Detect changes in gene and protein expression over time and varying dose levels upon drug treatment
Identifying and classifying drug-induced liver injury (DILI) using unbiased toxicogenomic-based methodologies (genomics, epigenomics, transcriptomics, proteomics) and next-generation sequencing can enhance reliability of DILI prediction (1). The superior physiological and mechanistic relevance of 3D InSight™ Human Liver Microtissues brings greater power to omics-based approaches for analysis of cellular responses to drug exposure. The analysis of 3D Human Liver Microtissues with HRM-MS proteomics, for example, allowed identification of novel NAPQI acceptor sites on proteins (2).
- Gain novel insights in liver function and response to drug treatment by combining physiologically relevant 3D liver models with high-density omics-based analytics
- Detect global and pathway-specific changes in cellular omics profiles using long-lived, complex models that reflect biology of differentiated hepatocytes
- Identify early-stage mechanistic stress and metabolic pathway activation on a transcriptomic or proteomic level under prolonged exposure, elucidating sub-toxic responses
Effects of Acetaminophen (APAP) treatment on 3D InSight™ Human Liver Microtissues (left). The candidate’s lists of the treatments compared with control (left), and induction of the biotransformation phase I, II and III enzymes upon APAP exposure already at clinically relevant concentrations (right) are depicted. The dashed line indicates unchanged expression compared with control. No significant detected changes = ns. Figure adapted from (2).
- Jiang et al, Expert Opinion on Drug Metabolism & Toxicology (2015)
- Bruderer et al, Molecular & Cellular Proteomics (2015)