Quantitative prediction of human pharmacokinetic responses to drugs via fluidically coupled vascularized organ chips

Anna Herland, Ben M. Maoz, Debarun Das, Mahadevabharath R. Somayaji, Rachelle Prantil-Baun, Richard Novak, Michael Cronce, Tessa Huffstater, Sauveur S.F. Jeanty, Miles Ingram, Angeliki Chalkiadaki, David Benson Chou, Susan Marquez, Aaron Delahanty, Sasan Jalili-Firoozinezhad, Yuka Milton, Alexandra Sontheimer-Phelps, Ben Swenor, Oren Levy, Kevin K. ParkerAndrzej Przekwas, Donald E. Ingber

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageAmerican English
Pages (from-to)421-436
Number of pages16
JournalNature Biomedical Engineering
Volume4
Issue number4
DOIs
StatePublished - 1 Apr 2020

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biotechnology
  • Biomedical Engineering
  • Medicine (miscellaneous)
  • Computer Science Applications

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