Effects of psychedelics on opioid use disorder: a scoping review of preclinical studies
Bibliographic record
Abstract
The current opioid crisis has had an unprecedented public health impact. Approved medications for opioid use disorder (OUD) exist, yet their limitations indicate a need for innovative treatments. Limited preliminary clinical studies suggest specific psychedelics might aid OUD treatment, though most clinical evidence remains observational, with few controlled trials. This review aims to bridge the gap between preclinical findings and potential clinical applications, following PRISMA-ScR guidelines. Searches included MEDLINE, Embase, Scopus, and Web of Science, focusing on preclinical in vivo studies involving opioids and psychedelics in animals, excluding pain studies and those lacking control groups. Forty studies met criteria, covering both classic and non-classic psychedelics. Most studies showed that 18-methoxycoronaridine (18-MC), ibogaine, noribogaine, and ketamine could reduce opioid self-administration, alleviate withdrawal symptoms, and change conditioned place preference. However, seven studies (two on 2,5-dimethoxy-4-methylamphetamine (DOM), three on ibogaine, one on 18-MC, and one on ketamine) showed no improvement over controls. A methodological quality assessment rated most of the studies as having unclear quality. Interestingly, most preclinical studies are limited to iboga derivatives, which were effective, but these agents may have higher cardiovascular risk than other psychedelics under-explored to date. This review strengthens support for translational studies testing psychedelics as potential innovative targets for OUD. It also suggests clinical studies need to include a broader range of agents beyond iboga derivatives but can also explore several ongoing questions in the field, such as the mechanism of action behind the potential therapeutic effect, safety profiles, doses, and frequency of administrations needed.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".