Drugs and genotypes: how pharmacogenetic information could improve smoking cessation treatment
Bibliographic record
Abstract
Current smoking cessation treatments are not optimal as the long-term abstinence rates are low. Nicotine replacement therapy and bupropion are the only pharmacotherapies widely available to smokers and there is a need to improve current cessation treatments and to develop new drug therapies. Our goal is to use pharmacogenetic information to improve smoking cessation treatments. Candidate genes include those involved in central mechanisms (such as genes encoding the nicotinic acetylcholine receptors, dopamine receptors, dopamine transporters and opioid receptors) and peripheral mechanisms (such as genes encoding the drug-metabolizing enzymes CYP2A6 and CYP2B6). Pharmacogenetics could be used to improve current smoking cessation treatments through two general approaches. The first would be to identify novel drug targets based on genetic variants that are associated with a natural protection against nicotine dependence, decreased smoking behaviour and/or increased smoking cessation. This knowledge could be used to develop drugs that can mimic these advantages, reducing the risk for acquisition, block the rewarding effects of smoking, decreasing the amount smoked and increasing cessation. The second would be to identify smokers with particular genetic variants who are likely to respond better to one treatment (or dose) versus another and to individualize their treatment to maximize long-term abstinence. This review will highlight examples of how pharmacogenetic information from central and peripheral candidate genes could contribute to improving smoking cessation outcomes by these two approaches.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".