Pharmacogenetics of alcohol, nicotine and drug addiction treatments
Bibliographic record
Abstract
The numerous premature deaths, medical complications and socio-economic repercussions of drug and alcohol addiction suggest that improvements in treatment strategies for addictive disorders are warranted. The use of pharmacogenetics to predict response to medication, side effects and appropriate dosages is relatively new in the field of drug addiction. However, increasing our understanding of the genetic factors influencing these processes may improve the treatment of addiction in the future. We examined the available scientific literature on pharmacogenetic advancements in the field of drug addiction with a focus on alcohol and tobacco to provide a summary of genes implicated in the effectiveness of pharmacotherapy for addiction. In addition, we reviewed pharmacogenetic research on cocaine and heroin dependence. Thus far, the most promising results were obtained for polymorphisms in the OPRM1 and CYP2A6 genes, which have been effective in predicting clinical response to naltrexone in alcoholism and nicotine replacement therapy in smoking, respectively. Opinions differ as to whether pharmacogenetic testing should be implemented in the clinic at this time because clinical utility and cost-effectiveness require further investigation. However, the data summarized in this review demonstrate that pharmacogenetic factors play a role in response to addiction pharmacotherapy and have the potential to aid in the personalization of addiction treatments. Such data may lead to improved cessation rates by allowing physicians to select medications for individuals based, at least in part, on genetic factors that predispose to treatment success or failure rather than on a trial and error basis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".