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A High Throughput Screen Identified Cyproheptadine That Decreases D-Type Cyclins, Arrests Cells in the G1 Phase, and Induces Apoptosis in Multiple Myeloma and Leukemia Cells.

2007· article· en· W2559499796 on OpenAlexaff
Xinliang Mao, A. Keith Stewart, Rose Hurren, Marcela Gronda, Kyle Lee, Sue Chow, Sheng‐Ben Liang, Suzanne Trudel, David W. Hedley, Aaron D. Schimmer

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsCyproheptadineCancer researchViability assayCyclin BCyclinCyclin DCyclin D1Cyclin B1Cyclin D3ApoptosisCell cycle checkpointCyclin ACell cycleCell growthBiologyCyclin ECyclin-dependent kinase 1Molecular biologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Cyclin Ds are key regulators of the cell cycle that are frequently over-expressed in multiple myeloma and leukemia and act to promote the phosphorylation of Rb, thereby facilitating the transition from G1 to S phase. Over-expression of cyclin Ds increases cell proliferation and chemoresistance. In contrast, reducing cyclin Ds levels directly or indirectly through G1 arrest can decrease cellular proliferation and induce apoptosis. To identify novel pharmaceutical inhibitors of cyclin D transactivation, we screened the LOPAC and Prestwick libraries of drugs and natural compounds (n = 2400) using NIH 3T3 cells stably expressing the Cyclin D2 promoter-driving a luciferase reporter gene. From this library screen and subsequent validation experiments we identified Cyproheptadine as a novel inhibitor of Cyclin D2 transactivation. Cyproheptadine has been used previously for the treatment of atopic dermatitis and anorexia, but its ability to inhibit cyclin D expression has not previously been reported. By immunoblotting, Cyproheptadine decreased expression of cyclin D1, D2 and D3 proteins in human myeloma and leukemia cell lines at low micromolar concentrations. Consistent with effects on the cyclin Ds, Cyproheptadine arrested cells in the G1 phase at concentrations associated with reduction in cyclin D expression. Decreased cyclin D expression and G1 arrest can induce apoptosis, so we tested the effects of Cyproheptadine on cell viability. Myeloma and leukemia cell lines were treated with increasing concentrations of Cyproheptadine and viability was measured by the MTS assay. Cyproheptadine reduced the viability of 7/10 myeloma and 7/8 AML cells lines with an IC50 ranging from 10–25μM. In contrast, it was less toxic to HeLa or NIH3T3 cells with IC20 > 50 μM. Cyproheptadine also reduced the viability of primary myeloma (8/8) and AML patient samples (7/9) with an IC50 <25 μM, but was less toxic to normal hematopoietic cells (IC20 > 50 μM). In a MDAY-D2 mouse model of leukemia, treatment with Cyproheptadine (50mg/kg/d) abolished formation of malignant leukemic ascites without untoward toxicity. Cyproheptadine-induced cell death was associated with reductions in mitochondrial membrane potential. Furthermore, reductions of pro-caspases -3 and -9 were observed prior to the reduction in pro-caspase-8, indicating that Cyproheptadine activates the mitochondrial pathway of caspase activation. Cyproheptadine is a known H1 histamine and serotonin receptor inhibitor, but pre-incubation with histamine, serotonin, or a combination of histamine and serotonin did not abrogate Cyproheptadine-induced cell death. Moreover, the structurally related H1 receptor inhibitor loratadine did not decrease cyclin D expression or reduce cell viability. Therefore, the pro-apoptotic activity of Cyproheptadine is not due to a competitive inhibition of the H1 and/or serotonin receptors, suggesting that Cyproheptadine has additional targets. In summary, Cyproheptadine arrests cells in G1, reduces cyclin D expression, and induces apoptosis via the mitochondrial pathway of caspase activation. Given the prior safety and toxicity record of Cyproheptadine, this drug could be rapidly advanced into clinical trial for the treatment of hematologic malignancies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.290
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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