title: Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data.
abstract: Hepatotoxicity accounts for a substantial number of drugs being withdrawn from the market. Using traditional animal models to detect hepatotoxicity is expensive and time-consuming. Alternative in vitro methods, in particular cell-based high-throughput screening (HTS) studies, have provided the research community with a large amount of data from toxicity assays. Among the various assays used to screen potential toxicants is the antioxidant response element beta lactamase reporter gene assay (ARE-bla), which identifies chemicals that have the potential to induce oxidative stress and was used to test > 10,000 compounds from the Tox21 program.
  
  
  
  The ARE-bla computational model and HTS data from a big data source (PubChem) were used to profile environmental and pharmaceutical compounds with hepatotoxicity data.
  
  
  
  Quantitative structure-activity relationship (QSAR) models were developed based on ARE-bla data. The models predicted the potential oxidative stress response for known liver toxicants when no ARE-bla data were available. Liver toxicants were used as probe compounds to search PubChem Bioassay and generate a response profile, which contained thousands of bioassays (> 10 million data points). By ranking the in vitro-in vivo correlations (IVIVCs), the most relevant bioassay(s) related to hepatotoxicity were identified.
  
  
  
  The liver toxicants profile contained the ARE-bla and relevant PubChem assays. Potential toxicophores for well-known toxicants were created by identifying chemical features that existed only in compounds with high IVIVCs.
  
  
  
  Profiling chemical IVIVCs created an opportunity to fully explore the source-to-outcome continuum of modern experimental toxicology using cheminformatics approaches and big data sources.
  
  
  
  Kim MT, Huang R, Sedykh A, Wang W, Xia M, Zhu H. 2016. Mechanism profiling of hepatotoxicity caused by oxidative stress using antioxidant response element reporter gene assay models and big data. Environ Health Perspect 124:634-641; http://dx.doi.org/10.1289/ehp.1509763.
authors.affiliation: Department of Chemistry, Rutgers University, Camden, New Jersey, USA.
authors.forename: Marlene Thai
authors.initials: MT
authors.lastname: Kim
authors.forename: Ruili
authors.initials: R
authors.lastname: Huang
authors.forename: Alexander
authors.initials: A
authors.lastname: Sedykh
authors.forename: Wenyi
authors.initials: W
authors.lastname: Wang
authors.forename: Menghang
authors.initials: M
authors.lastname: Xia
authors.forename: Hao
authors.initials: H
authors.lastname: Zhu
mesh_terms: D000818:Animals; D061745:Antioxidant Response Elements / Q000502:physiology*; D001681:Biological Assay; D002460:Cell Line; D003198:Computer Simulation; D017930:Genes, Reporter; D015386:Hazardous Substances / Q000633:toxicity*; D057166:High-Throughput Screening Assays / Q000379:methods; D016247:Information Storage and Retrieval; D008099:Liver; D018384:Oxidative Stress / Q000502:physiology*; D021281:Quantitative Structure-Activity Relationship; D018675:Toxicity Tests
chemical_list: D015386:Hazardous Substances