Annals of Biomedical Engineering

, Volume 40, Issue 6, pp 1228–1243 | Cite as

The Current Status of Alternatives to Animal Testing and Predictive Toxicology Methods Using Liver Microfluidic Biochips

  • Jean Matthieu Prot
  • Eric LeclercEmail author


In this paper, we will consider new in vitro cell culture platforms and the progress made, based on the microfluidic liver biochips dedicated to pharmacological and toxicological studies. Particular emphasis will be given to recent developments in the microfluidic tools dedicated to cell culture (more particularly liver cell culture), in silico opportunities for Physiologically Based PharmacoKinetic (PBPK) modelling, the challenge of the mechanistic interpretations offered by the approaches resulting from “multi-omics” data (transcriptomics, proteomics, metabolomics, cytomics) and imaging microfluidic platforms. Finally, we will discuss the critical features regarding microfabrication, design and materials, and cell functionality as the key points for the future development of new microfluidic liver biochips.


Liver Microfluidic biochips PBPK models Transcriptomics Proteomics Metabolomics Cytomics Alternative methods Predictive toxicology 



Mouse fibroblast cell line


Human lung alveolar carcinoma cell line


Absorption, Distribution, Metabolism and Excretion


Acetyl-P-AminoPhenol (acetaminophen)


Conseil Européen des Industries Chimiques


Cytochromes P450


Ethoxy Resorufin O Deethylase


Gene Set Enrichment Analysis




High Content Screening


Human colon carcinoma cell line


Human hepatoma cell line


Hepatoma-derived cell line


Human liver hepatocarcinoma cell line/subclone C3a


Renal tubular proximal cell line


Primary human preadipocyte


Inhibition Concentration of 50% of the analysed endpoints


Kyoto Encyclopedia of Genes and Genomes


Lethal dose leading to the death of 50% of population


Human breast adenocarcinoma cell line


Madin Darby Canine Kidney cell line


Multi Drug Resistance 1 (P-glycoprotein 1)


Multi drug resistant associated protein number 2 gene


Mass spectrometry


3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide


N-Acetyl-P-Benzoquinone Imine


Nuclear Magnetic Resonance spectrometry


Physiologically Based PharmacoKinetic






Registration, Evaluation and Authorization of Chemicals







Jean Matthieu Prot received a grant from the post grenelle 189 project “Activism”. The UTC liver microfluidic biochips project is supported by the foundation of the University of Technology of Compiègne “La Fondation UTC pour l’innovation” via the “puce à cellule” project. The project was also supported by the ANR PCV 2007 program via the “μHepaReTox” project and by the ANR CP2D 2007 program via the SysBioX project. Finally, we thank Frederic Bois and Céline Brochot for their help in the discussion-redaction of the review.

Conflict of interest

We declare to have no conflict of interest.


  1. 1.
    Abraham, V. C., D. L. Taylor, and J. R. Haskins. High content screening applied to large-scale cell biology. Trends Biotechnol. 22:15–22, 2004.PubMedCrossRefGoogle Scholar
  2. 2.
    Albrecht, D. R., G. H. Underhill, J. Resnikoff, A. Mendelson, S. N. Bhatia, and J. V. Shah. Microfluidics-integrated time-lapse imaging for analysis of cellular dynamics. Integr. Biol. 2:278–287, 2010.CrossRefGoogle Scholar
  3. 3.
    Anderson, J., D. Chiu, J. McDonald, R. Jackman, O. Cherniavskaya, H. Wu, S. Whitesides, and G. Whitesides. Fabrication of topologically complex three-dimensional microfluidic systems in PDMS by rapid prototyping. Anal. Chem. 72:3158–3164, 2000.PubMedCrossRefGoogle Scholar
  4. 4.
    Aninat, C., A. Piton, D. Glaise, T. Le Charpentier, S. Langouet, F. Morel, et al. Expression of cytochromes P450, conjugating enzymes and nuclear receptors in human hepatoma HepaRG cells. Drug Metab. Dispos. 34:75–83, 2006.PubMedCrossRefGoogle Scholar
  5. 5.
    Baudoin, R., A. Corlu, L. Griscom, C. Legallais, and E. Leclerc. Trends in the development of microfluidic cell biochips for in vitro hepatotoxicity. Toxicol. In Vitro 21:535–544, 2007.PubMedCrossRefGoogle Scholar
  6. 6.
    Baudoin, R., L. Griscom, J. M. Prot, C. Legallais, and E. Leclerc. Behavior of HepG2/C3A cell cultures in a microfluidic bioreactor. Biochem. Eng. J. 53:172–181, 2011.CrossRefGoogle Scholar
  7. 7.
    Becker, H., and C. Gärtner. Polymer microfabrication methods for microfluidic analytical applications. Electrophoresis 21:12–26, 2000.PubMedCrossRefGoogle Scholar
  8. 8.
    Beger, R., J. Sun, and L. Schnackenberg. Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity. Toxicol. Appl. Pharmacol. 243:154–166, 2010.PubMedCrossRefGoogle Scholar
  9. 9.
    Bhatia, S. N., M. L. Yarmush, and M. Toner. Controlling cell interactions by micropatterning in co-cultures: hepatocytes and 3T3 fibroblasts. J. Biomed. Mater. Res. 34:189–199, 1997.PubMedCrossRefGoogle Scholar
  10. 10.
    Bhogal, N., C. Grindon, R. Combes, and M. Balls. Toxicity testing: creating a revolution based on new technologies. Trends Biotechnol. 23:299–307, 2005.PubMedCrossRefGoogle Scholar
  11. 11.
    Blaauboer, B. J., and M. E. Andersen. The need for a new toxicity testing and risk analysis paradigm to implement REACH or any other large scale testing initiative. Arch. Toxicol. 81:385–387, 2007.PubMedCrossRefGoogle Scholar
  12. 12.
    Blanchard, N., E. Alexandre, C. Abadie, T. Lavé, B. Heyd, G. Mantion, D. Jaeck, L. Richert, and P. Coassolo. Comparison of clearance predictions using primary cultures and suspensions of human hepatocytes. Xenobiotica 35:1–15, 2005.PubMedCrossRefGoogle Scholar
  13. 13.
    Blanchard, N., L. Richert, B. Notter, F. Delobel, P. David, P. Coassolo, and T. Lavé. Impact of serum on clearance predictions obtained from suspensions and primary cultures of rat hepatocytes. Eur. J. Pharm. Sci. 23:189–199, 2004.PubMedCrossRefGoogle Scholar
  14. 14.
    Boverhof, D. R., L. D. Burgoon, C. Tashiro, B. Sharratt, B. Chittim, J. R. Harkema, et al. Comparative toxicogenomic analysis of the hepatotoxic effects of TCDD in Sprague Dawley rats and C57BL/6 mice. Toxicol. Sci. 94:398–416, 2006.PubMedCrossRefGoogle Scholar
  15. 15.
    Chao, P., T. Maguire, E. Novik, K. C. Cheng, and M. L. Yarmush. Evaluation of a microfluidic based cell culture platform with primary human hepatocytes for the prediction of hepatic clearance in human. Biochem. Pharmacol. 78:625–632, 2009.PubMedCrossRefGoogle Scholar
  16. 16.
    Charati, S., and S. Stern. Diffusion of gases in silicone polymer: molecular dynamics simulations. Macromolecules 31:5529–5535, 1998.CrossRefGoogle Scholar
  17. 17.
    Cheng, S., J. M. Prot, E. Leclerc, and F. Y. Bois. Zonation-related pathways in human hepatocellular carcinoma cells in dynamic vs. static culture microenvironments. BMC Genomics, 2011 (to appear).Google Scholar
  18. 18.
    Choucha-Snouber, L., L. Griscom, P. E. Poleni, F. Razan, C. Brochot, C. Aninat, A. Corlu, C. Legallais, and E. Leclerc. Liver kidney microfluidic bioreactor for cell coculture in drug studies. In: Proceedings of the Miniaturized Total Analysis System, μTAS, Conference 2010, Groningen, The Netherlands.Google Scholar
  19. 19.
    Claude, N., F. Goldfain-Blanc, and A. Guillouzo. La place des methods in silico, in vitro, in omic dans l’évaluation de la sécurité des medicaments. Med. Sci. 25:105–110, 2009.Google Scholar
  20. 20.
    Domansky, K., W. Inman, J. Serdy, A. Dash, M. H. Lim, and L. G. Griffith. Perfused multiwell plate for 3D liver tissue engineering. Lab Chip 10:51–58, 2010.PubMedCrossRefGoogle Scholar
  21. 21.
    Dorn, J. F., G. Danuser, and G. Yang. Computational processing and analysis of dynamic fluorescence image data. Methods Cell Biol. 85:497–538, 2008.PubMedCrossRefGoogle Scholar
  22. 22.
    Dumas, M. E., R. Barton, A. Toye, O. Cloarec, C. Blancher, A. Rothwell, J. Fearnside, R. Tatoud, V. Blanc, J. Lindon, S. V. Mitchell, E. Holmes, M. I. McCarthy, J. Scott, D. Gauguier, and J. Nicholson. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc. Natl. Acad. Sci. USA 103:12511–12516, 2006.PubMedCrossRefGoogle Scholar
  23. 23.
    Eisenbrand, G., B. Pool-Zobel, V. Baker, M. Balls, B. J. Blaauboer, A. Boobis, et al. Methods of in vitro toxicology. Food Chem. Toxicol. 40:193–236, 2002.PubMedCrossRefGoogle Scholar
  24. 24.
    Eschbach, E., S. S. Chatterjee, M. Noldner, E. Gottwald, H. Dertinger, K. F. Weibezahn, and G. Knedlitschek. Microstructured scaffolds for liver tissue cultures of high cell density: morphological and biochemical characterization of tissue aggregates. J. Cell. Biochem. 95:243–255, 2005.PubMedCrossRefGoogle Scholar
  25. 25.
    Fiehn, O., J. Kopka, P. Dörmann, T. Altmann, R. N. Trethewey, and L. Willmitzer. Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18:1157–1161, 2000.PubMedCrossRefGoogle Scholar
  26. 26.
    Figeys, D. Combining different ‘omics’ technologies to map and validate protein–protein interactions in humans. Brief Funct Genomic Proteomics 2:357–365, 2004.CrossRefGoogle Scholar
  27. 27.
    Gibney, M., M. Walsh, L. Brennan, H. Roche, B. German, and B. van Ommen. Metabolomics in human nutrition: opportunities and challenges. Am. J. Clin. Nutr. 82:497–503, 2005.PubMedGoogle Scholar
  28. 28.
    Greim, H. Toxicological comments to the discussion about REACH (H. Greim, M. Arand, H. Autrup, H.M. Bolt, J. Bridges, E. Dybing, R. Glomot, V. Foa, R. Schulte-Hermann, Arch Toxicol 2006, 80: 121–124). Reply to the letter to the editor: the need for a new toxicity testing and risk analysis paradigm to implement REACH or any other large scale testing initiative, by B.J. Blaauboer and M.E. Andersen (Arch Toxicol 2007, 81: 385–387). Arch. Toxicol. 80:121–124, 2007.CrossRefGoogle Scholar
  29. 29.
    Griffin, S. J., and J. B. Houston. Prediction of in vitro intrinsic clearance from hepatocytes: comparison of suspensions and monolayer cultures. Drug Metab. Dispos. 33:115–120, 2005.PubMedGoogle Scholar
  30. 30.
    Griffith, L., and G. Naughton. Tissue engineering—current challenges and expanding opportunities. Science 295:1009–1014, 2002.PubMedCrossRefGoogle Scholar
  31. 31.
    Gripon, P., S. Rumin, S. Urban, J. Le Seyec, D. Glaise, I. Cannie, et al. Infection of a human hepatoma cell line by hepatitis B virus. Proc. Natl. Acad. Sci. USA 99:15655–15660, 2002.PubMedCrossRefGoogle Scholar
  32. 32.
    Haenen, B., C. Rompelberg, K. Van Twillert, M. Hamzink, J. Dormans, and J. Van Eljkeren. Utility of rat liver slices to estimate hepatic clearance for application in physiologically based pharmacokinetic modeling: a study with tolbutamide, a compound with low extraction efficiency. Drug Metab. Dispos. 30:307–313, 2001.CrossRefGoogle Scholar
  33. 33.
    Haney, S. A., P. LaPan, J. Pan, and J. Zhang. High-content screening moves to the front of the line. Drug Discov. Today 11:889–894, 2006.PubMedCrossRefGoogle Scholar
  34. 34.
    Hartung, T., and C. Rovida. Chemical regulators have overreached. Nature 460:1080–1081, 2009.PubMedCrossRefGoogle Scholar
  35. 35.
    Herrera, G., L. Diaz, A. Martinez-Romero, A. Gomes, E. Villamon, R. C. Callaghan, et al. Cytomics: a multiparametric, dynamic approach to cell research. Toxicol. In Vitro 21:176–182, 2007.PubMedCrossRefGoogle Scholar
  36. 36.
    Hewitt, N. J., K. U. Bühring, J. Dasenbrock, J. Haunschild, B. Ladstetter, and D. Utesch. Studies comparing in vivo:in vitro metabolism of three pharmaceutical compounds in rat, dog, monkey, and human using cryopreserved hepatocytes, microsomes, and collagen gel immobilized hepatocyte cultures. Drug Metab. Dispos. 29:1042–1050, 2001.PubMedGoogle Scholar
  37. 37.
    Houston, J. B. Relevance of in vitro kinetic parameters to in vivo metabolism of xenobiotics. Toxicol. In Vitro 8:507–512, 1994.PubMedCrossRefGoogle Scholar
  38. 38.
    Houston, J. B., and D. J. Carlile. Prediction of hepatic clearance from microsomes, hepatocytes and liver slices. Drug Metab. Rev. 29:891–922, 1997.PubMedCrossRefGoogle Scholar
  39. 39.
    Ito, K., and J. B. Houston. Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm. Res. (NY) 21:785–792, 2004.CrossRefGoogle Scholar
  40. 40.
    Iwatsubo, T., N. Hirota, T. Ooie, H. Suzuki, N. Shimada, K. Chiba, T. Ishizaki, C. E. Green, C. A. Tyson, and Y. Sugiyama. Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacol. Ther. 73:147–171, 1997.PubMedCrossRefGoogle Scholar
  41. 41.
    Kane, B. J., M. J. Zinner, M. L. Yarmush, and M. Toner. Liver-specific functional studies in a microfluidic array of primary mammalian hepatocytes. Anal. Chem. 78:4291–4298, 2006.PubMedCrossRefGoogle Scholar
  42. 42.
    Kanehisa, M., S. Goto, M. Furumichi, M. Tanabe, and M. Hirakawa. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38:355–360, 2010.CrossRefGoogle Scholar
  43. 43.
    Khetani, S. R., and S. N. Bhatia. Microscale human liver tissue for drug development. Nat. Biotechnol. 26:120–126, 2008.PubMedCrossRefGoogle Scholar
  44. 44.
    Kidambi, S., R. Yarmush, E. Novik, P. B. Chao, M. L. Yarmush, and Y. Nahmias. Oxygen-mediated enhancement of metabolism, functional polarization, gene expression, and drug clearance in co-cultures of primary hepatocytes. PNAS 106:15714–15719, 2009.PubMedCrossRefGoogle Scholar
  45. 45.
    Kimura, H., H. Nakayama, T. Yamamoto, Y. Sakai, and T. Fujii. Development of on chip co culture system for cytotoxicity test using Caco-2 and HepG2. IEEJ 129:252–258, 2009.Google Scholar
  46. 46.
    Kimura, H., T. Yamamoto, H. Sakai, Y. Sakai, and T. Fujii. An integrated microfluidic system for long-term perfusion culture and on-line monitoring of intestinal tissue models”. Lab Chip 8:741–746, 2008.PubMedCrossRefGoogle Scholar
  47. 47.
    Korn, K., and E. Krausz. Cell-based high-content screening of small-molecule libraries. Curr. Opin. Chem. Biol. 11:503–510, 2007.PubMedCrossRefGoogle Scholar
  48. 48.
    Leclerc, E., Y. Sakai, and T. Fujii. Cell culture in 3-dimensional microfluidic structure of PDMS (polydimethylsiloxane). Biomed. Microdev. 5(2):109–114, 2003.CrossRefGoogle Scholar
  49. 49.
    Leclerc, E., Y. Sakai, and T. Fujii. Perfusion culture of fetal human hepatocytes in microfluidic-environments. Biochem. Eng. J. 20:143–148, 2004.CrossRefGoogle Scholar
  50. 50.
    Leclerc, E., Y. Sakai, and T. Fujii. Microfluidic PDMS (polydimethylsiloxane) bioreactors for large scale culture of hepatocytes. Biotech. Prog. 20:750–755, 2004.CrossRefGoogle Scholar
  51. 51.
    Lee, P., T. Gaige, and P. Hung. Microfluidic systems for live cell imaging. Methods Cell Biol. 102:77–103, 2011.PubMedCrossRefGoogle Scholar
  52. 52.
    Lee, P., N. Ghorashian, T. Gaige, and P. Hung. Microfluidic system for automated cell based assays. J. Assoc. Lab. Autom. 12:363–367, 2007.CrossRefGoogle Scholar
  53. 53.
    Lee, S., and B. J. Howell. High-content screening: emerging hardware and software technologies. Methods Enzymol. 414:468–483, 2006.PubMedCrossRefGoogle Scholar
  54. 54.
    Lee, M. Y., R. A. Kumar, S. M. Sukumaran, M. G. Hogg, D. S. Clark, and J. S. Dordick. Three-dimensional cellular microarray for high-throughput toxicology assays. Proc. Natl Acad. Sci. USA 105:59–63, 2008.PubMedCrossRefGoogle Scholar
  55. 55.
    Ma, B., G. Zhang, J. Qin, and B. Lin. Characterization of drug metabolites and cytotoxicity assay simultaneously using an integrated microfluidic device. Lab Chip 9:232–238, 2009.PubMedCrossRefGoogle Scholar
  56. 56.
    Madalinski, G., E. Godat, S. Alves, D. Lesage, E. Genin, P. Levi, J. Labarre, J. C. Tabet, E. Ezan, and C. Junot. Direct introduction of biological samples into a LTQ-Orbitrap hybrid mass spectrometer as a tool for fast metabolome analysis. Anal. Chem. 80:3291–3303, 2009.CrossRefGoogle Scholar
  57. 57.
    Mahler, G. J., M. B. Esch, R. P. Glahn, and M. L. Shuler. Characterization of a gastrointestinal tract microscale cell culture analog used to predict drug toxicity. Biotechnol. Bioeng. 104:193–205, 2009.PubMedCrossRefGoogle Scholar
  58. 58.
    Midwoud, P. M., G. M. M. van Groothuis Merema, M. T. Merema, and E. Verpoorte. Microfluidic biochip for the perifusion of precision-cut rat liver slices for metabolism and toxicology studies. Biotech. Bioeng. 105:184–194, 2010.CrossRefGoogle Scholar
  59. 59.
    Midwoud, P. M., M. T. van Merema, E. Verpoorte, and G. M. M. Groothuis. A microfluidic approach for in vitro assessment of interorgan interactions in drug metabolism using intestinal and liver slices. Lab Chip 10:2778–2786, 2010.PubMedCrossRefGoogle Scholar
  60. 60.
    Nicholson, J. K., J. Connelly, J. C. Lindon, and E. Holmes. Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1:153–161, 2002.PubMedCrossRefGoogle Scholar
  61. 61.
    Nicholson, J. K., J. C. Lindon, and E. Holmes. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29:1181–1189, 1999.PubMedCrossRefGoogle Scholar
  62. 62.
    Novik, E., T. J. Maguire, P. Chao, K. C. Cheng, and M. L. Yarmush. A microfluidic hepatic coculture platform for cell-based drug metabolism studies. Biochem. Pharmacol. 79:1036–1044, 2010.PubMedCrossRefGoogle Scholar
  63. 63.
    Paine, S., A. Parker, P. Gardiner, P. Webborn, and R. Riley. Prediction of the pharmacokinetics of atorvastatin, cerivastatin, and indomethacin using kinetic models applied to isolated rat hepatocytes. Drug Metab. Dispos. 36:1365–1374, 2008.PubMedCrossRefGoogle Scholar
  64. 64.
    Pang, K. S., and M. Rowland. Hepatic clearance of drugs. II. Experimental evidence for acceptance of the “well-stirred” model over the “parallel tube” model using lidocaine in the perfused rat liver in situ preparation. J. Pharmacokinet. Biopharm. 5:655–680, 1977.PubMedCrossRefGoogle Scholar
  65. 65.
    Pang, K. S., and M. Rowland. Hepatic clearance of drugs. I. Theoretical considerations of a “well-stirred” model and a “parallel tube” model. Influence of hepatic blood flow, plasma and blood cell binding, and the hepatocellular enzymatic activity on hepatic drug clearance. J. Pharmacokinet. Biopharm. 5:625–653, 1977.PubMedCrossRefGoogle Scholar
  66. 66.
    Paran, Y., M. Ilan, Y. Kashman, S. Goldstein, Y. Liron, B. Geiger, et al. High-throughput screening of cellular features using high-resolution light-microscopy; application for profiling drug effects on cell adhesion. J. Struct. Biol. 158:233–243, 2007.PubMedCrossRefGoogle Scholar
  67. 67.
    Pelkonen, O., and M. Turpeinen. In vitro-in vivo extrapolation of hepatic clearance: biological tools, scaling factors, model assumptions and correct concentrations. Xenobiotica. 37:1066–1089, 2007.PubMedCrossRefGoogle Scholar
  68. 68.
    Powers, M. J., D. Janigan, K. E. Wack, C. S. Baker, D. B. Stolz, and L. Griffith. Functional behavior of primary rat liver cells in a three-dimensional perfused microarray bioreactor. Tissue Eng. 8:499–508, 2002.PubMedCrossRefGoogle Scholar
  69. 69.
    Prot, J. M., C. Aninat, L. Griscom, F. Razan, C. Brochot, C. Guguen Guillouzo, C. Legallais, A. Corlu, and E. Leclerc. Improvement of HepG2/C3a cell functions in a microfluidic biochip. Biotechnol. Bioeng. 108:1704–1715, 2011.PubMedCrossRefGoogle Scholar
  70. 70.
    Prot, J. M., A. S. Briffaut, F. Letourneur, P. Chafey, F. Merlier, Y. Grandvalet, C. Legallais, and E. Leclerc. Integrated proteomic and transcriptomic investigation highlights original insight into paracetamol toxicity in liver biochip. PLoS One 6:e21268, 2011.PubMedCrossRefGoogle Scholar
  71. 71.
    Prot, J. M., A. Bunescu, B. Elena-Hermann, C. Aninat, L. Choucha-Snouber, L. Griscom, F. Bois, C. Cécile Legallais, C. Céline Brochot, A. Anne Corlu, M. D. Dumas, and E. Leclerc. Alternative method in predictive toxicology using systemic biology on chip: application to acetaminophen injury. Toxicol. Appl. Pharmacol., 2011 (to appear).Google Scholar
  72. 72.
    Prot, J. M., O. Videau, C. Brochot, C. Legallais, H. Benech, and E. Leclerc. A cocktail of metabolic probes demonstrates the relevance of primary human hepatocyte cultures in a microfluidic biochip for pharmaceutical drug screening. Int. J. Pharm. 408:67–75, 2011.PubMedCrossRefGoogle Scholar
  73. 73.
    Riley, R., D. F. McGinnity, and R. P. Austn. A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocyte and microsomes. Drug Metab. Dispos. 33:1304–1311, 2005.PubMedCrossRefGoogle Scholar
  74. 74.
    Sheikh-Bahaei, S., S. H. J. Kim, S. Sheikh-Bahaei, and C. A. Hunt. Understanding the role of liver zonation in toxin elimination. Int. J. Intell. Control Syst. 14:33–40, 2009.Google Scholar
  75. 75.
    Shibata, Y., H. Takahashi, M. Chiba, and Y. Ishii. Prediction of hepatic clearance and availability by cryopreserved human hepatocytes: an application of serum incubation method. Drug Metab. Dispos. 30:892–896, 2002.PubMedCrossRefGoogle Scholar
  76. 76.
    Shibata, Y., H. Takahashi, and Y. Ishii. A convenient in vitro screening method for predicting in vivo drug metabolic clearance using isolated hepatocytes suspended in serum. Drug Metab. Dispos. 28:1518–1523, 2000.PubMedGoogle Scholar
  77. 77.
    Shintu, L., R. Baudoin, V. Navratil, J. M. Prot, C. Pontoizeau, M. Defernez, B. Blaise, C. Domange, A. Péry, P. Toulhoat, C. Legallais, C. Brochot, E. Leclerc, and M. E. Dumas. Metabolomics-on-a-chip and predictive systems toxicology in microfluidic. Bioartificial organs. Anal. Chem., 2011 (to appear).Google Scholar
  78. 78.
    Sivaraman, A., J. K. Leach, S. Townsend, T. Iida, B. J. Hogan, D. B. Stolz, R. Fry, L. D. Samson, S. R. Tannenbaum, and L. G. Griffith. A microscale in vitro physiological model of the liver: predictive screens for drug metabolism and enzyme induction. Curr. Drug Metab. 6:569–592, 2005.PubMedCrossRefGoogle Scholar
  79. 79.
    Sung, J. H., C. Kam, and M. J. Shuler. A microfluidic device for a pharmacokinetic–pharmacodynamic (PK-PD) model on a chip. Lab Chip 10:446–455, 2010.PubMedCrossRefGoogle Scholar
  80. 80.
    Tilles, A., H. Baskaran, P. Roy, M. Yarmush, and M. Toner. Effects of oxygenation and flow on the viability and function of rat hepatocytes cocultured in a microchannel flat-plate bioreactor. Biotech. Bioeng. 73:379–389, 2001.CrossRefGoogle Scholar
  81. 81.
    Toh, Y. C., T. C. Lim, D. Tai, G. Xiao, D. van Noort, and H. Yu. A microfluidic 3D hepatocyte chip for drug toxicity testing. Lab Chip 9:2026–2031, 2009.PubMedCrossRefGoogle Scholar
  82. 82.
    Viravaidya, K., and M. L. Shuler. Incorporation of 3T3-L1 cells to mimic bioaccumulation in a microscale cell culture analog device for toxicity studies. Biotechnol. Prog. 20:590–597, 2004.PubMedCrossRefGoogle Scholar
  83. 83.
    Walker, G. M., H. C. Zeringue, and D. J. Beebe. Microenvironment design considerations for cellular scale studies. Lab Chip 4:91–97, 2004.PubMedCrossRefGoogle Scholar
  84. 84.
    Wood, D. K., D. M. Weingeist, S. N. Bhatia, and B. P. Engelward. Single cell trapping and DNA damage analysis using microwell arrays. PNAS 107:10008–10013, 2010.PubMedCrossRefGoogle Scholar
  85. 85.
    Yu, H., I. Meyvantsson, I. A. Shkel, and D. J. Beebe. Diffusion dependent cell behavior in microenvironments. Lab Chip 5:1089–1095, 2005.PubMedCrossRefGoogle Scholar
  86. 86.
    Zhang, S., W. Tong, B. Zheng, T. Susanto, L. Xia, C. Zhang, A. Ananthanarayanan, X. Tuo, R. Sakban, R. Jia, C. Iliescu, K. H. Chai, M. McMillian, S. Shen, H. Leo, and H. Yu. Robust high-throughput sandwich cell-based drug screening platform. Biomaterials 32:1229–1241, 2011.PubMedCrossRefGoogle Scholar
  87. 87.
    Zhang, C., Z. Zhao, N. Abdul Rahim, D. van Noort, and H. Yu. Towards a human-on-chip: culturing multiple cell types on a chip with compartmentalized microenvironments. Lab Chip 9:3165–3312, 2009.CrossRefGoogle Scholar
  88. 88.
    Zuegge, J., G. Schneider, P. Coassolo, and T. Lave. Prediction of hepatic metabolic clearance: comparison and assessment of prediction models. Clin. Pharmacokinet. 40:553–563, 2001.PubMedCrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2011

Authors and Affiliations

  1. 1.CNRS UMR 6600, Laboratoire de Biomécanique et BioingénierieUniversité de Technologie de CompiègneCompiègneFrance

Personalised recommendations