Towards a three-dimensional microfluidic liver platform for predicting drug efficacy and toxicity in humans

Although the process of drug development requires efficacy and toxicity testing in animals prior to human testing, animal models have limited ability to accurately predict human responses to xenobiotics and other insults. Societal pressures are also focusing on reduction of and, ultimately, replacem...

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Hauptverfasser: Bhushan, Abhinav, Senutovitch, Nina, Bale, Shyam S, McCarty, William J, Hegde, Manjunath, Jindal, Rohit, Golberg, Inna, Berk Usta, O, Yarmush, Martin L, Vernetti, Lawrence, Gough, Albert, Bakan, Ahmet, Shun, Tong Ying, Biasio, Richard, Lansing Taylor, D
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Sprache:eng
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Zusammenfassung:Although the process of drug development requires efficacy and toxicity testing in animals prior to human testing, animal models have limited ability to accurately predict human responses to xenobiotics and other insults. Societal pressures are also focusing on reduction of and, ultimately, replacement of animal testing. However, a variety of in vitro models, explored over the last decade, have not been powerful enough to replace animal models. New initiatives sponsored by several US federal agencies seek to address this problem by funding the development of physiologically relevant human organ models on microscopic chips. The eventual goal is to simulate a human-on-a-chip, by interconnecting the organ models, thereby replacing animal testing in drug discovery and development. As part of this initiative, we aim to build a three-dimensional human liver chip that mimics the acinus, the smallest functional unit of the liver, including its oxygen gradient. Our liver-on-a-chip platform will deliver a microfluidic three-dimensional co-culture environment with stable synthetic and enzymatic function for at least 4 weeks. Sentinel cells that contain fluorescent biosensors will be integrated into the chip to provide multiplexed, real-time readouts of key liver functions and pathology. We are also developing a database to manage experimental data and harness external information to interpret the multimodal data and create a predictive platform.
ISSN:1757-6512
1757-6512
DOI:10.1186/scrt377