Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling
| Autor: | Javen Jun Hao Heng, Lit-Hsin Loo, Sreetama Basu, Daniele Zink, Faezah Hussain |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Drug Health Toxicology and Mutagenesis In silico media_common.quotation_subject Primary Cell Culture Cell Computational biology 010501 environmental sciences Biology Toxicology Proto-Oncogene Mas Risk Assessment 01 natural sciences Machine Learning 03 medical and health sciences Toxicity Tests Image Processing Computer-Assisted medicine Humans Cellular Senescence 0105 earth and related environmental sciences media_common Cell Death Dose-Response Relationship Drug Transcription Factor RelA In vitro toxicology Hep G2 Cells General Medicine Phenotype In vitro High-Throughput Screening Assays Actin Cytoskeleton 030104 developmental biology medicine.anatomical_structure Microscopy Fluorescence Hepatocyte Toxicity Hepatocytes Chemical and Drug Induced Liver Injury DNA Damage |
| Zdroj: | Archives of Toxicology. 94:2749-2767 |
| ISSN: | 1432-0738 0340-5761 |
| Popis: | Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and regulators. The goal of the current study was to develop an in vitro/in silico method for the rapid and accurate prediction of drug- and chemical-induced hepatocyte injury in humans. HepaRG cells were employed for high-throughput imaging in combination with phenotypic profiling. A reference set of 69 drugs and chemicals was screened at a range of 7 concentrations, and the cellular response values were used for training a supervised classifier and for determining assay performance by using tenfold cross-validation. The results showed that the best performing phenotypic features were related to nuclear translocation of RELA (RELA proto-oncogene, NF-kB subunit; also known as NF-kappa B p65), DNA organization, and the F-actin cytoskeleton. Using a subset of 30 phenotypic features, direct hepatocyte toxicity in humans could be predicted with a test sensitivity, specificity and balanced accuracy of 73%, 92%, and 83%, respectively. The method was applied to another set of 26 drugs and chemicals with unclear annotation and their hepatocyte toxicity in humans was predicted. The results also revealed that the identified discriminative phenotypic changes were related to cell death and cellular senescence. Whereas cell death-related endpoints are widely applied in in vitro toxicology, cellular senescence-related endpoints are not, although cellular senescence can be induced by various drugs and other small molecule compounds and plays an important role in liver injury and disease. These findings show how phenotypic profiling can reveal unexpected chemical-induced mechanisms in toxicology. |
| Databáze: | OpenAIRE |
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