The development of an algorithm model thatintegrates clinical data (wide range of symptomatology assessment, treatment side effects, presence of childhood trauma) and -omics features (genomic, transcriptomic and miRNomic profiling) for the prediction of treatment response in MDD patients will enable tailoring the right therapeutic strategy for the right person at the right time.
Patient empowerment is crucial to shared decision-making but to date it is not widely adopted in the field of psychiatry. In this project, active patients’ and clinicians’ participation will be established as a critical component for successful consideration of patients’ perspective and needs on the use of predictive tools for MDD treatment
The development of an innovative predictive algorithm that predicts response to MDD treatments will enable early detection of non-responder patients. The project addresses the great challenge to reduce the probability of developing a recurrent and chronic course of MDD, to decrease suffering of patients and their families as well as to reduce the enormous economic burden produced by MDD in European countries.
Major depressive disorder (MDD) is the most common psychiatric disease worldwide, with huge socio-economic impacts. Pharmacotherapy represents the first-line therapeutic choice, but about 30% of patients are classified as resistant to treatment (TRD). TRD is associated with specific clinical and biological features; however, taken individually, these signatures have limited power in response prediction. The project’s aim is the development of an innovative algorithm for the early detection of non-responder patients, more prone to later develop TRD. Phase 1 will involve 300 patients with MDD already recruited, including 150 TRD/150 responders, considered as “extremes” in relation to treatment response. A full clinical assessment will be performed, together with a comprehensive molecular evaluation (genomic, transcriptomic and miRNomic profiling). An algorithm integrating all these data will be developed in order to predict response to therapy. In phase 2, a new cohort of 300 MDD patients will be recruited to assess, in real-world conditions, the ability of the algorithm to correctly predict treatment outcome. Moreover, an active participation of patients will be established to consider their perspectives and needs. Project results will provide a new predictive tool for future use in the clinical practice, enabling a better prevention and management of MDD treatment resistance
The project’s aims include two main objectives: 1) the development and testing of an innovative algorithm, based on a precision medicine approach, for the prediction of MDD treatment outcome. 2) The development of new tools to establish an active participation of patients and clinicians, considering their perspectives and needs as a critical component for the successful application of predictive tools for MDD treatment in clinical practice.