Novel and established CYP2A6 alleles impair in vivo nicotine metabolism in a population of Black African descent
Bibliographic record
Abstract
Cytochrome P450 2A6 (CYP2A6) is a human enzyme best known for metabolizing tobacco-related compounds, such as nicotine, cotinine (COT), and nitrosamine procarcinogens. CYP2A6 genetic variants have been associated with smoking status, cigarette consumption, and tobacco-related cancers. Our objective was to functionally characterize four nonsynonymous CYP2A6 sequence variants with respect to their haplotype, allele frequency, and association with in vivo CYP2A6 activity. In vivo, nicotine was administered orally to 281 volunteers of Black African descent. Blood samples were collected for kinetic phenotyping and CYP2A6 genotyping. In vitro, nicotine C-oxidation catalytic efficiencies of heterologously expressed variant enzymes were assessed. The four uncharacterized sequence variants were found in seven novel alleles CYP2A6(*)24A&B ; (*)25, (*)26, (*)27, and *28A&B, most were associated with impaired in vivo CYP2A6 activity. Nicotine metabolism groupings, based on the in vivo data of variant alleles, were created. Mean trans-3'-hydroxycotinine/cotinine (3HC/COT) differed (P<0.001) between normal (100%), intermediate (64%), and slow (40%) groups. Systemic exposure to nicotine following oral administration also differed (P<0.001) between normal (100%), intermediate (139%), and slow (162%) metabolism groups. In addition, alleles of individuals with unusual phenotype-genotype relationships were sequenced, resulting in the discovery of five novel uncharacterized alleles and at least one novel duplication allele. A total of 7% of this population of Black African descent had at least one of the eight novel characterized alleles and 29% had at least one previously established allele. These findings are important for increasing the accuracy of association studies between CYP2A6 genotype and behavioral, disease, or pharmacological phenotypes.
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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 | 0.001 |
| 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) | 0.002 | 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".