Bispecific antibodies in the treatment of multiple myeloma
Bibliographic record
Abstract
The treatment paradigm in myeloma is constantly changing. Upfront use of monoclonal antibodies like daratumumab along with proteasome inhibitors (PI)s, and immune modulators (IMiD)s have significantly improved survival and outcomes, but also cause unique challenges at the time of relapse. Engaging immune T cells for tumour cell kill with chimeric antigenic T-cell (CAR T-cell) therapy and bispecific antibodies have become important therapeutic options in relapsed multiple myeloma. Bispecific antibodies are dual antigen targeting constructs that engage the T cells to plasma cells through various target antigens like B-cell membrane antigen (BCMA), G-protein-coupled receptor family C group 5 member D (GPRC5D), and Fc receptor-homolog 5 (FcRH5). These agents have proven to induce deep and durable responses in heavily pre-treated myeloma patients with a predictable safety profile and the ease of off-the-shelf availability. Significant research is ongoing to overcome resistance mechanisms like T cell exhaustion, target antigen mutation or loss and high disease burden. Various trials are also studying these agents as first line options in the newly diagnosed setting. These agents play an important role in the relapsed setting, and efforts are underway to optimize their sequencing in the myeloma treatment algorithm.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".