AI could help us to work out what psychedelic drugs to do our brains by analyzing the words used in trip reports

by

Randomized clinical trials, which involve giving some participants a drug, others a placebo, and comparing the effects of both, are considered the gold standard in such studies.  

But such trials are slow and expensive, and tend to involve only a small number of participants. “[It takes] multiple years, costs a seven-digit amount of money, [and] the ethics approvals take forever,” says Bzdok.  

Instead, his team used natural language processing to assess 6,850 written accounts of hallucinogenic drug use. Each account was written by a person who took one of 27 drugs—including ketamine, MDMA, LSD and psilocin—in a real-world setting rather than as part of a lab-based experiment. The accounts were accessed from the website of Erowid, a member-supported drug information organization. 

Bzdok’s team then integrated this data with records of which receptors in the brain each drug is known to interact with. Together, these steps allow the team to identify which neurotransmitter receptors are linked to words associated with specific drug experiences.  

For example, words linked to mystical experiences, such as “space,” “universe,” “consciousness,” “dimension,” and “breakthrough” were associated with drugs that bind to specific dopamine, serotonin, and opioid receptors.  

Bzdok says the approach could provide new starting points for drug development. In theory, drugs that are designed to target these receptors should elicit specific aspects of psychedelic drug experiences, says Bzdok, whose work was published today in the journal Science Advances.

Frederick Barrett, a psychedelics neuroscientist at Johns Hopkins University in Baltimore, isn’t wholly convinced. “Folks don’t always know [what drug they’re taking],” he says. “Doses are not always well calibrated in the real world, and there’s a lot more variation that goes into real-world experiences than it may be possible to even fully recognize.”