Microphysiological flux balance platform unravels the dynamics of drug induced steatosis

Avner Ehrlich, Sabina Tsytkin-Kirschenzweig, Konstantinos Ioannidis, Muneef Ayyash, Anne Riu, Reine Note, Gladys Ouedraogo, Jan Vanfleteren, Merav Cohen, Yaakov Nahmias*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Drug development is currently hampered by the inability of animal experiments to accurately predict human response. While emerging organ on chip technology offers to reduce risk using microfluidic models of human tissues, the technology still mostly relies on end-point assays and biomarker measurements to assess tissue damage resulting in limited mechanistic information and difficulties to detect adverse effects occurring below the threshold of cellular damage. Here we present a sensor-integrated liver on chip array in which oxygen is monitored using two-frequency phase modulation of tissue-embedded microprobes, while glucose, lactate and temperature are measured in real time using microfluidic electrochemical sensors. Our microphysiological platform permits the calculation of dynamic changes in metabolic fluxes around central carbon metabolism, producing a unique metabolic fingerprint of the liver's response to stimuli. Using our platform, we studied the dynamics of human liver response to the epilepsy drug Valproate (Depakine™) and the antiretroviral medication Stavudine (Zerit™). Using E6/E7LOW hepatocytes, we show TC50 of 2.5 and 0.8 mM, respectively, coupled with a significant induction of steatosis in 2D and 3D cultures. Time to onset analysis showed slow progressive damage starting only 15-20 hours post-exposure. However, flux analysis showed a rapid disruption of metabolic homeostasis occurring below the threshold of cellular damage. While Valproate exposure led to a sustained 15% increase in lipogenesis followed by mitochondrial stress, Stavudine exposure showed only a transient increase in lipogenesis suggesting disruption of β-oxidation. Our data demonstrates the importance of tracking metabolic stress as a predictor of clinical outcome.

Original languageAmerican English
Pages (from-to)2510-2522
Number of pages13
JournalLab on a Chip
Issue number17
StatePublished - 7 Sep 2018

Bibliographical note

Publisher Copyright:
© 2018 The Royal Society of Chemistry.


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