Selinexor, daratumumab, and dexamethasone in patients with relapsed or refractory multiple myeloma
Bibliographic record
Abstract
We assessed the safety, efficacy, maximum tolerated dose (MTD), and the recommended phase 2 dose (RP2D) of selinexor, a first in class oral selective inhibitor of nuclear export (100 mg once weekly [QW] or 60 mg twice weekly), in combination with daratumumab (16 mg/kg per label) and dexamethasone (40 mg QW) (SDd) in patients with relapsed refractory multiple myeloma (RRMM). Thirty-four patients (median prior therapies, 3 [range, 2-10]) were enrolled; MM was refractory to proteasome inhibitor (PI) in 85%, immunomodulatory agent (IMiD) in 76%, both in 74%, and daratumumab in 6% of patients. Two dose-limiting toxicities (DLTs) were reported in the selinexor 60 mg twice-weekly cohort with no DLTs in the 100 mg QW cohort, making 100 mg QW the MTD and RP2D. Common treatment-related adverse events included thrombocytopenia (70.6%), nausea (70.6%), fatigue (61.8%), anemia (61.8%), and neutropenia (50.0%). Overall response rate was 73% and median progression-free survival 12.5 months in daratumumab-naïve patients. SDd was well tolerated and its promising efficacy suggests that further study of this PI- and IMiD-free regimen in RRMM patients who had at least one prior line of therapy including a PI and an IMiD but whose disease is naïve to daratumumab is warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".