Opioid Antagonists and the A118G Polymorphism in the μ-Opioid Receptor Gene: Effects of GSK1521498 and Naltrexone in Healthy Drinkers Stratified by OPRM1 Genotype
Bibliographic record
Abstract
The A118G single-nucleotide polymorphism (SNP rs1799971) in the μ-opioid receptor gene, OPRM1, has been much studied in relation to alcohol use disorders. The reported effects of allelic variation at this SNP on alcohol-related behaviors, and on opioid receptor antagonist treatments, have been inconsistent. We investigated the pharmacogenetic interaction between A118G variation and the effects of two μ-opioid receptor antagonists in a clinical lab setting. Fifty-six overweight and moderate-heavy drinkers were prospectively stratified by genotype (29 AA homozygotes, 27 carriers of at least 1 G allele) in a double-blind placebo-controlled, three-period crossover design with naltrexone (NTX; 25 mg OD for 2 days, then 50 mg OD for 3 days) and GSK1521498 (10 mg OD for 5 days). The primary end point was regional brain activation by the contrast between alcohol and neutral tastes measured using functional magnetic resonance imaging (fMRI). Secondary end points included other fMRI contrasts, subjective responses to intravenous alcohol challenge, and food intake. GSK1521498 (but not NTX) significantly attenuated fMRI activation by appetitive tastes in the midbrain and amygdala. GSK1521498 (and NTX to a lesser extent) significantly affected self-reported responses to alcohol infusion. Both drugs reduced food intake. Across all end points, there was less robust evidence for significant effects of OPRM1 allelic variation, or for pharmacogenetic interactions between genotype and drug treatment. These results do not support strong modulatory effects of OPRM1 genetic variation on opioid receptor antagonist attenuation of alcohol- and food-related behaviors. However, they do support further investigation of GSK1521498 as a potential therapeutic for alcohol use and eating disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".