Novel quinolone chalcones targeting colchicine-binding pocket kill multidrug-resistant cancer cells by inhibiting tubulin activity and MRP1 function
Bibliographic record
Abstract
Agents targeting colchicine-binding pocket usually show a minimal drug-resistance issue, albeit often associated with high toxicity. Chalcone-based compounds, which may bind to colchicine-binding site, are found in many edible fruits, suggesting that they can be effective drugs with less toxicity. Therefore, we synthesized and examined 24 quinolone chalcone compounds, from which we identified ((E)-3-(3-(2-Methoxyphenyl)-3-oxoprop-1-enyl) quinolin-2(1H)-one) (CTR-17) and ((E)-6-Methoxy-3-(3-(2-methoxyphenyl)-3-oxoprop-1-enyl) quinolin-2(1H)-one) (CTR-20) as promising leads. In particular, CTR-20 was effective against 65 different cancer cell lines originated from 12 different tissues, largely in a cancer cell-specific manner. We found that both CTR-17 and CTR-20 reversibly bind to the colchicine-binding pocket on β-tubulin. Interestingly however, both the CTRs were highly effective against multidrug-resistant cancer cells while colchicine, paclitaxel and vinblastine were not. Our study with CTR-20 showed that it overcomes multidrug-resistance through its ability to impede MRP1 function while maintaining strong inhibition against microtubule activity. Data from mice engrafted with the MDA-MB-231 triple-negative breast cancer cells showed that both CTR-17 and CTR-20 possess strong anticancer activity, alone or in combination with paclitaxel, without causing any notable side effects. Together, our data demonstrates that both the CTRs can be effective and safe drugs against many different cancers, especially against multidrug-resistant tumors.
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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".