Characteristic molecular and proteomic signatures of drug‐induced liver injury in a rat model. Issue 2 (18th September 2014)

• Record Type: Journal Article
• Authors: Eun, Jung Woo; Bae, Hyun Jin; Shen, Qingyu; Park, Se Jin; Kim, Hyung Seok; Shin, Woo Chan; Yang, Hee Doo; Jin, Chan Young; You, Jueng Soo; Kang, Hyun Joo; Kim, Hoguen; Ahn, Young Min; Park, Won Sang; Lee, Jung Young; Nam, Suk Woo
• Is Part Of: Journal of applied toxicology. Volume 35:Issue 2(2015)
• Page Start: 152
• Page End: 164
• Publication Date: 2014-09-18
• Subjects: Toxicology -- Periodicals; Industrial toxicology -- Periodicals; Environmentally induced diseases -- Periodicals; Toxicology -- Periodicals; 615.9005
• DOI: 10.1002/jat.3062
• Languages: English
• ISSNs: 0260-437X

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>Drug‐induced liver injury (DILI) is a major safety concern during drug development and remains one of the main reasons for withdrawal of drugs from the market. Although it is crucial to develop methods that will detect potential hepatotoxicity of drug candidates as early and as quickly as possible, there is still a lack of sensitive and specific biomarkers for DILI that consequently leads to a scarcity of reliable hepatotoxic data. Hence, in this study, we assessed characteristic molecular signatures in rat liver treated with drugs (pyrazinamide, ranitidine, enalapril, carbamazepine and chlorpromazine) that are known to cause DILI in humans. Unsupervised hierarchical clustering analysis of transcriptome changes induced by DILI‐causing drugs resulted in three different subclusters on dendrogram, i.e., hepatocellular, cholestatic and mixed type of DILI at early time points (2 days), and multiclassification analysis suggested 31 genes as discernible markers for each DILI pattern. Further analysis for characteristic molecular signature of each DILI pattern provided a molecular basis for different modes of DILI action. A proteomics study of the same rat livers was used to confirm the results, and the two sets of data showed 60 matching classifiers. In conclusion, the data of different DILI‐causing drug treatments from genomic analysis in a rat model suggest that DILI‐specific molecular signatures can discriminate different patterns of DILI at an early exposure time point, and that they provide useful information for mechanistic studies that may lead to a better understanding of the molecular basis of DILI. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>