2026-04-26T23:29:43Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/71422024-10-31T11:48:51Zcom_20.500.12105_19604com_20.500.12105_2051col_20.500.12105_19605col_20.500.12105_19607
Repisalud
author
Jimenez-Carretero, Daniel
author
Abrishami, Vahid
author
Fernandez-de-Manuel, Laura
author
Palacios, Irene
author
Quilez-Alvarez, Antonio
author
Diez-Sanchez, Alberto
author
del Pozo, Miguel Angel
author
Montoya, Maria
funder
Ministerio de Ciencia, Innovación y Universidades (España)
funder
Unión Europea. Comisión Europea
funder
Fundación ProCNIC
funder
Worldwide Cancer Research
funder
Fundación La Caixa
2019-02-07T13:51:09Z
2019-02-07T13:51:09Z
2018
PLoS Comput Biol. 2018; 14(11):e1006238
1553-7358
http://hdl.handle.net/20.500.12105/7142
30500821
10.1371/journal.pcbi.1006238
1553-7358
PLoS computational biology
Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction of toxicity based on nucleus pattern recognition. Deep learning algorithms obtain abstract representations of images through an automated process, allowing them to efficiently classify complex patterns, and have become the state-of-the art in machine learning for computer vision. Here, deep convolutional neural networks (CNN) were trained to predict toxicity from images of DAPI-stained cells pre-treated with a set of drugs with differing toxicity mechanisms. Different cropping strategies were used for training CNN models, the nuclei-cropping-based Tox_CNN model outperformed other models classifying cells according to health status. Tox_CNN allowed automated extraction of feature maps that clustered compounds according to mechanism of action. Moreover, fully automated region-based CNNs (RCNN) were implemented to detect and classify nuclei, providing per-cell toxicity prediction from raw screening images. We validated both Tox_(R)CNN models for detection of pre-lethal toxicity from nuclei images, which proved to be more sensitive and have broader specificity than established toxicity readouts. These models predicted toxicity of drugs with mechanisms of action other than those they had been trained for and were successfully transferred to other cell assays. The Tox_(R)CNN models thus provide robust, sensitive, and cost-effective tools for in vitro screening of drug-induced toxicity. These models can be adopted for compound prioritization in drug screening campaigns, and could thereby increase the efficiency of drug discovery.
eng
Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening
journal article
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