Reproducing human and cross-species drug toxicities using a Liver-Chip.

Nonclinical rodent and nonrodent toxicity models used to support clinical trials of candidate drugs may produce discordant results or fail to predict complications in humans, contributing to drug failures in the clinic. Here, we applied microengineered Organs-on-Chips technology to design a rat, dog, and human Liver-Chip containing species-specific primary hepatocytes interfaced with liver sinusoidal endothelial cells, with or without Kupffer cells and hepatic stellate cells, cultured under physiological fluid flow. The Liver-Chip detected diverse phenotypes of liver toxicity, including hepatocellular injury, steatosis, cholestasis, and fibrosis, and species-specific toxicities when treated with tool compounds. A multispecies Liver-Chip may provide a useful platform for prediction of liver toxicity and inform human relevance of liver toxicities detected in animal studies to better determine safety and human risk.

Integrated in vitro models for hepatic safety and metabolism: evaluation of a human Liver-Chip and liver spheroid.

Drug-induced liver injury remains a frequent reason for drug withdrawal. Accordingly, more predictive and translational models are required to assess human hepatotoxicity risk. This study presents a comprehensive evaluation of two promising models to assess mechanistic hepatotoxicity, microengineered Organ-Chips and 3D hepatic spheroids, which have enhanced liver phenotype, metabolic activity and stability in culture not attainable with conventional 2D models. Sensitivity of the models to two hepatotoxins, acetaminophen (APAP) and fialuridine (FIAU), was assessed across a range of cytotoxicity biomarkers (ATP, albumin, miR-122, α-GST) as well as their metabolic functionality by quantifying APAP, FIAU and CYP probe substrate metabolites. APAP and FIAU produced dose- and time-dependent increases in miR-122 and α-GST release as well as decreases in albumin secretion in both Liver-Chips and hepatic spheroids. Metabolic turnover of CYP probe substrates, APAP and FIAU, was maintained over the 10-day exposure period at concentrations where no cytotoxicity was detected and APAP turnover decreased at concentrations where cytotoxicity was detected. With APAP, the most sensitive biomarkers were albumin in the Liver-Chips (EC50 5.6 mM, day 1) and miR-122 and ATP in the liver spheroids (14-fold and EC50 2.9 mM, respectively, day 3). With FIAU, the most sensitive biomarkers were albumin in the Liver-Chip (EC50 126 µM) and miR-122 (15-fold) in the liver spheroids, both on day 7. In conclusion, both models exhibited integrated toxicity and metabolism, and broadly similar sensitivity to the hepatotoxicants at relevant clinical concentrations, demonstrating the utility of these models for improved hepatotoxicity risk assessment.

Introducing an automated high content confocal imaging approach for Organs-on-Chips.

Organ-Chips are micro-engineered systems that aim to recapitulate the organ microenvironment. Implementation of Organ-Chips within the pharmaceutical industry aims to improve the probability of success of drugs reaching late stage clinical trial by generating models for drug discovery that are of human origin and have disease relevance. We are adopting the use of Organ-Chips for enhancing pre-clinical efficacy and toxicity evaluation and prediction. Whilst capturing cellular phenotype via imaging in response to drug exposure is a useful readout in these models, application has been limited due to difficulties in imaging the chips at scale. Here we created an end-to-end, automated workflow to capture and analyse confocal images of multicellular Organ-Chips to assess detailed cellular phenotype across large batches of chips. By automating this process, we not only reduced acquisition time, but we also minimised process variability and user bias. This enabled us to establish, for the first time, a framework of statistical best practice for Organ-Chip imaging, creating the capability of using Organ-Chips and imaging for routine testing in drug discovery applications that rely on quantitative image data for decision making. We tested our approach using benzbromarone, whose mechanism of toxicity has been linked to mitochondrial damage with subsequent induction of apoptosis and necrosis, and staurosporine, a tool inducer of apoptosis. We also applied this workflow to assess the hepatotoxic effect of an active AstraZeneca drug candidate illustrating its applicability in drug safety assessment beyond testing tool compounds. Finally, we have demonstrated that this approach could be adapted to Organ-Chips of different shapes and sizes through application to a Kidney-Chip.

Morphological single cell profiling of the epithelial-mesenchymal transition.

Single cells respond heterogeneously to biochemical treatments, which can complicate the analysis of in vitro and in vivo experiments. In particular, stressful perturbations may induce the epithelial-mesenchymal transition (EMT), a transformation through which compact, sensitive cells adopt an elongated, resistant phenotype. However, classical biochemical measurements based on population averages over large numbers cannot resolve single cell heterogeneity and plasticity. Here, we use high content imaging of single cell morphology to classify distinct phenotypic subpopulations after EMT. We first characterize a well-defined EMT induction through the master regulator Snail in mammary epithelial cells over 72 h. We find that EMT is associated with increased vimentin area as well as elongation of the nucleus and cytoplasm. These morphological features were integrated into a Gaussian mixture model that classified epithelial and mesenchymal phenotypes with >92% accuracy. We then applied this analysis to heterogeneous populations generated from less controlled EMT-inducing stimuli, including growth factors (TGF-ß1), cell density, and chemotherapeutics (Taxol). Our quantitative, single cell approach has the potential to screen large heterogeneous cell populations for many types of phenotypic variability, and may thus provide a predictive assay for the preclinical assessment of targeted therapeutics.