title: Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity.
abstract: BACKGROUND
  Quantitative high-throughput screening (qHTS) assays are increasingly being used to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the National Institutes of Health Chemical Genomics Center.
  
  
  OBJECTIVE
  Our goal was to test a hypothesis that dose-response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, improve the accuracy of quantitative structure-activity relationship (QSAR) models applied to prediction of in vivo toxicity end points.
  
  
  METHODS
  We obtained cell viability qHTS concentration-response data for 1,408 substances assayed in 13 cell lines from PubChem; for a subset of these compounds, rodent acute toxicity half-maximal lethal dose (LD50) data were also available. We used the k nearest neighbor classification and random forest QSAR methods to model LD50 data using chemical descriptors either alone (conventional models) or combined with biological descriptors derived from the concentration-response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data.
  
  
  RESULTS
  Both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models were superior to conventional models.
  
  
  CONCLUSIONS
  Concentration-response qHTS data may serve as informative biological descriptors of molecules that, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity end points.
authors.affiliation: Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Sedykh
authors.forename: Hao
authors.initials: H
authors.lastname: Zhu
authors.forename: Hao
authors.initials: H
authors.lastname: Tang
authors.forename: Liying
authors.initials: L
authors.lastname: Zhang
authors.forename: Ann
authors.initials: A
authors.lastname: Richard
authors.forename: Ivan
authors.initials: I
authors.lastname: Rusyn
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
mesh_terms: D000818:Animals; D002460:Cell Line; D004305:Dose-Response Relationship, Drug; D015386:Hazardous Substances / Q000032:analysis / Q000633:toxicity*; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D051381:Rats; D018675:Toxicity Tests / Q000379:methods
chemical_list: D015386:Hazardous Substances