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Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires
Kinase inhibitors are often used as cancer targeting agents for their ability to prevent the activation of cell growth and proliferation signals. Cardiotoxic effects have been identified for some marketed kinase inhibitors that were not detected during clinical trials. We hypothesize that more predi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5603510/ https://www.ncbi.nlm.nih.gov/pubmed/28924210 http://dx.doi.org/10.1038/s41598-017-12048-5 |
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author | Conant, Genevieve Ahadian, Samad Zhao, Yimu Radisic, Milica |
author_facet | Conant, Genevieve Ahadian, Samad Zhao, Yimu Radisic, Milica |
author_sort | Conant, Genevieve |
collection | PubMed |
description | Kinase inhibitors are often used as cancer targeting agents for their ability to prevent the activation of cell growth and proliferation signals. Cardiotoxic effects have been identified for some marketed kinase inhibitors that were not detected during clinical trials. We hypothesize that more predictive cardiac functional assessments of kinase inhibitors on human myocardium can be established by combining a high-throughput two-dimensional (2D) screening assay and a high-content three-dimensional (3D) engineered cardiac tissue (Biowire(TM)) based assay, and using human induced pluripotent stem cell-derived CMs (hiPSC-CMs). A subset (80) of compounds from the GlaxoSmithKline published kinase inhibitor set were tested on hiPSC-CM monolayers and significant effects on cell viability, calcium transients, and contraction frequency were observed. Artificial neural network modelling was then used to analyze the experimental results in an efficient and unbiased manner to select for kinase inhibitors with minimal effects on cell viability and function. Inhibitors of specific interest based on the modeling were evaluated in the 3D Biowire tissues. The three-dimensional Biowire platform eliminated oversensitivity in detecting both Ca(2+) transient amplitude enhancements as well as the acute detrimental effects on cell viability due to the kinase inhibitor application as compared to the monolayer testing. |
format | Online Article Text |
id | pubmed-5603510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-56035102017-09-20 Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires Conant, Genevieve Ahadian, Samad Zhao, Yimu Radisic, Milica Sci Rep Article Kinase inhibitors are often used as cancer targeting agents for their ability to prevent the activation of cell growth and proliferation signals. Cardiotoxic effects have been identified for some marketed kinase inhibitors that were not detected during clinical trials. We hypothesize that more predictive cardiac functional assessments of kinase inhibitors on human myocardium can be established by combining a high-throughput two-dimensional (2D) screening assay and a high-content three-dimensional (3D) engineered cardiac tissue (Biowire(TM)) based assay, and using human induced pluripotent stem cell-derived CMs (hiPSC-CMs). A subset (80) of compounds from the GlaxoSmithKline published kinase inhibitor set were tested on hiPSC-CM monolayers and significant effects on cell viability, calcium transients, and contraction frequency were observed. Artificial neural network modelling was then used to analyze the experimental results in an efficient and unbiased manner to select for kinase inhibitors with minimal effects on cell viability and function. Inhibitors of specific interest based on the modeling were evaluated in the 3D Biowire tissues. The three-dimensional Biowire platform eliminated oversensitivity in detecting both Ca(2+) transient amplitude enhancements as well as the acute detrimental effects on cell viability due to the kinase inhibitor application as compared to the monolayer testing. Nature Publishing Group UK 2017-09-18 /pmc/articles/PMC5603510/ /pubmed/28924210 http://dx.doi.org/10.1038/s41598-017-12048-5 Text en © The Author(s) 2017 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 Conant, Genevieve Ahadian, Samad Zhao, Yimu Radisic, Milica Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires |
title | Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires |
title_full | Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires |
title_fullStr | Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires |
title_full_unstemmed | Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires |
title_short | Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires |
title_sort | kinase inhibitor screening using artificial neural networks and engineered cardiac biowires |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5603510/ https://www.ncbi.nlm.nih.gov/pubmed/28924210 http://dx.doi.org/10.1038/s41598-017-12048-5 |
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