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Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field?

Major depression, affecting an estimated 350 million people worldwide, poses a serious social and economic threat to modern societies. There are currently two major problems calling for innovative research approaches, namely, the absence of biomarkers predicting antidepressant response and the lack...

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Detalles Bibliográficos
Autores principales: Labermaier, Christiana, Masana, Mercè, Müller, Marianne B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3774965/
https://www.ncbi.nlm.nih.gov/pubmed/24167346
http://dx.doi.org/10.1155/2013/984845
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author Labermaier, Christiana
Masana, Mercè
Müller, Marianne B.
author_facet Labermaier, Christiana
Masana, Mercè
Müller, Marianne B.
author_sort Labermaier, Christiana
collection PubMed
description Major depression, affecting an estimated 350 million people worldwide, poses a serious social and economic threat to modern societies. There are currently two major problems calling for innovative research approaches, namely, the absence of biomarkers predicting antidepressant response and the lack of conceptually novel antidepressant compounds. Both, biomarker predicting a priori whether an individual patient will respond to the treatment of choice as well as an early distinction of responders and nonresponders during antidepressant therapy can have a significant impact on improving this situation. Biosignatures predicting antidepressant response a priori or early in treatment would enable an evidence-based decision making on available treatment options. However, research to date does not identify any biologic or genetic predictors of sufficient clinical utility to inform the selection of specific antidepressant compound for an individual patient. In this review, we propose an optimized translational research strategy to overcome some of the major limitations in biomarker discovery. We are confident that early transfer and integration of data between both species, ideally leading to mutual supportive evidence from both preclinical and clinical studies, are most suitable to address some of the obstacles of current depression research.
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spelling pubmed-37749652013-10-01 Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field? Labermaier, Christiana Masana, Mercè Müller, Marianne B. Dis Markers Review Article Major depression, affecting an estimated 350 million people worldwide, poses a serious social and economic threat to modern societies. There are currently two major problems calling for innovative research approaches, namely, the absence of biomarkers predicting antidepressant response and the lack of conceptually novel antidepressant compounds. Both, biomarker predicting a priori whether an individual patient will respond to the treatment of choice as well as an early distinction of responders and nonresponders during antidepressant therapy can have a significant impact on improving this situation. Biosignatures predicting antidepressant response a priori or early in treatment would enable an evidence-based decision making on available treatment options. However, research to date does not identify any biologic or genetic predictors of sufficient clinical utility to inform the selection of specific antidepressant compound for an individual patient. In this review, we propose an optimized translational research strategy to overcome some of the major limitations in biomarker discovery. We are confident that early transfer and integration of data between both species, ideally leading to mutual supportive evidence from both preclinical and clinical studies, are most suitable to address some of the obstacles of current depression research. Hindawi Publishing Corporation 2013 2013-07-21 /pmc/articles/PMC3774965/ /pubmed/24167346 http://dx.doi.org/10.1155/2013/984845 Text en Copyright © 2013 Christiana Labermaier et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Labermaier, Christiana
Masana, Mercè
Müller, Marianne B.
Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field?
title Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field?
title_full Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field?
title_fullStr Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field?
title_full_unstemmed Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field?
title_short Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field?
title_sort biomarkers predicting antidepressant treatment response: how can we advance the field?
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3774965/
https://www.ncbi.nlm.nih.gov/pubmed/24167346
http://dx.doi.org/10.1155/2013/984845
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