Skip to main content
SpringerLink
Log in

Loss of Function ABCG2 c.421C>A (rs2231142) Polymorphism Increases Steady-State Exposure to Mycophenolic Acid in Stable Renal Transplant Recipients: An Exploratory Matched Cohort Study

  • Original Research
  • Published:
Advances in Therapy Aims and scope Submit manuscript

Abstract

Introduction

Polymorphism ABCG2 c.421C>A (rs2231142) results in reduced activity of the important drug efflux transporter breast cancer-resistance protein (BCRP/ABCG2). One study has suggested that it may affect enterohepatic recirculation of mycophenolic acid (MPA). We evaluated the effect of rs2231142 on steady-state exposure to MPA in renal transplant recipients.

Methods

Consecutive, stable adult (age ≥ 16 years) renal transplant recipients on standard MPA-based immunosuppressant protocols (N = 68; 43 co-treated with cyclosporine, 25 with tacrolimus) underwent routine therapeutic drug monitoring after a week of initial treatment, and were genotyped for ABCG2 c.421C>A and 11 polymorphisms in genes encoding enzymes and transporters implicated in MPA pharmacokinetics. ABCG2 c.421C>A variant versus wild-type (wt) patients were matched with respect to demographic, biopharmaceutic, and genetic variables (full optimal combined with exact matching) and compared for dose-adjusted steady-state MPA pharmacokinetics [frequentist and Bayes (skeptical neutral prior) estimates of geometric means ratios, GMR].

Results

Raw data (12 variant versus 56 wt patients) indicated around 40% higher total exposure (frequentist GMR = 1.45, 95% CI 1.10–1.91; Bayes = 1.38, 95% CrI 1.07–1.81) and around 30% lower total body clearance (frequentist GMR = 0.66, 0.58–0.90; Bayes = 0.71, 0.53–0.95) in variant carriers than in wt controls. The estimates were similar in matched data (11 variant versus 43 wt patients): exposure GMR = 1.41 (1.11–1.79) frequentist, 1.39 (1.15–1.81) Bayes, with 90.7% and 85.5% probability of GMR > 1.20, respectively; clearance GMR = 0.73 (0.58–0.93) frequentist, 0.71 (0.54–0.95) Bayes. Sensitivity analysis indicated low susceptibility of the estimates to unmeasured confounding.

Conclusions

Loss-off-function polymorphism ABCG2 c.421C>A increases steady-state exposure to MPA in stable renal transplant patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Staatz CE, Tett SE. Clinical pharmacokinetics and pharmacodynamics of mycophenolate in solid organ transplant recipients. Clin Pharmacokinet. 2007;46:13–58.

    Article  CAS  Google Scholar 

  2. Tett SE, Saint-Marcoux F, Staatz CE, Brunet M, Vinks AA, Miura M, et al. Mycophenolate, clinical pharmacokinetics, formulations, and methods for assessing drug exposure. Transplant Rev. 2011;5:47–57.

    Article  Google Scholar 

  3. Lamba V, Sanhavi K, Fish A, Altman RB, Klein TE. PharmGKB summary: mycophenolic acid pathway. Pharmacogenet Genom. 2014;24:73–9.

    Article  CAS  Google Scholar 

  4. Dalla VecchiaGenvigir F, Cerda A, Dominguez Crespo Hirata T, Hirata MH, Hirata DC. Mycophenolic acid pharmacogenomics in kidney transplantation. J Transl Genet Genom. 2020;4:320–55.

    Google Scholar 

  5. Bergan S, Brunet M, Hesselink DA, Johnson-Davis KL, Kunicki PK, Lemaitre F, et al. Personalized therapy for mycophenolate: consensus report by the International association on therapeutic drug monitoring and clinical toxicology. Ther Drug Monit. 2021;43:150–200.

    Article  CAS  Google Scholar 

  6. Giacomini KM, Huang SM, Tweedie DJ, Benet LZ, Brouwer KL, Chu X, et al. International transporter consortium: membrane transporters in drug development. Nat Rev Drug Discov. 2010;9:215–36.

    Article  CAS  Google Scholar 

  7. Giacomini KM, Balimane PV, Cho SK, Eadon M, Edeki T, Hillgren KM, et al. International transporter consortium commentary on clinically important transporter polymorphisms. Clin Pharmacol Ther. 2013;94:23–6.

    Article  CAS  Google Scholar 

  8. Foher AE, Brackman DJ, Giacomini KM, Altman RB, Klein TE. Pharm GKB summary: very important pharmacogene information for ABCG2. Pharmacogenet Genom. 2017;27:420–7.

    Article  Google Scholar 

  9. Kondo C, Suzuki H, Itoda M, Ozawa S, Kobayashi D, et al. Functional analysis of SNPs variants of BCRP/ABCG2. Pharm Res. 2004;21:1895–903.

    Article  CAS  Google Scholar 

  10. Furukawa T, Wakabayashi K, Tamura A, Nakagawa H, Morishima Y, Osawa Y, et al. Major SNP (Q141K) variant of human ABC transporter ABCG2 undergoes lysosomal and proteosomal degradations. Pharm Res. 2009;26:469–79.

    Article  CAS  Google Scholar 

  11. Miura M, Kagaya H, Satoh S, Inoue K, Saito M, Habuchi T, Suzuki T. Influence of drug transporters and UGT polymorphisms on pharmacokinetics of phenolic glucuronide metabolite of mycophenolic acid in Japanese renal transplant recipients. Ther Drug Monit. 2008;30:559–64.

    Article  CAS  Google Scholar 

  12. Trkulja V, Lalić Z, Nađ-Škegro S, Lebo A, Granić P, Lovrić M, et al. Effect of cyclosporine on steady-state pharmacokinetics of MPA in renal transplant recipients is not affected by the MPA formulation: analysis based on therapeutic drug monitoring data. Ther Drug Monit. 2014;36:456–64.

    Article  CAS  Google Scholar 

  13. Božina N, Lalić Z, NađŠkegro S, Borić-Bilušić A, Božina T, Kaštelan Ž, Trkulja V. Steady-state pharmacokinetics of mycophenolic acid in renal transplant patients: exploratory analysis of the effects of cyclosporine, recipients’ and donors’ ABCC2 gene variants and their interactions. Eur J Clin Pharmacol. 2017;73:1129–40.

    Article  Google Scholar 

  14. Hu DG, Meech R, Lu L, McKinnon RA, Mackenzie PI. Polymorphisms and haplotypes of the UDP-glucuronosyltransferase 2B7 gene promoter. Drug Metab Dispos. 2014;42:854–62.

    Article  Google Scholar 

  15. Pearl J. Causality: models, reasoning and inference. 2nd ed. Cambridge: Cambridge University Press; 2009.

    Book  Google Scholar 

  16. Knol MJ, VanderWeele TJ. Recommendations for presenting analyses of effect modification and interaction. Int J Epidemiol. 2016;42:514–20.

    Google Scholar 

  17. VenderWeele TJ, Rothman KJ, Lash TL. Confounding and confounders. In: Lash TL, VanderWeele TJ, Haneuse S, Rothman KJ, editors. Modern epidemiology. 4th ed. Philadephia: Wolters Kluwver; 2021. p. 610–67.

    Google Scholar 

  18. Textor J, van der Zander B, Gilthorpe MS, Liskiewicz M, Elliot GTH. Robust causal inference using directed acyclic graphs: the R package “dagitty.” Int J Epidemiol. 2016;45:1887–94.

    Google Scholar 

  19. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2020.

  20. Endrenyi L, Gritsch S, Yan W. Cmax/AUC is a clearer measure than Cmax for absorption rates in investigations of bioequivalence. Int J Clin Pharmacol Ther Toxicol. 1991;29:394–9.

    CAS  Google Scholar 

  21. Ho DE, Imai K, King G, Stuart EA. MatchIT: nonparmetric preprocessing for parametric causal inference. J Stat Software. 2011;42:1–28. https://doi.org/10.18637/jss.v042.i08.

    Article  Google Scholar 

  22. Hansen BB, Olsen KS. Optimal full matching and related designs via network flows. J Comput Global Stat. 2006;15:609–27.

    Article  Google Scholar 

  23. King G, Nielsen R. Why propensity scores should not be used for matching. Polit Anal. 2019;27:435–54.

    Article  Google Scholar 

  24. Goodrich B, Gabry J, Ali I, Brilleman S. rstanarm: Bayesian applied regression modeling via Stan. R package version 2.21.3, 2022, https://mc-stan.org/rstanarm/

  25. Gaunt TR, Rodríguez S, Day IN. Cubic exact solutions for the estimation of pairwise haplotype frequencies: implications for linkage disequilibrium analyses and a web tool “CubeX.” BMC Bioinform. 2007;8(1):428. https://doi.org/10.1186/1471-2105-8-428.

    Article  CAS  Google Scholar 

  26. VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med. 2017;167:268–74.

    Article  Google Scholar 

  27. Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiological database studies of therapeutics. Pharmacoepidemol Drug Saf. 2006;15:291–303.

    Article  Google Scholar 

  28. Haine D. The episensr package: basic sensitivity analysis of epidemiological results. https://doi.org/10.5281/zenodo.4554553, R package version 1.1.0, https://dhaine.github.io/episensr

  29. Balduzzi S, Rücker G, Schwarzer G. How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health. 2019;22:153–60.

    Article  Google Scholar 

  30. Friedrich JO, Adhikari NKJ, Beyene J. The ratio of means method as an alternative to mean differences for analyzing continuous outcome variables in meta-analysis: a simulation study. BMC Med Res Methodol. 2008;8:32. https://doi.org/10.1186/1471-2288-8-32.

    Article  Google Scholar 

  31. Boumar R, Hessenlink DA, van Schaik RHN, Weimar W, van der Heiden I, de Fijter JW, et al. Mycophenolic acid-related diarrhea is not associated with polymorphisms in SLCO1B nor with ABCB1 in renal transplant recipients. Pharmacogen Genom. 2012;22:399–407.

    Article  Google Scholar 

  32. van Schaik RHN, van Agteren M, de Fijter JW, Hartmann A, Schmidt J, Budde K, et al. UGT1A9 -275T>A/-2152C>T polymorphisms correlate with low MPA exposure and acute rejection in MMF/tacrolimus-treated kidney transplant patients. Clin Pharmacol Ther. 2009;86:319–27.

    Article  Google Scholar 

  33. Picard N, Yee SW, Woillard JB, Lebranchu Y, Le Meur Y, Giacomini KM, Marquet P. The role of organic antion-transporting polypeptides and their common genetic variants in mycophenolic acid pharmacokinetics. Clin Pharm Ther. 2010;87:100–8.

    Article  CAS  Google Scholar 

  34. Geng F, Jiao Z, Dao YJ, Qiu XY, Ding JJ, Shi X, et al. The association of the UGT1A8, SLCO1B3 and ABCC2/ABCG2 genetic polymorphisms with the pharmacokinetics of mycophenolic aid and its phenolic glucuronide metabolite in Chinese individuals. Clin Chim Acta. 2012;423:683–90.

    Google Scholar 

  35. Miura M, Satoh S, Inoue K, Kagaya H, Saito M, Inoue M, et al. Influence of SLCO1B1, 1B3, 2B1 and ABCC2 genetic polymorphisms on mycophenolic acid pharmacokinetics in Japanese renal transplant recipients. Eur J Clin Pharmacol. 2007;63:1161–9.

    Article  CAS  Google Scholar 

  36. Bernard O, Cuillemette C. The main role of UGT1A9 in the hepatic metabolism of mycophenolic acid and the effectsof naturally occurring variants. Drug Metab Dispos. 2004;32:775–8.

    Article  CAS  Google Scholar 

  37. Kuypers DR, Naesens M, Vermeire S, Vanrentghem Y. The impact of uridine diphosphate-glucuronosyltrasferase 1A9 (UGT1A9) gene promoter region single-nucleotide polymorphisms T-275A and C-2152T on early mycophenolic acid dose-interval exposure in de novo renal allograft recipients. Clin Pharmacol Ther. 2005;78:351–61.

    Article  CAS  Google Scholar 

  38. Zhao W, Fakhoury M, Deschenes G, Roussey G, Brochard K, Niaudet P, et al. Population pharmacokinetics and pharmacogenetics of mycophenolic acid following administration of mycophenolate mofetil in de novo pediatric renal transplant patients. J Clin Pharmacol. 2010;50:1280–91.

    Article  CAS  Google Scholar 

  39. Yang CI, Shen CC, Liao GY, Yong S, Feng LJ, Xia Q, et al. Genetic polymorphisms in metabolic enzymes and transporters have no impact on mycophenolic acid pharmacokinetics in adult kindey transplant patients co-treated with tacrolimus: a population analysis. J Clin Pharm Ther. 2021;00:1–12. https://doi.org/10.1111/jcpt.13488.

    Article  CAS  Google Scholar 

  40. UGT alleles Nomenclature. Available at https://www.pharmacogenomics.pha.ulaval.ca/ugt-alleles-nomenclature/ (last accessed July 11, 2022)

  41. Takuathung MN, Sakuludomkan W, Koonrungsesomboon N. The impact of genetic polymorphisms on the pharmacokinetics and pharmacodynamics of mycophenolic acid: systematic review and meta-analysis. Clin Pharmacokinet. 2021;60:1291–302.

    Article  Google Scholar 

  42. Wolking S, Schaeffeler E, Lerche H, Schwab M, Nies AT. Impact of genetic polymorphisms of ABCB1 (MDR1, P-glycoprotein) on drug disposition and potential clinical implications: update of the literature. Clin Pharmacokinet. 2015;54:709–35.

    Article  CAS  Google Scholar 

  43. Bruckmueller H, Cascorbi I. ABCB1, ABCG2, ABCC1, ABCC2 and ABCC3 drug transporter polymorphisms and their impact on drug bioavailability: what is our current understanding. Exp Opin Drug Metab Toxicol. 2021;17:369–96.

    Article  CAS  Google Scholar 

  44. Barbarino JM, Staatz CE, Venkataramanan R, Klein TE, Altman RB. PharmGKB summary: cyclosporine and tacrolimus pathways. Pharmacogenet Genomics. 2013;23:563–85.

    Article  CAS  Google Scholar 

  45. Gupta A, Dai Y, Vethanayagam RR, Herber MF, Thummel KE, Unadkat JD, et al. Cyclosporin A, tacrolimus and sirolimus are potent inhibitors of the human breast cancer resistance protein (ABCG2) and reverse resistance to mitoxantrone and topotecan. Cancer Chemother Pharmacol. 2006;58:374–83.

    Article  CAS  Google Scholar 

  46. Bakhsheshian J, Hall MD, Robey RW, Herrmann MA, Chen JQ, Bates SE, Gottesman MM. Overlapping substrate and inhibitor specificity of human and murine ABCG2. Drug Metab Dispos. 2013;41:1805–12.

    Article  CAS  Google Scholar 

  47. Li LQ, Chen DN, Li CJ, Li QP, Chen Y, Fang P, et al. Impact of UGT2B7 and ABCC2 genetic polymorphisms on mycophenolic acid metabolism in Chinese renal transplant recipients. Pharmacogenomics. 2018;19:1323–34.

    Article  CAS  Google Scholar 

  48. Dalla VecchiaGenvigir F, Campus-Salazar AB, Rosso Felipe C, Tedesco-Silv H, Medina-Pestana JO, de Quateli DS, et al. CYP3A5*3 and CYP2C8*3 variants influence exposure and clinical outcomes of tacrolimus-based therapy. Pharmacogenomics. 2020;21:7–21.

    Article  Google Scholar 

Download references

Acknowledgements

Funding

No funding or sponsorship was received for this study or publication of this article.

Author Contributions

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Disclosures

Ana Borić Bilušić, Nada Božina, Zdenka Lalić, Mila Lovrić, Sandra Nađ-Škegro, Luka Penezić, Karmela Barišić and Vladimir Trkulja have nothing to disclose.

Compliance with Ethics Guidelines

Study was approved by the Ethics Committee of the University Hospital Center Zagreb (approval No. 8.1-17/242-2 02/21, January 30, 2018). All procedures performed in the study were in accordance with the 1964 Declaration of Helsinki and its later amendments. All patients included in the present analysis underwent standard routine therapeutic drug monitoring in their post-transplant period. Those meeting inclusion criteria were included only if they signed an informed consent for genotyping of pharmacogenes for research purposes.

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Author information

Authors and Affiliations

  1. Agency for Medicinal Products and Medical Devices of Croatia, Zagreb, Croatia

    A Ana Borić-Bilušić

  2. Department of Pharmacology, Zagreb University School of Medicine, Šalata 11, 10000, Zagreb, Croatia

    Nada Božina & Vladimir Trkulja

  3. Department of Laboratory Diagnostics, Analytical Toxicology and Pharmacology Division, University Hospital Center Zagreb, Zagreb, Croatia

    Zdenka Lalić & Mila Lovrić

  4. Department of Urology, University Hospital Center Zagreb, Zagreb, Croatia

    Sandra Nađ-Škegro & Luka Penezić

  5. Department of Medical Biochemistry and Hematology, Faculty of Pharmacy and Biochemistry, Zagreb University, Zagreb, Croatia

    Karmela Barišić

Authors
  1. A Ana Borić-Bilušić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nada Božina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zdenka Lalić
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mila Lovrić
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sandra Nađ-Škegro
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Luka Penezić
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Karmela Barišić
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Vladimir Trkulja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladimir Trkulja.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 1388 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Borić-Bilušić, A.A., Božina, N., Lalić, Z. et al. Loss of Function ABCG2 c.421C>A (rs2231142) Polymorphism Increases Steady-State Exposure to Mycophenolic Acid in Stable Renal Transplant Recipients: An Exploratory Matched Cohort Study. Adv Ther 40, 601–618 (2023). https://doi.org/10.1007/s12325-022-02378-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12325-022-02378-w

Keywords

Search

Navigation