Archives of Toxicology

, Volume 92, Issue 3, pp 1295–1310 | Cite as

Prediction of metabolism-induced hepatotoxicity on three-dimensional hepatic cell culture and enzyme microarrays

  • Kyeong-Nam Yu
  • Sashi Nadanaciva
  • Payal Rana
  • Dong Woo Lee
  • Bosung Ku
  • Alexander D. Roth
  • Jonathan S. Dordick
  • Yvonne Will
  • Moo-Yeal LeeEmail author
Organ Toxicity and Mechanisms


Human liver contains various oxidative and conjugative enzymes that can convert nontoxic parent compounds to toxic metabolites or, conversely, toxic parent compounds to nontoxic metabolites. Unlike primary hepatocytes, which contain myriad drug-metabolizing enzymes (DMEs), but are difficult to culture and maintain physiological levels of DMEs, immortalized hepatic cell lines used in predictive toxicity assays are easy to culture, but lack the ability to metabolize compounds. To address this limitation and predict metabolism-induced hepatotoxicity in high-throughput, we developed an advanced miniaturized three-dimensional (3D) cell culture array (DataChip 2.0) and an advanced metabolizing enzyme microarray (MetaChip 2.0). The DataChip is a functionalized micropillar chip that supports the Hep3B human hepatoma cell line in a 3D microarray format. The MetaChip is a microwell chip containing immobilized DMEs found in the human liver. As a proof of concept for generating compound metabolites in situ on the chip and rapidly assessing their toxicity, 22 model compounds were dispensed into the MetaChip and sandwiched with the DataChip. The IC50 values obtained from the chip platform were correlated with rat LD50 values, human C max values, and drug-induced liver injury categories to predict adverse drug reactions in vivo. As a result, the platform had 100% sensitivity, 86% specificity, and 93% overall predictivity at optimum cutoffs of IC50 and C max values. Therefore, the DataChip/MetaChip platform could be used as a high-throughput, early stage, microscale alternative to conventional in vitro multi-well plate platforms and provide a rapid and inexpensive assessment of metabolism-induced toxicity at early phases of drug development.


Metabolism-induced hepatotoxicity Three-dimensional (3D) cell culture array Metabolizing enzyme microarray DataChip/MetaChip High-throughput toxicity screening 



We acknowledge support from the National Institute of Environmental Health Sciences (ES018022, ES012619, and ES025779) and National Science Foundation (IIP-0740592). This work was partly supported by Samsung Electro-Mechanics Co. (SEMCO), Ltd. The authors are grateful to Dr. Byeong-Cheon Koh (former Executive Vice President) and members of the cell chip research group in SEMCO for helpful suggestions and assistance with chip fabrication.

Compliance with ethical standards

Conflict of interest

The research was partly supported by Samsung Electro-Mechanics Co. (SEMCO). Thus, the authors declare that there might be potential conflict of interest.

Supplementary material

204_2017_2126_MOESM1_ESM.docx (3.5 mb)
Supplementary material 1 (DOCX 3557 KB)


  1. Andersson DA, Gentry C, Alenmyr L, Killander D, Lewis SE, Andersson A, Bucher B, Galzi JL, Sterner O, Bevan S, Högestätt ED, Zygmunt PM (2011) TRPA1 mediates spinal antinociception induced by acetaminophen and the cannabinoid ∆(9)-tetrahydrocannabiorcol. Nat Commun 22(2):551. CrossRefGoogle Scholar
  2. Astrid S, Helmut S, Roland S (2007) Drug metabolism as catalyzed by human cytochrome P450 systems. In: Metal ions in life science. Volume 3: the ubiquitous roles of cytochrome P450 proteins. Wiley Online Library, EnglandGoogle Scholar
  3. Balan G, Timmins P, Greene DS, Marathe PH (2001) In vitro–in vivo correlation (IVIVC) models for metformin after administration of modified-release (MR) oral dosage forms to healthy human volunteers. J Pharm Sci 90:1176–1185. CrossRefPubMedGoogle Scholar
  4. Bale SS, Moore L, Yarmush M, Jindal R (2016) Emerging in vitro liver technologies for drug metabolism and inter-organ interactions. Tissue Eng Part B Rev 22:383–394. CrossRefPubMedPubMedCentralGoogle Scholar
  5. Brandon EF, Raap CD, Meijerman I, Beijnen JH, Schellens JH (2003) An update on in vitro test methods in human hepatic drug biotransformation research: pros and cons. Toxicol Appl Pharmacol 189:233–246. CrossRefPubMedGoogle Scholar
  6. Bui PH, Quesada A, Handforth A, Hankinson O (2008) The Mibefradil derivative NNC55-0396, a specific T-type calcium channel antagonist, exhibits less CYP3A4 inhibition than mibefradil. Drug Metab Dispos 36:1291–1299. CrossRefPubMedPubMedCentralGoogle Scholar
  7. Combes R, Balls M, Bansil L, Barratt M, Bell D, Botham P, Broadhead C, Clothier R, George E, Fentem J, Jackson M, Indans I, Loizou G, Navaratnam V, Pentreath V, Phillips B, Stemplewski H, Stewart J (2002) An assessment of progress in the use of alternatives in toxicity testing since the publication of the report of the second FRAME Toxicity Committee (1991). Altern Lab Anim 30:365–406PubMedGoogle Scholar
  8. Costas I (2008) Cytochromes P450: role in the metabolism and toxicity of drugs and other xenobiotics. Royal Society of Chemistry, CambridgeGoogle Scholar
  9. DiMasi JA, Grabowski HG (2012) R&D costs and returns to new drug development: a review of the evidence. Oxford University Press, Oxford, pp 21–46Google Scholar
  10. Emami J (2006) In vitro–in vivo correlation: from theory to applications. J Pharm Pharm Sci 9:169–189PubMedGoogle Scholar
  11. Emara LH, El-Menshavi BS, Estefan MY (2000) In vitro-in vivo correlation and comparative bioavailability of vincamine in prolonged-release preparation. Drug Dev Ind Phar 26:243–251. CrossRefGoogle Scholar
  12. Gómez-Lechón MJ, Donato MT, Castell JV, Jover R (2004) Human hepatocytes in primary culture: the choice to investigate drug metabolism in man. Curr Drug Metab 5:443–462. CrossRefPubMedGoogle Scholar
  13. Gustafsson F, Foster AJ, Sarda S, Bridgland-Taylor MH, Kenna JG (2014) A correlation between the in vitro drug toxicity of drugs to cell lines that express human P450s and their propensity to cause liver injury in humans. Toxicol Sci 137:189–211. CrossRefPubMedGoogle Scholar
  14. Hariparsad N, Sane RS, Strom SC, Desai PB (2006) In vitro methods in human drug biotransformation research: implications for cancer chemotherapy. Toxicol In Vitro 20:135–153. CrossRefPubMedGoogle Scholar
  15. Hewitt NJ, Lechón MJ, Houston JB, Hallifax D, Brown HS, Maurel P, Kenna JG, Gustavsson L, Lohmann C, Skonberg C, Guillouzo A, Tuschl G, Li AP, LeCluyse E, Groothuis GM, Hengstler JG (2007) Primary hepatocytes: current understanding of the regulation of metabolic enzymes and transporter proteins, and pharmaceutical practice for the use of hepatocytes in metabolism, enzyme induction, transporter, clearance, and hepatotoxicity studies. Drug Metab Rev 39:159–234. CrossRefPubMedGoogle Scholar
  16. Huch M, Gehart H, van Boxtel R, Hamer K, Blokzijl F, Verstegen MM, Ellis E, van Wenum M, Fuchs SA, de Ligt J, van de Wetering M, Sasaki N, Boers SJ, Kemperman H, de Jonge J, Ijzermans JN, Nieuwenhuis EE, Hoekstra R, Strom S, Vries RR, van der Laan LJ, Cuppen E, Clevers H (2015) Long-term culture of genome-stable bipotent stem cells from adult human liver. Cell 160:299–312. CrossRefPubMedPubMedCentralGoogle Scholar
  17. Hughes JP, Rees S, Kalindjian SB, Philpott KL (2011) Principles of early drug discovery. Br J Pharmacol 162:1239–1249. CrossRefPubMedPubMedCentralGoogle Scholar
  18. Jang GR, Harris RZ, Lau DT (2001) Pharmacokinetics and its role in small molecule drug discovery research. Med Res Rev 21:382–396. CrossRefPubMedGoogle Scholar
  19. Johnson TN, Rostami-Hodjegan A, Tucker GT (2006) Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin Pharmacokinet 45:931–956. CrossRefPubMedGoogle Scholar
  20. Kennedy JP, Williams L, Bridges TM, Daniels RN, Weaver D, Lindsley CW (2008) Application of combinatorial chemistry science on modern drug discovery. J Comb Chem 10:345–354. CrossRefPubMedGoogle Scholar
  21. Lee MY, Dordick JS (2006) High-throughput human metabolism and toxicity analysis. Curr Opin Biotechnol 17:619–627. CrossRefPubMedGoogle Scholar
  22. Lee MY, Park CB, Dordick JS, Clark DS (2005) Metabolizing enzyme toxicology assay chip (MetaChip) for high-throughput microscale toxicity analyses. Proc Natl Acad Sci USA 102:983–987. CrossRefPubMedPubMedCentralGoogle Scholar
  23. Lee MY, Kumar RA, Sukumaran SM, Hogg MG, Clark DS, Dordick JS (2008) Three-dimensional cellular microarray for high-throughput toxicology assays. Proc Natl Acad Sci USA 105:59–63. CrossRefPubMedGoogle Scholar
  24. Lee DW, Yi SH, Jeomg SH, Ku B, Kim J, Lee MY (2013) Plastic Pillar inserts for three-dimensional(3D) cell cultures in 96-well plates. Sensors Actuators B 177:78–85. CrossRefGoogle Scholar
  25. Lee DW, Choi YS, Seo YJ, Lee MY, Jeon SY, Ku B, Kim S, Yi SH, Nam DH (2014a) High-throughput screening (HTS) of anticancer drug efficacy on a micropillar/microwell chip platform. Anal Chem 86:535–542. CrossRefPubMedGoogle Scholar
  26. Lee DW, Lee MY, Ku B, Yi SH, Ryu JH, Jeon R, Yang M (2014b) Application of the DataChip/MetaChip technology for the evaluation of ajoene toxicity in vitro. Arch Toxicol 88:283–290. CrossRefPubMedGoogle Scholar
  27. Mahayni H, Rekhi GS, Uppoor RS, Marroum P, Hussain AS, Augsburger LL, Eddington ND (2000) Evaluation of external predictability of an in vitro–in vivo correlation for an extended-release formulation containing metoprolol tartrate. J Pharm Sci 89:1354–1361.<1354::AID-JPS13>3.0.CO;2-P CrossRefPubMedGoogle Scholar
  28. Masubuchi Y, Kano S, Horie T (2006) Mitochondrial permeability transition as a potential determinant of hepatotoxicity of antidiabetic thiazolidinediones. Toxicology 222:233–239. CrossRefPubMedGoogle Scholar
  29. OECD (2002) Guidelines for the testing of chemicals/OECD series on testing and assessment harmonised integrated classification system for human health and environmental hazards of chemical substances and mixtures. OECDGoogle Scholar
  30. Osburn WO, Kensler TW (2008) Nrf2 signaling: an adaptive response pathway for protection against environmental toxic insults. Mutat Res 659:31–39. CrossRefPubMedGoogle Scholar
  31. Reddy VB, Karanam BV, Gruber WL, Wallace MA, Vincent SH, Franklin RB, Baillie TA (2005) Mechanistic studies on the metabolic scission of thiazolidinedione derivatives to acyclic thiols. Chem Res Toxicol 18:880–888. CrossRefPubMedGoogle Scholar
  32. Sakore S, Chakraborty B (2011) In vitro–in vivo correlation (IVIVC): a strategic tool in drug development. J Bioequiv Availab S3. Google Scholar
  33. Schadt EE, Friend SH, Shaywitz DA (2009) A network view of disease and compound screening. Nat Rev Drug Discov 8:286–295. CrossRefPubMedGoogle Scholar
  34. Shanks N, Greek R, Greek J (2009) Are animal models predictive for humans? Philos Ethics Humanit Med 15:4:2. CrossRefGoogle Scholar
  35. Shukla SJ, Huang R, Austin CP, Xia M (2010) The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platform. Drug Discov Today 15:997–1007. CrossRefPubMedPubMedCentralGoogle Scholar
  36. Sivaraman A, Leach JK, Townsend S, Iida T, Hogan BJ, Stolz DB, Fry R, Samson LD, Tannenbaum SR, Griffith LG (2005) A microscale in vitro physiological model of the liver: predictive screens for drug metabolism and enzyme induction. Curr Drug Metab 6:569–591. CrossRefPubMedGoogle Scholar
  37. Soldatow VY, Lecluyse EL, Griffith LG, Rusyn I (2013) In vitro models for liver toxicity testing. Toxicol Res (Camb) 2:23–39. CrossRefGoogle Scholar
  38. Tak PP, Firestein GS (2001) NF-kappaB: a key role in inflammatory diseases. J Clin Invest 107:7–11. CrossRefPubMedPubMedCentralGoogle Scholar
  39. Watanabe T, Shibata N, Westerman KA, Okitsu T, Allain JE, Sakaguchi M, Totsugawa T, Maruyama M, Matsumura T, Noguchi H, Yamamoto S, Hikida M, Ohmori A, Reth M, Weber A, Tanaka N, Leboulch P, Kobayashi N (2003) Establishment of immortalized human hepatic stellate scavenger cells to develop bioartificial livers. Transplantation 75(11):1873–1880. CrossRefPubMedGoogle Scholar
  40. Westra IM, Mutsaers HA, Luangmonkong T, Hadi M, Oosterhuis D, de Jong KP, Groothuis GM, Olinga P (2016) Human precision-cut liver slices as a model to test antifibrotic drugs in the early onset of liver fibrosis. Toxicol In Vitro 35:77–85. CrossRefPubMedGoogle Scholar
  41. Wetmore BA, Allen B, Clewell HJ, Parker T, Wambaugh JF, Almond LM, Thomas RS (2014) Incorporating population variability and susceptible subpopulations into dosimetry for high-throughput toxicity testing. Toxicol Sci 142:210–224. CrossRefPubMedGoogle Scholar
  42. Xu JJ, Henstock PV, Dunn MC, Smith AR, Chabot JR, de Graaf D (2008) Cellular imaging predictions of clinical drug-induced liver injury. Toxicol Sci 105:97–105. CrossRefPubMedGoogle Scholar
  43. Yoon M, Clewell HJ 3rd (2016) Addressing early life sensitivity using physiologically based pharmacokinetic modeling and in vitro to in vivo extrapolation. Toxicol Res 32:15–20. CrossRefPubMedPubMedCentralGoogle Scholar
  44. Yoon M, Kedderis GL, Yan GZ, Clewell HJ 3rd (2015) Use of in vitro data in developing a physiologically based pharmacokinetic model: carbaryl as a case study. Toxicology 332:52–66. CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Kyeong-Nam Yu
    • 1
  • Sashi Nadanaciva
    • 2
  • Payal Rana
    • 2
  • Dong Woo Lee
    • 3
  • Bosung Ku
    • 4
  • Alexander D. Roth
    • 1
  • Jonathan S. Dordick
    • 5
  • Yvonne Will
    • 2
  • Moo-Yeal Lee
    • 1
    Email author return OK on get
  1. 1.Department of Chemical and Biomedical EngineeringCleveland State UniversityClevelandUSA
  2. 2.Compound Safety PredictionPfizer Inc.GrotonUSA
  3. 3.Department of Biomedical EngineeringKonyang UniversityDaejeonRepublic of Korea
  4. 4.Central R & D CenterMedical & Bio Device (MBD) Co., LtdSuwonRepublic of Korea
  5. 5.Department of Chemical and Biological Engineering, and Center for Biotechnology and Interdisciplinary StudiesRensselaer Polytechnic InstituteTroyUSA

Personalised recommendations