Global Access to Chimeric Antigen Receptor (CAR) T-Cell Therapies: Health Technology Assessment (HTA) of CAR T in G7 Countries and Australia
Notice bibliographique
Résumé
Introduction CAR T-cells represent a major treatment advance for certain patients with hematologic malignancies. Barriers to accessing this technology include its prohibitive cost, the autologous nature of CAR T-cells (which necessitates a delay between apheresis and infusion), and the need for a specialized manufacturing process requiring a sophisticated logistical infrastructure. In countries outside the US, decisions to reimburse therapies depend on formal evaluations by health technology assessment (HTA) bodies, which account for factors including clinical benefits and comparative effectiveness. Cost-effectiveness studies of CAR T-cells have found mixed results. Given the high price of CAR T-cell therapies, we hypothesized that their variable efficacy and cost-effectiveness would affect HTA decision-making and reimbursement decisions. We also hypothesized that reimbursement decisions would differ between clinical settings for which there is demonstrated curative potential (e.g., DLBCL) versus those for which the therapy does not yet appear to be curative (e.g., myeloma). Methods To address these questions, we performed a cross-sectional analysis of reimbursement decisions made by HTA bodies of the G7 countries and Australia for all CAR T-cell indications approved in the US through January 1, 2024. We used the FDA and National Comprehensive Cancer Network websites to define the full set of drug-indication pairs and obtained corresponding full-form HTA reports from the websites of each HTA body. From each report, we abstracted (1) the CAR T-cell product and indication for use, (2) final reimbursement decision, and (3) factors used to justify HTA decision-making. For drug-indication pairs with multiple HTA evaluations, the most recent HTA decision prior to January 1, 2024 was used to determine reimbursement status. For Germany and Japan, where HTA bodies are utilized for price negotiations following drug approval and do not provide reimbursement recommendations per se, drug approval was used to infer reimbursement status. Drugs recommended with conditional clinical requirements were considered reimbursed. Results We identified 6 CAR T-cell products linked to 12 indications approved by the FDA through January 1, 2024. Of the 84 drug-indication pairs across countries (excluding the US), 59 (70%) were recommended for reimbursement, 8 (10%) were not recommended for reimbursement following an HTA evaluation, and 17 (20%) were non-reimbursed for other reasons (e.g., an application was never submitted). Countries with the most recommendations for funding were France and Germany (11, 92%). Japan recommended reimbursement for 9 (75%) indications, while Italy and Canada did for 8 (67%). Countries with the fewest reimbursed indications included England (6, 50%) and Australia (4, 33%). The most recommended drug-indication pair was axicabtagene ciloleucel (axi-cel) for DLBCL after two or more lines of systemic therapy, the only drug-indication pair to be approved in all countries. The fewest recommendations were linked to idecabtagene vicleucel (ide-cel) and ciltacabtagene autoleucel (cilta-cel) for myeloma as fifth or later line treatment, and lisocabtagene maraleucel for DLBCL relapsed or refractory to first-line therapy. For HTA bodies that provided rationale for drug reimbursement decisions, an analysis identified (1) inadequate comparator arms, (2) a lack of long-term survival data, (3) uncertainty regarding the true magnitude of clinical benefit, and (4) cost-effectiveness as common themes for recommendation against reimbursement. Discussion Despite cited concerns regarding cost-effectiveness and disparities in access, we find that the majority of CAR T-cell indications were recommended for reimbursement in this sample of the highest-income countries (HICs) globally. Among the CAR-Ts, HICs most frequently provided reimbursement for axi-cel for DLBCL (the only indication with demonstrated overall survival benefit) and least often provided reimbursement for ide-cel and cilta-cel for myeloma (for which CAR-T cells do not yet appear to have curative potential, though this may be one of multiple reasons). Our results demonstrate that HTA decisions are not necessarily the bottleneck in the HICs for access to CAR-T among patients for which the data indicate the greatest therapeutic potential, and that approaches are heterogeneous between jurisdictions.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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