MindMed Reveals Results Of A Study On Personalized MDMA Dosing

Psychedelic medicine biotech company MindMed MMED MMEDF MMQ reported Monday the results of a study on MDMA dosing optimization utilizing personalized medicine.

The study was conducted at the University Hospital Basel Liechti Lab, in Basel, Switzerland. The company said it offers “the first scientific” information for predicting reactions to MDMA and optimizing dosing.

These results could help take full advantage of the medical benefits of the treatment, and minimize adverse reactions.

Key Findings

• Predictors known prior to dosing, such as body weight, sex, genetics, age, personality trait measures and mood before treatment could help with MDMA dose optimization.
• Genetic testing of the drug-metabolizing enzyme CYP2D6 could also help with adjusting the dosing.
• Patients with a bigger personality trait of “openness to a new experience” displayed more feelings of closeness and positive drug effects acutely induced by MDMA.
• Those patients who were more anxious and depressed prior to MDMA treatment experienced more anxiety after MDMA administration.

In conclusion, personalizing MDMA dosing could help optimize the acute MDMA experience.

“To our knowledge, this is the first study on personalized MDMA treatment including pharmacogenetics, personality assessments, and psychometrics in one analysis,” Dr. Matthias Liechti stated. “The method used allowed us to evaluate the relative importance of the different predictors. Very clearly, the dose of MDMA has the greatest impact on the acute subjective response. However, genetics, personality, and mood before MDMA use also moderated the response to MDMA use.”

Liechti further explained that because the studies were conducted in a safe environment and in healthy volunteers with mostly no prior MDMA experience, more data in patients is required

MindMed Executive President, Dr. Miri Halperin Wernli added, “With the promise of advances in psychedelic medicine we are aiming to step away from the rather rigid and inflexible drug-centric approach of ‘one size fits all’ and start leveraging data and machine learning to help health professionals and patients engage new personalized therapies in the treatment paradigms.

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