Characteristics of Ongoing Clinical Trials for Cocaine Use Disorder Registered on Global Clinical Trial Databases
Bibliographic record
Abstract
OBJECTIVES: Cocaine use disorder (CUD) affects 1.4 million people in the United States, yet no FDA-approved treatments exist. In 2023, the Food and Drug Administration (FDA) released a draft guideline on treatments for stimulant use disorders, providing direction for trial design, outcomes, and population selection. In this study, we aimed to review ongoing clinical trials for CUD and assess their alignment with the FDA's recommendations. METHODS: We conducted a systematic search of the 6 major clinical trial databases (United States, Australia, Canada, Iran, Netherlands, and Switzerland) to identify ongoing interventional studies for CUD. We included trials evaluating pharmacological, behavioral, device-based, and mixed treatments. We extracted data on intervention type, target population, study design, duration, and primary outcomes. Trials were assessed for alignment with 5 key FDA recommendations, including trial duration, use of both self-reported and biological outcome measures, randomization, placebo control, and double blinding. RESULTS: In total, 38 trials were identified, primarily from the United States (32). Most trials were randomized: 36 (94.7%), while 21 (55.3%) trials had combined endpoints or a 3-month minimum duration. Only 7 trials (18.4%) met all 5 key FDA recommendations. New treatment approaches were identified, including psilocybin and the dAd5GNE vaccine, as well as digital platforms for behavioral therapies. CONCLUSIONS: A variety of promising treatments for CUD are under investigation. However, many trials fall short of current FDA design recommendations. Improved adherence to regulatory guidance and stronger collaboration between researchers and regulators will be essential to advance effective, scalable treatments for CUD.
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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 | 0.071 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.020 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".