Inhibition of procarcinogen‐bioactivating human CYP1A1, CYP1A2 and CYP1B1 enzymes by melatonin
Bibliographic record
Abstract
Administration of melatonin to rodents decreases the incidence of tumorigenesis initiated by benzo[a]pyrene or 7,12-dimethylbenz[a]anthracene, which requires bioactivation by cytochrome P450 enzymes, such as CYP1A1, CYP1A2 and CYP1B1, to produce carcinogenic metabolites. The present study tested the hypothesis that melatonin is a modulator of human CYP1 catalytic activity and gene expression. As a comparison, we also investigated the effect of melatonin on the catalytic activity of CYP2A6, which is also a procarcinogen-bioactivating enzyme. Melatonin (3-300 microm) decreased 7-ethoxyresorufin O-dealkylation catalyzed by human hepatic microsomes and recombinant CYP1A1, CYP1A2 and CYP1B1, whereas it did not affect coumarin 7-hydroxylation catalyzed by hepatic microsomes or recombinant CYP2A6. Melatonin inhibited CYP1 enzymes by mixed inhibition, with apparent K(i) values (mean +/- S.E.M.) of 59 +/- 1 (CYP1A1), 12 +/- 1 (CYP1A2), 14 +/- 2 (CYP1B1) and 46 +/- 8 microm (hepatic microsomes). Additional experiments indicated that melatonin decreased benzo[a]pyrene hydroxylation catalyzed by hepatic microsomes and CYP1A2 but not by CYP1A1 or CYP1B1. Treatment of MCF-10A human mammary epithelial cells with melatonin (up to 300 microm) did not affect basal or benzo[a]pyrene-inducible CYP1A1 or CYP1B1 gene expression. Consistent with this finding, melatonin did not influence reporter activity in aryl hydrocarbon receptor-dependent pGudluc6.1-transfected MCF-10A cells treated with or without benzo[a]pyrene, as assessed in an in vitro cell-based luciferase reporter gene assay. Overall, melatonin is an in vitro inhibitor of human CYP1 catalytic activity, and it may be useful to develop potent analogues of melatonin as potential cancer chemopreventive agents that block CYP1-mediated chemical carcinogenesis.
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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".