Psilocybin Therapy for Treatment Resistant Depression: Prediction of Clinical Outcome by Natural Language Processing
OpenAlex – September 30, 2022
Source: OpenAlex
Summary
Predicting long-term mood improvement from psilocybin for major depressive episode is now 85-88% accurate. This breakthrough in clinical psychology utilizes artificial intelligence and machine learning, specifically logistic regression, to analyze patient-therapist dialogue from psychological support sessions. Focusing on COMP360, a synthetic psilocybin alkaloid, this advancement in medicine and psychedelics and drug studies offers personalized prognostication across the population. Such insights are vital for mental health research topics, optimizing care for individuals with severe depression.
Abstract
Background: Therapeutic administration of psychedelic drugs has shown significant potential in historical accounts and in recent clinical trials in the treatment of depression and other mood disorders. A recent randomized double-blind phase-IIb study demonstrated the safety and efficacy of COMP360, COMPASS Pathways’ proprietary synthetic formulation of psilocybin, in participants with treatment resistant depression. While promising, the treatment works for a portion of the population and early prediction of outcome is a key objective.Methods: Transcripts were made from audio recordings of the psychological support session between participant and therapist one day post COMP360 administration. A zero-shot machine learning classifier based on the BART large language model was used to compute two-dimensional sentiment (valence and arousal) for the participant and therapist from the transcript. These scores, combined with the Emotional Breakthrough Index (EBI) and treatment arm were used to predict treatment outcome as measured by MADRS scores. Code and data are available at https://github.com/compasspathways/Sentiment2DResults: Two multinomial logistic regression models were fit to predict responder status at week 3 and through week 12. Cross-validation of these models resulted in 85% and 88% accuracy and AUC values of 88% and 85%.Conclusions: A machine learning algorithm using NLP and EBI accurately predicts long term patient response, allowing rapid prognostication of personalized response to psilocybin treatment and insight into therapeutic model optimization. Further research is required to understand if language data from earlier stages in the therapeutic process hold similar predictive power.