Nicotine Dependence Pharmacogenetics: Role of Genetic Variation in Nicotine-Metabolizing Enzymes
Bibliographic record
Abstract
Nicotine-dependence pharmacogenetics research is an emerging field, and a number of studies have begun to characterize the clinical relevance and predictive power of genetic variation in drug-metabolizing enzymes and drug target genes for response to medication. The present paper focuses on evidence for the role of nicotine-metabolizing enzymes in smoking behavior and response to treatment. Nicotine metabolism is mediated primarily by cytochrome P450 2A6 (CYP2A6). Genetic variation in the CYP2A6 gene has been linked with several smoking behavior phenotypes. Individuals who carry null or reduced activity alleles for CYP2A6 smoke fewer cigarettes per day, are less dependent on nicotine, and may have an easier time quitting smoking. A phenotypic measure of CYP2A6 enzyme activity, defined as the ratio of the nicotine metabolites 3'hydroxycotinine/cotinine, also predicts successful quitting with the transdermal nicotine patch, and counseling alone. Faster metabolizers of nicotine respond more poorly to these treatments; however, they may be excellent candidates for non-nicotine therapies, such as bupropion. Inherited variation in the CYP2B6 enzyme is also associated with response to bupropion treatment and counseling alone for smoking cessation. Inhibition of the CYP2A6 enzyme to slow nicotine metabolism is a promising approach to increase nicotine availability and potentially reduce harm from tobacco smoking.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".