Modifying the Metabolism of Nicotine as a Therapeutic Strategy
Bibliographic record
Abstract
CYP2A6 is the enzyme responsible for the metabolic inactivation of around 90% of nicotine to cotinine. Individuals with genetically decreased CYP2A6 have slower rates of nicotine inactivation. We have found that slow nicotine inactivators are roughly twice less likely to be current adult smokers and those who are smoke 7–10 fewer cigarettes per day than people with normal metabolic rates. Slow nicotine inactivators also smoke for a shorter duration before quitting and may have increased success in quitting. Recently we have shown that imitating the protection offered by the slow metabolism, by inhibiting CYP2A6 activity in vivo, can decrease smoking. CYP2A6 is also involved in the activation of tobacco-smoke nitrosamines. Slow metabolizes are at lower risk for lung cancer and we have shown that CYP2A6 inhibitors can also decrease the nitrosamine activation (rerouting them to detoxified glucuronides). CYP2A6 inhibitors can be used alone, or with nicotine to make a nicotine oral pill, to inhibit the first-pass metabolism. CYP2A6 inhibitors can also increase nicotine plasma levels (and bioavailability) of nicotine when given with nicotine patch or gum. These approaches together may provide a better understanding of smoking behaviour and provide novel therapeutic approaches to smoking reduction and cessation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.008 | 0.002 |
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".