title: Dose-response modeling of high-throughput screening data.
abstract: The National Toxicology Program is developing a high-throughput screening (HTS) program to set testing priorities for compounds of interest, to identify mechanisms of action, and potentially to develop predictive models for human toxicity. This program will generate extensive data on the activity of large numbers of chemicals in a wide variety of biochemical- and cell-based assays. The first step in relating patterns of response among batteries of HTS assays to in vivo toxicity is to distinguish between positive and negative compounds in individual assays. Here, the authors report on a statistical approach developed to identify compounds positive or negative in an HTS cytotoxicity assay based on data collected from screening 1353 compounds for concentration-response effects in 9 human and 4 rodent cell types. In this approach, the authors develop methods to normalize the data (removing bias due to the location of the compound on the 1536-well plates used in the assay) and to analyze for concentration-response relationships. Various statistical tests for identifying significant concentration-response relationships and for addressing reproducibility are developed and presented.
authors.affiliation: National Institutes of Health (NIH)/National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina 27709, USA. parham@niehs.nih.gov
authors.forename: Fred
authors.initials: F
authors.lastname: Parham
authors.forename: Chris
authors.initials: C
authors.lastname: Austin
authors.forename: Noel
authors.initials: N
authors.lastname: Southall
authors.forename: Ruili
authors.initials: R
authors.lastname: Huang
authors.forename: Raymond
authors.initials: R
authors.lastname: Tice
authors.forename: Christopher
authors.initials: C
authors.lastname: Portier
mesh_terms: D000818:Animals; D002460:Cell Line; D004305:Dose-Response Relationship, Drug; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D015203:Reproducibility of Results