The role of E3 ubiquitin ligase in multiple myeloma: potential for cereblon E3 ligase modulators in the treatment of relapsed/refractory disease
Bibliographic record
Abstract
INTRODUCTION: Insights into the mechanisms of protein homeostasis and proteasomal degradation have led to new strategies of redirecting the ubiquitin-proteasome system (UPS) to reduce or eliminate proteins or survival factors key to malignant pathobiology, multiple myeloma (MM) in particular. These strategies have enabled researchers to target proteins that were previously considered difficult to modulate by pharmacological means. AREAS COVERED: E3 ubiquitin ligase complex, and summarizes current strategies for co-opting the UPS, including CELMoD compounds, SNIPERs, PROTACs, and degronimids. A detailed discussion is provided on lead CELMoD compounds iberdomide and mezigdomide, which are currently being evaluated in clinical trials in patients with MM. EXPERT OPINION: Since a high proportion of patients develop drug resistance, it is vital to have novel therapeutic agents for treating relapsed patients with MM more effectively. It is encouraging that the expanding pathophysiological insight into cellular signaling pathways in MM increasingly translates into the development of novel therapeutic agents such as targeted protein degraders. This holds promise for improving outcomes in MM and beyond.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".