JMIR cancer · 2026
Cost-Effectiveness of AI-Powered Decentralized Production for Advanced Therapies in the Hospital-Generated Chimeric Antigen Receptor T-Cell Therapy for Multiple Myeloma From a German Hospital Perspective: Early Health Technology Assessment Study.
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Boot Iris W A, Weltin Anna Louisa, de Graaf Ysanne, Vrijhoef Hubertus J M
Аннотация
BACKGROUND: Although effective, current chimeric antigen receptor T-cell (CAR-T) production methods-centralized, manual, and complex-are cost-intensive, time-consuming, and prone to variability. AIDPATH (AI-Powered Decentralized Production for Advanced Therapies in the Hospital) proposes a decentralized, automated alternative that integrates patient-specific data; optimizes resource use; and potentially improves cell viability, manufacturing efficiency, and patient outcomes.
OBJECTIVE: The aim of this study was to compare CAR-T therapy produced by the AIDPATH platform to both ciltacabtagene autoleucel (cilta-cel) and standard of care (SoC) for patients with triple-class refractory multiple myeloma (MM) over a 40-year time horizon from the hospital perspective in Germany.
METHODS: A partitioned survival model reflecting 3 health states (progression-free disease, progressed disease, and death) was used. The analysis used clinical trial data for cilta-cel, real-world data for SoC, and estimated parameters for AIDPATH due to the developmental status of the platform. The primary outcome was the incremental cost-effectiveness ratio; secondary outcomes included sensitivity and scenario analyses.
RESULTS: On the basis of the model used in this analysis, AIDPATH was dominant compared to both cilta-cel and SoC. Most costs for CAR-T therapies were driven by acquisition and adverse events. Sensitivity analyses showed that the results were most influenced by discount rates and assumptions about progression-free survival. Scenario analyses, including reduced adverse events and shorter vein-to-vein times for AIDPATH, further supported its cost-effectiveness.
CONCLUSIONS: This is the first study to assess the cost-effectiveness of a CAR-T product generated with AI support in Germany from the hospital perspective. AIDPATH was found to be a cost-effective alternative to both cilta-cel and SoC, making it a promising option for future implementation. Although further data are needed, this study provides valuable guidance for health care stakeholders, reimbursement discussions, and future research.
OBJECTIVE: The aim of this study was to compare CAR-T therapy produced by the AIDPATH platform to both ciltacabtagene autoleucel (cilta-cel) and standard of care (SoC) for patients with triple-class refractory multiple myeloma (MM) over a 40-year time horizon from the hospital perspective in Germany.
METHODS: A partitioned survival model reflecting 3 health states (progression-free disease, progressed disease, and death) was used. The analysis used clinical trial data for cilta-cel, real-world data for SoC, and estimated parameters for AIDPATH due to the developmental status of the platform. The primary outcome was the incremental cost-effectiveness ratio; secondary outcomes included sensitivity and scenario analyses.
RESULTS: On the basis of the model used in this analysis, AIDPATH was dominant compared to both cilta-cel and SoC. Most costs for CAR-T therapies were driven by acquisition and adverse events. Sensitivity analyses showed that the results were most influenced by discount rates and assumptions about progression-free survival. Scenario analyses, including reduced adverse events and shorter vein-to-vein times for AIDPATH, further supported its cost-effectiveness.
CONCLUSIONS: This is the first study to assess the cost-effectiveness of a CAR-T product generated with AI support in Germany from the hospital perspective. AIDPATH was found to be a cost-effective alternative to both cilta-cel and SoC, making it a promising option for future implementation. Although further data are needed, this study provides valuable guidance for health care stakeholders, reimbursement discussions, and future research.