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This psychiatry month in review spotlights new research on detecting and treating major depressive disorder with AI, FDA’s Complete Response Letter for roluperidone, and more.
From data finding memories triggered by positive smells alleviate depressive symptoms to artificial intelligence (AI) weaving its way into psychiatry clinical trials, February was a big month for psychiatry research. Studies published in February present promising initial data that could transform psychiatry treatment in the future.
This month in review highlights everything from FDA news, studies experimenting with AI devices, and how antidepressant dispensing rates differed between female and male adolescents.
On February 27, 2024, Minerva Neurosciences announced receipt of a Complete Response Letter from the US Food and Drug Administration (FDA) for roluperidone in treating negative symptoms in schizophrenia—a letter rejecting the drug’s approval. The letter stated how the submission had insufficient evidence to prove effectiveness as there was a lack of data on both concomitant antipsychotic administration and clinical meaningfulness of change in negative symptoms.
Additionally, the submitted safety database did not include enough subjects. According to Minerva Neurosciences, the FDA said they must submit ≥ 1 additional positive, adequate, and well-controlled study to support the safety and efficacy of roluperidone.
Tested in a clinical trial, a brain scan equipped with an AI algorithm predicted within a week if the antidepressant, sertraline, would work to reduce depressive symptoms. Investigators conducted a secondary analysis of the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite, double-blind, placebo-controlled randomized trial.
The analysis found 1/3 of patients would respond to sertraline and 2/3 would not, suggesting the brain scan can help prevent many unnecessary prescriptions of sertraline which not only wastes a lot of time and money but averts people from experiencing unpleasant adverse events. Overall, the brain scan algorithm has a balanced accuracy of 68%.
Dartmouth investigators developed the first depression-detecting smartphone app using AI that captures a photo with the front camera the moment someone unlocks their phone and identifies depression based on facial expressions alone. Along with AI, the app, MoodCapture, uses facial-image processing software.
Investigators tested the app on people already diagnosed with major depressive disorder, and MoodCapture accurately diagnosed 75% of participants with early symptoms of depression. Due to the promising data, investigators surmise the smartphone app could be released to the public in 5 years but may present ethical and privacy concerns to it must be necessary to ensure user consent, data security, and transparency of using personal data.
A study saw an increase in adolescents and young adults taking antidepressants during the COVID-19 pandemic, particularly among females. The dispensing rate for adolescents and young adults aged 12 – 25 years rose nearly 64% faster after March 2020, and the pandemic was linked to a slope increase of 10.8 per month in the dispensing rate.
Females drove the rise in the antidepressant dispensing rate during the pandemic; after March 2020, the rate increased 130% faster among female adolescents aged 12 – 17 years and 60% faster among female young adults aged 18 – 25 years. In contrast, the antidepressant rate had little change for male young adults and declined for male adolescents. Investigators suggested this could be due to male adolescents skipping yearly physicals or other healthcare appointments during the pandemic.