tx4004165_si_001.xls (70.5 kB)
Classification of Hepatotoxicants Using HepG2 Cells: A Proof of Principle Study
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posted on 2014-03-17, 00:00 authored by Wim F. P. M. Van den Hof, Maarten L. J. Coonen, Marcel van Herwijnen, Karen Brauers, Will K. W. H. Wodzig, Joost H. M. van Delft, Jos C. S. KleinjansWith
the number of new drug candidates increasing every year, there
is a need for high-throughput human toxicity screenings. As the liver
is the most important organ in drug metabolism and thus capable of
generating relatively high levels of toxic metabolites, it is important
to find a reliable strategy to screen for drug-induced hepatotoxicity.
Microarray-based transcriptomics is a well-established technique in
toxicogenomics research and is an ideal approach to screen for drug-induced
injury at an early stage. The aim of this study was to prove the principle
of classifying known hepatotoxicants and nonhepatotoxicants using
their distinctive gene expression profiles in vitro in HepG2 cells. Furthermore, we undertook to subclassify the hepatotoxic
compounds by investigating the subclass of cholestatic compounds.
Prediction analysis for microarrays was used for classification of
hepatotoxicants and nonhepatotoxicants, which resulted in an accuracy
of 92% on the training set and 91% on the validation set, using 36
genes. A second model was set up with the goal of finding classifiers
for cholestasis, resulting in 12 genes that appeared capable of correctly
classifying 8 of the 9 cholestatic compounds, resulting in an accuracy
of 93%. We were able to prove the principle that transcriptomic analyses
of HepG2 cells can indeed be used to classify chemical entities for
hepatotoxicity. Genes selected for classification of hepatotoxicity
and cholestasis indicate that endoplasmic reticulum stress and the
unfolded protein response may be important cellular effects of drug-induced
liver injury. However, the number of compounds in both the training
set and the validation set should be increased to improve the reliability
of the prediction.
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drug candidates9 cholestatic compoundshepatotoxicantdrug metabolismPrediction analysisPrinciple StudyWithtoxicity screeningstranscriptomic analysesprotein responsegene expression profilescholestasiclassificationcholestatic compoundstrainingendoplasmic reticulum stresshepatotoxicityhepatotoxic compoundsHepG 2 CellsinjuryHepG 2 cells12 genesnonhepatotoxicant36 genesaccuracychemical entitiestoxicogenomics researchprinciplevalidation
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