title: Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity.
abstract: BACKGROUND
  To develop efficient approaches for rapid evaluation of chemical toxicity and human health risk of environmental compounds, the National Toxicology Program (NTP) in collaboration with the National Center for Chemical Genomics has initiated a project on high-throughput screening (HTS) of environmental chemicals. The first HTS results for a set of 1,408 compounds tested for their effects on cell viability in six different cell lines have recently become available via PubChem.
  
  
  OBJECTIVE
  We have explored these data in terms of their utility for predicting adverse health effects of the environmental agents.
  
  
  RESULTS
  Initially, the classification k nearest neighbor (kNN) quantitative structure-activity relationship (QSAR) modeling method was applied to the HTS data only, for a curated data set of 384 compounds. The resulting models had prediction accuracies for training, test (containing 275 compounds together), and external validation (109 compounds) sets as high as 89%, 71%, and 74%, respectively. We then asked if HTS results could be of value in predicting rodent carcinogenicity. We identified 383 compounds for which data were available from both the Berkeley Carcinogenic Potency Database and NTP-HTS studies. We found that compounds classified by HTS as "actives" in at least one cell line were likely to be rodent carcinogens (sensitivity 77%); however, HTS "inactives" were far less informative (specificity 46%). Using chemical descriptors only, kNN QSAR modeling resulted in 62.3% prediction accuracy for rodent carcinogenicity applied to this data set. Importantly, the prediction accuracy of the model was significantly improved (72.7%) when chemical descriptors were augmented by HTS data, which were regarded as biological descriptors.
  
  
  CONCLUSIONS
  Our studies suggest that combining NTP-HTS profiles with conventional chemical descriptors could considerably improve the predictive power of computational approaches in toxicology.
authors.affiliation: Carolina Environmental Bioinformatics Research Center, University of North Carolina, Chapel Hill, NC 27599-7360, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Zhu
authors.forename: Ivan
authors.initials: I
authors.lastname: Rusyn
authors.forename: Ann
authors.initials: A
authors.lastname: Richard
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
mesh_terms: D000818:Animals; D002274:Carcinogens, Environmental / Q000633:toxicity*; D002460:Cell Line; D002470:Cell Survival / Q000187:drug effects*; D006801:Humans; D009374:Neoplasms, Experimental / Q000139:chemically induced; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D051381:Rats
chemical_list: D002274:Carcinogens, Environmental