Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Cost-effectiveness analysis of CYP3A5 genotype-guided tacrolimus dosing in solid organ transplantation using real-world data

The Pharmacogenomics Journal volume 24, Article number: 14 (2024) Cite this article

Subjects

Abstract

The objective of this study was to estimate the cost-effectiveness of CYP3A5 genotype-guided tacrolimus dosing in kidney, liver, heart, and lung transplant recipients relative to standard of care (SOC) tacrolimus dosing, from a US healthcare payer perspective. We developed decision-tree models to compare economic and clinical outcomes between CYP3A5 genotype-guided and SOC tacrolimus therapy in the first six months post-transplant. We derived inputs for CYP3A5 phenotype frequencies and physician use of genotype test results to inform clinical care from literature; tacrolimus exposure [high vs low tacrolimus time in therapeutic range using the Rosendaal algorithm (TAC TTR-Rosendaal)] and outcomes (incidences of acute tacrolimus nephrotoxicity, acute cellular rejection, and death) from real-world data; and costs from the Medicare Fee Schedule and literature. We calculated cost per avoided event and performed sensitivity analyses to evaluate the robustness of the results to changes in inputs. Incremental costs per avoided event for CYP3A5 genotype-guided vs SOC tacrolimus dosing were $176,667 for kidney recipients, $364,000 for liver recipients, $12,982 for heart recipients, and $93,333 for lung recipients. The likelihood of CYP3A5 genotype-guided tacrolimus dosing leading to cost-savings was 19.8% in kidney, 32.3% in liver, 51.8% in heart, and 54.1% in lung transplant recipients. Physician use of genotype results to guide clinical care and the proportion of patients with a high TAC TTR-Rosendaal were key parameters driving the cost-effectiveness of CYP3A5 genotype-guided tacrolimus therapy. Relative to SOC, CYP3A5 genotype-guided tacrolimus dosing resulted in a slightly greater benefit at a higher cost. Further economic evaluations examining intermediary outcomes (e.g., dose modifications) are needed, particularly in populations with higher frequencies of CYP3A5 expressers.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Decision tree model for CYP3A5 genotype-guided vs SOC tacrolimus dosing.
Fig. 2: Univariate sensitivity analysis.
Fig. 3: Scatterplot of probabilistic sensitivity analysis.

Similar content being viewed by others

Data availability

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

References

  1. Brunet M, van Gelder T, Asberg A, Haufroid V, Hesselink DA, Langman L, et al. Therapeutic drug monitoring of tacrolimus-personalized therapy: second consensus report. Ther Drug Monit. 2019;41:261–307.

    Article  CAS  PubMed  Google Scholar 

  2. Kang JS, Lee MH. Overview of therapeutic drug monitoring. Korean J Intern Med. 2009;24:1–10.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Dasari BVM, Hodson J, Nassir A, Widmer J, Isaac J, Mergentel H, et al. Variations in practice to therapeutic monitoring of tacrolimus following primary adult liver transplantation. Int J Organ Transplant Med. 2016;7:1–8.

    PubMed  Google Scholar 

  4. Chang DH, Youn J-C, Dilibero D, Patel JK, Kobashigawa JA. Heart transplant immunosuppression strategies at cedars-sinai medical center. Int J Heart Fail. 2021;3:15–30.

    Article  PubMed  Google Scholar 

  5. Kamdem LK. Contribution of CYP3A5 to the in vitro hepatic clearance of tacrolimus. Clinical Chemistry. 2005;51:1374–81.

    Article  CAS  PubMed  Google Scholar 

  6. Rojas LE, Herrero MJ, Boso V, Garcia-Eliz M, Poveda JL, Librero J, et al. Meta-analysis and systematic review of the effect of the donor and recipient CYP3A5 6986A>G genotype on tacrolimus dose requirements in liver transplantation. Pharmacogenet Genomics. 2013;23:509–17.

    Article  CAS  PubMed  Google Scholar 

  7. Rojas L, Neumann I, Herrero MJ, Boso V, Reig J, Poveda JL, et al. Effect of CYP3A5*3 on kidney transplant recipients treated with tacrolimus: a systematic review and meta-analysis of observational studies. Pharmacogenomics J. 2015;15:38–48.

    Article  CAS  PubMed  Google Scholar 

  8. Deininger KM, Vu A, Page RL 2nd, Ambardekar AV, Lindenfeld J, Aquilante CL. CYP3A pharmacogenetics and tacrolimus disposition in adult heart transplant recipients. Clin Transplant. 2016;30:1074–81.

    Article  CAS  PubMed  Google Scholar 

  9. Calabrese DR, Florez R, Dewey K, Hui C, Torgerson D, Chong T, et al. Genotypes associated with tacrolimus pharmacokinetics impact clinical outcomes in lung transplant recipients. Clin Transplant. 2018;32:e13332.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Birdwell KA, Decker B, Barbarino JM, Peterson JF, Stein CM, Sadee W, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP3A5 Genotype and Tacrolimus Dosing. Clin Pharmacol Ther. 2015;98:19–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Thervet E, Loriot MA, Barbier S, Buchler M, Ficheux M, Choukroun G, et al. Optimization of initial tacrolimus dose using pharmacogenetic testing. Clin Pharmacol Ther. 2010;87:721–6.

    CAS  PubMed  Google Scholar 

  12. Shuker N, Bouamar R, van Schaik RH, Clahsen-van Groningen MC, Damman J, Baan CC, et al. A randomized controlled trial comparing the efficacy of Cyp3a5 Genotype-Based With Body-Weight-Based Tacrolimus Dosing After Living Donor Kidney Transplantation. Am J Transplant. 2016;16:2085–96.

    Article  CAS  PubMed  Google Scholar 

  13. Anutrakulchai S, Pongskul C, Kritmetapak K, Limwattananon C, Vannaprasaht S. Therapeutic concentration achievement and allograft survival comparing usage of conventional tacrolimus doses and CYP3A5 genotype-guided doses in renal transplantation patients. Br J Clin Pharmacol. 2019;85:1964–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Reininger KA, Onyeaghala G, Anderson-Haag T, Schladt DS, Wu B, Guan W, et al. Higher number of tacrolimus dose adjustments in kidney transplant recipients who are extensive and intermediate CYP3A5 metabolizers. Clin Transplant. 2023;37:e14893.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Natasha P, Baye J, Aifaoui A, Wilke RA, Lupu RA, Savageau J, et al. Implementation of wide-scale pharmacogenetic testing in primary care. Pharmacogenomics. 2019;20:903–13.

    Article  Google Scholar 

  16. Bielinski SJ, St Sauver JL, Olson JE, Larson NB, Black JL, Scherer SE, et al. Cohort Profile: the right drug, right dose, right time: using genomic data to individualize treatment protocol (RIGHT Protocol). Int J Epidemiol. 2020;49:23–24.

    Article  PubMed  Google Scholar 

  17. Liu M, Vnencak-Jones CL, Roland BP, Gatto CL, Mathe JL, Just SL, et al. A tutorial for pharmacogenomics implementation through end-to-end clinical decision support based on ten years of experience from PREDICT. Clin Pharmacol Ther. 2021;109:101–15.

    Article  PubMed  Google Scholar 

  18. Pasternak AL, Marshall VD, Gersch CL, Rae JM, Englesbe M, Park JM. Evaluating the Impact of CYP3A5 genotype on post-transplant healthcare resource utilization in pediatric renal and heart transplant recipients receiving tacrolimus. Pharmacogenomics Personalized Med. 2021;14:319–26.

    Article  Google Scholar 

  19. Wang L, Scherer SE, Bielinski SJ, Muzny DM, Jones LA, Black JL 3rd, et al. Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study. Genet Med. 2022;24:1062–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Tillman E, Nikirk MG, Chen J, Skaar TC, Shugg T, Maddatu JP, et al. Implementation of Clinical Cytochrome P450 3A Genotyping for Tacrolimus Dosing in a Large Kidney Transplant Program. J Clin Pharmacol. 2023;63:961–7.

    Article  CAS  PubMed  Google Scholar 

  21. Prograf prescribing information: http://www.astellas.us/docs/prograf.pdf.

  22. Phan L, Jin Y, Zhang H, Qiang W, Shekhtman E, Shao D, et al. “ALFA: Allele Frequency Aggregator” (2022). National Center for Biotechnology Information, U.S. National Library of Medicine; Available from: www.ncbi.nlm.nih.gov/snp/docs/gsr/alfa/.

  23. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Khan BA, Robinson R, Fohner AE, Muzquiz LI, Schilling BD, Beans JA, et al. Cytochrome P450 genetic variation associated with tamoxifen biotransformation in american indian and alaska native people. Clin Transl Sci. 2018;11:312–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mohamed ME, Schladt DP, Guan W, Wu B, van Setten J, Keating BJ, et al. Tacrolimus troughs and genetic determinants of metabolism in kidney transplant recipients: A comparison of four ancestry groups. Am J Transplant. 2019;19:2795–804.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fohner AE, Dalton R, Skagen K, Jackson K, Claw KG, Hopkins SE, et al. Characterization of CYP3A pharmacogenetic variation in American Indian and Alaska Native communities, targeting CYP3A4*1G allele function. Clin Transl Sci. 2021;14:1292–302.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Deininger KM, Tran JN, Tsunoda SM, Young GK, Lee YM, Anderson HD, et al. Stakeholder perspectives of the clinical utility of pharmacogenomic testing in solid organ transplantation. Pharmacogenomics. 2019;20:1291–302.

    Article  CAS  PubMed  Google Scholar 

  28. Peterson JF, Field JR, Unertl KM, Schildcrout JS, Johnson DC, Shi Y, et al. Physician response to implementation of genotype-tailored antiplatelet therapy. Clin Pharmacol Ther. 2016;100:67–74.

    Article  CAS  PubMed  Google Scholar 

  29. Deininger KM, Tsunoda SM, Hirsch JD, Anderson H, Lee YM, McIlvennan CK, et al. National survey of physicians’ perspectives on pharmacogenetic testing in solid organ transplantation. Clin Transpl. 2020;34:e14037.

    Article  Google Scholar 

  30. Salah LM, Oreschak K, Ambardekar AV, Page RL II, Lindenfeld J, Aquilante CL. Effect of CYP3A Genetic Variants on Different Measures of Tacrolimus Variability in Heart Transplant Recipients. J Heart Lung Transpl. 2020;39:S212.

    Article  Google Scholar 

  31. Davis S, Gralla J, Klem P, Tong S, Wedermyer G, Freed B, et al. Lower tacrolimus exposure and time in therapeutic range increase the risk of de novo donor-specific antibodies in the first year of kidney transplantation. Am J Transplant. 2018;18:907–15.

    Article  CAS  PubMed  Google Scholar 

  32. Ensor CR, Iasella CJ, Harrigan KM, Morrell MR, Moore CA, Shigemura N, et al. Increasing tacrolimus time-in-therapeutic range is associated with superior one-year outcomes in lung transplant recipients. Am J Transplant. 2018;18:1527–33.

    Article  CAS  PubMed  Google Scholar 

  33. Song T, Yin S, Jiang Y, Huang Z, Liu J, Wang Z, et al. Increasing time in therapeutic range of tacrolimus in the first year predicts better outcomes in living-donor kidney transplantation. Front Immunol. 2019;10:2912.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Davis S, Gralla J, Klem P, Stites E, Wiseman A, Cooper JE. Tacrolimus intrapatient variability, time in therapeutic range, and risk of de novo donor-specific antibodies. Transplantation. 2020;104:881–7.

    Article  CAS  PubMed  Google Scholar 

  35. Adie SK, Bitar A, Konerman MC, Dorsch MP, Andrews CA, Pogue K, et al. Tacrolimus time in therapeutic range and long-term outcomes in heart transplant recipients. Pharmacotherapy. 2021;42:106–11.

    Article  PubMed  Google Scholar 

  36. Leino AD, Park JM, Pasternak AL. Impact of CYP3A5 phenotype on tacrolimus time in therapeutic range and clinical outcomes in pediatric renal and heart transplant recipients. Pharmacotherapy. 2021;41:649–57.

    Article  CAS  PubMed  Google Scholar 

  37. Yin S, Huang Z, Wang Z, Fan Y, Wang X, Song T, et al. Early monitoring and subsequent gain of tacrolimus time-in-therapeutic range may improve clinical outcomes after living kidney transplantation. Ther Drug Monit. 2021;43:728–35.

    Article  CAS  PubMed  Google Scholar 

  38. Rosendaal FR, Cannegieter SC, van der Meer FJ, Briet E. A method to determine the optimal intensity of oral anticoagulant therapy. Thromb Haemost. 1993;69:236–9.

    Article  CAS  PubMed  Google Scholar 

  39. Pierce DR, West-Thielke P, Hajjiri Z, Gaitonde S, Tzvetanov I, Benedetti E, et al. Clinical implications of tacrolimus time in therapeutic range and intrapatient variability in urban renal transplant recipients undergoing early corticosteroid withdrawal. Transplant Direct. 2021;7:e698.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120:c179–184.

    Article  PubMed  Google Scholar 

  41. Oetting WS, Wu B, Schladt DP, Guan W, van Setten J, Keating BJ, et al. Genetic variants associated with immunosuppressant pharmacokinetics and adverse effects in the DeKAF genomics genome-wide association studies. Transplantation. 2019;103:1131–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Broce JC, Price LL, Liangos O, Uhlig K, Jaber BL. Hospital-acquired acute kidney injury: an analysis of nadir-to-peak serum creatinine increments stratified by baseline estimated GFR. Clin J Am Soc Nephrol. 2011;6:1556–65.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Banff schema for grading liver allograft rejection: an international consensus document. Hepatology. 1997;25:658-63.

  44. Stewart S, Winters GL, Fishbein MC, Tazelaar HD, Kobashigawa J, Abrams J, et al. Revision of the 1990 working formulation for the standardization of nomenclature in the diagnosis of heart rejection. J Heart Lung Transplant. 2005;24:1710–20.

    Article  PubMed  Google Scholar 

  45. Stewart S, Fishbein MC, Snell GI, Berry GJ, Boehler A, Burke MM, et al. Revision of the 1996 working formulation for the standardization of nomenclature in the diagnosis of lung rejection. J Heart Lung Transplant. 2007;26:1229–42.

    Article  PubMed  Google Scholar 

  46. Roufosse C, Simmonds N, Clahsen-van Groningen M, Haas M, Henriksen KJ, Horsfield C, et al. A 2018 Reference Guide to the Banff Classification of Renal Allograft Pathology. Transplantation. 2018;102:1795–814.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Quest Diagnostics. Pain Management, CYP450 3A5 Genotype, Qualitative. https://testdirectory.questdiagnostics.com/test/test-detail/91618/pain-management-cyp450-3a5-genotype-qualitative?p=h&cc=MASTER.

  48. Laboratory Corporation of America. Cytochrome P450 3A4/3A5 Genotyping. https://www.labcorp.com/tests/504155/cytochrome-p450-3a4-3a5-genotyping.

  49. ARUP Laboratories. CYP3A4 and CYP3A5. https://ltd.aruplab.com/Tests/Pub/3001518.

  50. Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project (HCUP). https://datatools.ahrq.gov/hcupnet/.

  51. Centers for Medicare & Medicaid Services. 2022 ASP Drug Pricing Files. https://www.cms.gov/medicare/payment/part-b-drugs/asp-pricing-files.

  52. Centers for Medicare & Medicaid. Physician Fee Schedule. https://www.cms.gov/medicare/physician-fee-schedule/search.

  53. Vannaprasaht S, Limwattananon C, Anutrakulchai S, Chan-On C. Effect of CYP3A5 genotype on hospitalization cost for kidney transplantation. Int J Clin Pharm. 2019;41:88–95.

    Article  CAS  PubMed  Google Scholar 

  54. Nguyen TT, Pearson RA, Mohamed ME, Schladt DP, Berglund D, Rivers Z, et al. Pharmacogenomics in kidney transplant recipients and potential for integration into practice. J Clin Pharm Ther. 2020;45:1457–65.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Plumpton CO, Pirmohamed M, Hughes DA. Cost-effectiveness of panel tests for multiple pharmacogenes associated with adverse drug reactions: an evaluation framework. Clin Pharmacol Ther. 2019;105:1429–38.

    Article  PubMed  Google Scholar 

  56. McWilliam A, Lutter R, Nardinelli C. Healthcare impact of personalized medicine using genetic testing: an exploratory analysis for warfarin. Per Med. 2008;5:279–84.

    Article  CAS  PubMed  Google Scholar 

  57. You JH, Chan FW, Wong RS, Cheng G. The potential clinical and economic outcomes of pharmacogenetics-oriented management of warfarin therapy - a decision analysis. Thromb Haemost. 2004;92:590–7.

    Article  CAS  PubMed  Google Scholar 

  58. You JH, Tsui KK, Wong RS, Cheng G. Potential clinical and economic outcomes of CYP2C9 and VKORC1 genotype-guided dosing in patients starting warfarin therapy. Clin Pharmacol Ther. 2009;86:540–7.

    Article  CAS  PubMed  Google Scholar 

  59. Mizzi C, Dalabira E, Kumuthini J, Dzimiri N, Balogh I, Başak N, et al. A european spectrum of pharmacogenomic biomarkers: implications for clinical pharmacogenomics. PLoS One. 2016;11:e0162866.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Al-Mahayri ZN, Patrinos GP, Wattanapokayakit S, Iemwimangsa N, Fukunaga K, Mushiroda T, et al. Variation in 100 relevant pharmacogenes among emiratis with insights from understudied populations. Sci Rep. 2020;10:21310.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Runcharoen C, Fukunaga K, Sensorn I, Iemwimangsa N, Klumsathian S, Tong H, et al. Prevalence of pharmacogenomic variants in 100 pharmacogenes among Southeast Asian populations under the collaboration of the Southeast Asian Pharmacogenomics Research Network (SEAPharm). Hum Genome Var. 2021;8:7.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Sanders GD, Neumann PJ, Basu A, Brock DW, Feeny D, Krahn M, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine. JAMA. 2016;316:1093–103.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study was funded by a PhRMA Foundation Health Outcomes Research Pre-Doctoral Fellowship (to KMD) and supported by the Health Data Compass Data Warehouse project (healthdatacompass.org).

Funding

This study was funded by a PhRMA Foundation Health Outcomes Research Pre-Doctoral Fellowship (to KMD).

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Sciences, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, USA

    Kimberly M. Deininger & Christina L. Aquilante

  2. Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, USA

    Heather D. Anderson

  3. Department of Pharmacy, University of Patras School of Health Sciences, Patras, Greece

    George P. Patrinos

  4. Department of Genetics and Genomics, United Arab Emirates University, College of Medicine and Health Sciences, Al-Ain, Abu Dhabi, UAE

    George P. Patrinos & Christina Mitropoulou

  5. Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi, UAE

    George P. Patrinos

  6. The Golden Helix Foundation, London, UK

    Christina Mitropoulou

  7. Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Christina L. Aquilante

Authors
  1. Kimberly M. Deininger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heather D. Anderson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. George P. Patrinos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christina Mitropoulou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christina L. Aquilante
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

KMD and CLA conceived of and designed the study. KMD acquired and analyzed the study data. KMD, HDA, GPP, CM, and CLA played an important role in interpreting the results. KMD drafted the manuscript. CLA, HDA, GPP, and CM critically revised the manuscript and approved the final version. CLA had full access to the study data and final responsibility for the decision to submit for publication.

Corresponding author

Correspondence to Christina L. Aquilante.

Ethics declarations

Competing interests

KMD was an employee of the University of Colorado at the time of this work and is currently an employee of Amgen Pharmaceuticals. None of the other authors have any competing financial interests to declare.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

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

Deininger, K.M., Anderson, H.D., Patrinos, G.P. et al. Cost-effectiveness analysis of CYP3A5 genotype-guided tacrolimus dosing in solid organ transplantation using real-world data. Pharmacogenomics J 24, 14 (2024). https://doi.org/10.1038/s41397-024-00334-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41397-024-00334-1

Search

Advanced search

Quick links