Optimizing and Predicting Antidepressant Efficacy in Patients with Major Depressive Disorder Using Multi-Omics Analysis and the Opade AI Prediction Tools

Giulio Corrivetti, Francesco Monaco, Annarita Vignapiano, Alessandra Marenna*, Kaia Palm, Salvador Fernández-Arroyo, Eva Frigola-Capell, Volker Leen, Oihane Ibarrola, Burak Amil, Mattia Marco Caruson, Lorenzo Chiariotti, Maria Alejandra Palacios-Ariza, Pieter J. Hoekstra, Hsin Yin Chiang, Alexandru Floareș, Andrea Fagiolini, Alessio Fasano

*Corresponding author voor dit werk

OnderzoeksoutputAcademicpeer review

2 Citaten (Scopus)
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Samenvatting

According to the World Health Organization (WHO), major depressive disorder (MDD) is the fourth leading cause of disability worldwide and the second most common disease after cardiovascular events. Approximately 280 million people live with MDD, with incidence varying by age and gender (female to male ratio of approximately 2:1). Although a variety of antidepressants are available for the different forms of MDD, there is still a high degree of individual variability in response and tolerability. Given the complexity and clinical heterogeneity of these disorders, a shift from “canonical treatment” to personalized medicine with improved patient stratification is needed. OPADE is a non-profit study that researches biomarkers in MDD to tailor personalized drug treatments, integrating genetics, epigenetics, microbiome, immune response, and clinical data for analysis. A total of 350 patients between 14 and 50 years will be recruited in 6 Countries (Italy, Colombia, Spain, The Netherlands, Turkey) for 24 months. Real-time electroencephalogram (EEG) and patient cognitive assessment will be correlated with biological sample analysis. A patient empowerment tool will be deployed to ensure patient commitment and to translate patient stories into data. The resulting data will be used to train the artificial intelligence/machine learning (AI/ML) predictive tool.

Originele taal-2English
Artikelnummer658
Aantal pagina's15
TijdschriftBrain Sciences
Volume14
Nummer van het tijdschrift7
DOI's
StatusPublished - jul.-2024

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