Effect of<i>trans</i>-resveratrol on 7-benzyloxy-4-trifluoromethylcoumarin<i>O</i>-dealkylation catalyzed by human recombinant CYP3A4 and CYP3A5
Bibliographic record
Abstract
Red wine concentrate has been reported to inhibit the catalytic activity of human recombinant cytochrome P450 (CYP) 3A4. Wine contains many polyphenolic compounds, including trans-resveratrol, which is also available commercially as a nutraceutical product. In the present study, we examined the in vitro effect of trans-resveratrol on human CYP3A catalytic activity by employing recombinant CYP3A4 and CYP3A5 as model enzymes and 7-benzyloxy-4-trifluoromethylcoumarin (BFC) as a CYP3A substrate. Trans-resveratrol inhibited BFC O-dealkylation catalyzed by CYP3A4 and CYP3A5 in a concentration-dependent manner. In each case, the inhibition was noncompetitive, as determined by Lineweaver-Burk and Dixon plots of the enzyme kinetic data. The apparent Ki values (mean +/- SEM) for the inhibition by trans-resveratrol of BFC O-dealkylation catalyzed by CYP3A4 and CYP3A5 were 10.2+/-1.1 microM and 14.7+/-0.3 microM, respectively. Preincubation of trans-resveratrol with NADPH and CYP3A4 or CYP3A5 for 10 or 15 min prior to initiation of substrate oxidation did not enhance the inhibitory effect, suggesting that this compound was not a mechanism-based inactivator of CYP3A4 or CYP3A5 when BFC was used as the substrate. Overall, our study provides the first demonstration that trans-resveratrol inhibits, in vitro, a substrate oxidation reaction catalyzed by human recombinant CYP3A4 and CYP3A5.
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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".