Literature DB >> 17522070

Acute hepatotoxicity: a predictive model based on focused illumina microarrays.

Nadine Zidek1, Juergen Hellmann, Peter-Juergen Kramer, Philip G Hewitt.   

Abstract

Drug-induced hepatotoxicity is a major issue for drug development, and toxicogenomics has the potential to predict toxicity during early toxicity screening. A bead-based Illumina oligonucleotide microarray containing 550 liver specific genes has been developed. We have established a predictive screening system for acute hepatotoxicity by analyzing differential gene expression profiles of well-known hepatotoxic and nonhepatotoxic compounds. Low and high doses of tetracycline, carbon tetrachloride (CCL4), 1-naphthylisothiocyanate (ANIT), erythromycin estolate, acetaminophen (AAP), or chloroform as hepatotoxicants, clofibrate, theophylline, naloxone, estradiol, quinidine, or dexamethasone as nonhepatotoxic compounds, were administered as a single dose to male Sprague-Dawley rats. After 6, 24, and 72 h, livers were taken for histopathological evaluation and for analysis of gene expression alterations. All hepatotoxic compounds tested generated individual gene expression profiles. Based on leave-one-out cross-validation analysis, gene expression profiling allowed the accurate discrimination of all model compounds, 24 h after high dose treatment. Even during the regeneration phase, 72 h after treatment, CCL4, ANIT, and AAP were predicted to be hepatotoxic, and only these three compounds showed histopathological changes at this time. Furthermore, we identified 64 potential marker genes responsible for class prediction, which reflected typical hepatotoxicity responses. These genes and pathways, commonly deregulated by hepatotoxicants, may be indicative of the early characterization of hepatotoxicity and possibly predictive of later hepatotoxicity onset. Two unknown test compounds were used for prevalidating the screening test system, with both being correctly predicted. We conclude that focused gene microarrays are sufficient to classify compounds with respect to toxicity prediction.

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Year:  2007        PMID: 17522070     DOI: 10.1093/toxsci/kfm131

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  24 in total

1.  Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data.

Authors:  Ivan Rusyn; Alexander Sedykh; Yen Low; Kathryn Z Guyton; Alexander Tropsha
Journal:  Toxicol Sci       Date:  2012-03-02       Impact factor: 4.849

Review 2.  The evolution of bioinformatics in toxicology: advancing toxicogenomics.

Authors:  Cynthia A Afshari; Hisham K Hamadeh; Pierre R Bushel
Journal:  Toxicol Sci       Date:  2010-12-22       Impact factor: 4.849

Review 3.  Use of transcriptomics in understanding mechanisms of drug-induced toxicity.

Authors:  Yuxia Cui; Richard S Paules
Journal:  Pharmacogenomics       Date:  2010-04       Impact factor: 2.533

4.  In vitro transcriptomic prediction of hepatotoxicity for early drug discovery.

Authors:  Feng Cheng; Dan Theodorescu; Ira G Schulman; Jae K Lee
Journal:  J Theor Biol       Date:  2011-08-27       Impact factor: 2.691

5.  Predicting the future: opportunities and challenges for the chemical industry to apply 21st-century toxicity testing.

Authors:  Raja S Settivari; Nicholas Ball; Lynea Murphy; Reza Rasoulpour; Darrell R Boverhof; Edward W Carney
Journal:  J Am Assoc Lab Anim Sci       Date:  2015-03       Impact factor: 1.232

6.  Dose-dependent effects of alpha-naphthylisothiocyanate disconnect biliary fibrosis from hepatocellular necrosis.

Authors:  Nikita Joshi; Jessica L Ray; Anna K Kopec; James P Luyendyk
Journal:  J Biochem Mol Toxicol       Date:  2016-09-08       Impact factor: 3.642

7.  Human primary cultured hepatic stellate cells can be cryopreserved.

Authors:  Anna Nakamura; Takato Ueno; Yumihiko Yagi; Koji Okuda; Toshiro Ogata; Toru Nakamura; Takuji Torimura; Hideki Iwamoto; Sivakumar Ramadoss; Michio Sata; Victor Tsutsumi; Kaori Yasuda; Yumi Tomiyasu; Kenichi Obayashi; Kosuke Tashiro; Satoru Kuhara
Journal:  Med Mol Morphol       Date:  2010-08-04       Impact factor: 2.309

Review 8.  Biomarkers for drug-induced renal damage and nephrotoxicity-an overview for applied toxicology.

Authors:  Tobias Christian Fuchs; Philip Hewitt
Journal:  AAPS J       Date:  2011-10-04       Impact factor: 4.009

9.  Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.

Authors:  Yen Low; Takeki Uehara; Yohsuke Minowa; Hiroshi Yamada; Yasuo Ohno; Tetsuro Urushidani; Alexander Sedykh; Eugene Muratov; Viktor Kuz'min; Denis Fourches; Hao Zhu; Ivan Rusyn; Alexander Tropsha
Journal:  Chem Res Toxicol       Date:  2011-07-21       Impact factor: 3.739

10.  Tissue factor-dependent coagulation contributes to alpha-naphthylisothiocyanate-induced cholestatic liver injury in mice.

Authors:  James P Luyendyk; Glenn H Cantor; Daniel Kirchhofer; Nigel Mackman; Bryan L Copple; Ruipeng Wang
Journal:  Am J Physiol Gastrointest Liver Physiol       Date:  2009-01-29       Impact factor: 4.052

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