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Project Details

Summary

  • Major depressive disorder (MDD) is the most common psychiatric dosorder worldwide, with a huge socio-economic impact.
  • Pharmacotherapy is the first-line treatment of choice, but approximately 30% of patients are classified as treatment resistant (TRD).
  • TRD is associated with specific clinical and biological features; however, taken individually, these signatures alone have limited power in predicting response.
  • The aim of the project is to develop of an innovative algorithm for the early identification of non-responders, who are more prone to develop TRD.

  • The PROMPT project is divided into two phases: In Phase 1, 300 patients with MDD have already been recruited, including 150 TRD/150 responders, which are considered “extremes” in terms of treatment response. A complete 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 to predict treatment response. In phase 2, a new cohort of 300 MDD patients will be recruited to assess the ability of the algorithm to correctly predict treatment outcome under real-world conditions. In addition, active patient involvement will be established to consider their perspectives and needs.

  • The project results will provide a new predictive tool for future use in the clinical practice, enabling better prevention and management of MDD treatment resistance.

 — Personalized —

— Participation —

— Prevention —

The development of an algorithm model that integrates clinical data (broad symptom assessment, treatment side effects, presence of childhood trauma) and omics features (genomic, transcriptomic and miRNomic profiling) for predicting treatment response in MDD patients will allow tailoring the right therapeutic strategy to the right person at the right time.

Patient empowerment is critical to shared decision-making (SMD), but has not been widely adopted in the field of psychiatry. In this project, active patient and clinician participation will be established as a critical component for a successful consideration of patients’ perspective and needs in the use of predictive tools for MDD treatment.

The development of an innovative predictive algorithm that predicts response to MDD treatments will enable early identification of non-respondes. The project addresses the major challenge of reducing the likelihood of developing a recurrent and chronic course of MDD, reducing the suffering of patients and their families, and reducing the enormous economic burden of MDD in European countries.

2-phase design:

  • The discovery phase 1 includes the multi-omics profiling of a retrospective cohort of 300 MDD patients.
  • Phase 2 consists of the prospective validation of the algorithm.