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Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles

In vitro assay data have recently emerged as a potential alternative to traditional animal toxicity studies to aid in the prediction of adverse effects of chemicals on humans. Here we evaluate the data generated from a battery of quantitative high-throughput screening (qHTS) assays applied to a larg...

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Autores principales: Huang, Ruili, Xia, Menghang, Sakamuru, Srilatha, Zhao, Jinghua, Lynch, Caitlin, Zhao, Tongan, Zhu, Hu, Austin, Christopher P., Simeonov, Anton
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830476/
https://www.ncbi.nlm.nih.gov/pubmed/29491351
http://dx.doi.org/10.1038/s41598-018-22046-w
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author Huang, Ruili
Xia, Menghang
Sakamuru, Srilatha
Zhao, Jinghua
Lynch, Caitlin
Zhao, Tongan
Zhu, Hu
Austin, Christopher P.
Simeonov, Anton
author_facet Huang, Ruili
Xia, Menghang
Sakamuru, Srilatha
Zhao, Jinghua
Lynch, Caitlin
Zhao, Tongan
Zhu, Hu
Austin, Christopher P.
Simeonov, Anton
author_sort Huang, Ruili
collection PubMed
description In vitro assay data have recently emerged as a potential alternative to traditional animal toxicity studies to aid in the prediction of adverse effects of chemicals on humans. Here we evaluate the data generated from a battery of quantitative high-throughput screening (qHTS) assays applied to a large and diverse collection of chemicals, including approved drugs, for their capacity in predicting human toxicity. Models were built with animal in vivo toxicity data, in vitro human cell-based assay data, as well as in combination with chemical structure and/or drug-target information to predict adverse effects observed for drugs in humans. Interestingly, we found that the models built with the human cell-based assay data performed close to those of the models based on animal in vivo toxicity data. Furthermore, expanding the biological space coverage of assays by including additional drug-target annotations was shown to significantly improve model performance. We identified a small set of targets, which, when added to the current suite of in vitro human cell-based assay data, result in models that greatly outperform those built with the existing animal toxicity data. Assays can be developed for this set of targets to screen compounds for construction of robust models for human toxicity prediction.
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spelling pubmed-58304762018-03-05 Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles Huang, Ruili Xia, Menghang Sakamuru, Srilatha Zhao, Jinghua Lynch, Caitlin Zhao, Tongan Zhu, Hu Austin, Christopher P. Simeonov, Anton Sci Rep Article In vitro assay data have recently emerged as a potential alternative to traditional animal toxicity studies to aid in the prediction of adverse effects of chemicals on humans. Here we evaluate the data generated from a battery of quantitative high-throughput screening (qHTS) assays applied to a large and diverse collection of chemicals, including approved drugs, for their capacity in predicting human toxicity. Models were built with animal in vivo toxicity data, in vitro human cell-based assay data, as well as in combination with chemical structure and/or drug-target information to predict adverse effects observed for drugs in humans. Interestingly, we found that the models built with the human cell-based assay data performed close to those of the models based on animal in vivo toxicity data. Furthermore, expanding the biological space coverage of assays by including additional drug-target annotations was shown to significantly improve model performance. We identified a small set of targets, which, when added to the current suite of in vitro human cell-based assay data, result in models that greatly outperform those built with the existing animal toxicity data. Assays can be developed for this set of targets to screen compounds for construction of robust models for human toxicity prediction. Nature Publishing Group UK 2018-02-28 /pmc/articles/PMC5830476/ /pubmed/29491351 http://dx.doi.org/10.1038/s41598-018-22046-w 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
Huang, Ruili
Xia, Menghang
Sakamuru, Srilatha
Zhao, Jinghua
Lynch, Caitlin
Zhao, Tongan
Zhu, Hu
Austin, Christopher P.
Simeonov, Anton
Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles
title Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles
title_full Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles
title_fullStr Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles
title_full_unstemmed Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles
title_short Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles
title_sort expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830476/
https://www.ncbi.nlm.nih.gov/pubmed/29491351
http://dx.doi.org/10.1038/s41598-018-22046-w
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