SLC Neurotransmitter Transporters as Therapeutic Targets for Alcohol Use Disorder: A Narrative Review
Bibliographic record
Abstract
Alcohol use disorder (AUD) is 1 of the most prevalent of all substance use disorders and contributes significantly to global disease burden. Despite its prevalence, <10% of individuals with AUD receive treatment. A significant barrier to receiving treatment is a lack of effective pharmacotherapies. While 3 medications have been approved by the FDA for AUD (disulfiram, acamprosate, naltrexone), their efficacy remains low. Furthermore, a number of undesirable side effects associated with these drugs further reduce patient compliance. Thus, research into new effective pharmacotherapies for AUD is warranted. Due to their involvement in regulating synaptic neurotransmitter levels, solute carrier (SLC) transporters could be targeted for developing effective treatment strategies for AUD. Indeed, a number of studies have shown beneficial reductions in alcohol consumption through the use of drugs that target transporters of dopamine, serotonin, glutamate, glycine, and GABA. The purpose of this narrative review is to summarize preclinical and clinical studies from the last 2 decades targeting SLC neurotransmitter transporters for the treatment of AUD. Limitations, as well as future directions for expanding this field, are also discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".