Lee, JY.J., Miller, J.A., Basu, S. et al. Arch Toxicol (2018). https://doi.org/10.1007/s00204-018-2213-0
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 “Highthroughput 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.
We thank members of the Loo Lab for support and discussions, and the National Supercomputing Centre Singapore (NSCC) for providing access to the high-performance computing system. The work was supported by a grant from the Biomedical Research Council (BMRC)—Economic Development Board (EDB) Industry Alignment Fund (IAF311017G), Agency for Science, Technology and Research (A*STAR), Singapore, and partially supported by the Lush Prize (Science Award) 2016.