Cyproheptadine displays preclinical activity in myeloma and leukemia
Bibliographic record
Abstract
D-cyclins are regulators of cell division that act in a complex with cyclin-dependent kinases to commit cells to a program of DNA replication. D-cyclins are overexpressed in many tumors, including multiple myeloma and leukemia, and contribute to disease progression and chemoresistance. To better understand the role and impact of D-cyclins in hematologic malignancies, we conducted a high throughput screen for inhibitors of the cyclin D2 promoter and identified the drug cyproheptadine. In myeloma and leukemia cells, cyproheptadine decreased expression of cyclins D1, D2, and D3 and arrested these cells in the G(0)/G(1) phase. After D-cyclin suppression, cyproheptadine induced apoptosis in myeloma and leukemia cell lines and primary patient samples preferentially over normal hematopoietic cells. In mouse models of myeloma and leukemia, cyproheptadine inhibited tumor growth without significant toxicity. Cyproheptadine-induced apoptosis was preceded by activation of the mitochondrial pathway of caspase activation and was independent of the drug's known activity as an H1 histamine and serotonin receptor antagonist. Thus, cyproheptadine represents a lead for a novel therapeutic agent for the treatment of malignancy. Because the drug is well tolerated and already approved in multiple countries for clinical use as an antihistamine and appetite stimulant, it could be moved directly into clinical trials for cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".