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Using neurocognitive models to optimise the treatment of depression
ABSTRACT: Conventional antidepressants, such as SSRIs, are an effective treatment for many patients with depression. However, for a significant proportion of patients SSRIs either lack efficacy or are poorly tolerated. Even when SSRIs are effective in treating mood symptoms, there are often residual...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Cambridge University Press
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417918/ http://dx.doi.org/10.1192/j.eurpsy.2023.40 |
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author | Murphy, S. |
author_facet | Murphy, S. |
author_sort | Murphy, S. |
collection | PubMed |
description | ABSTRACT: Conventional antidepressants, such as SSRIs, are an effective treatment for many patients with depression. However, for a significant proportion of patients SSRIs either lack efficacy or are poorly tolerated. Even when SSRIs are effective in treating mood symptoms, there are often residual symptoms that are not well treated, including cognitive impairment and anhedonia. The development of novel treatment for depression is particularly challenging given the limited predictive validity of animal models. Human neurocognitive models of antidepressant action can help to bridge the translational gap and allow rapid investigation of novel compounds in healthy volunteers and people with depression. In this talk, using the 5-HT(4) receptor as an example of a novel target of interest, I will outline how these objective neurocognitive models can be used as a translational tool to understand antidepressant treatment mechanisms, guide treatment selection and test novel putative antidepressants early in development. DISCLOSURE OF INTEREST: S. Murphy Grant / Research support from: Zogenix, UCB, Janssen, Consultant of: Zogenix, Sumitomo Danippon, Janssen, UCB, Speakers bureau of: Zogenix |
format | Online Article Text |
id | pubmed-10417918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-104179182023-08-12 Using neurocognitive models to optimise the treatment of depression Murphy, S. Eur Psychiatry Abstract ABSTRACT: Conventional antidepressants, such as SSRIs, are an effective treatment for many patients with depression. However, for a significant proportion of patients SSRIs either lack efficacy or are poorly tolerated. Even when SSRIs are effective in treating mood symptoms, there are often residual symptoms that are not well treated, including cognitive impairment and anhedonia. The development of novel treatment for depression is particularly challenging given the limited predictive validity of animal models. Human neurocognitive models of antidepressant action can help to bridge the translational gap and allow rapid investigation of novel compounds in healthy volunteers and people with depression. In this talk, using the 5-HT(4) receptor as an example of a novel target of interest, I will outline how these objective neurocognitive models can be used as a translational tool to understand antidepressant treatment mechanisms, guide treatment selection and test novel putative antidepressants early in development. DISCLOSURE OF INTEREST: S. Murphy Grant / Research support from: Zogenix, UCB, Janssen, Consultant of: Zogenix, Sumitomo Danippon, Janssen, UCB, Speakers bureau of: Zogenix Cambridge University Press 2023-07-19 /pmc/articles/PMC10417918/ http://dx.doi.org/10.1192/j.eurpsy.2023.40 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Abstract Murphy, S. Using neurocognitive models to optimise the treatment of depression |
title | Using neurocognitive models to optimise the treatment of depression |
title_full | Using neurocognitive models to optimise the treatment of depression |
title_fullStr | Using neurocognitive models to optimise the treatment of depression |
title_full_unstemmed | Using neurocognitive models to optimise the treatment of depression |
title_short | Using neurocognitive models to optimise the treatment of depression |
title_sort | using neurocognitive models to optimise the treatment of depression |
topic | Abstract |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417918/ http://dx.doi.org/10.1192/j.eurpsy.2023.40 |
work_keys_str_mv | AT murphys usingneurocognitivemodelstooptimisethetreatmentofdepression |