Hutson, MS, Alexander, PG, Allwardt, V et al. (20 more authors) (2016) Organs-on-Chips as Bridges for Predictive Toxicology. Applied In Vitro Toxicology, 2 (2). pp. 97-102. ISSN 2332-1512
Abstract
The next generation of chemical toxicity testing will use organs-on-chips (OoCs)—3D cultures of heterotypic cells with appropriate extracellular matrices to better approximate the in vivo cellular microenvironment. Researchers are already working to validate whether OoCs are predictive of toxicity in humans. Here, we review two other key aspects of how OoCs may advance predictive toxicology—each taking advantage of OoCs as systems of intermediate complexity that remain experimentally accessible. First, the intermediate complexity of OoCs will help elucidate the scale(s) of organismal complexity that currently confound computational predictions of in vivo toxicity from in vitro data sets. Identifying the strongest confounding factors will help researchers improve the computational models underlying such predictions. Second, the experimental accessibility of OoCs will allow researchers to analyze chemical-exposure responses in OoCs using an array of high-content readouts—from fluorescent biosensors that report dynamic changes in specific cell signaling pathways to unbiased searches over broader biochemical space using technologies like ion mobility-mass spectrometry. Such high-content information on OoC responses will help determine the details of adverse outcome pathways. We note these possible uses of OoCs so that researchers and engineers can consider them in the design of next-generation OoC control, perfusion, and analysis platforms.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Keywords: | computational model; environmental; fetal membrane; limb development; liver; mammary gland; organotypic culture model; toxicant |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Pollard Institute (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Oct 2019 13:14 |
Last Modified: | 02 Nov 2021 14:53 |
Status: | Published |
Publisher: | Mary Ann Liebert |
Identification Number: | 10.1089/aivt.2016.0003 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151608 |