Identification of Docetaxel as a Potential Drug to Promote HDL Biogenesis
Bibliographic record
Abstract
Objective: Our recent studies showed that desmocollin 1 (DSC1) binds to apoA-I in order to inhibit apoA-I-mediated high density lipoprotein (HDL) biogenesis in atherosclerotic plaques. To promote HDL biogenesis in the plaque, here we search for small molecules that block apoA-I-DSC1 interactions. Approach and Results: We combined mutational and computational mapping methods to show that amino acid residues 442–539 in the mature DSC1 protein form an apoA-I binding site (AIBS). Using a crystal structure of the AIBS, we carried out virtual screening of 10 million small molecules to estimate their binding affinities to the AIBS, followed by the selection of 51 high-affinity binding molecules as potential inhibitors of apoA-I-DSC1 interactions. Among the 51, the chemotherapy drug docetaxel showed the highest potency in promoting apoA-I-mediated HDL biogenesis in primary human skin fibroblasts with the half-maximal effective concentration of 0.72 nM. In silico docking studies suggest that the taxane ring in docetaxel binds to the AIBS and that the carbon-13 sidechain of the taxane tightens/stabilizes the binding. The HDL biogenic effect of docetaxel was also observed in two predominant cell types in atherosclerosis, macrophages and smooth muscle cells. Importantly, docetaxel promoted HDL biogenesis at concentrations much lower than those required for inducing cytotoxicity. Conclusion: Determination of the AIBS in DSC1 and AIBS structure-based virtual screening allowed us to identify docetaxel as a strong HDL biogenic agent. With the remarkable potency in promoting HDL biogenesis, a chemotherapy drug docetaxel may be repurposed to enhance atheroprotective HDL functions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".