Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00204-018-2213-0
Title: Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence
Authors: Lee, J.-Y.J
Miller, J.A
Basu, S
Kee, T.-Z.V
Loo, L.-H 
Keywords: amiodarone
aristolochic acid
bleomycin
butylcresol
cadmium chloride
carbamazepine
DNA
doxorubicin
lithium chloride
myrcene
nitrofurantoin
nystatin
ochratoxin
paraquat
patulin
phalloidin
phenylenediamine
skatole
tenofovir
xenobiotic agent
Article
artificial intelligence
automation
BEAS-2B cell line
cell viability assay
chemical structure
controlled study
diagnostic accuracy
diagnostic test accuracy study
DNA damage response
DNA strand breakage
HBEC cell line (bronchial epithelium)
high throughput in vitro phenotypic profiling for toxicity prediction
high throughput screening
human
human cell
in vitro study
in vivo study
lung cell line
lung toxicity
phenotype
predictive value
priority journal
sensitivity and specificity
A-549 cell line
bronchus
cell line
cell survival
chemistry
drug effect
high throughput screening
lung
pathology
procedures
toxicity testing
A549 Cells
Artificial Intelligence
Bronchi
Cell Line
Cell Survival
High-Throughput Screening Assays
Humans
Lung
Predictive Value of Tests
Sensitivity and Specificity
Toxicity Tests
Xenobiotics
Issue Date: 2018
Publisher: Springer Verlag
Citation: Lee, J.-Y.J, Miller, J.A, Basu, S, Kee, T.-Z.V, Loo, L.-H (2018). Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence. Archives of Toxicology 92 (6) : 2055-2075. ScholarBank@NUS Repository. https://doi.org/10.1007/s00204-018-2213-0
Rights: Attribution 4.0 International
Abstract: Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are not always clear, and thus, a general in vitro assay for testing pulmonotoxicity applicable to a wide variety of chemicals is not currently available. Here, we report a study that uses high-throughput imaging and artificial intelligence to build an in vitro pulmonotoxicity assay by automatically comparing and selecting human lung-cell lines and their associated quantitative phenotypic features most predictive of in vivo pulmonotoxicity. This approach is called “High-throughput In vitro Phenotypic Profiling for Toxicity Prediction” (HIPPTox). We found that the resulting assay based on two phenotypic features of a human bronchial epithelial cell line, BEAS-2B, can accurately classify 33 reference chemicals with human pulmonotoxicity information (88.8% balance accuracy, 84.6% sensitivity, and 93.0% specificity). In comparison, the predictivity of a standard cell-viability assay on the same set of chemicals is much lower (77.1% balanced accuracy, 84.6% sensitivity, and 69.5% specificity). We also used the assay to evaluate 17 additional test chemicals with unknown/unclear human pulmonotoxicity, and experimentally confirmed that many of the pulmonotoxic reference and predicted-positive test chemicals induce DNA strand breaks and/or activation of the DNA-damage response (DDR) pathway. Therefore, HIPPTox helps us to uncover these common modes-of-action of pulmonotoxic chemicals. HIPPTox may also be applied to other cell types or models, and accelerate the development of predictive in vitro assays for other cell-type- or organ-specific toxicities. © 2018, The Author(s).
Source Title: Archives of Toxicology
URI: https://scholarbank.nus.edu.sg/handle/10635/179035
ISSN: 03405761
DOI: 10.1007/s00204-018-2213-0
Rights: Attribution 4.0 International
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1007_s00204-018-2213-0.pdf9.77 MBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons