Docetaxel as a Model Compound to Promote HDL (High-Density Lipoprotein) Biogenesis and Reduce Atherosclerosis
Bibliographic record
Abstract
The recent identification of the cell-surface protein DSC1 (desmocollin 1) as a negative regulator of HDL (high-density lipoprotein) biogenesis has attracted us to revisit the old HDL biogenesis hypothesis: HDL biogenesis reduces atherosclerosis. The location and function of DSC1 suggest that DSC1 is a druggable target for the promotion of HDL biogenesis, and the discovery of docetaxel as a potent inhibitor of the DSC1 sequestration of apolipoprotein A-I has provided us with new opportunities to test this hypothesis. The FDA-approved chemotherapy drug docetaxel promotes HDL biogenesis at low-nanomolar concentrations that are far lower than used in chemotherapy. Docetaxel has also been shown to inhibit atherogenic proliferation of vascular smooth muscle cells. In accordance with these atheroprotective effects of docetaxel, animal studies have shown that docetaxel reduces dyslipidemia-induced atherosclerosis. In the absence of HDL-directed therapies for atherosclerosis, DSC1 constitutes an important new target for the promotion of HDL biogenesis, and the DSC1-targeting compound docetaxel serves as a model compound to prove the hypothesis. In this brief review, we discuss opportunities, challenges, and future directions for using docetaxel in the prevention and treatment of atherosclerosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".