Sterol-like drugs potentiate statin-triggered prostate cancer cell death by inhibiting SREBP2 nuclear translocation
Bibliographic record
Abstract
There is an urgent need to provide immediate and effective options for the treatment of prostate cancer (PCa) to prevent progression to lethal castration-resistant PCa (CRPC). The mevalonate (MVA) pathway is dysregulated in PCa, and statin drugs commonly prescribed for hypercholesterolemia, effectively target this pathway. Statins exhibit anti-PCa activity, however the resulting intracellular depletion of cholesterol triggers a feedback loop that restores MVA pathway activity, thus diminishing statin efficacy and contributing to resistance. To identify drugs that block this feedback response and enhance the pro-apoptotic activity of statins, we performed a high-content image-based screen of a 1508 drug library, enriched for FDA-approved compounds. Two of the validated hits, Galeterone (GAL) and Quinestrol, share the cholesterol-related tetracyclic structure, which is also evident in the FDA-approved CRPC drug Abiraterone (ABI). Molecular modeling revealed that GAL, Quinestrol and ABI not only share structural similarity with 25-hydroxy-cholesterol (25HC) but were also predicted to bind similarly to a known protein-binding site of 25HC. This suggested GAL, Quinestrol and ABI are sterol-mimetics and thereby inhibit the statin-induced feedback response. Cell-based assays demonstrated that these agents inhibit nuclear translocation of sterol-regulatory element binding protein 2 (SREBP2) and the transcription of MVA genes. Sensitivity was independent of androgen status and the Fluva-GAL combination significantly impeded CRPC tumor xenograft growth. By identifying cholesterol-mimetic drugs that inhibit SREBP2 activation upon statin treatment, we provide a potent "one-two punch" against CRPC progression and pave the way for innovative therapeutic strategies to combat additional diseases whose etiology is associated with SREBP2 dysregulation.
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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 | 0.000 |
| 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".