The Power of Resolution: Contextualized Understanding of Chemical-biological Interactions

Prediction of human response to chemical exposures is a major challenge in both pharmaceutical and toxicological research. Transcriptomics has been a powerful tool to explore chemical-biological interactions. However, limited throughput, high-costs and complexity of transcriptomic interpretations have yielded numerous studies lacking sufficient experimental context for predictive application. We utilized a novel high-throughput transcriptomics platform to explore a broad range of exposures to 24 reference compounds in both differentiated and undifferentiated human HepaRG cultures. Our goals were to 1) explore transcriptomic characteristics distinguishing liver injury compounds, 2) assess impacts of differentiation state on baseline and compound-induced responses (e.g., metabolically-activated), and 3) identify and resolve reference biological-response pathways and their quantitative translation to human exposures. Study data revealed the predictive utility of transcriptomic concentration-response modeling to quantitatively identify human liver injury compounds by their respective benchmark concentrations (BMCs), and model hepatic responses to classical reference compounds yielding plausibly-relevant estimations of human potency.

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