Psilocybin-assisted Treatment for Cannabis Use Disorder

This open-label, proof-of-concept trial (n=12) will evaluate the therapeutic potential of psilocybin in individuals with Cannabis Use Disorder (CUD). Participants will undergo a 12-week treatment course, including two psilocybin (25mg or 35mg) sessions with psychological support, followed by assessments at three and six months post-treatment.

The study, conducted by Johns Hopkins University, involves preparatory cognitive behavioural therapy (CBT) before administering a 25 mg dose of psilocybin in week 5 and either another 25 mg or an increased 35 mg dose in week 7. Follow-up integration meetings will take place within three days after each session, with weekly support continuing throughout the study.

Researchers will assess cannabis use reduction using self-reported data and biomarkers, alongside secondary measures such as depression and sleep quality. The study is set to run from November 2024 to September 2026.

Topic Addiction
Compound Psilocybin
Country United States of America
Visit trial
Status Recruiting
Results Published No
Start date 08 November 2024
End date 01 September 2026
Phase Phase I
Design Open
Type Interventional
Generation First
Participants 12
Sex All
Age 18- 99
Therapy Yes

Trial Details

This pilot study will evaluate the therapeutic potential of psilocybin in people with Cannabis Use Disorder (CUD). This study will examine the impact of psilocybin treatment on cannabis use and related variables in 12 people with CUD. This is an open-label proof-of-concept trial in which participants will complete a 12-week course of study treatment including two psilocybin sessions with psychological support, and follow-up assessments 3 and 6 months after the first psilocybin session.

Trial Number NCT06660381

Sponsors & Collaborators

Johns Hopkins University
Johns Hopkins University (Medicine) is host to the Center for Psychedelic and Consciousness Research, which is one of the leading research institutes into psychedelics. The center is led by Roland Griffiths and Matthew Johnson.

Data attribution

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