Background and importance Chimeric antigen receptor T-cell therapies (CAR-T) are based on the ex vivo modification of T-lymphocytes for the expression of an antigen receptor that provides the specific union with tumour cells for their consequent destruction. CAR-T introduction into clinical practices presents challenges from a clinical and economic perspective. Traditional pharmacoeconomic studies may be limited in their ability to act as a valid decision-making tool in the access management of CAR-T and alternative methodological approaches may have to be considered.
Aim and objectives A literature review of CAR-T pharmacoeconomic studies has been carried out with the aim of reviewing the current literature on the economic evaluation of these drugs and to determine if traditional pharmacoeconomic studies represent a valid tool for decision-making in the access management of CAR-T.
Material and methods A systematic search was carried out in Scopus, Pubmed and Cochrane Library databases, using terms related to CAR-T and Pharmacoeconomics. We included published articles and accepted manuscripts written in English or Spanish up to 15 August 2021. For the quality evaluation of the identified studies, CHEERS and Drummon checklists were used.
Results 17 pharmacoeconomic studies were identified. The most studied CAR-T drug was tisagenlecleucel for diffuse large B-cell lymphoma in adults, with a median cost per quality-adjusted life year (QALY) of €291 924.51. CAR-T therapies represent a clinically and potentially cost-effective therapeutic alternative. The quality of the identified studies was good according to the quality assessment scores.
Conclusion and relevance Cost-effectiveness of CAR-T therapies depends on its long-term results, the duration of the study conducted, and the cure rate used of the clinical study. Because of this, pharmacoeconomic studies in CAR-T exhibit certain limitations and could not be robust tools for decision-making solely based on their findings. There is a need to develop pharmacoeconomic methods that can avoid the uncertainty of many assumptions and incorporate more data, including real-life data.
Conflict of interest No conflict of interest
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