Review

Assessing the utility of measurement methods applied in economic evaluations of pharmacogenomics applications

The Golden Helix Foundation, London, SE1 8RT, UK

University of Patras, School of Health Sciences, Department of Pharmacy, Laboratory of Pharmacogenomics & Individualized Therapy, 26504, Rio, Patras, Greece

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Korina Tzerefou

University of Piraeus, Economics Department, 18534, Piraeus, Greece

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Konstantinos Koufou

The Golden Helix Foundation, London, SE1 8RT, UK

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George P Patrinos

https://orcid.org/0000-0002-0519-7776

University of Patras, School of Health Sciences, Department of Pharmacy, Laboratory of Pharmacogenomics & Individualized Therapy, 26504, Rio, Patras, Greece

United Arab Emirates University, College of Medicine & Health Sciences, Department of Genetics & Genomics, P.O. Box. 15551, Al-Ain, Abu Dhabi, United Arab Emirates

United Arab Emirates University, Zayed Center for Health Sciences, P.O. Box. 15551, Al-Ain, Abu Dhabi, United Arab Emirates

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Christina Mitropoulou

*Author for correspondence: Tel.: 0030 698 321 0644;

E-mail Address: c.mitropoulou@goldenhelix.org

https://orcid.org/0000-0002-9184-4668

The Golden Helix Foundation, London, SE1 8RT, UK

United Arab Emirates University, Zayed Center for Health Sciences, P.O. Box. 15551, Al-Ain, Abu Dhabi, United Arab Emirates

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Published Online:30 Jan 2024https://doi.org/10.2217/pgs-2023-0221

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Abstract

An increasing number of economic evaluations are published annually investigating the economic effectiveness of pharmacogenomic (PGx) testing. This work was designed to provide a comprehensive summary of the available utility methods used in cost–effectiveness/cost–utility analysis studies of PGx interventions. A comprehensive review was conducted to identify economic analysis studies using a utility valuation method for PGx testing. A total of 82 studies met the inclusion criteria. A majority of studies were from the USA and used the EuroQol-5D questionnaire, as the utility valuation method. Cardiovascular disorders was the most studied therapeutic area while discrete-choice studies mainly focused on patients' willingness to undergo PGx testing. Future research in applying other methodologies in PGx economic evaluation studies would improve the current research environment and provide better results.

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References

1. Klein ME, Parvez MM, Shin JG. Clinical implementation of pharmacogenomics for personalized precision medicine: barriers and solutions. J. Pharm. Sci. 106(9), 2368–2379 (2017).
Medline CASGoogle Scholar
2. Pandi MT, Koromina M, Vonitsanos G, van der Spek PJ, Patrinos GP, Mitropoulou C. Development of an optimized and generic cost-utility model for analyzing genome-guided treatment data. Pharmacol. Res. 178, 106187 (2022).
Medline CASGoogle Scholar
3. Chenoweth MJ, Giacomini KM, Pirmohamed M et al. Global pharmacogenomics within precision medicine: challenges and opportunities. Clin. Pharmacol. Ther. 107(1), 57–61 (2020).
MedlineGoogle Scholar
4. Fragoulakis V, Mitropoulou C, Williams M, Patrinos GP. Economic Evaluation in Genomic Medicine. ScienceDirect, UK (2015).
Google Scholar
5. Salomon JA. Techniques for valuing health states. Elsevier eBooks, 454–458 (2014).
Google Scholar
6. Grosse SD, Wordsworth S, Payne K. Economic methods for valuing the outcomes of genetic testing: beyond cost-effectiveness analysis. Genet. Med. 10(9), 648–654 (2008).
MedlineGoogle Scholar
7. Brazier J, Ratcliffe J. Measuring and Valuing Health Benefits for Economic Evaluation (2nd Edition 2016; online edition). Oxford Academic, Oxford, UK (2016).
Google Scholar
8. Hickey A, Barker M, McGee H, O'Boyle C. Measuring health-related quality of life in older patient populations: a review of current approaches. Pharmacoeconomics 23(10), 971–993 (2005).
MedlineGoogle Scholar
9. Rowen D, Azzabi Zouraq I, Chevrou-Severac H, van Hout B. International regulations and recommendations for utility data for health technology assessment. Pharmacoeconomics 35(Suppl. 1), 11–19 (2017).
MedlineGoogle Scholar
10. Fragoulakis V, Mitropoulou C, Williams MS, Patrinos GP. Economic Evaluation in Genomic Medicine. Elsevier/Academic Press, UK, 37–63 (2015).
Google Scholar
11. Attema AE, Edelaar-Peeters Y, Versteegh MM, Stolk EA. Time trade-off: one methodology, different methods. Eur. J. Health Econ. 14(Suppl. 1),S53–S64 (2013).
MedlineGoogle Scholar
12. Clark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics 32(9), 883–902 (2014).
MedlineGoogle Scholar
13. Soekhai V, de Bekker-Grob EW, Ellis AR, Vass CM. Discrete choice experiments in health economics: past, present and future. Pharmacoeconomics 37(2), 201–226 (2019).
MedlineGoogle Scholar
14. Zoratti MJ, Pickard AS, Stalmeier PFM et al. Evaluating the conduct and application of health utility studies: a review of critical appraisal tools and reporting checklists. Eur. J. Health Econ. 22(5), 723–733 (2021).
MedlineGoogle Scholar
15. Verbelen M, Weale ME, Lewis CM. Cost-effectiveness of pharmacogenetic-guided treatment: are we there yet? Pharmacogenomics J. 17(5), 395–402 (2017).
Medline CASGoogle Scholar
16. Deverka PA, Vernon J, McLeod HL. Economic opportunities and challenges for pharmacogenomics. Annu. Rev. Pharmacol. Toxicol. 50, 423–437 (2010).
Medline CASGoogle Scholar
17. Koufaki MI, Karamperis K, Vitsa P et al. Adoption of pharmacogenomic testing: a marketing perspective. Front. Pharmacol. 12, 72431 (2021).
Google Scholar
18. Charbonneau DH. The Cochrane Library. J. Med. Libr. Assoc. 93(3), 409–410 (2005).
Google Scholar
19. Misra DP, Ravindran V. An overview of the functionalities of PubMed. J. Royal College Physicians Edinburgh 52(1), 8–9 (2022).
Google Scholar
20. NHS Centre for Reviews and Dissemination. The NHS Economic Evaluation Database (NHS EED) York: University of York. Effectiveness Matters 6(1). 2002 Effectiveness Matters. York: University of York 6(1), (2002). www.crd.york.ac.uk/CRDWeb/ShowRecord.asp?ID=32002000739
Google Scholar
21. Alagoz O, Durham D, Kasirajan K. Cost-effectiveness of one-time genetic testing to minimize lifetime adverse drug reactions. Pharmacogenomics J. 16(2), 129–136 (2016).
Medline CASGoogle Scholar
22. Banerjee S, Kumar A, Lopez N et al. Cost-effectiveness analysis of genetic testing and tailored first-line therapy for patients with metastatic gastrointestinal stromal tumors. JAMA Netw. Open 3(9), e2013565 (2020).
MedlineGoogle Scholar
23. Bereza BG, Coyle D, So DY et al. Stated preferences for attributes of a CYP2C19 pharmacogenetic test among the general population presented with a hypothetical acute coronary syndrome scenario. Clinicoecon. Outcomes Res. 12, 167–175 (2020).
MedlineGoogle Scholar
24. Berm EJ, Hak E, Postma M et al. Effects and cost-effectiveness of pharmacogenetic screening for CYP2D6 among older adults starting therapy with nortriptyline or venlafaxine: study protocol for a pragmatic randomized controlled trial (CYSCEtrial). Trials 16, 37 (2015).
MedlineGoogle Scholar
25. Biltaji E, Walker B, Au T et al. Can cost-effectiveness analysis inform genotype-guided aspirin use for primary colorectal cancer prevention? Cancer Epidemiol. Biomarkers Prev. 30(6), 1106–1113 (2021).
MedlineGoogle Scholar
26. Blázquez-Pérez A, San Miguel R, Mar J. Cost-effectiveness analysis of triple therapy with protease inhibitors in treatment-naive hepatitis C patients. Pharmacoeconomics 31(10), 919–931 (2013).
MedlineGoogle Scholar
27. Brooks GA, Tapp S, Daly AT, Busam JA, Tosteson ANA. Cost-effectiveness of DPYD genotyping prior to fluoropyrimidine-based adjuvant chemotherapy for colon cancer. Clinical Colorectal Cancer 21(3), e189–e195 (2022).
MedlineGoogle Scholar
28. Cai Z, Cai D, Wang R, Wang H, Yu Z, Gao F. Cost-effectiveness of CYP2C19 genotyping to guide antiplatelet therapy for acute minor stroke and high-risk transient ischemic attack. Sci. Rep. 11(1), 7383 (2021).
MedlineGoogle Scholar
29. Carta A, Del Zompo M, Meloni A et al. Cost-utility analysis of pharmacogenetic testing based on CYP2C19 or CYP2D6 in major depressive disorder: assessing the drivers of different cost-effectiveness levels from an Italian societal perspective. Clin. Drug Investig. 42(9), 733–746 (2022).
MedlineGoogle Scholar
30. Chan SL, Wen Low JJ, Lim YW et al. Willingness-to-pay and preferences for warfarin pharmacogenetic testing in Chinese warfarin patients and the Chinese general public. Per. Med. 10(2), 127–137 (2013).
CASGoogle Scholar
31. Chen Z, Liew D, Kwan P. Real-world cost-effectiveness of pharmacogenetic screening for epilepsy treatment. Neurology 86(12), 1086–1094 (2016).
Medline CASGoogle Scholar
32. Chong HY, Saokaew S, Dumrongprat K et al. Cost-effectiveness analysis of pharmacogenetic-guided warfarin dosing in Thailand. Thromb. Res. 134(6), 1278–1284 (2014).
Medline CASGoogle Scholar
33. Claassens DMF, van Dorst PWM, Vos GJA et al. Cost effectiveness of a CYP2C19 genotype-guided strategy in patients with acute myocardial infarction: results from the POPular Genetics trial. Am. J. Cardiovasc. Drugs 22(2), 195–206 (2022).
MedlineGoogle Scholar
34. Crespin DJ, Federspiel JJ, Biddle AK, Jonas DE, Rossi JS. Ticagrelor versus genotype-driven antiplatelet therapy for secondary prevention after acute coronary syndrome: a cost-effectiveness analysis. Value Health 14(4), 483–491 (2011).
MedlineGoogle Scholar
35. Dong OM, Friede KA, Chanfreau-Coffinier C, Voora D. Cost-effectiveness of CYP2C19-guided P2Y12 inhibitors in veterans undergoing percutaneous coronary intervention for acute coronary syndromes. Eur. Heart J. Qual. Care Clin. Outcomes 9(3), 249–257 (2023).
MedlineGoogle Scholar
36. Dong OM, Wheeler SB, Cruden G et al. Cost-effectiveness of multigene pharmacogenetic testing in patients with acute coronary syndrome after percutaneous coronary intervention. Value Health 23(1), 61–73 (2020).
MedlineGoogle Scholar
37. Dong D, Tan-Koi WC, Teng GG, Finkelstein E, Sung C. Cost-effectiveness analysis of genotyping for HLA-B*5801 and an enhanced safety program in gout patients starting allopurinol in Singapore. Pharmacogenomics 16(16), 1781–1793 (2015).
CASGoogle Scholar
38. Dong D, Ozdemir S, Mong Bee Y, Toh SA, Bilger M, Finkelstein E. Measuring high-risk patients' preferences for pharmacogenetic testing to reduce severe adverse drug reaction: a discrete choice experiment. Value Health 19(6), 767–775 (2016).
MedlineGoogle Scholar
39. Duong KN, Nguyen DV, Chaiyakunapruk N, Nelson RE, Malone DC. Cost-effectiveness of HLA-B*58:01 testing to prevent Stevens-Johnson syndrome/toxic epidermal necrolysis in Vietnam. Pharmacogenomics 24(13), 713–724 (2023).
CASGoogle Scholar
40. Fariman SA, Jahangard Rafsanjani Z, Hasanzad M, Niksalehi K, Nikfar S. Upfront DPYD genotype-guided treatment for fluoropyrimidine-based chemotherapy in advanced and metastatic colorectal cancer: a cost-effectiveness analysis. Value Health Reg. Issues 37, 71–80 (2023).
MedlineGoogle Scholar
41. Fragoulakis V, Roncato R, Bignucolo A et al. Cost-utility analysis and cross-country comparison of pharmacogenomics-guided treatment in colorectal cancer patients participating in the U-PGx PREPARE study. Pharmacol. Res. 197, 106949 (2023).
Medline CASGoogle Scholar
42. Fragoulakis V, Roncato R, Fratte CD et al. Estimating the effectiveness of DPYD genotyping in Italian individuals suffering from cancer based on the cost of chemotherapy-induced toxicity. Am. J. Hum. Genet. 104(6), 1158–1168 (2019).
Medline CASGoogle Scholar
43. Fragoulakis V, Bartsakoulia M, Díaz-Villamarín X et al. Cost-effectiveness analysis of pharmacogenomics-guided clopidogrel treatment in Spanish patients undergoing percutaneous coronary intervention. Pharmacogenomics J. 19(5), 438–445 (2019).
Medline CASGoogle Scholar
44. Fu Y, Zhang XY, Qin SB. Cost-effectiveness of CYP2C19 LOF-guided antiplatelet therapy in Chinese patients with acute coronary syndrome. Pharmacogenomics 21(1), 33–42 (2020).
CASGoogle Scholar
45. Girardin FR, Poncet A, Perrier A. Cost-effectiveness of HLA-DQB1/HLA-B pharmacogenetic-guided treatment and blood monitoring in US patients taking clozapine. Pharmacogenomics J. 19(2), 211–218 (2019).
Medline CASGoogle Scholar
46. Gordon LG, Elliott TM, Bennett C, Hollway G, Waddell N, Vadlamudi L. Early cost-utility analysis of genetically guided therapy for patients with drug-resistant epilepsy. Epilepsia 63(12), 3111–3121 (2022).
MedlineGoogle Scholar
47. Groessl EJ, Tally SR, Hillery N, Maciel A, Garces JA. Cost-effectiveness of a pharmacogenetic test to guide treatment for major depressive disorder. J. Manag. Care Spec. Pharm. 24(8), 726–734 (2018).
MedlineGoogle Scholar
48. Hafeez A, Cipriano LE, Kim RB et al. Cost-effectiveness analysis of pharmacogenomics (PGx)-based warfarin, apixaban, and rivaroxaban versus standard warfarin for the management of atrial fibrillation in Ontario, Canada. Pharmacoeconomics 22(1), 69–90 (2024).
Google Scholar
49. Hornberger J, Li Q, Quinn B. Cost-effectiveness of combinatorial pharmacogenomic testing for treatment-resistant major depressive disorder patients. Am. J. Manag. Care 21(6), e357–e365 (2015).
MedlineGoogle Scholar
50. Jiang M, You JH. CYP2C19 LOF and GOF-guided antiplatelet therapy in patients with acute coronary syndrome: a cost-effectiveness analysis. Cardiovasc. Drugs Ther. 31(1), 39–49 (2017).
Medline CASGoogle Scholar
51. Jiang M, You JH. Cost-effectiveness analysis of personalized antiplatelet therapy in patients with acute coronary syndrome. Pharmacogenomics 17(7), 701–713 (2016).
CASGoogle Scholar
52. Jutkowitz E, Dubreuil M, Lu N, Kuntz KM, Choi HK. The cost-effectiveness of HLA-B*5801 screening to guide initial urate-lowering therapy for gout in the United States. Semin. Arthritis Rheum. 46(5), 594–600 (2017).
MedlineGoogle Scholar
53. Kauf TL, Farkouh RA, Earnshaw SR, Watson ME, Maroudas P, Chambers MG. Economic efficiency of genetic screening to inform the use of abacavir sulfate in the treatment of HIV. Pharmacoeconomics 28(11), 1025–1039 (2010).
MedlineGoogle Scholar
54. Kazi DS, Garber AM, Shah RU et al. Cost-effectiveness of genotype-guided and dual antiplatelet therapies in acute coronary syndrome. Ann. Intern. Med. 160(4), 221–232 (2014).
MedlineGoogle Scholar
55. Ke CH, Chung WH, Wen YH et al. Cost-effectiveness analysis for genotyping before allopurinol treatment to prevent severe cutaneous adverse drug reactions. J. Rheumatol. 44(6), 835–843 (2017).
Medline CASGoogle Scholar
56. Kim JH, Tan DS, Chan MY. Cost-effectiveness of CYP2C19-guided antiplatelet therapy for acute coronary syndromes in Singapore. Pharmacogenomics J. 21(2), 243–250 (2021).
MedlineGoogle Scholar
57. Kim K, Touchette DR, Cavallari LH, Ardati AK, DiDomenico RJ. Cost-effectiveness of strategies to personalize the selection of P2Y₁₂ inhibitors in patients with acute coronary syndrome. Cardiovasc. Drugs Ther. 33(5), 533–546 (2019).
Medline CASGoogle Scholar
58. Kim DJ, Kim HS, Oh M, Kim EY, Shin JG. Cost effectiveness of genotype-guided warfarin dosing in patients with mechanical heart valve replacement under the fee-for-service system. Appl. Health Econ. Health Policy 15(5), 657–667 (2017).
MedlineGoogle Scholar
59. Koufaki MI, Fragoulakis V, Díaz-Villamarín X et al. Economic evaluation of pharmacogenomic-guided antiplatelet treatment in Spanish patients suffering from acute coronary syndrome participating in the U-PGx PREPARE study. Hum. Genomics 17(1), 51 (2023).
MedlineGoogle Scholar
60. Lala A, Berger JS, Sharma G et al. Genetic testing in patients with acute coronary syndrome undergoing percutaneous coronary intervention: a cost-effectiveness analysis. J. Thromb. Haemost. 11(1), 81–91 (2013).
Medline CASGoogle Scholar
61. Limdi NA, Cavallari LH, Lee CR et al. Cost-effectiveness of CYP2C19-guided antiplatelet therapy in patients with acute coronary syndrome and percutaneous coronary intervention informed by real-world data. Pharmacogenomics J. 20(5), 724–735 (2020).
Medline CASGoogle Scholar
62. Martes-Martinez C, Méndez-Sepúlveda C, Millán-Molina J et al. Cost-utility study of warfarin genotyping in the VACHS affiliated anticoagulation clinic of Puerto Rico. P R Health Sci. J. 36(3), 165–172 (2017).
MedlineGoogle Scholar
63. Mitchell D, Guertin JR, Iliza AC, Fanton-Aita F, LeLorier J. Economic evaluation of a pharmacogenomics test for statin-induced myopathy in cardiovascular high-risk patients initiating a statin. Mol. Diag. Ther. 21(1), 95–105 (2017).
Medline CASGoogle Scholar
64. Mitchell D, Guertin JR, Dubois A et al. A discrete event simulation model to assess the economic value of a hypothetical pharmacogenomics test for statin-induced myopathy in patients initiating a statin in secondary cardiovascular prevention. Mol. Diag. Ther. 22(2), 241–254 (2018).
MedlineGoogle Scholar
65. Mitropoulou C, Fragoulakis V, Bozina N et al. Economic evaluation of pharmacogenomic-guided warfarin treatment for elderly Croatian atrial fibrillation patients with ischemic stroke. Pharmacogenomics 16(2), 137–148 (2015).
CASGoogle Scholar
66. Moretti ME, Lato DF, Berger H et al. A cost-effectiveness analysis of maternal CYP2D6 genetic testing to guide treatment for postpartum pain and avert infant adverse events. Pharmacogenomics J. 18(3), 391–397 (2017).
MedlineGoogle Scholar
67. Narasimhalu K, Ang YK, Tan DSY, De Silva DA, Tan KB. Cost effectiveness of genotype-guided antiplatelet therapy in Asian ischemic stroke patients: ticagrelor as an alternative to clopidogrel in patients with CYP2C19 loss of function mutations. Clin. Drug Investig. 40(11), 1063–1070 (2020).
Medline CASGoogle Scholar
68. Ninomiya K, Saito T, Okochi T et al. Cost effectiveness of pharmacogenetic-guided clozapine administration based on risk of HLA variants in Japan and the UK. Transl. Psychiatry 11(1), 362 (2021).
Medline CASGoogle Scholar
69. Ninomiya K, Saito T, Ikeda M, Iwata N, Girardin FR. Pharmacogenomic-guided clozapine administration based on HLA-DQB1, HLA-B and SLCO1B3-SLCO1B7 variants: an effectiveness and cost-effectiveness analysis. Front. Pharmacol. 13, 1016669 (2022).
Medline CASGoogle Scholar
70. Nshimyumukiza L, Duplantie J, Gagnon M et al. Dabigatran versus warfarin under standard or pharmacogenetic-guided management for the prevention of stroke and systemic thromboembolism in patients with atrial fibrillation: a cost/utility analysis using an analytic decision model. Thrombosis J. 11, 14 (2013).
MedlineGoogle Scholar
71. Panattoni L, Brown PM, Te Ao B, Webster M, Gladding P. The cost effectiveness of genetic testing for CYP2C19 variants to guide thienopyridine treatment in patients with acute coronary syndromes: a New Zealand evaluation. Pharmacoeconomics 30(11), 1067–1084 (2012).
MedlineGoogle Scholar
72. Patel V, Lin FJ, Ojo O, Rao S, Yu S, Zhan L et al. Cost-utility analysis of genotype-guided antiplatelet therapy in patients with moderate-to-high risk acute coronary syndrome and planned percutaneous coronary intervention. Pharm. Pract. 12(3), 438 (2014).
MedlineGoogle Scholar
73. Payne K, Fargher EA, Roberts SA et al. Valuing pharmacogenetic testing services: a comparison of patients' and health care professionals' preferences. Value Health 14(1), 121–134 (2011).
MedlineGoogle Scholar
74. Pink J, Pirmohamed M, Lane S, Hughes DA. Cost-effectiveness of pharmacogenetics-guided warfarin therapy vs. alternative anticoagulation in atrial fibrillation. Clin. Pharmacol. Ther. 95(2), 199–207 (2014).
Medline CASGoogle Scholar
75. Plumpton CO, Alfirevic A, Pirmohamed M, Hughes DA. Cost effectiveness analysis of HLA-B*58:01 genotyping prior to initiation of allopurinol for gout. Rheumatology (Oxford) 56(10), 1729–1739 (2017).
Medline CASGoogle Scholar
76. Plumpton CO, Yip VL, Alfirevic A, Marson AG, Pirmohamed M, Hughes DA. Cost-effectiveness of screening for HLA-A*31:01 prior to initiation of carbamazepine in epilepsy. Epilepsia 56(4), 556–563 (2015).
MedlineGoogle Scholar
77. 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. 105(6), 1429–1438 (2019).
MedlineGoogle Scholar
78. Powell G, Holmes EA, Plumpton CO et al. Pharmacogenetic testing prior to carbamazepine treatment of epilepsy: patients' and physicians' preferences for testing and service delivery. Br. J. Clin. Pharmacol. 80(5), 1149–1159 (2015).
Medline CASGoogle Scholar
79. Rattanavipapong W, Koopitakkajorn T, Praditsitthikorn N, Mahasirimongkol S, Teerawattananon Y. Economic evaluation of HLA-B*15:02 screening for carbamazepine-induced severe adverse drug reactions in Thailand. Epilepsia 54(9), 1628–1638 (2013).
Medline CASGoogle Scholar
80. Rens NE, Uyl-de Groot CA, Goldhaber-Fiebert JD, Croda J, Andrews JR. Cost-effectiveness of a pharmacogenomic test for stratified isoniazid dosing in treatment of active tuberculosis. Clin. Infectious Dis. 71(12), 3136–3143 (2020).
MedlineGoogle Scholar
81. Saokaew S, Tassaneeyakul W, Maenthaisong R, Chaiyakunapruk N. Cost-effectiveness analysis of HLA-B*5801 testing in preventing allopurinol-induced SJS/TEN in Thai population. PLOS One 9(4), e94294 (2014).
MedlineGoogle Scholar
82. Schackman BR, Haas DW, Park SS, Li XC, Freedberg KA. Cost-effectiveness of CYP2B6 genotyping to optimize efavirenz dosing in HIV clinical practice. Pharmacogenomics 16(18), 2007–2018 (2015).
CASGoogle Scholar
83. Sluiter RL, Kievit W, van der Wilt GJ et al. Cost-effectiveness analysis of genotype-guided treatment allocation in patients with alcohol use disorders using naltrexone or acamprosate, using a modeling approach. Eur. Addict. Res. 24(5), 245–254 (2018).
MedlineGoogle Scholar
84. Sluiter RL, Janzing J, Van Der Wilt GJ, Kievit W, Teichert M. An economic model of the cost-utility of pre-emptive genetic testing to support pharmacotherapy in patients with major depression in primary care. Pharmacogenomics J. 19(5), 480–489 (2019).
Medline CASGoogle Scholar
85. Sorich MJ, Horowitz JD, Sorich W et al. Cost-effectiveness of using CYP2C19 genotype to guide selection of clopidogrel or ticagrelor in Australia. Pharmacogenomics 14(16), 2013–2021 (2013).
CASGoogle Scholar
86. Tanner JA, Davies PE, Overall CC et al. Cost-effectiveness of combinatorial pharmacogenomic testing for depression from the Canadian public payer perspective. Pharmacogenomics 21(8), 521–531 (2020).
CASGoogle Scholar
87. Teng GG, Tan-Koi WC, Dong D, Sung C. Is HLA-B*58:01 genotyping cost effective in guiding allopurinol use in gout patients with chronic kidney disease? Pharmacogenomics 21(4), 279–291 (2020).
CASGoogle Scholar
88. Thompson AJ, Newman WG, Elliott RA et al. The cost-effectiveness of a pharmacogenetic test: a trial-based evaluation of TPMT genotyping for azathioprine. Value Health 17(1), 22–33 (2014).
MedlineGoogle Scholar
89. Turongkaravee S, Praditsitthikorn N, Ngamprasertchai T et al. Economic evaluation of multiple-pharmacogenes testing for the prevention of adverse drug reactions in people living with HIV. Clinicoecon. Outcomes Res. 14, 447–463 (2022).
MedlineGoogle Scholar
90. Verhoef TI, Redekop WK, Langenskiold S et al. Cost-effectiveness of pharmacogenetic-guided dosing of warfarin in the United Kingdom and Sweden. Pharmacogenomics J. 16(5), 478–484 (2016).
Medline CASGoogle Scholar
91. Verhoef TI, Redekop WK, Veenstra DL. Cost-effectiveness of pharmacogenetic-guided dosing of phenprocoumon in atrial fibrillation. Pharmacogenomics 14(8), 869–883 (2013).
CASGoogle Scholar
92. Wang Y, Yan BP, Liew D, Lee VWY. Cost-effectiveness of cytochrome P450 2C19 *2 genotype-guided selection of clopidogrel or ticagrelor in Chinese patients with acute coronary syndrome. Pharmacogenomics J. 18(1), 113–120 (2018).
MedlineGoogle Scholar
93. Wee JW, Png WY, Wong XY et al. Measuring preferences for CYP2C19 genotyping in patients with acute coronary syndrome–a discrete choice experiment. Future Cardiol. 16(6), 663–674 (2020).
CASGoogle Scholar
94. Wei X, Sun H, Zhuang J et al. Cost-effectiveness analysis of CYP2D6*10 pharmacogenetic testing to guide the adjuvant endocrine therapy for postmenopausal women with estrogen receptor positive early breast cancer in China. Clin. Drug Investig. 40(1), 25–32 (2020).
Medline CASGoogle Scholar
95. Wei X, Cai J, Zhuang J et al. CYP2D6*10 pharmacogenetic-guided SERM could be a cost-effective strategy in Chinese patients with hormone receptor-positive breast cancer. Pharmacogenomics 21(1), 43–53 (2020).
CASGoogle Scholar
96. Wei X, Cai J, Sun H et al. Cost-effectiveness analysis of UGT1A1*6/*28 genotyping for preventing FOLFIRI-induced severe neutropenia in Chinese colorectal cancer patients. Pharmacogenomics 20(4), 241–249 (2019).
CASGoogle Scholar
97. You JH. Pharmacogenetic-guided selection of warfarin versus novel oral anticoagulants for stroke prevention in patients with atrial fibrillation: a cost-effectiveness analysis. Pharmacogenet. Genomics 24(1), 6–14 (2014).
Medline CASGoogle Scholar
98. You JH, Tsui KK, Wong RS, Cheng G. Cost-effectiveness of dabigatran versus genotype-guided management of warfarin therapy for stroke prevention in patients with atrial fibrillation. PLOS One 7(6), (2012).
Google Scholar
99. Yuliwulandari R, Shin JG, Kristin E et al. Cost-effectiveness analysis of genotyping for HLA-B*15:02 in Indonesian patients with epilepsy using a generic model. Pharmacogenomics J. 21(4), 476–483 (2021).
Medline CASGoogle Scholar
100. Zhu Y, Moriarty JP, Swanson KM et al. A model-based cost-effectiveness analysis of pharmacogenomic panel testing in cardiovascular disease management: preemptive, reactive, or none? Genet. Med. 23(3), 461–470 (2021).
Medline CASGoogle Scholar
101. Zhu Y, Swanson KM, Rojas RL et al. Systematic review of the evidence on the cost-effectiveness of pharmacogenomics-guided treatment for cardiovascular diseases. Genet. Med. 22(3), 475–486 (2022).
Google Scholar
102. Morris SA, Alsaidi AT Verbyla A et al. Cost effectiveness of pharmacogenetic testing for drugs with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines: a systematic review. Clin. Pharmacol. Ther. 112(6), 1318–1328 (2022).
Medline CASGoogle Scholar
103. Plöthner M, Ribbentrop D, Hartman JP, Frank M. Cost-effectiveness of pharmacogenomic and pharmacogenetic test-guided personalized therapies: a systematic review of the approved active substances for personalized medicine in Germany. Adv. Ther. 33(9), 1461–1480 (2016).
MedlineGoogle Scholar
104. Ozdemir S, Lee JJ, Chaudhry I, Ocampo RRQ. A systematic review of discrete choice experiments and conjoint analysis on genetic testing. Patient 15(1), 39–54 (2022).
MedlineGoogle Scholar
105. Fragoulakis V, Mitropoulou C, van Schaik RH, Maniadakis N, Patrinos GP. An alternative methodological approach for cost-effectiveness analysis and decision making in genomic medicine. OMICS 20(5), 274–282 (2016).
Medline CASGoogle Scholar
106. Fragoulakis V, Mitropoulou C, Katelidou D et al. Performance ratio based resource allocation decision-making in genomic medicine. OMICS 21(2), 67–73 (2017).
Medline CASGoogle Scholar

Vol. 25, No. 2

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Received 21 November 2023

Accepted 8 January 2024

Published online 30 January 2024

Published in print January 2024

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Keywords

Author contributions

C Mitropoulou conceived the idea and the study's design and conducted supervision. K Tzerefou, K Koufou, MI Koufaki and V Fragoulakis developed the search strategy and performed study selection, data extraction and management. MI Koufaki and V Fragoulakis performed final study selection and data analysis. MI Koufaki prepared the tables and figures and edited the final manuscript. K Tzerefou, K Koufou and MI Koufaki drafted the first version of the paper. MI Koufaki, V Fragoulakis, GP Patrinos and C Mitropoulou drafted and edited all subsequent versions of the paper. All authors reviewed and approved the manuscript for publication.

Acknowledgments

This study was supported by the Golden Helix Foundation research budget with funding provided to C Mitropoulou.

Financial disclosure

This work was partly funded by the Golden Helix Foundation research budget. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

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