Autor: |
Bernhard T. Baune, Alessandra Minelli, Bernardo Carpiniello, Martina Contu, Jorge Domínguez Barragán, Chus Donlo, Ewa Ferensztajn-Rochowiak, Rosa Glaser, Britta Kelch, Paulina Kobelska, Grzegorz Kolasa, Dobrochna Kopeć, María Martínez de Lagrán Cabredo, Paolo Martini, Miguel-Angel Mayer, Valentina Menesello, Pasquale Paribello, Júlia Perera Bel, Giulia Perusi, Federica Pinna, Marco Pinna, Claudia Pisanu, Cesar Sierra, Inga Stonner, Viktor T. H. Wahner, Laura Xicota, Johannes C. S. Zang, Massimo Gennarelli, Mirko Manchia, Alessio Squassina, Marie-Claude Potier, Filip Rybakowski, Ferran Sanz, Mara Dierssen |
Jazyk: |
angličtina |
Rok vydání: |
2024 |
Předmět: |
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Zdroj: |
Frontiers in Psychiatry, Vol 14 (2024) |
Druh dokumentu: |
article |
ISSN: |
1664-0640 |
DOI: |
10.3389/fpsyt.2023.1279688 |
Popis: |
Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. Here we describe the methodology of the PROMPT project that aims at the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extremes phenotypes of response. A deep clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and sex-related data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients’ empowerment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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