Predicting Placebo Response in Major Depressive Disorder on Clinical Trials Data Using Artificial Intelligence (AI)Machine Learning (ML)

Lead Investigator: Mariam Khayretdinova, Brainify.AI
Title of Proposal Research: Predicting Placebo Response in Major Depressive Disorder on Clinical Trials Data Using Artificial Intelligence (AI)Machine Learning (ML)
Vivli Data Request: 9844
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Depression is a complex condition that affects over 350 million people worldwide. It can be difficult to diagnose and treat because it manifests differently in everyone, making it a challenge to find the right treatment. Some people don’t improve with standard treatments, a situation known as treatment-resistant depression (TRD), affecting about 30% of those with Major Depressive Disorder (MDD). This highlights the need for treatments that are tailored to the individual’s specific needs. Now, let’s talk about the placebo effect. This happens when someone experiences an improvement in their condition after receiving a treatment that has no active ingredients, simply because they believe they’re receiving a real treatment. It’s fascinating because it shows how powerful our expectations and beliefs can be in influencing our health. In clinical trials for new antidepressants, the placebo effect can make it hard to tell if the actual drug is working, since many people feel better just because they think they’re being treated.

Researchers use electroencephalography (EEG), a method that records the electrical activity of the brain, to find clues or “biomarkers” that can help predict how a person might respond to a treatment. By analyzing EEG data with machine learning, scientists can identify patterns that might indicate whether someone will benefit from a specific treatment. This approach is part of a bigger movement towards precision medicine in psychiatry, aiming to provide personalized treatments based on individual characteristics, such as their brain activity patterns. Our research focuses on refining these machine learning models to better predict placebo responses and identify effective treatments for depression. By understanding and reducing the impact of the placebo effect, and recognizing the unique aspects of each patient’s condition, we hope to advance the development of more effective antidepressants. This work is crucial as it could lead to better, more targeted treatment options for those who have not found relief with current therapies, significantly improving patient care and outcomes in depression treatment.

Requested Studies:

Duloxetine Versus Placebo in the Prevention of Recurrence of Major Depressive Disorder
Data Contributor: Lilly
Study ID: NCT00105989
Sponsor ID: 8606