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Record W2049901733 · doi:10.1038/clpt.2013.159

Genetic and Pharmacokinetic Determinants of Response to Transdermal Nicotine in White, Black, and Asian Nonsmokers

2013· article· en· W2049901733 on OpenAlexafffund
Delia Dempsey, Gideon St. Helen, Peyton Jacob, Rachel F. Tyndale, Neal L. Benowitz

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNicotinic Acetylcholine Receptors Study
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
FundersNational Center for Research ResourcesNIH Clinical CenterNational Institutes of HealthNational Cancer InstituteNational Institute on Drug AbuseCanadian Institutes of Health ResearchUniversity of California, San Francisco
KeywordsNicotineCYP2A6PharmacokineticsPharmacologyCotinineMedicineToxicityClinical pharmacologyPhysiologyInternal medicineMetabolism

Abstract

fetched live from OpenAlex

The aim of the study was to examine genetic, pharmacokinetic, and demographic factors that influence sensitivity to nicotine in never-smokers. Sixty never-smokers, balanced for gender and race (white, black, and Asian), wore 7-mg nicotine skin patches for up to 8 h. Serial plasma nicotine concentrations and subjective and cardiovascular effects were measured, and genetic variation in the CYP2A6 gene, encoding the primary enzyme responsible for nicotine metabolism, was assessed. Nicotine toxicity requiring patch removal developed in nine subjects and was strongly associated with rate of increase and peak concentrations of plasma nicotine. Toxicity and subjective and cardiovascular effects of nicotine were associated with the presence of reduced-function CYP2A6 alleles, presumably reflecting slow nicotine metabolic inactivation. This study has implications for understanding individual differences in responses to nicotine medications, particularly when they are used for treating medical conditions in nonsmokers, and possibly in vulnerability to developing nicotine dependence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.365
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2013
Admission routes2
Has abstractyes

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