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Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks

BACKGROUND: Blockage of some ion channels and in particular, the hERG (human Ether-a’-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefor...

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Autores principales: Sharifi, Mohsen, Buzatu, Dan, Harris, Stephen, Wilkes, Jon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751783/
https://www.ncbi.nlm.nih.gov/pubmed/29297274
http://dx.doi.org/10.1186/s12859-017-1895-2
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author Sharifi, Mohsen
Buzatu, Dan
Harris, Stephen
Wilkes, Jon
author_facet Sharifi, Mohsen
Buzatu, Dan
Harris, Stephen
Wilkes, Jon
author_sort Sharifi, Mohsen
collection PubMed
description BACKGROUND: Blockage of some ion channels and in particular, the hERG (human Ether-a’-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts ((13)C and (15)N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model. RESULTS: An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. CONCLUSION: A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-017-1895-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-57517832018-01-05 Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks Sharifi, Mohsen Buzatu, Dan Harris, Stephen Wilkes, Jon BMC Bioinformatics Research BACKGROUND: Blockage of some ion channels and in particular, the hERG (human Ether-a’-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts ((13)C and (15)N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model. RESULTS: An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. CONCLUSION: A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-017-1895-2) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-28 /pmc/articles/PMC5751783/ /pubmed/29297274 http://dx.doi.org/10.1186/s12859-017-1895-2 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Sharifi, Mohsen
Buzatu, Dan
Harris, Stephen
Wilkes, Jon
Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks
title Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks
title_full Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks
title_fullStr Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks
title_full_unstemmed Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks
title_short Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks
title_sort development of models for predicting torsade de pointes cardiac arrhythmias using perceptron neural networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751783/
https://www.ncbi.nlm.nih.gov/pubmed/29297274
http://dx.doi.org/10.1186/s12859-017-1895-2
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