Pomalidomide, dexamethasone, and daratumumab in relapsed refractory multiple myeloma after lenalidomide treatment
Bibliographic record
Abstract
Patients with multiple myeloma who have relapsed after or become refractory to lenalidomide in early treatment lines represent a clinically important population in need of effective therapies. The safety and efficacy of pomalidomide, low-dose dexamethasone, and daratumumab was evaluated in lenalidomide-pretreated patients with relapsed or refractory multiple myeloma (RRMM) after one to two prior treatment lines in the phase 2 MM-014 study. Patients received pomalidomide 4 mg daily from days 1-21 and dexamethasone 40 mg weekly (28-day cycles). Daratumumab 16 mg/kg was administered per label. Primary endpoint was overall response rate (ORR); secondary endpoints included progression-free survival (PFS) and safety. Per protocol, all patients (N = 112) had received lenalidomide in their most recent prior regimen (75.0% lenalidomide refractory). ORR was 77.7% (76.2% in lenalidomide-refractory patients); median follow-up was 17.2 months. Median PFS was not reached (1-year PFS rate 75.1%). The most common hematologic grade 3/4 treatment-emergent adverse event was neutropenia (62.5%). Grade 3/4 infections were reported in 31.3% of patients, including 13.4% with grade 3/4 pneumonia. These results demonstrate the safety and efficacy of pomalidomide-based therapy as early as second line in patients with RRMM, even immediately after lenalidomide failure, indicating that switching from the immunomodulatory agent class is not necessary.
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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) | 0.000 | 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".