Daratumumab With Cetrelimab, an Anti–PD-1 Monoclonal Antibody, in Relapsed/Refractory Multiple Myeloma
Bibliographic record
Abstract
BACKGROUND: Daratumumab is approved for relapsed or refractory multiple myeloma (RRMM) as monotherapy or in combination regimens. We evaluated daratumumab plus cetrelimab, a programmed death receptor-1 inhibitor, in RRMM. PATIENTS AND METHODS: This open-label, multiphase study enrolled adults with RRMM with ≥ 3 prior lines of therapy. Part 1 was a safety run-in phase examining dose-limiting toxicities of daratumumab (16 mg/kg intravenously weekly for cycles 1-2, biweekly for cycles 3-6, and monthly thereafter) plus cetrelimab (240 mg intravenously biweekly, all cycles). In Parts 2 and 3, patients were to be randomized to daratumumab with or without cetrelimab (same schedule as Part 1). Endpoints included safety, overall response rate, pharmacokinetics, and biomarker analyses. RESULTS: Nine patients received daratumumab plus cetrelimab in the safety run-in, and 1 received daratumumab in Part 2 before administrative study termination following a data monitoring committee's global recommendation to stop any trial including daratumumab combined with inhibitors of programmed death receptor-1 or its ligand (programmed death-ligand 1). The median follow-up times were 6.7 months (safety run-in) and 0.3 months (Part 2). No dose-limiting toxicities occurred. All 10 patients had ≥ 1 treatment-emergent adverse event; 7 patients had grade 3 to 4 treatment-emergent adverse events, and none led to treatment discontinuation or death. In the safety run-in, 7 (77.7%) patients had ≥ 1 infusion-related reaction (most grade 1-2), and 1 had a grade 2 immune-mediated reaction. Among safety run-in patients, the overall response rate was 44.4%. CONCLUSIONS: No new safety concerns were identified for daratumumab plus cetrelimab in RRMM. The short study duration and small population limit complete analysis of this combination.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".