Enitociclib, a selective CDK9 inhibitor: in vitro and in vivo preclinical studies in multiple myeloma
Bibliographic record
Abstract
• Targeting cyclin-dependent kinase 9 with enitociclib induces apoptotic cell death in MM and demonstrates synergy in drug combinations. • Enitociclib may be an effective treatment for MM as a single agent or in combination therapies in future clinical studies. Multiple myeloma (MM) is a cancer of plasma cells that remains incurable despite advances in treatment options. In this study, a library of 216 clinically feasible small-molecule inhibitors was screened to identify agents that selectively inhibit MM cell proliferation. Enitociclib, a cyclin-dependent kinase 9–specific small-molecule inhibitor, was found to be highly effective in decreasing cell viability and inducing apoptosis in 4 MM cell lines. Enitociclib inhibited the phosphorylation of the carboxy-terminal domain (CTD) of RNA polymerase II at Ser2/Ser5 and repressed the protein expression of oncogenes c-Myc, myeloid cell leukemia-1 (Mcl-1), and proliferating cell nuclear antigen (PCNA) in MM cells. Additionally, enitociclib demonstrated synergistic effects with several anti-MM agents, including bortezomib, lenalidomide, pomalidomide, and venetoclax. These results suggest that enitociclib may represent a promising therapeutic option for the treatment of MM, either as a single agent or in combination with other anti-MM agents.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".