Autoxidative and Cyclooxygenase-2 Catalyzed Transformation of the Dietary Chemopreventive Agent Curcumin
Bibliographic record
Abstract
The efficacy of the diphenol curcumin as a cancer chemopreventive agent is limited by its chemical and metabolic instability. Non-enzymatic degradation has been described to yield vanillin, ferulic acid, and feruloylmethane through cleavage of the heptadienone chain connecting the phenolic rings. Here we provide evidence for an alternative mechanism, resulting in autoxidative cyclization of the heptadienone moiety as a major pathway of degradation. Autoxidative transformation of curcumin was pH-dependent with the highest rate at pH 8 (2.2 μM/min) and associated with stoichiometric uptake of O(2). Oxidation was also catalyzed by recombinant cyclooxygenase-2 (COX-2) (50 nm; 7.5 μM/min), and the rate was increased ≈10-fold by the addition of 300 μM H(2)O(2). The COX-2 catalyzed transformation was inhibited by acetaminophen but not indomethacin, suggesting catalysis occurred by the peroxidase activity. We propose a mechanism of enzymatic or autoxidative hydrogen abstraction from a phenolic hydroxyl to give a quinone methide and a delocalized radical in the heptadienone chain that undergoes 5-exo cyclization and oxygenation. Hydration of the quinone methide (measured by the incorporation of O-18 from H(2)(18)O) and rearrangement under loss of water gives the final dioxygenated bicyclopentadione product. When curcumin was added to RAW264.7 cells, the bicyclopentadione was increased 1.8-fold in cells activated by LPS; vanillin and other putative cleavage products were negligible. Oxidation to a reactive quinone methide is the mechanistic basis of many phenolic anti-cancer drugs. It is possible, therefore, that oxidative transformation of curcumin, a prominent but previously unrecognized reaction, contributes to its cancer chemopreventive activity.
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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".