Isatuximab for the treatment of multiple myeloma: current clinical advances and future directions
Bibliographic record
Abstract
INTRODUCTION: The addition of the anti-CD38 monoclonal antibody isatuximab to standard therapies is transforming the care of patients with newly diagnosed multiple myeloma (NDMM), as previously seen in the relapsed/refractory setting. This is particularly important for patients with NDMM as early treatment with effective, well tolerated therapies may ensure better clinical outcomes. AREAS COVERED: Here, we examine recent results from pivotal Phase 3 and 2 clinical trials that demonstrate efficacy and safety of isatuximab across multiple combinations, for both transplant-ineligible and transplant-eligible NDMM patients. We then evaluate long-term outcomes from the IKEMA and ICARIA-MM trials as well as real-world evidence emerging from analyses conducted in patients with relapsed/refractory MM (RRMM). Further, we address current approaches to optimize treatment with isatuximab-based combinations involving changes in bortezomib or dexamethasone dosing. Lastly, we review current findings with new administration modalities developed to optimize delivery of isatuximab in the clinic. EXPERT OPINION: Supported by multiple lines of high-level evidence, isatuximab in combination with standard-of-care backbone therapies produces triplet or quadruplet regimens with enhanced efficacy and consistent safety for the treatment of patients with NDMM and RRMM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".