Antibodies and bispecifics for multiple myeloma: effective effector therapy
Bibliographic record
Abstract
The therapeutic landscape in multiple myeloma (MM) has changed dramatically over the last 2 decades. With the introduction of novel immunotherapies, patients with MM can expect deeper responses, longer remissions, and improved overall survival. Since its approval by the US Food and Drug Administration in 2015, the monoclonal antibody specific for CD38, daratumumab, has been incorporated into both frontline and relapsed treatment regimens. Its role as a maintenance therapy is currently being explored. Subsequently, a variety of novel antibody therapeutics have evolved from the success of daratumumab, using similar concepts to target the malignant plasma cell clone. Noteworthy naked monoclonal antibodies include isatuximab, another agent directed against CD38, and elotuzumab, an agent directed against SLAM family member 7. Antibody-drug conjugates, complex molecules composed of an antibody tethered to a cytotoxic drug, target malignant cells and deliver a lethal payload. The first to market is belantamab mafodotin, which targets B-cell maturation antigen (BCMA) on malignant plasma cells and delivers a potent microtubule inhibitor, monomethyl auristatin F. Additionally, bispecific T-cell antibodies are in development that engage the immune system directly by simultaneously binding CD3 on T cells and a target epitope-such as BCMA, G-protein coupled receptor family C group 5 member D (GPRC5d), and Fc receptor homologue 5 (FcRH5)-on malignant cells. Currently, teclistamab, an anti-BCMA bispecific, is closest to approval for commercial use. In this review, we explore the evolving landscape of antibodies in the treatment of MM, including their role in frontline and relapse settings.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".