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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".