Antibodies and bispecifics for multiple myeloma: effective effector therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it