CAPE Analogs Induce Growth Arrest and Apoptosis in Breast Cancer Cells
Bibliographic record
Abstract
Breast cancer is the second leading cause of death amongst women worldwide. As a result, many have turned their attention to new alternative approaches to treat this disease. Caffeic acid phenylethyl ester (CAPE), a well-known active compound from bee propolis, has been previously identified as a strong antioxidant, anti-inflammatory, antiviral and anticancer molecule. In fact, CAPE is well documented as inducing cell death by inhibiting NFκB and by inducing pro-apoptotic pathways (i.e., p53). With the objective of developing stronger anticancer compounds, we studied 18 recently described CAPE derivatives for their ability to induce apoptosis in breast cancer cell lines. Five of the said compounds, including CAPE, were selected and subsequently characterised for their anticancer mechanism of action. We validated that CAPE is a potent inducer of caspase-dependent apoptosis. Interestingly, some newly synthesized CAPE derivatives also showed greater cell death activity than the lead CAPE structure. Similarly to CAPE, analog compounds elicited p53 activation. Interestingly, one compound in particular, analog 10, induced apoptosis in a p53-mutated cell line. These results suggest that our new CAPE analog compounds may display the capacity to induce breast cancer apoptosis in a p53-dependent and/or independent manner. These CAPE analogs could thus provide new therapeutic approaches for patients with varying genotypic signatures (such as p53 mutations) in a more specific and targeted fashion.
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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 | 0.001 |
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".