizer and extensive metabolizer groups, respectively. Multiple linear regression analysis revealed that, taken together, both CYP3A4 intron 6 and CYP3A5*3 SNPs explained more than 60 and 20% of the variability observed in dose-adjusted [Tac](0) and [CsA](0), respectively.
CONCLUSION: The CYP3A4 intron 6 C>T polymorphism is associated with altered Tac and CsA metabolism. CYP3A4 intron 6 C>T along with CYP3A5*3 (especially for Tac) pharmacogenetic testing performed just before transplantation may help identifying patients at risk of CNI overexposure and contribute to limit CNI-related nephrotoxicity by refining the starting dose according to their genotype. Original submitted 5 May 2011; Revision submitted 29 June 2011.

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Year:  2011        PMID: 21902502     DOI: 10.2217/pgs.11.90

Source DB:  PubMed          Journal:  Pharmacogenomics        ISSN: 1462-2416            Impact factor:   2.533


  45 in total

1.  Personalizing initial calcineurin inhibitor dosing by adjusting to donor CYP3A-status in liver transplant patients.

Authors:  Katalin Monostory; Katalin Tóth; Ádám Kiss; Edit Háfra; Nóra Csikány; József Paulik; Enikő Sárváry; László Kóbori
Journal:  Br J Clin Pharmacol       Date:  2015-10-26       Impact factor: 4.335

2.  Program in pharmacogenomics at the Ohio State University Medical Center.

Authors:  Joseph P Kitzmiller; Peter J Embi; Kandamurugu Manickam; Kevin M Sweet; Mitch A Phelps; Rebecca D Jackson; Clay B Marsh; Wolfgang Sadee
Journal:  Pharmacogenomics       Date:  2012-05       Impact factor: 2.533

Review 3.  PharmGKB summary: cyclosporine and tacrolimus pathways.

Authors:  Julia M Barbarino; Christine E Staatz; Raman Venkataramanan; Teri E Klein; Russ B Altman
Journal:  Pharmacogenet Genomics       Date:  2013-10       Impact factor: 2.089

Review 4.  Clinical implementation of pharmacogenetics in kidney transplantation: calcineurin inhibitors in the starting blocks.

Authors:  Laure Elens; Rachida Bouamar; Nauras Shuker; Dennis A Hesselink; Teun van Gelder; Ron H N van Schaik
Journal:  Br J Clin Pharmacol       Date:  2014-04       Impact factor: 4.335

5.  Multigene predictors of tacrolimus exposure in kidney transplant recipients.

Authors:  Rebecca A Pulk; David S Schladt; William S Oetting; Weihua Guan; Ajay K Israni; Arthur J Matas; Rory P Remmel; Pamala A Jacobson
Journal:  Pharmacogenomics       Date:  2015-06-12       Impact factor: 2.533

6.  The CYP3A4*22 allele affects the predictive value of a pharmacogenetic algorithm predicting tacrolimus predose concentrations.

Authors:  Laure Elens; Dennis A Hesselink; Ron H N van Schaik; Teun van Gelder
Journal:  Br J Clin Pharmacol       Date:  2013-06       Impact factor: 4.335

Review 7.  Pharmacogenetics and immunosuppressive drugs in solid organ transplantation.

Authors:  Teun van Gelder; Ron H van Schaik; Dennis A Hesselink
Journal:  Nat Rev Nephrol       Date:  2014-09-23       Impact factor: 28.314

8.  Impact of genetic and nongenetic factors on interindividual variability in 4β-hydroxycholesterol concentration.

Authors:  Kristine Hole; C Gjestad; K M Heitmann; T Haslemo; E Molden; S Bremer
Journal:  Eur J Clin Pharmacol       Date:  2016-12-14       Impact factor: 2.953

9.  Erlotinib in African Americans with advanced non-small cell lung cancer: a prospective randomized study with genetic and pharmacokinetic analyses.

Authors:  M A Phelps; T E Stinchcombe; J S Blachly; W Zhao; L J Schaaf; S L Starrett; L Wei; M Poi; D Wang; A Papp; J Aimiuwu; Y Gao; J Li; G A Otterson; W J Hicks; M A Socinski; M A Villalona-Calero
Journal:  Clin Pharmacol Ther       Date:  2014-04-29       Impact factor: 6.875

10.  CYP3A4/5 combined genotype analysis for predicting statin dose requirement for optimal lipid control.

Authors:  Joseph Paul Kitzmiller; Danielle M Sullivan; Mitchell A Phelps; Danxin Wang; Wolfgang Sadee
Journal:  Drug Metabol Drug Interact       Date:  2013
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