Gene–gene interactions between CYP2B6 and CYP2A6 in nicotine metabolism
Bibliographic record
Abstract
OBJECTIVES: CYP2A6 is the major enzyme involved in nicotine metabolism, yet large interindividual variations in the rate of nicotine metabolism exist within groups of individuals having the same CYP2A6 genotype. We investigated the influence of genetic variation in another potential nicotine-metabolizing enzyme, CYP2B6, and its interaction with CYP2A6, on the metabolism of nicotine. METHODS: Two hundred and twelve healthy Caucasian adult twin volunteers underwent an intravenous infusion of stable isotope-labeled nicotine and its major metabolite, cotinine, for characterization of pharmacokinetic and metabolism phenotypes. Five CYP2B6 genetic polymorphisms causing amino acid substitutions (R22C, Q172 H, S259R, K262R, and R487C) were analyzed. RESULTS: We observed that the CYP2B6*6 haplotype (defined as having both Q172 H and K262R variants) was associated with faster nicotine and cotinine clearance, and that such associations were more prominent among individuals having decreased-activity CYP2A6 genotypes. Statistically significant interactions between CYP2B6 and CYP2A6 genotypes were observed (P<0.003 for nicotine clearance and P<0.002 for cotinine clearance). CONCLUSIONS: Our results indicate that CYP2B6 genetic variation is associated with the metabolism of nicotine and cotinine among individuals with decreased CYP2A6 activity. Further investigation of the roles of CYP2B6 and the interaction between CYP2B6 and CYP2A6 genotypes in mediating nicotine dependence and tobacco-related diseases is merited.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".