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. To evaluate the activity within qHTS studies, we developed a two-stage decision tree statistical model and applied it to normalized concentration-response data from twelve cell-based agonist nuclear receptor assays (AR, ER, FXR, GR, LXR, PPARd, PPARg, RXR, TRb, VDR, PXR-human, PXR-rat), and agonist assays for p53 and ELG1. We also applied the model to nuclear receptor antagonist assays (AR, FXR, GR, LXR, PPARd, PPARg, RXR, TRb, VDR) and cytotoxicity assays from various chicken cell lines. In the first stage, data obtained from 1408 substances 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) for each substance exhibiting at least 25% efficacy, using different significance thresholds, identified active compounds within the tested concentration range (5 x 10-10 M to 10-4 M). In the second stage, compounds not detected as active in the first stage were evaluated by comparing the distribution of measured responses to a control value, at an alpha value of 0.05. Using this approach, we identified a greater number of active compounds in each assay than a previously utilized heuristic approach.

Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2010
Total Cost
$80,095
Indirect Cost
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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