Variable CYP2A6-mediated nicotine metabolism alters smoking behavior and risk.
Bibliographic record
Abstract
Nicotine is the psychoactive substance responsible for tobacco dependence; smokers adjust their cigarette consumption to maintain brain nicotine levels. In humans, 70 to 80% of nicotine is metabolized to the inactive metabolite cotinine by the enzyme CYP2A6. CYP2A6 can also activate tobacco smoke procarcinogens [e.g., NNK, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone]. In initial studies we found that there was an under-representation of individuals carrying defective CYP2A6 alleles in a tobacco-dependent population, and that among smokers, those with deficient nicotine metabolism smoked fewer cigarettes. We have since reproduced this data in a prospective smoking study (400 male and female, heavy and light smokers) examining the role of the CYP2A6 genotype on carbon monoxide levels, plasma and urine nicotine and cotinine levels, and cigarette counts. We have also recently identified deletion and duplication variants in the CYP2A6 gene locus and have examined their impact on smoking. These data provide the impetus to examine how inhibition of CYP2A6 activity might be useful in a therapeutic context. Both kinetic and behavioral experiments in human smokers demonstrated that inhibiting CYP2A6 in vivo decreased nicotine metabolism and smoking behavior. This article summarizes the preliminary results from our studies.
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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) | 0.015 | 0.001 |
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".