Benchmark Concentrations for Untargeted Metabolomics Versus Transcriptomics for Liver Injury Compounds in In Vitro Liver Models. (22nd March 2021)

• Record Type: Journal Article
• Authors: Crizer, David M; Ramaiahgari, Sreenivasa C; Ferguson, Stephen S; Rice, Julie R; Dunlap, Paul E; Sipes, Nisha S; Auerbach, Scott S; Merrick, Bruce Alex; DeVito, Michael J
• Is Part Of: Toxicological sciences. Volume 181:Number 2(2021)
• Page Start: 175
• Page End: 186
• Publication Date: 2021-03-22
• Subjects: toxicometabolomics -- toxicogenomics -- benchmark concentration analysis -- HepaRG cells -- liver injury; Toxicology -- Periodicals; Toxicology -- Periodicals; Toxicology; Periodicals; 615.9
• DOI: 10.1093/toxsci/kfab036
• Languages: English
• ISSNs: 1096-6080

Abstract

Abstract: Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.