TY - JOUR
T1 - Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data
AU - Donovan, Shauna D. O.
AU - Cavill, Rachel
AU - Wimmenauer, Florian
AU - Lukas, Alexander
AU - Stumm, Tobias
AU - Smirnov, Evgueni
AU - Lenz, Michael
AU - Ertaylan, Gokhan
AU - Jennen, Danyel G. J.
AU - van Riel, Natal A. W.
AU - Driessens, Kurt
AU - Peeters, Ralf L. M.
AU - de Kok, Theo M. C. M.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85178503290&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/38032940
U2 - 10.1371/journal.pone.0292030
DO - 10.1371/journal.pone.0292030
M3 - Article
C2 - 38032940
SN - 1932-6203
VL - 18
JO - PLOS ONE
JF - PLOS ONE
IS - 11 November
M1 - e0292030
ER -