Analyses of drug pharmacokinetics (PKs) and pharmacodynamics (PDs) performed in animals are often not predictive of drug PKs and PDs in humans, and in vitro PK and PD modelling does not provide quantitative PK parameters. Here, we show that physiological PK modelling of first-pass drug absorption, metabolism and excretion in humans—using computationally scaled data from multiple fluidically linked two-channel organ chips—predicts PK parameters for orally administered nicotine (using gut, liver and kidney chips) and for intravenously injected cisplatin (using coupled bone marrow, liver and kidney chips). The chips are linked through sequential robotic liquid transfers of a common blood substitute by their endothelium-lined channels (as reported by Novak et al. in an associated Article) and share an arteriovenous fluid-mixing reservoir. We also show that predictions of cisplatin PDs match previously reported patient data. The quantitative in-vitro-to-in-vivo translation of PK and PD parameters and the prediction of drug absorption, distribution, metabolism, excretion and toxicity through fluidically coupled organ chips may improve the design of drug-administration regimens for phase-I clinical trials.
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All data supporting the results in this study are available within the Article and its Supplementary Information. The broad range of raw datasets acquired and analysed (or any subsets of it), which for reuse would require contextual metadata, are available from the corresponding author on reasonable request.
The CoBi code used to simulate individual organs and their network, as well as individual organ models, is freely available at http://medicalavatars.cfdrc.com/index.php/cobi-tools, under the folder ‘Microphysiological Organs and Systems Models’.
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We thank M. Rosnach for his artwork, B. Fountaine and S. Kroll for their help with photography, N. Dimitrakakis for statistical analysis and C. Vidoudez for MS analysis. This research was sponsored by the Wyss Institute for Biologically Inspired Engineering at Harvard University, the Defense Advanced Research Projects Agency under cooperative agreement no. W911NF-12-2-0036 and US Food and Drug Administration grant HHSF223201310079C. Funding from Knut och Alice Wallenberg’s stiftelse (grant no. 2015-0178; to A.H.) is acknowledged. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency, or the US Government. This work was performed in part at the Center for Nanoscale Systems (CNS), a member of the National Nanotechnology Coordinated Infrastructure Network, which is supported by the National Science Foundation under award no. 1541959. The CNS is part of Harvard University, the Harvard Materials Research Science and Engineering Center (DMR-1420570).
D.E.I. is a founder and holds equity in Emulate, and chairs its scientific advisory board. K.K.P. is a consultant to Emulate. S.S.F.J. is an employee of and holds equity in Emulate. M.C., A.H., D.E.I., M.I., O.L., B.M.M., Y.M., R.N., K.K.P., A.S.-P. and D.E.I. are listed as inventors on intellectual property licensed to Emulate. The remaining authors declare no competing interests.
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Herland, A., Maoz, B.M., Das, D. et al. Quantitative prediction of human pharmacokinetic responses to drugs via fluidically coupled vascularized organ chips. Nat Biomed Eng 4, 421–436 (2020). https://doi.org/10.1038/s41551-019-0498-9
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