Emerging pharmacological strategies for the treatment of cannabis use disorder
Bibliographic record
Abstract
INTRODUCTION: Cannabis use disorder (CUD) is a growing global health concern, with limited pharmacological treatments currently available despite increasing prevalence and legalization trends. AREAS COVERED: This review explores the landscape of pharmacotherapies for CUD, including both repurposed agents and emerging investigational compounds. We summarize findings from recent systematic reviews and meta-analyses, with attention to mechanisms of action and clinical relevance. Agents discussed include gabapentin, N-acetylcysteine, synthetic cannabinoids, fatty acid amide hydrolase (FAAH) inhibitors, orexin receptor antagonists, and psychedelics. A narrative approach was used, informed by targeted searches of PubMed, Google Scholar, and clinical trial registries from 2000 to 2025, focusing on human studies, randomized trials, and meta-analyses relevant to pharmacologic management of CUD. EXPERT OPINION: The pharmacologic treatment of CUD is in its early stages, with no approved agents and modest efficacy demonstrated to date. Novel compounds targeting endocannabinoid tone and motivational circuits show promise, but significant research is still needed. Future progress depends on better integration with behavioral care, trial stratification by clinical phenotype, and increased investment in translational research to move beyond withdrawal symptom management toward sustained recovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".