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O’Donovan SD, Cavill R, Wimmenauer F, Lukas A, Stumm T, Smirnov E, Lenz M, Ertaylan G, Jennen DGJ, van Riel NAW, Driessens K, Peeters RLM, de Kok TMCM. Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data. PLoS One 2023; 18:e0292030. [PMID: 38032940 PMCID: PMC10688741 DOI: 10.1371/journal.pone.0292030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/11/2023] [Indexed: 12/02/2023] Open
Abstract
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.
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Affiliation(s)
- Shauna D. O’Donovan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Rachel Cavill
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Florian Wimmenauer
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Alexander Lukas
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Tobias Stumm
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Evgueni Smirnov
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Michael Lenz
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
- Preventive Cardiology and Preventative Medicine – Center for Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Gokhan Ertaylan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Sustainable Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Danyel G. J. Jennen
- Dept. of Toxicogenomics, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Natal A. W. van Riel
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Kurt Driessens
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Ralf L. M. Peeters
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Theo M. C. M. de Kok
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Toxicogenomics, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
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O’Donovan SD, Driessens K, Lopatta D, Wimmenauer F, Lukas A, Neeven J, Stumm T, Smirnov E, Lenz M, Ertaylan G, Jennen DGJ, van Riel NAW, Cavill R, Peeters RLM, de Kok TMCM. Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes. PLoS One 2020; 15:e0236392. [PMID: 32780735 PMCID: PMC7418976 DOI: 10.1371/journal.pone.0236392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 07/06/2020] [Indexed: 11/19/2022] Open
Abstract
In clinical trials, animal and cell line models are often used to evaluate the potential toxic effects of a novel compound or candidate drug before progressing to human trials. However, relating the results of animal and in vitro model exposures to relevant clinical outcomes in the human in vivo system still proves challenging, relying on often putative orthologs. In recent years, multiple studies have demonstrated that the repeated dose rodent bioassay, the current gold standard in the field, lacks sufficient sensitivity and specificity in predicting toxic effects of pharmaceuticals in humans. In this study, we evaluate the potential of deep learning techniques to translate the pattern of gene expression measured following an exposure in rodents to humans, circumventing the current reliance on orthologs, and also from in vitro to in vivo experimental designs. Of the applied deep learning architectures applied in this study the convolutional neural network (CNN) and a deep artificial neural network with bottleneck architecture significantly outperform classical machine learning techniques in predicting the time series of gene expression in primary human hepatocytes given a measured time series of gene expression from primary rat hepatocytes following exposure in vitro to a previously unseen compound across multiple toxicologically relevant gene sets. With a reduction in average mean absolute error across 76 genes that have been shown to be predictive for identifying carcinogenicity from 0.0172 for a random regression forest to 0.0166 for the CNN model (p < 0.05). These deep learning architecture also perform well when applied to predict time series of in vivo gene expression given measured time series of in vitro gene expression for rats.
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Affiliation(s)
- Shauna D. O’Donovan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Kurt Driessens
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Daniel Lopatta
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Florian Wimmenauer
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Alexander Lukas
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Jelmer Neeven
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Tobias Stumm
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Evgueni Smirnov
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Michael Lenz
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
- Preventive Cardiology and Preventative Medicine—Center for Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Gokhan Ertaylan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Danyel G. J. Jennen
- Dept. of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Natal A. W. van Riel
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Biomedical Engineering, Eindhoven University of Technology, The Netherlands
| | - Rachel Cavill
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Ralf L. M. Peeters
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Theo M. C. M. de Kok
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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