Cyclic AMP‐hydrolyzing phosphodiesterase inhibitors potentiate statin‐induced cancer cell death
Bibliographic record
Abstract
Dipyridamole, an antiplatelet drug, has been shown to synergize with statins to induce cancer cell-specific apoptosis. However, given the polypharmacology of dipyridamole, the mechanism by which it potentiates statin-induced apoptosis remains unclear. Here, we applied a pharmacological approach to identify the activity of dipyridamole specific to its synergistic anticancer interaction with statins. We evaluated compounds that phenocopy the individual activities of dipyridamole and assessed whether they could potentiate statin-induced cell death. Notably, we identified that a phosphodiesterase (PDE) inhibitor, cilostazol, and other compounds that increase intracellular cyclic adenosine monophosphate (cAMP) levels potentiate statin-induced apoptosis in acute myeloid leukemia and multiple myeloma cells. Additionally, we demonstrated that both dipyridamole and cilostazol further inhibit statin-induced activation of sterol regulatory element-binding protein 2, a known modulator of statin sensitivity, in a cAMP-independent manner. Taken together, our data support that PDE inhibitors such as dipyridamole and cilostazol can potentiate statin-induced apoptosis via a dual mechanism. Given that several PDE inhibitors are clinically approved for various indications, they are immediately available for testing in combination with statins for the treatment of hematological malignancies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".