The ability of a substance to induce a toxicological response is better understood by analyzing the response profile over a broad range of concentrations (or doses) rather than by evaluating effects that occur at a single concentration (or dose). In vitro qHTS assays are multiple-concentration experiments that play an important role in NTPs efforts to advance toxicology from a predominantly observational science at the level of disease-specific models to a predominantly predictive science based on broad inclusion of target-specific, mechanism-based, biological observations. The analysis of qHTS data has largely been motivated by the conservative focus of pharmaceutical applications (i.e., minimizing the risk of Type I error) and generally has relied on heuristics rather than statistical tests to make activity calls. Here, we developed a multiple-stage activity call algorithm and applied it to qHTS data from nine cell-based nuclear receptor agonist assays (AR, ER, FXR, GR, PPARa, PPARg, RXR, TRb, VDR). Data were fit to a four-parameter Hill equation and an overall F-test comparing the best fit to the Hill equation and a horizontal line (no response) was calculated for each chemical. Substances with a robust dose-response were identified in the first stage. In the second stage, compounds not detected as active in the first stage were evaluated for a maximal response at the lowest dose by comparing the distribution of measured responses to a control value. Chemicals with a weak dose-response were identified in the third stage, and the final stage separated substances exhibiting a cytotoxic response at the lowest dose from inactive compounds. Our model identified more active compounds than a previously utilized heuristic approach.

Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2011
Total Cost
$139,446
Indirect Cost
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State
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Zip Code
Shockley, Keith R (2016) Estimating Potency in High-Throughput Screening Experiments by Maximizing the Rate of Change in Weighted Shannon Entropy. Sci Rep 6:27897
Pei, Ying; Peng, Jun; Behl, Mamta et al. (2016) Comparative neurotoxicity screening in human iPSC-derived neural stem cells, neurons and astrocytes. Brain Res 1638:57-73
Shockley, Keith R (2015) Quantitative high-throughput screening data analysis: challenges and recent advances. Drug Discov Today 20:296-300
Chen, Shiuan; Hsieh, Jui-Hua; Huang, Ruili et al. (2015) Cell-Based High-Throughput Screening for Aromatase Inhibitors in the Tox21 10K Library. Toxicol Sci 147:446-57
Ray, Mitas; Shockley, Keith; Kissling, Grace (2014) Minimizing Systematic Errors in Quantitative High Throughput Screening Data Using Standardization, Background Subtraction, and Non-Parametric Regression. J Exp Second Sci 3:
Huang, Ruili; Sakamuru, Srilatha; Martin, Matt T et al. (2014) Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway. Sci Rep 4:5664
Shockley, Keith R (2014) Using weighted entropy to rank chemicals in quantitative high-throughput screening experiments. J Biomol Screen 19:344-53
Teng, Christina; Goodwin, Bonnie; Shockley, Keith et al. (2013) Bisphenol A affects androgen receptor function via multiple mechanisms. Chem Biol Interact 203:556-64
Shockley, Keith R (2012) A three-stage algorithm to make toxicologically relevant activity calls from quantitative high throughput screening data. Environ Health Perspect 120:1107-15