Evaluation of methoxsalen, tranylcypromine, and tryptamine as specific and selective CYP2A6 inhibitors in vitro.
Bibliographic record
Abstract
CYP2A6 is the principle enzyme metabolizing nicotine to its inactive metabolite cotinine. In this study, the selective probe reactions for each major cytochrome P450 (P450) were used to evaluate the specificity and selectivity of the CYP2A6 inhibitors methoxsalen, tranylcypromine, and tryptamine in cDNA-expressing and human liver microsomes. Phenacetin O-deethylation (CYP1A2), coumarin 7-hydroxylation (CYP2A6), diclofenac 4'-hydroxylation (CYP2C9), omeprazole 5-hydroxylation (CYP2C19), dextromethorphan O-demethylation (CYP2D6), 7-ethoxy-4-trifluoromethylcoumarin deethylation (CYP2B6), p-nitrophenol hydroxylation (CYP2E1), and omeprazole sulfonation (CYP3A4) were used as index reactions. Apparent K(i) values for inhibition of P450s' (1A2, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, and 3A4) activities showed that tranylcypromine, methoxsalen, and tryptamine have high specificity and relative selectivity for CYP2A6. In cDNA-expressing microsomes, tranylcypromine inhibited CYP2A6 (K(i) = 0.08 microM) with about 60- to 5000-fold greater potency relative to other P450s. Methoxsalen inhibited CYP2A6 (K(i) = 0.8 microM) with about 3.5- 94-fold greater potency than other P450s, except for CYP1A2 (K(i) = 0.2 microM). Tryptamine inhibited CYP2A6 (K(i) = 1.7 microM) with about 6.5- 213-fold greater potency relative to other P450s, except for CYP1A2 (K(i) = 1.7 microM). Similar results were also obtained with methoxsalen and tranylcypromine in human liver microsomes. R-(+)-Tranylcypromine, (+/-)-tranylcypromine, and S-(-)-tranylcypromine competitively inhibited CYP2A6-mediated metabolism of nicotine with apparent K(i) values of 0.05, 0.08, and 2.0 microM, respectively. Tranylcypromine [particularly R-(+) isomer], tryptamine, and methoxsalen are specific and relatively selective for CYP2A6 and may be useful in vivo to decrease smoking by inhibiting nicotine metabolism with a low risk of metabolic drug interactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".