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Recalibrating Health Technology Assessment Methods for Cell and Gene Therapies

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Abstract

Recently licensed cell and gene therapies have promising but highly uncertain clinical benefits. They are entering the market at very high prices, with the latest entrants costing hundreds of thousands of dollars. The significant long-term uncertainty posed by these therapies has already complicated the use of conventional economic evaluation approaches such as cost-effectiveness and cost-utility analyses, which are widely used for assessing the value of new health interventions. Cell and gene therapies also risk jeopardising healthcare systems’ financial sustainability. As a result, there is a need to recalibrate the current health technology assessment methods used to measure and compensate their value. In this paper, we outline a set of technical adaptations and methodological refinements to address key challenges in the appraisal of cell and gene therapies’ value, including the assessment of efficiency and affordability. We also discuss the potential role of alternative financing mechanisms. Ultimately, uncertainties associated with cell and gene therapies can only be meaningfully addressed by improving the evidence base supporting their approval and adoption in healthcare systems.

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References

  1. Office of Technology Assessment. The implications of cost-effectiveness analysis of medical technology. 1980. https://catalog.hathitrust.org/Record/000771079. Accessed 27 Aug 2020.

  2. Therapy ASoGC. Gene therapy & cell therapy defined. 2020. https://annualmeeting.asgct.org/general-public/educational-resources/gene-therapy--and-cell-therapy-defined. Accessed 27 Aug 2020.

  3. Bersenev A. Cell therapy: definitions, classifications and trends. 2019. https://celltrials.info/2016/10/14/presentation-cell-therapy-definitions-classifications/. Accessed 27 Aug 2020.

  4. Mukherjee S. The promise and price of cellular therapies. The New Yorker, 2019.

  5. Bender E. Regulating the gene-therapy revolution. Nature. 2018;564:S20–S2222.

    CAS  PubMed  Google Scholar 

  6. FDA. Framework for the regulation of regenerative medicine products. 2020. https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products/framework-regulation-regenerative-medicine-products. Accessed 27 Aug 2020.

  7. Yano K, Watanabe N, Tsuyuki K, et al. Regulatory approval for autologous human cells and tissue products in the United States, the European Union, and Japan. Regen Ther. 2015;1:45–56.

    PubMed  Google Scholar 

  8. EMA. Advanced therapy medicinal products: overview. 2020. https://www.ema.europa.eu/en/human-regulatory/overview/advanced-therapy-medicinal-products-overview. Accessed 27 Aug 2020.

  9. Iglesias-López C, Agustí A, Obach M, et al. Regulatory framework for advanced therapy medicinal products in Europe and United States. Front Pharmacol. 2019;10:921.

    PubMed  PubMed Central  Google Scholar 

  10. US FDA. Cellular and gene therapy products. 2020. https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products. Accessed 27 Aug 2020.

  11. Chambers JD, Neumann PJ. Listening to Provenge: what a costly cancer treatment says about future Medicare policy. N Engl J Med. 2011;364:1687–9.

    CAS  PubMed  Google Scholar 

  12. EMA. EPAR summary for the public: Provenge. European Medicines Agency, London. 2013.

  13. EMA. Public statement: Provenge. European Medicines Agency; London. 2015.

  14. Carroll J. Gene therapies seize the top of the list of the most expensive drugs on the planet—and that trend has just begun. Endpoint News. 2019. https://endpts.com/gene-therapies-seize-the-top-of-the-list-of-the-most-expensive-drugs-on-the-planet-and-that-trend-has-just-begun. Accessed 27 Aug 2020.

  15. Harrison RP, Zylberberg E, Ellison S, et al. Chimeric antigen receptor-T cell therapy manufacturing: modelling the effect of offshore production on aggregate cost of goods. Cytotherapy. 2019;21:224–33.

    PubMed  Google Scholar 

  16. Ramanayake S, Bilmon I, Bishop D, et al. Low-cost generation of good manufacturing practice-grade CD19-specific chimeric antigen receptor-expressing T cells using piggyBac gene transfer and patient-derived materials. Cytotherapy. 2015;17:1251–67.

    CAS  PubMed  Google Scholar 

  17. Zhu F, Shah N, Xu H, et al. Closed-system manufacturing of CD19 and dual-targeted CD20/19 chimeric antigen receptor T cells using the CliniMACS Prodigy device at an academic medical center. Cytotherapy. 2018;20:394–406.

    CAS  PubMed  Google Scholar 

  18. CMS. Trump administration makes CAR T-cell cancer therapy available to Medicare beneficiaries nationwide. 2019. https://www.cms.gov/newsroom/press-releases/trump-administration-makes-car-t-cell-cancer-therapy-available-medicare-beneficiaries-nationwide. Accessed 27 Aug 2020.

  19. ASCO P. CMS finalizes decision to cover CAR T-cell therapy for Medicare beneficiaries. 2019. https://www.ascopost.com/issues/august-25-2019/cms-finalizes-decision-to-cover-car-t-cell-therapy-for-medicare-beneficiaries/. Accessed 27 Aug 2020.

  20. Wouters OJ, McKee M, Luyten J. Estimated research and development investment needed to bring a new medicine to market, 2009–2018. JAMA. 2020;323:844–53.

    PubMed  PubMed Central  Google Scholar 

  21. Hodgson TA, Meiners MR. Cost-of-illness methodology: a guide to current practices and procedures. Milbank Mem Fundam Q Health Soc. 1982;60:429–62.

    CAS  Google Scholar 

  22. Byford S, Torgerson DJ, Raftery J. Cost of illness studies. BMJ. 2000;320:1335.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Angelis A, Tordrup D, Kanavos P. Socio-economic burden of rare diseases: a systematic review of cost of illness evidence. Health Policy. 2015;119:964–79.

    PubMed  Google Scholar 

  24. Drummond MF, Neumann PJ, Sullivan SD, et al. Analytic considerations in applying a general economic evaluation reference case to gene therapy. Value Health. 2019;22:661–8.

    PubMed  Google Scholar 

  25. Gaddipati H, Liu K, Pariser A, et al. Rare cancer trial design: lessons from FDA approvals. Clin Cancer Res. 2012;18:5172–8.

    PubMed  Google Scholar 

  26. Hee SW, Willis A, Tudur Smith C, et al. Does the low prevalence affect the sample size of interventional clinical trials of rare diseases? An analysis of data from the aggregate analysis of clinicaltrials.gov. Orphanet J Rare Dis. 2017;12:44.

    PubMed  PubMed Central  Google Scholar 

  27. Nordon C, Karcher H, Groenwold RHH, et al. The “efficacy-effectiveness gap”: historical background and current conceptualization. Value Health. 2016;19:75–81.

    PubMed  Google Scholar 

  28. Darrow JJ. Luxturna: FDA documents reveal the value of a costly gene therapy. Drug Discov Today. 2019;24:949–54.

    PubMed  Google Scholar 

  29. Jönsson B, Hampson G, Michaels J, et al. Advanced therapy medicinal products and health technology assessment principles and practices for value-based and sustainable healthcare. Eur J Health Econ. 2019;20:427–38.

    PubMed  Google Scholar 

  30. Garrison LP. Value-based pricing for emerging gene therapies: the economic case for a higher cost-effectiveness threshold. J Manag Care Spec Pharm. 2019;25:793–9.

    PubMed  Google Scholar 

  31. Institute for Economic and Clinical Review. Valuing a cure: final White Paper and methods adaptations. 2019. https://icer-review.org/wpcontent/uploads/2019/01/ICER_SST_FinalAdaptations_111219.pdf.

  32. Briggs AH. Decision modelling for health economic evaluation. Oxford: Oxford University Press; 2006.

    Google Scholar 

  33. Briggs AH, Weinstein MC, Fenwick EAL, et al. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6. Med Decis Mak. 2012;32:722–32.

    Google Scholar 

  34. Bojke L, Claxton K, Sculpher M, et al. Characterizing structural uncertainty in decision analytic models: a review and application of methods. Value Health. 2009;12:739–49.

    PubMed  Google Scholar 

  35. Chahal HS, Marseille EA, Tice JA, et al. Cost-effectiveness of early treatment of hepatitis C virus genotype 1 by stage of liver fibrosis in a US treatment-naive population. JAMA Intern Med. 2016;176:65–73.

    PubMed  PubMed Central  Google Scholar 

  36. Iyengar S, Tay-Teo K, Vogler S, et al. Prices, costs, and affordability of new medicines for hepatitis C in 30 countries: an economic analysis. PLoS Med. 2016;13:e1002032.

    PubMed  PubMed Central  Google Scholar 

  37. PhRMA. Medicines in development for cell and gene therapy 2018 report. Pharmaceutical Research and Manufacturers of America: Washnigton DC, 2018.

  38. ClinicalTrials.gov. Search results: CRISPR| United States. 2020. https://clinicaltrials.gov/ct2/results?cond=&term=CRISPR&cntry=US&state=&city=&dist=&Search=Search. Accessed 27 Aug 2020.

  39. ClinicalTrials.gov. Search results: CRISPR| China. 2020. https://clinicaltrials.gov/ct2/results?cond=&term=CRISPR&cntry=CN&state=&city=&dist=&Search=Search. Accessed 27 Aug 2020.

  40. NICE. Tisagenlecleucel for treating relapsed or refractory B-cell acute lymphoblastic leukaemia in people aged up to 25 years. Technology appraisal guidance TA554, 2018. https://www.nice.org.uk/guidance/ta554.

  41. Walton M, Sharif S, Simmonds M, et al. Tisagenlecleucel for the treatment of relapsed or refractory B-cell acute lymphoblastic leukaemia in people aged up to 25 years: an evidence review group perspective of a NICE single technology appraisal. Pharmacoeconomics. 2019;37:1209–17.

    PubMed  Google Scholar 

  42. NICE. Cancer Drugs Fund Managed Access Agreement. Tisagenlecleucel for treating relapsed or refractory B-cell acute lymphoblastic leukaemia in people aged up to 25 years. ID1167, 2018. https://www.nice.org.uk/guidance/ta554/resources/managed-access-agreementdecember-2018-pdf-6651288397.

  43. Grieve R, Abrams K, Claxton K, et al. Cancer Drugs Fund requires further reform. BMJ. 2016;354:i5090.

    PubMed  Google Scholar 

  44. NICE. Exploring the assessment and appraisal of regenerative medicines and cell therapy products. National Institute for Health and Care Excellence; 2016. https://www.nice.org.uk/Media/Default/About/what-we-do/Science%20policy%20and%20research/Regenerative-medicine-studymarch-2016.pdf

  45. Sanders GD, Neumann PJ, Basu A, 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.

    Google Scholar 

  46. Bach PB. New math on drug cost-effectiveness. N Engl J Med. 2015;373:1797–9.

    PubMed  Google Scholar 

  47. Bach PB, Giralt SA, Saltz LB. FDA approval of tisagenlecleucel: promise and complexities of a $475,000 cancer drug. JAMA. 2017;318:1861–2.

    PubMed  Google Scholar 

  48. Espin J. IMPACT-HTA Work Package 3: developing a costing methodology and a database of unit costs. 2020. https://www.impact-hta.eu/work-package-3. Accessed 27 Aug 2020.

  49. Gray A, ProQuest, Gray AM. Applied methods of cost-effectiveness analysis in health care. Oxford: Oxford University Press; 2011.

    Google Scholar 

  50. Briggs AH, Gray AM. Handling uncertainty in economic evaluations of healthcare interventions. BMJ. 1999;319:635.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Gibson E, Koblbauer I, Begum N, et al. Modelling the survival outcomes of immuno-oncology drugs in economic evaluations: a systematic approach to data analysis and extrapolation. Pharmacoeconomics. 2017;35:1257–70.

    PubMed  PubMed Central  Google Scholar 

  52. Othus M, Bansal A, Koepl L, et al. Accounting for cured patients in cost-effectiveness analysis. Value Health. 2017;20:705–9.

    PubMed  Google Scholar 

  53. Ouwens MJNM, Mukhopadhyay P, Zhang Y, et al. Estimating lifetime benefits associated with immuno-oncology therapies: challenges and approaches for overall survival extrapolations. Pharmacoeconomics. 2019;37:1129–38.

    PubMed  PubMed Central  Google Scholar 

  54. Bullement A, Latimer NR, Bell GH. Survival extrapolation in cancer immunotherapy: a validation-based case study. Value Health. 2019;22:276–83.

    PubMed  Google Scholar 

  55. Othus M, Barlogie B, LeBlanc ML, et al. Cure models as a useful statistical tool for analyzing survival. Clin Cancer Res. 2012;18:3731–6.

    PubMed  PubMed Central  Google Scholar 

  56. Keeney R, Raiffa H. Decisions with multiple objectives: preferences and value trade-offs. 1993rd ed. Cambridge: Cambridge University Press; 1976.

    Google Scholar 

  57. Angelis A, Phillips L. Advancing structured decision-making in drug regulation at the FDA and EMA. Br J Clin Pharmacol. 2020. https://doi.org/10.1111/bcp.14425.

    Article  PubMed  Google Scholar 

  58. Phelps CE, Lakdawalla DN, Basu A, et al. Approaches to aggregation and decision making: a health economics approach: an ISPOR Special Task Force Report [5]. Value Health. 2018;21:146–54.

    PubMed  PubMed Central  Google Scholar 

  59. Angelis A, Kanavos P, Phillips L. ICER Value Framework 2020 update: recommendations on the aggregation of benefits and contextual considerations. Value Health. 2020;. https://doi.org/10.1016/j.jval.2020.04.1828.

    Article  PubMed  Google Scholar 

  60. Angelis A, Kanavos P. Multiple criteria decision analysis (MCDA) for evaluating new medicines in health technology assessment and beyond: the Advance Value Framework. Soc Sci Med. 2017;188:137–56.

    PubMed  Google Scholar 

  61. Angelis A, Lange A, Kanavos P. Using health technology assessment to assess the value of new medicines: results of a systematic review and expert consultation across eight European countries. Eur J Health Econ. 2018;19:123–52.

    PubMed  Google Scholar 

  62. Oliveira M, Mataloto I, Kanavos P. Multi-criteria decision analysis for health technology assessment: addressing methodological challenges to improve the state of the art. Eur J Health Econ. 2019;20:891–918.

    PubMed  PubMed Central  Google Scholar 

  63. Garrison LP, Neumann PJ, Erickson P, et al. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force Report. Value Health. 2007;10:326–35.

    PubMed  Google Scholar 

  64. Ford I, Norrie J. Pragmatic trials. N Engl J Med. 2016;375:454–63.

    PubMed  Google Scholar 

  65. Collins R, Bowman L, Landray M, et al. The magic of randomization versus the myth of real-world evidence. N Engl J Med. 2020;382:674–8.

    PubMed  Google Scholar 

  66. Faria R, Hernandez Alava M, Manca A, et al. The use of observational data to inform estimates of treatment effectiveness in technology appraisal: methods for comparative individual patient data. NICE DSU technical support document 17. Sheffield: Decision Support Unit, ScHARR, University of Sheffield; 2015.

  67. Bell H, Wailoo AJ, Hernandez M, et al. The use of real world data for the estimation of treatment effects in NICE decision making. Decision Support Unit, ScHARR, University of Sheffield; Sheffield, 2016.

  68. Jonsson P, Salcher M. IMPACT- HTA Work Package 6: methodological guidance on the analysis and interpretation of non-randomised studies to inform health economic evaluation. 2020. https://www.impact-hta.eu/work-package-6. Accessed 27 Aug 2020.

  69. Pearson SD, Ollendorf DA, Chapman RH. New cost-effectiveness methods to determine value-based prices for potential cures: what are the options? Value Health. 2019;22:656–60.

    PubMed  Google Scholar 

  70. Phelps C, Madhavan G. Resource allocation in decision support frameworks. Cost Eff Resour Alloc. 2018;16(Suppl. 1):48.

    PubMed  PubMed Central  Google Scholar 

  71. ICER. A guide to ICER’s methods for health technology assessment. Institute for Clinical and Economic Review; 2018. http://icer-review.org/wp-content/uploads/2018/08/ICER-HTA-Guide_082018.pdf.

  72. NICE. Budget impact test. National Institute for Health and Care Excellence; 2020. https://www.nice.org.uk/about/what-we-do/our-programmes/nice-guidance/nice-technology-appraisal-guidance/budgetimpact-test.

  73. Claxton K, Martin S, Soares M, et al. Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold. Health Technol Assesst. 2015;19:1–503.

    Google Scholar 

  74. Salo A, Keisler J, Morton A. Portfolio decision analysis: improved methods for resource allocation. New York: Springer; 2011.

    Google Scholar 

  75. Schaffer SK, Messner D, Mestre-Ferrandiz J, et al. Paying for cures: perspectives on solutions to the “affordability issue”. Value Health. 2018;21:276–9.

    PubMed  Google Scholar 

  76. Ferrario A, Kanavos P. Dealing with uncertainty and high prices of new medicines: a comparative analysis of the use of managed entry agreements in Belgium, England, the Netherlands and Sweden. Soc Sci Med. 2015;124:39–47.

    PubMed  Google Scholar 

  77. Kanavos P, Ferrario A, Tafuri G, et al. Managing risk and uncertainty in health technology introduction: the role of managed entry agreements. Glob Policy. 2017;8:84–92.

    Google Scholar 

  78. Antonanzas F, Juárez-Castelló C, Lorente R, et al. The use of risk-sharing contracts in healthcare: theoretical and empirical assessments. Pharmacoeconomics. 2019;37:1469–83.

    PubMed  Google Scholar 

  79. Hampson G, Towse A, Pearson SD, et al. Gene therapy: evidence, value and affordability in the US health care system. J Comp Eff Res. 2018;7:15–28.

    PubMed  Google Scholar 

  80. Seeley E. Outcomes-based pharmaceutical contracts: an answer to high U.S. drug spending? Issue Brief (Commonw Fund). 2017;2017:1–8.

    PubMed  Google Scholar 

  81. Neumann P, Chambers J, Simon F, et al. Risk-sharing arrangements that link payment for drugs to health outcomes are proving hard to implement. Health Aff (Millwood). 2011;30:2329–37.

    Google Scholar 

  82. Wouters OJ. Lobbying expenditures and campaign contributions by the pharmaceutical and health product industry in the United States, 1999–2018. JAMA Intern Med. 2020;180(5):1–10.

    PubMed Central  Google Scholar 

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Acknowledgements

The authors thank James Chambers (Tufts Medical Center), Andrew Briggs (London School of Hygiene and Tropical Medicine), Christopher Carswell (Editor in Chief) and three anonymous reviewers for valuable comments and feedback that helped to improve the manuscript.

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Correspondence to Aris Angelis.

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Aris Angelis, Huseyin Naci, and Allan Hackshaw have no conflicts of interest that are directly relevant to the content of this article.

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AA was responsible for the first draft of the paper and the collection of any data. AA and HN were responsible for the conception of the paper. AA, HN and AH made substantial contributions to the paper, critically drafted and revised the paper for important intellectual content, approved the final version of the paper and agree to be accountable for all aspects of the work.

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Angelis, A., Naci, H. & Hackshaw, A. Recalibrating Health Technology Assessment Methods for Cell and Gene Therapies. PharmacoEconomics 38, 1297–1308 (2020). https://doi.org/10.1007/s40273-020-00956-w

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