Curcumin analog GO‐Y030 is a novel inhibitor of IKKβ that suppresses NF‐κB signaling and induces apoptosis
Bibliographic record
Abstract
Curcumin is a dietary constituent with tumor-suppressing potential, inhibiting various pathways involved in carcinogenesis. However, because of its low bioavailability, the use of curcumin in in vivo trials has been limited. To overcome this problem, we synthesized more than 50 analogs and identified a monoketone analog, GO-Y030, which has a 30-fold higher potential to suppress tumor cell growth compared with curcumin. We investigated the inhibitory effect of GO-Y030 on NF-κB activation. In thyroid, pancreatic cancers and cholangiocarcinoma cells, in which NF-κB is activated, NF-κB activation was suppressed to 8-62% of the control value following treatment with 1 μM GO-Y030, an effect comparable to that of 10 μM curcumin. Direct inhibition of IKKβ kinase activity and suppression of nuclear translocation of the NF-κB p65 subunit were observed. The 50% growth inhibition concentrations of GO-Y030 ranged from one-11th to one-14th of those of curcumin. GO-Y030 also induced cell death comparable to that induced by curcumin but at a 10-fold lower concentration. In pancreatic and thyroid cancer cells, the growth-inhibitory effect of GO-Y030 was 4- and 15-fold greater, respectively, than that of curcumin. GO-Y030 was a much stronger inducer of apoptosis compared with curcumin. The enhanced potency of GO-Y030 may make it more useful than curcumin, which suffers from low bioavailability. GO-Y030 is a good lead compound for the development of useful compounds for practical cancer chemotherapy.
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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 | 0.001 |
| 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".