Selective Targeting of Cancer Cells by Oxidative Vulnerabilities with Novel Curcumin Analogs
Bibliographic record
Abstract
Recently, research has focused on targeting the oxidative and metabolic vulnerabilities in cancer cells. Natural compounds like curcumin that target such susceptibilities have failed further clinical advancements due to the poor stability and bioavailability as well as the need of high effective doses. We have synthesized and evaluated the anti-cancer activity of several monocarbonyl analogs of curcumin. Interestingly, two novel analogs (Compound A and I) in comparison to curcumin, have increased chemical stability and have greater anti-cancer activity in a variety of human cancer cells, including triple-negative, inflammatory breast cancer cells. In particular, the generation of reactive oxygen species was selective to cancer cells and occurred upstream of mitochondrial collapse and execution of apoptosis. Furthermore, Compound A in combination with another cancer-selective/pro-oxidant, piperlongumine, caused an enhanced anti-cancer effect. Most importantly, Compound A was well tolerated by mice and was effective in inhibiting the growth of human triple-negative breast cancer and leukemia xenografts in vivo when administered intraperitoneally. Thus, exploiting oxidative vulnerabilities in cancer cells could be a selective and efficacious means to eradicate malignant cells as demonstrated by the curcumin analogs presented in this report with high therapeutic potential.
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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) | 0.001 | 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".