Cost per Responder Analysis to Assess the Value of CAR-T Therapy for Relapsed or Refractory Multiple Myeloma
Bibliographic record
Abstract
Abstract New classes of therapies have emerged for treating Relapsed or Refractory Multiple Myeloma (RRMM) patients, including chimeric antigen receptor T-cell (CAR-T) therapies targeting the B-cell maturation antigen. While CAR-T therapies are expected to be a more expensive class of treatment compared to chemotherapy, 1 they have been shown to have a high overall response rate (ORR) and progression-free survival (PFS). 2,3 As newer, more innovative RRMM therapies are developed and brought to market, payers will need to balance their higher efficacy and total treatment costs when assessing potential value. To assess the value of RRMM CAR-T therapies (ciltacabtagene autoleucel [cilta-cel] and idecabtagene vicleucel [ide-cel]), we developed a cost per responder (CPR) model that incorporates efficacy and total cost of treatment. In the absence of head-to-head trial data for CAR-T therapies, indirect treatment comparisons (ITCs) can be used to evaluate comparative efficacy, and the results can be used to inform a CPR model. Matching-adjusted indirect comparisons (MAIC) is a form of ITC that involves matching and adjusting a treatment group from a clinical study with individual patient-level data (IPD) available to a comparator for which only summary-level data are available. This method mitigates potential bias arising from differences in patient characteristics between trials and is widely used and accepted in comparative effectiveness research. Unanchored matching adjusted indirect comparison (MAIC) analyses were used to inform the comparative efficacy of cilta-cel versus ide-cel in our CPR model. MAIC results indicated that cilta-cel was associated with statistically significantly improved ORR (odds ratio [OR]: 87.99 [95% confidence interval [CI]: 20.32, 381.01; p < .0001]), complete response or better (≥CR) rate (OR: 5.96 [95% CI: 2.76, 12.88; p < .0001]) and PFS (hazard ratio [HR]: 0.36 [95% CI: 0.22, 0.59; p < .0001]) when compared with ide-cel. 4 To adequately capture total treatment costs for each treatment of interest, CPR models should include all costs related to acquisition and delivery of treatment. Relevant costs of CAR-T therapy for RRMM include the cost of apheresis, bridging therapy, costs of CAR-T acquisition and administration, supportive care and monitoring costs, adverse event management costs, and any costs associated with delivery of inpatient or outpatient clinical services. Preliminary results of the CPR analysis indicate that ide-cel is associated with a cost per ORR of approximately $743,000, a cost per CR or better of $1.66 million and a cost per month in PFS of approximately $55,000. Corresponding results for cilta-cel will be generated after the PDUFA date (November 29, 2021), and presented at ASH 2021. In conclusion, CPR models have significant potential to assist payers in evaluating the value of newer, more innovative RRMM therapies by integrating information on both total costs and efficacy. References 1 Pagliarulo N. FDA approves first CAR-T cell therapy for multiple myeloma. https://www.biopharmadive.com/news/fda-car-t-multiple-myeloma-approval-bristol-myersbluebird/597438/#:~:text=The%20pharma%2C%20which%20licensed%20the,other%20approve d%20CAR%2DT%20therapies. Published 27 March 2021. Accessed 22 June 2021. 2 Munshi NC, Anderson Jr LD, Shah N, et al. Idecabtagene vicleucel in relapsed and refractory multiple myeloma. N Engl J Med. 2021;384(8):705-16. 3 Madduri D, Berdeja JG, Usmani SZ, et al. CARTITUDE-1: phase 1b/2 study of ciltacabtagene autoleucel, a B-cell maturation antigen-directed chimeric antigen receptor T-cell therapy, in relapsed/refractory multiple myeloma [abstract]. Blood. 2020;136(1 supplement). Abstract 177. 4 Martin T, Usmani SZ, Schecter JM, Vogel M, Jackson CC et al. (2021) Matching-adjusted indirect comparison of efficacy outcomes for ciltacabtagene autoleucel in CARTITUDE-1 versus idecabtagene vicleucel in KarMMa for the treatment of patients with relapsed or refractory multiple myeloma. Curr Med Res Opin 1-10. Disclosures Martin: Sanofi: Research Funding; Oncopeptides: Consultancy; Janssen: Research Funding; Amgen: Research Funding; GlaxoSmithKline: Consultancy. Usmani: Pharmacyclics: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Merck: Consultancy, Research Funding; SkylineDX: Consultancy, Research Funding; Bristol-Myers Squibb: Research Funding; Takeda: Consultancy, Research Funding, Speakers Bureau; Janssen Oncology: Consultancy, Research Funding; Abbvie: Consultancy; Array BioPharma: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding, Speakers Bureau; Celgene/BMS: Consultancy, Research Funding, Speakers Bureau; GSK: Consultancy, Research Funding; EdoPharma: Consultancy; Janssen: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Research Funding, Speakers Bureau. Joseph: Johnson and Johnson: Current Employment, Current equity holder in publicly-traded company. Crivera: Johnson & Johnson: Current Employment, Current equity holder in publicly-traded company. Valluri: Janssen: Current Employment, Current equity holder in publicly-traded company. Jackson: Memorial Sloan Kettering Cancer Center: Consultancy; Janssen: Current Employment. Cohen: Eversana Life Science Services: Current Employment, Other: Eversana Life Science Services was contracted by Janssen to work on this project.. Singh: Eversana Life Science Services: Current Employment, Other: Eversana Life Science Services was contracted by Janssen to work on this project..
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".