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Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data
The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to eval...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688741/ https://www.ncbi.nlm.nih.gov/pubmed/38032940 http://dx.doi.org/10.1371/journal.pone.0292030 |
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author | O’Donovan, Shauna D. Cavill, Rachel Wimmenauer, Florian Lukas, Alexander Stumm, Tobias Smirnov, Evgueni Lenz, Michael Ertaylan, Gokhan Jennen, Danyel G. J. van Riel, Natal A. W. Driessens, Kurt Peeters, Ralf L. M. de Kok, Theo M. C. M. |
author_facet | O’Donovan, Shauna D. Cavill, Rachel Wimmenauer, Florian Lukas, Alexander Stumm, Tobias Smirnov, Evgueni Lenz, Michael Ertaylan, Gokhan Jennen, Danyel G. J. van Riel, Natal A. W. Driessens, Kurt Peeters, Ralf L. M. de Kok, Theo M. C. M. |
author_sort | O’Donovan, Shauna D. |
collection | PubMed |
description | The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains. |
format | Online Article Text |
id | pubmed-10688741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106887412023-12-01 Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data O’Donovan, Shauna D. Cavill, Rachel Wimmenauer, Florian Lukas, Alexander Stumm, Tobias Smirnov, Evgueni Lenz, Michael Ertaylan, Gokhan Jennen, Danyel G. J. van Riel, Natal A. W. Driessens, Kurt Peeters, Ralf L. M. de Kok, Theo M. C. M. PLoS One Research Article The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains. Public Library of Science 2023-11-30 /pmc/articles/PMC10688741/ /pubmed/38032940 http://dx.doi.org/10.1371/journal.pone.0292030 Text en © 2023 O’Donovan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article O’Donovan, Shauna D. Cavill, Rachel Wimmenauer, Florian Lukas, Alexander Stumm, Tobias Smirnov, Evgueni Lenz, Michael Ertaylan, Gokhan Jennen, Danyel G. J. van Riel, Natal A. W. Driessens, Kurt Peeters, Ralf L. M. de Kok, Theo M. C. M. Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data |
title | Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data |
title_full | Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data |
title_fullStr | Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data |
title_full_unstemmed | Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data |
title_short | Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data |
title_sort | application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688741/ https://www.ncbi.nlm.nih.gov/pubmed/38032940 http://dx.doi.org/10.1371/journal.pone.0292030 |
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