Immediate Utility of Two Approved Agents to Target Both the Metabolic Mevalonate Pathway and Its Restorative Feedback Loop
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
New therapies are urgently needed for hematologic malignancies, especially in patients with relapsed acute myelogenous leukemia (AML) and multiple myeloma. We and others have previously shown that FDA-approved statins, which are used to control hypercholesterolemia and target the mevalonate pathway (MVA), can trigger tumor-selective apoptosis. Our goal was to identify other FDA-approved drugs that synergize with statins to further enhance the anticancer activity of statins in vivo. Using a screen composed of other FDA approved drugs, we identified dipyridamole, used for the prevention of cerebral ischemia, as a potentiator of statin anticancer activity. The statin-dipyridamole combination was synergistic and induced apoptosis in multiple myeloma and AML cell lines and primary patient samples, whereas normal peripheral blood mononuclear cells were not affected. This novel combination also decreased tumor growth in vivo. Statins block HMG-CoA reductase (HMGCR), the rate-limiting enzyme of the MVA pathway. Dipyridamole blunted the feedback response, which upregulates HMGCR and HMG-CoA synthase 1 (HMGCS1) following statin treatment. We further show that dipyridamole inhibited the cleavage of the transcription factor required for this feedback regulation, sterol regulatory element-binding transcription factor 2 (SREBF2, SREBP2). Simultaneously targeting the MVA pathway and its restorative feedback loop is preclinically effective against hematologic malignancies. This work provides strong evidence for the immediate evaluation of this novel combination of FDA-approved drugs in clinical trials.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it