Relapsed/Refractory Multiple Myeloma: A Review of Available Therapies and Clinical Scenarios Encountered in Myeloma Relapse
Bibliographic record
Abstract
Multiple myeloma remains an incurable disease with the usual disease course requiring induction therapy, autologous stem cell transplantation for eligible patients, and long-term maintenance. Risk stratification tools and cytogenetic alterations help inform individualized therapeutic choices for patients in hopes of achieving long-term remissions with preserved quality of life. Unfortunately, relapses occur at different stages of the course of the disease owing to the biological heterogeneity of the disease. Addressing relapse can be complex and challenging as there are both therapy- and patient-related factors to consider. In this broad scoping review of available therapies in relapsed/refractory multiple myeloma (RRMM), we cover the pharmacologic mechanisms underlying active therapies such as immunomodulatory agents (IMiDs), proteasome inhibitors (PIs), monoclonal antibodies (mAbs), traditional chemotherapy, and Venetoclax. We then review the clinical data supporting the use of these therapies, organized based on drug resistance/refractoriness, and the role of autologous stem cell transplant (ASCT). Approaches to special situations during relapse such as renal impairment and extramedullary disease are also covered. Lastly, we look towards the future by briefly reviewing the clinical data supporting the use of chimeric antigen receptor (CAR-T) therapy, bispecific T cell engagers (BITE), and Cereblon E3 Ligase Modulators (CELMoDs).
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".