High-throughput and combinatorial gene expression on a chip for metabolism-induced toxicology screening

Seok Joon Kwon, Dong Woo Lee, Dhiral A. Shah, Bosung Ku, Sang Youl Jeon, Kusum Solanki, Jessica D. Ryan, Douglas S. Clark (), Jonathan S. Dordick () and Moo-Yeal Lee ()
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Seok Joon Kwon: Rensselaer Polytechnic Institute
Dong Woo Lee: Samsung Electro-Mechanics Co, Central R & D Institute
Dhiral A. Shah: Rensselaer Polytechnic Institute
Bosung Ku: Samsung Electro-Mechanics Co, Central R & D Institute
Sang Youl Jeon: Samsung Electro-Mechanics Co, Central R & D Institute
Kusum Solanki: Rensselaer Polytechnic Institute
Jessica D. Ryan: Solidus Biosciences Inc.
Douglas S. Clark: University of California at Berkeley
Jonathan S. Dordick: Rensselaer Polytechnic Institute
Moo-Yeal Lee: Solidus Biosciences Inc.

Nature Communications, 2014, vol. 5, issue 1, 1-12

Abstract: Abstract Differential expression of various drug-metabolizing enzymes (DMEs) in the human liver may cause deviations of pharmacokinetic profiles, resulting in interindividual variability of drug toxicity and/or efficacy. Here, we present the ‘Transfected Enzyme and Metabolism Chip’ (TeamChip), which predicts potential metabolism-induced drug or drug-candidate toxicity. The TeamChip is prepared by delivering genes into miniaturized three-dimensional cellular microarrays on a micropillar chip using recombinant adenoviruses in a complementary microwell chip. The device enables users to manipulate the expression of individual and multiple human metabolizing-enzyme genes (such as CYP3A4, CYP2D6, CYP2C9, CYP1A2, CYP2E1 and UGT1A4) in THLE-2 cell microarrays. To identify specific enzymes involved in drug detoxification, we created 84 combinations of metabolic-gene expressions in a combinatorial fashion on a single microarray. Thus, the TeamChip platform can provide critical information necessary for evaluating metabolism-induced toxicity in a high-throughput manner.

Date: 2014
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DOI: 10.1038/ncomms4739

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