The clinical journey of belantamab mafodotin in relapsed or refractory multiple myeloma: lessons in drug development
Bibliographic record
Abstract
Patients with relapsed/refractory multiple myeloma (RRMM) have a poor prognosis and a need remains for novel effective therapies. Belantamab mafodotin, an anti-B-cell maturation antigen antibody-drug conjugate, was granted accelerated/conditional approval for patients with RRMM who have received at least 4 prior lines of therapy, based on response rates observed in DREAMM-1/DREAMM-2. Despite the 41% response rate and durable responses observed with belantamab mafodotin in the Phase III confirmatory DREAMM-3 trial, the marketing license for belantamab mafodotin was later withdrawn from US and European markets when the trial did not meet its primary endpoint of superiority for progression-free survival compared with pomalidomide and dexamethasone. This review reflects on key lessons arising from the clinical journey of belantamab mafodotin in RRMM. It considers how incorporating longer follow-up in DREAMM-3 may have better captured the clinical benefits of belantamab mafodotin, particularly given its multimodal, immune-related mechanism of action with responses deepening over time. A non-inferiority hypothesis may have been more appropriate rather than superiority in the context of a monotherapy versus an active doublet therapy. Further, anticipation of, and planning for, non-proportional hazards arising from response heterogeneity may have mitigated loss of statistical power. With the aim of improving the efficacy of belantamab mafodotin, other Phase III trials in the RRMM development program (DREAMM-7 and DREAMM-8) proceeded to evaluate the synergistic potential of combination regimens in earlier lines of treatment. The aim was to increase the proportion of patients responding to belantamab mafodotin (and thus the likelihood of seeing a clear separation of the progression-free survival curve versus comparator regimens). Protocol amendments reflecting DREAMM-3 learnings could also be implemented prospectively on the combinations trials to optimize the follow-up duration and mitigate risk. The wider implications of the lessons learned for clinical research in RRMM and in earlier treatment settings are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".