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Abstract

The liver is a heterogeneous organ with many vital functions, including metabolism of pharmaceutical drugs and is highly susceptible to injury from these substances. The etiology of drug-induced liver disease is still debated although generally regarded as a continuum between an activated immune response and hepatocyte metabolic dysfunction, most often resulting from an intermediate reactive metabolite. This debate stems from the fact that current animal and in vitro models provide limited physiologically relevant information, and their shortcomings have resulted in “silent” hepatotoxic drugs being introduced into clinical trials, garnering huge financial losses for drug companies through withdrawals and late stage clinical failures. As we advance our understanding into the molecular processes leading to liver injury, it is increasingly clear that (a) the pathologic lesion is not only due to liver parenchyma but is also due to the interactions between the hepatocytes and the resident liver immune cells, stellate cells, and endothelial cells; and (b) animal models do not reflect the human cell interactions. Therefore, a predictive human, in vitro model must address the interactions between the major human liver cell types and measure key determinants of injury such as the dosage and metabolism of the drug, the stress response, cholestatic effect, and the immune and fibrotic response. In this mini-review, we first discuss the current state of macro-scale in vitro liver culture systems with examples that have been commercialized. We then introduce the paradigm of microfluidic culture systems that aim to mimic the liver with physiologically relevant dimensions, cellular structure, perfusion, and mass transport by taking advantage of micro and nanofabrication technologies. We review the most prominent liver-on-a-chip platforms in terms of their physiological relevance and drug response. We conclude with a commentary on other critical advances such as the deployment of fluorescence-based biosensors to identify relevant toxicity pathways, as well as computational models to create a predictive tool.

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Article first published online: April 24, 2014
Issue published: September 2014

Keywords

  1. Drug-induced liver injury
  2. liver on chip
  3. hepatotoxicity
  4. high content screening
  5. predictive modeling

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History

Published online: April 24, 2014
Issue published: September 2014

Authors

Affiliations

Shyam Sundhar Bale*
Center for Engineering in Medicine (CEM) at Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, MA 02114, USA
Lawrence Vernetti*
University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260, USA
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
Nina Senutovitch
University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260, USA
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
Rohit Jindal
Center for Engineering in Medicine (CEM) at Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, MA 02114, USA
Manjunath Hegde
Center for Engineering in Medicine (CEM) at Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, MA 02114, USA
Albert Gough
University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260, USA
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
William J McCarty
Center for Engineering in Medicine (CEM) at Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, MA 02114, USA
Ahmet Bakan
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
Abhinav Bhushan
Center for Engineering in Medicine (CEM) at Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, MA 02114, USA
Tong Y Shun
University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260, USA
Inna Golberg
Center for Engineering in Medicine (CEM) at Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, MA 02114, USA
Richard DeBiasio
University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260, USA
O Berk Usta
Center for Engineering in Medicine (CEM) at Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, MA 02114, USA
D Lansing Taylor
University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260, USA
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
Martin L Yarmush
Center for Engineering in Medicine (CEM) at Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, MA 02114, USA

Notes

*
These authors contributed equally to this publication.
Martin L Yarmush. Email: [email protected]

Author Contributions

The entire team had input into conceiving and outlining the manuscript. SSB and LV contributed equally to the writing of the manuscript. The entire team proof-read and edited the manuscript.

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