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

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.

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Fig. 1: Development of a first-pass multi-organ-chip system.
Fig. 2: Mass spectrometry data and DMPK model of the first-pass multi-organ-chip system.
Fig. 3: IVIVT of human PK parameters for nicotine using the multi-organ-chip first-pass system.
Fig. 4: Prediction of cisplatin PK and PD parameters using the multi-organ-chip IVIVT system.

Data availability

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.

Code availability

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’.

References

  1. 1.

    Shanks, N., Greek, R. & Greek, J. Are animal models predictive for humans? Philos. Ethics Humanit. Med. 4, 2 (2009).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Malinowski, H. et al. Draft guidance for industry extended-release solid oral dosage forms. Development, evaluation and application of in vitro-in vivo correlations. Adv. Exp. Med. Biol. 423, 269–288 (1997).

    CAS  PubMed  Google Scholar 

  3. 3.

    Danhof, M., de Lange, E. C. M., Della Pasqua, O. E., Ploeger, B. A. & Voskuyl, R. A. Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research. Trends Pharmacol. Sci. 29, 186–191 (2008).

    CAS  PubMed  Google Scholar 

  4. 4.

    Abaci, H. E. & Shuler, M. L. Human-on-a-chip design strategies and principles for physiologically based pharmacokinetics/pharmacodynamics modeling. Integr. Biol. 7, 383–391 (2015).

    Google Scholar 

  5. 5.

    Esch, M. B., Ueno, H., Applegate, D. R. & Shuler, M. L. Modular, pumpless body-on-a-chip platform for the co-culture of GI tract epithelium and 3D primary liver tissue. Lab Chip 16, 2719–2729 (2016).

    CAS  PubMed  Google Scholar 

  6. 6.

    Coppeta, J. R. et al. A portable and reconfigurable multi-organ platform for drug development with onboard microfluidic flow control. Lab Chip 17, 134–144 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Xiao, S. et al. A microfluidic culture model of the human reproductive tract and 28-day menstrual cycle. Nat. Commun. 8, 14584 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Wagner, I. et al. A dynamic multi-organ-chip for long-term cultivation and substance testing proven by 3D human liver and skin tissue co-culture. Lab Chip 13, 3538–3547 (2013).

    CAS  PubMed  Google Scholar 

  9. 9.

    Stokes, C. L., Cirit, M. & Lauffenburger, D. A. Physiome-on-a-Chip: the challenge of “scaling” in design, operation, and translation of microphysiological systems. CPT Pharmacomet. Pharmacol. 4, 559–562 (2015).

    CAS  Google Scholar 

  10. 10.

    Bovard, D. et al. A lung/liver-on-a-chip platform for acute and chronic toxicity studies. Lab Chip 18, 3814–3829 (2018).

    CAS  PubMed  Google Scholar 

  11. 11.

    Oleaga, C. et al. Multi-organ toxicity demonstration in a functional human in vitro system composed of four organs. Sci. Rep. 6, 20030 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Maschmeyer, I. et al. A four-organ-chip for interconnected long-term co-culture of human intestine, liver, skin and kidney equivalents. Lab Chip 15, 2688–2699 (2015).

    CAS  PubMed  Google Scholar 

  13. 13.

    Edington, C. D. et al. Interconnected microphysiological systems for quantitative biology and pharmacology Studies. Sci. Rep. 8, 4530 (2018).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Vernetti, L. et al. Functional coupling of human microphysiology systems: intestine, liver, kidney proximal tubule, blood-brain barrier and skeletal muscle. Sci. Rep. 7, 42296 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Kim, H. J., Li, H., Collins, J. J. & Ingber, D. E. Contributions of microbiome and mechanical deformation to intestinal bacterial overgrowth and inflammation in a human gut-on-a-chip. Proc. Natl Acad. Sci. USA 113, E7–E15 (2016).

    CAS  PubMed  Google Scholar 

  16. 16.

    Novak, R. et al. Robotic fluidic coupling and interrogation of multiple vascularized organ chips. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-019-0497-x (2020).

  17. 17.

    Jang, K.-J. et al. Human kidney proximal tubule-on-a-chip for drug transport and nephrotoxicity assessment. Integr. Biol. 5, 1119–1129 (2013).

    CAS  Google Scholar 

  18. 18.

    Prantil-Baun, R. et al. Physiologically based pharmacokinetic and pharmacodynamic analysis enabled by microfluidically linked organs-on-chips. Annu. Rev. Pharmacol. Toxicol. 58, 37–64 (2018).

    CAS  PubMed  Google Scholar 

  19. 19.

    Auner, A. W., Tasneem, K. M., Markov, D. A., McCawley, L. J. & Hutson, M. S. Chemical-PDMS binding kinetics and implications for bioavailability in microfluidic devices. Lab Chip 19, 864–874 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Jalili-Firoozinezhad, S. et al. A complex human gut microbiome cultured in an anaerobic intestine-on-a-chip. Nat. Biomed. Eng. 3, 520–531 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Kasendra, M. et al. Development of a primary human small intestine-on-a-chip using biopsy-derived organoids. Sci. Rep. 8, 2871 (2018).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Kim, H. J., Huh, D., Hamilton, G. & Ingber, D. E. Human gut-on-a-chip inhabited by microbial flora that experiences intestinal peristalsis-like motions and flow. Lab Chip 12, 2165–2174 (2012).

    CAS  PubMed  Google Scholar 

  23. 23.

    Jang, K.-J. et al. Reproducing human and cross-species toxicities using a Liver-Chip. Science Transl. Med. 11, eaax5516 (2019).

    CAS  Google Scholar 

  24. 24.

    Pullan, R. D. et al. Transdermal nicotine for active ulcerative colitis. N. Engl. J. Med. 330, 811–815 (1994).

    CAS  PubMed  Google Scholar 

  25. 25.

    Benowitz, N. L., Hukkanen, J. & Jacob, P. Nicotine chemistry, metabolism, kinetics and biomarkers. Handb. Exp. Pharmacol. 192, 29–60 (2009).

  26. 26.

    Dancik, Y., Anissimov, Y. G., Jepps, O. G. & Roberts, M. S. Convective transport of highly plasma protein bound drugs facilitates direct penetration into deep tissues after topical application. Br. J. Clin. Pharm. 73, 564–578 (2012).

    CAS  Google Scholar 

  27. 27.

    Varma, M. V. S. et al. Physicochemical determinants of human renal clearance. J. Med. Chem. 52, 4844–4852 (2009).

    CAS  PubMed  Google Scholar 

  28. 28.

    Digard, H., Proctor, C., Kulasekaran, A., Malmqvist, U. & Richter, A. Determination of nicotine absorption from multiple tobacco products and nicotine gum. Nicotine Tob. Res. 15, 255–261 (2013).

    CAS  PubMed  Google Scholar 

  29. 29.

    Prytz, H., Benoni, C. & Tagesson, C. Does smoking tighten the gut? Scand. J. Gastroenterol. 24, 1084–1088 (1989).

    CAS  PubMed  Google Scholar 

  30. 30.

    Suenaert, P. et al. In vivo influence of nicotine on human basal and NSAID-induced gut barrier function. Scand. J. Gastroenterol. 38, 399–408 (2003).

    CAS  PubMed  Google Scholar 

  31. 31.

    McGilligan, V. E., Wallace, J. M. W., Heavey, P. M., Ridley, D. L. & Rowland, I. R. The effect of nicotine in vitro on the integrity of tight junctions in Caco-2 cell monolayers. Food Chem. Toxicol. 45, 1593–1598 (2007).

    CAS  PubMed  Google Scholar 

  32. 32.

    Rodriguez-Gaztelumendi, A., Alvehus, M., Andersson, T. & Jacobsson, S. O. P. Comparison of the effects of nicotine upon the transcellular electrical resistance and sucrose permeability of human ECV304/rat C6 co-cultures and human CaCo2 cells. Toxicol. Lett. 207, 1–6 (2011).

    CAS  PubMed  Google Scholar 

  33. 33.

    Jones, H. M. & Rowland-Yeo, K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacomet. Pharmacol. 2, 1–12 (2013).

    Google Scholar 

  34. 34.

    Yamazaki, H. et al. Human blood concentrations of cotinine, a biomonitoring marker for tobacco smoke, extrapolated from nicotine metabolism in rats and humans and physiologically based pharmacokinetic modeling. Int. J. Environ. Res. Publ. Health 7, 3406–3421 (2010).

    CAS  Google Scholar 

  35. 35.

    Hartmann, J. T. & Lipp, H.-P. Toxicity of platinum compounds. Expert Opin. Pharmacother. 4, 889–901 (2003).

    CAS  PubMed  Google Scholar 

  36. 36.

    Chou, D. B. et al. On-chip recapitulation of clinical bone marrow toxicities and patient-specific pathophysiology. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-019-0495-z (2020).

  37. 37.

    Sparreboom, A., Nooter, K., Loos, W. J. & Verweij, J. The (ir)relevance of plasma protein binding of anticancer drugs. Neth. J. Med. 59, 196–207 (2001).

    CAS  PubMed  Google Scholar 

  38. 38.

    Rajkumar, P. et al. Cisplatin concentrations in long and short duration infusion: implications for the optimal time of radiation delivery. J. Clin. Diagn. Res. 10, XC01–XC04 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Wikswo, J. P. et al. Scaling and systems biology for integrating multiple organs-on-a-chip. Lab Chip 13, 3496–3511 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Maass, C., Stokes, C. L., Griffith, L. G. & Cirit, M. Multi-functional scaling methodology for translational pharmacokinetic and pharmacodynamic applications using integrated microphysiological systems (MPS). Integr. Biol. 9, 290–302 (2017).

    Google Scholar 

  41. 41.

    Neault, J. F. & Tajmir-Riahi, H. A. Interaction of cisplatin with human serum albumin. Drug binding mode and protein secondary structure. Biochim. Biophys. Acta 1384, 153–159 (1998).

    CAS  PubMed  Google Scholar 

  42. 42.

    Vickers, A. E. M. et al. Kidney slices of human and rat to characterize cisplatin-induced injury on cellular pathways and morphology. Toxicol. Pathol. 32, 577–590 (2004).

    CAS  PubMed  Google Scholar 

  43. 43.

    Huang, Q. et al. Assessment of cisplatin-induced nephrotoxicity by microarray technology. Toxicol. Sci. 63, 196–207 (2001).

    CAS  PubMed  Google Scholar 

  44. 44.

    Maass, C. et al. Establishing quasi-steady state operations of microphysiological systems (MPS) using tissue-specific metabolic dependencies. Sci. Rep. 8, 8015 (2018).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Huh, D. et al. Microfabrication of human organs-on-chips. Nat. Protoc. 8, 2135–2157 (2013).

    CAS  PubMed  Google Scholar 

  46. 46.

    Park, T. -E. et al. Hypoxia-enhanced blood-brain barrier chip recapitulates human barrier function, drug penetration, and antibody shuttling properties. Nat. Commun. 10, 2621 (2019).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Elamin, E. E. et. al. in Molecular Aspects of Alcohol and Nutrition: A Volume in the Molecular Nutrition Series (ed. Patel, V. B.) Ch. 14 (Elsevier, 2016).

  48. 48.

    Henry, O. Y. F. et al. Organs-on-chips with integrated electrodes for trans-epithelial electrical resistance (TEER) measurements of human epithelial barrier function. Lab Chip 17, 2264–2271 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Maoz, B. M. et al. Organs-on-Chips with combined multi-electrode array and transepithelial electrical resistance measurement capabilities. Lab Chip 17, 2294–2302 (2017).

    CAS  PubMed  Google Scholar 

  50. 50.

    Przekwas, A., Friend, T., Teixeira, R., Chen, Z. & Wilkerson, P. Spatial Modeling Tools for Cell Biology (Air Force Research Laboratory Information Directorate Rome Research Site, 2006).

  51. 51.

    Adams, B. M. et al. Dakota, a Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (OSTI, 2014).

  52. 52.

    Amidon, G. L., Lennernäs, H., Shah, V. P. & Crison, J. R. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm. Res. 12, 413–420 (1995).

    CAS  PubMed  Google Scholar 

  53. 53.

    O’Hara, T. et al. In vivo-in vitro correlation (IVIVC) modeling incorporating a convolution step. J. Pharmacokinet. Pharmacodyn. 28, 277–298 (2001).

    PubMed  Google Scholar 

  54. 54.

    Howell, B. A. et al. In vitro to in vivo extrapolation and species response comparisons for drug-induced liver injury (DILI) using DILIsymTM: a mechanistic, mathematical model of DILI. J. Pharmacokinet. Pharmacodyn. 39, 527–541 (2012).

    PubMed  Google Scholar 

  55. 55.

    Poulin, P. & Haddad, S. Toward a new paradigm for the efficient in vitro-in vivo extrapolation of metabolic clearance in humans from hepatocyte data. J. Pharm. Sci. 102, 3239–3251 (2013).

    CAS  PubMed  Google Scholar 

  56. 56.

    Chen, Y., Jin, J. Y., Mukadam, S., Malhi, V. & Kenny, J. R. Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies. Biopharm. Drug Dispos. 33, 85–98 (2012).

    PubMed  Google Scholar 

  57. 57.

    Rostami-Hodjegan, A. Physiologically based pharmacokinetics joined with in vitro-in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin. Pharmacol. Ther. 92, 50–61 (2012).

    CAS  PubMed  Google Scholar 

  58. 58.

    Cirit, M. & Stokes, C. L. Maximizing the impact of microphysiological systems with in vitro-in vivo translation. Lab Chip 18, 1831–1837 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Zhu, C., Jiang, L., Chen, T.-M. & Hwang, K.-K. A comparative study of artificial membrane permeability assay for high throughput profiling of drug absorption potential. Eur. J. Med. Chem. 37, 399–407 (2002).

    CAS  PubMed  Google Scholar 

  60. 60.

    Hukkanen, J., Jacob, P. & Benowitz, N. L. Metabolism and disposition kinetics of nicotine. Pharmacol. Rev. 57, 79–115 (2005).

    CAS  PubMed  Google Scholar 

  61. 61.

    Riley, R. J., McGinnity, D. F. & Austin, R. P. A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes. Drug Metab. Dispos. 33, 1304–1311 (2005).

    CAS  PubMed  Google Scholar 

  62. 62.

    Chiba, M., Ishii, Y. & Sugiyama, Y. Prediction of hepatic clearance in human from in vitro data for successful drug development. AAPS J. 11, 262–276 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Jamei, M. et al. A mechanistic framework for in vitro-in vivo extrapolation of liver membrane transporters: prediction of drug-drug interaction between rosuvastatin and cyclosporine. Clin. Pharmacokinet. 53, 73–87 (2014).

    CAS  PubMed  Google Scholar 

  64. 64.

    Sluka, J. P. et al. A liver-centric multiscale modeling framework for xenobiotics. PLoS ONE 11, e0162428 (2016).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Clancy, C. E. et al. Multiscale modeling in the clinic: drug design and development. Ann. Biomed. Eng. 44, 2591–2610 (2016).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Kannan, R. R., Singh, N. & Przekwas, A. A compartment-quasi-3D multiscale approach for drug absorption, transport, and retention in the human lungs. Int. J. Numer. Method Biomed. Eng. 34, e2955 (2018).

    PubMed  Google Scholar 

  67. 67.

    Tsamandouras, N. et al. Integrated gut and liver microphysiological systems for quantitative in vitro pharmacokinetic studies. AAPS J. 19, 1499–1512 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Gong, C. et al. Hepatotoxicity and pharmacokinetics of cisplatin in combination therapy with a traditional Chinese medicine compound of Zengmian Yiliu granules in ICR mice and SKOV-3-bearing nude mice. BMC Complement. Altern. Med. 15, 283 (2015).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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).

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Contributions

A.H., B.M.M., R.N. and R.P.-B. helped to design and manage the multi-organ linking studies, led the data analysis for generation of figures as well as assembly of the manuscript with D.E.I. D.D., M.R.S. and A.P. were responsible for DM-PBPK/PD model development and data analysis working closely with A.H., B.M.M., R.P.-B. and R.N. A.H., B.M.M., M.C., T.H. and S.S.F.J. planned and performed biological experiments with the help of R.N., Y.M., B.S., A.S.P. and S.J.-F., all under the supervision of O.L., A.C., R.N., R.P.-B., K.K.P. and D.E.I. R.N., B.C., K.K.P. and D.E.I. were responsible for chip development and fabrication. M.I., S.M, A.D. and R.N. were responsible for software and hardware engineering and operation of the linking studies. R.P.-B., R.N., K.K.P. and D.E.I. were responsible for overseeing the entire effort, including preparation of the manuscript.

Corresponding author

Correspondence to Donald E. Ingber.

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Competing interests

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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