Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity*1
Bibliographic record
Abstract
BACKGROUND: Nicotine and a variety of other drugs and toxins are metabolized by cytochrome P450 (CYP) 2A6. Our objective was to evaluate the use of oral nicotine with measurement of the trans-3'-hydroxycotinine (3HC)/cotinine (COT) metabolite ratio as a noninvasive probe of CYP2A6 activity. METHODS: Sixty-two healthy volunteers received an oral solution of deuterium-labeled nicotine (2 mg) and its metabolite cotinine (10 mg). Plasma nicotine and plasma and saliva cotinine and 3HC concentrations were measured over time. RESULTS: The 3HC/COT ratio derived from deuterium-labeled cotinine, measured in either plasma (2-8 hours after administration) or saliva (at 6 hours), was strongly correlated with the oral clearance of nicotine (r = 0.76-0.83, depending on the time of measurement). The 6-hour 3HC/COT ratio from nicotine derived from tobacco in 14 smokers was highly correlated with the ratio derived from deuterium-labeled nicotine (r = 0.88) and was also highly correlated with the oral clearance of nicotine (r = 0.90). Two subjects homozygous for inactive CYP2A6 alleles produced no 3HC, confirming the specificity of the metabolite ratio. The 3HC/COT ratio was also highly correlated with the clearance and half-life of cotinine, consistent with the fact that cotinine is also primarily metabolized by CYP2A6. CONCLUSIONS: The 3HC/COT ratio derived from nicotine either administered as a probe drug or from tobacco use, measured in either plasma or saliva, is highly correlated with the oral clearance of nicotine. The ratio appears to be a useful noninvasive marker of the rate of nicotine metabolism (which is important in studying nicotine addiction and smoking behavior), as well as a general marker of CYP2A6 activity (which is important in studying drug and toxin metabolism).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".