title: Quantitative high-throughput screening data analysis: challenges and recent advances.
abstract: In vitro HTS holds much potential to advance drug discovery and provide cell-based alternatives for toxicity testing. In quantitative HTS, concentration-response data can be generated simultaneously for thousands of different compounds and mixtures. However, nonlinear modeling in these multiple-concentration assays presents important statistical challenges that are not problematic for linear models. The uncertainty of parameter estimates obtained from the widely used Hill equation model can be extremely large when using standard designs. Failure to properly consider standard errors of these parameter estimates would greatly hinder chemical genomics and toxicity testing efforts. In this light, optimal study designs should be developed to improve nonlinear parameter estimation; or alternative approaches with reliable performance characteristics should be used to describe concentration-response profiles.
authors.affiliation: Biostatistics and Computational Biology Branch, The National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA. Electronic address: shockleykr@niehs.nih.gov.
authors.forename: Keith R
authors.initials: KR
authors.lastname: Shockley
mesh_terms: D003627:Data Interpretation, Statistical; D055808:Drug Discovery / Q000706:statistics & numerical data; D057166:High-Throughput Screening Assays / Q000706:statistics & numerical data*; D017711:Nonlinear Dynamics; D012044:Regression Analysis