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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292030&type=printable |
| _version_ | 1797349356636995584 |
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| author | Shauna D O'Donovan Rachel Cavill Florian Wimmenauer Alexander Lukas Tobias Stumm Evgueni Smirnov Michael Lenz Gokhan Ertaylan Danyel G J Jennen Natal A W van Riel Kurt Driessens Ralf L M Peeters Theo M C M de Kok |
| author_facet | Shauna D O'Donovan Rachel Cavill Florian Wimmenauer Alexander Lukas Tobias Stumm Evgueni Smirnov Michael Lenz Gokhan Ertaylan Danyel G J Jennen Natal A W van Riel Kurt Driessens Ralf L M Peeters Theo M C M de Kok |
| author_sort | Shauna D O'Donovan |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-08T12:29:14Z |
| format | Article |
| id | doaj.art-6585bbc2c6084525bdf1b8f9ae8698b8 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-08T12:29:14Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-6585bbc2c6084525bdf1b8f9ae8698b82024-01-22T05:31:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-011811e029203010.1371/journal.pone.0292030Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data.Shauna D O'DonovanRachel CavillFlorian WimmenauerAlexander LukasTobias StummEvgueni SmirnovMichael LenzGokhan ErtaylanDanyel G J JennenNatal A W van RielKurt DriessensRalf L M PeetersTheo M C M de KokThe 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292030&type=printable |
| spellingShingle | Shauna D O'Donovan Rachel Cavill Florian Wimmenauer Alexander Lukas Tobias Stumm Evgueni Smirnov Michael Lenz Gokhan Ertaylan Danyel G J Jennen Natal A W van Riel Kurt Driessens Ralf L M Peeters Theo M C M de Kok Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data. PLoS ONE |
| 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 |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292030&type=printable |
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