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Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects
Neurological drugs are often associated with serious side effects, yet drug screens typically focus only on efficacy. We demonstrate a novel paradigm utilizing high-throughput in vivo electrophysiology and brain activity patterns (BAPs). A platform with high sensitivity records local field potential...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5768723/ https://www.ncbi.nlm.nih.gov/pubmed/29335539 http://dx.doi.org/10.1038/s41467-017-02404-4 |
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author | Eimon, Peter M. Ghannad-Rezaie, Mostafa De Rienzo, Gianluca Allalou, Amin Wu, Yuelong Gao, Mu Roy, Ambrish Skolnick, Jeffrey Yanik, Mehmet Fatih |
author_facet | Eimon, Peter M. Ghannad-Rezaie, Mostafa De Rienzo, Gianluca Allalou, Amin Wu, Yuelong Gao, Mu Roy, Ambrish Skolnick, Jeffrey Yanik, Mehmet Fatih |
author_sort | Eimon, Peter M. |
collection | PubMed |
description | Neurological drugs are often associated with serious side effects, yet drug screens typically focus only on efficacy. We demonstrate a novel paradigm utilizing high-throughput in vivo electrophysiology and brain activity patterns (BAPs). A platform with high sensitivity records local field potentials (LFPs) simultaneously from many zebrafish larvae over extended periods. We show that BAPs from larvae experiencing epileptic seizures or drug-induced side effects have substantially reduced complexity (entropy), similar to reduced LFP complexity observed in Parkinson’s disease. To determine whether drugs that enhance BAP complexity produces positive outcomes, we used light pulses to trigger seizures in a model of Dravet syndrome, an intractable genetic epilepsy. The highest-ranked compounds identified by BAP analysis exhibit far greater anti-seizure efficacy and fewer side effects during subsequent in-depth behavioral assessment. This high correlation with behavioral outcomes illustrates the power of brain activity pattern-based screens and identifies novel therapeutic candidates with minimal side effects. |
format | Online Article Text |
id | pubmed-5768723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57687232018-01-19 Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects Eimon, Peter M. Ghannad-Rezaie, Mostafa De Rienzo, Gianluca Allalou, Amin Wu, Yuelong Gao, Mu Roy, Ambrish Skolnick, Jeffrey Yanik, Mehmet Fatih Nat Commun Article Neurological drugs are often associated with serious side effects, yet drug screens typically focus only on efficacy. We demonstrate a novel paradigm utilizing high-throughput in vivo electrophysiology and brain activity patterns (BAPs). A platform with high sensitivity records local field potentials (LFPs) simultaneously from many zebrafish larvae over extended periods. We show that BAPs from larvae experiencing epileptic seizures or drug-induced side effects have substantially reduced complexity (entropy), similar to reduced LFP complexity observed in Parkinson’s disease. To determine whether drugs that enhance BAP complexity produces positive outcomes, we used light pulses to trigger seizures in a model of Dravet syndrome, an intractable genetic epilepsy. The highest-ranked compounds identified by BAP analysis exhibit far greater anti-seizure efficacy and fewer side effects during subsequent in-depth behavioral assessment. This high correlation with behavioral outcomes illustrates the power of brain activity pattern-based screens and identifies novel therapeutic candidates with minimal side effects. Nature Publishing Group UK 2018-01-15 /pmc/articles/PMC5768723/ /pubmed/29335539 http://dx.doi.org/10.1038/s41467-017-02404-4 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Eimon, Peter M. Ghannad-Rezaie, Mostafa De Rienzo, Gianluca Allalou, Amin Wu, Yuelong Gao, Mu Roy, Ambrish Skolnick, Jeffrey Yanik, Mehmet Fatih Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects |
title | Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects |
title_full | Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects |
title_fullStr | Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects |
title_full_unstemmed | Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects |
title_short | Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects |
title_sort | brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5768723/ https://www.ncbi.nlm.nih.gov/pubmed/29335539 http://dx.doi.org/10.1038/s41467-017-02404-4 |
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