The Role of CELMoD Agents in Multiple Myeloma
Bibliographic record
Abstract
Although recent decades have seen continued improvements in survival for patients with multiple myeloma, the disease remains largely incurable, and most patients will experience relapse and/or become refractory to treatment. There thus remains an urgent unmet need for novel treatments, particularly for those patients with relapsed or refractory multiple myeloma. Novel treatment modalities, such as targeted protein degradation, have attracted particular interest due to their ability to expand the range of druggable protein targets in myeloma cells. Iberdomide (CC-220) and mezigdomide (CC-92480) are promising oral CELMoD™ agents currently being evaluated for the treatment of patients with multiple myeloma. Preclinical data from lenalidomide- and pomalidomide-resistant cell lines and mouse models suggest that iberdomide and mezigdomide have the potential to provide therapeutic benefit even in patients who are refractory to lenalidomide and pomalidomide. The optimized specificity, potency, and safety profile of iberdomide and mezigdomide supports their clinical use and aligns with the need for longer durations of a well-tolerated oral CELMoD agent with synergistic combinability with other immune approaches (such as anti-CD38 monoclonal antibodies) and proteasome inhibitors (such as bortezomib and carfilzomib). Although neither iberdomide or mezigdomide has yet received regulatory approval for the treatment of multiple myeloma, based on their mechanism of action and the data available to date, we propose that both drugs may be attractive options for the treatment of patients with relapsed or refractory multiple myeloma; based on their efficacy and safety profiles, iberdomide is likely better suited for use in newly diagnosed, first relapse, or maintenance settings, whereas mezigdomide may also be better suited for use in patients with early relapse or a greater number of prior antimyeloma treatments. Iberdomide and mezigdomide are currently being evaluated for the treatment of patients with multiple myeloma in several trials, and results so far are promising.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".