Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence

• Main Authors: Lee, J.-Y.J, Miller, J.A, Basu, S, Kee, T.-Z.V, Loo, L.-H
• Other Authors: DEPT OF PHARMACOLOGY
• Format: Article
• Published: Springer Verlag 2020
• Subjects: 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; 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
• Online Access: https://scholarbank.nus.edu.sg/handle/10635/179035
• Institution: National University of Singapore

10.1007/s00204-018-2213-0