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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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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. |
format | Online Article Text |
id | pubmed-3774965 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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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