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Environ Health Perspect. 2012 Aug;120(8):1107-15. doi: 10.1289/ehp.1104688. Epub 2012 May 10.

A three-stage algorithm to make toxicologically relevant activity calls from quantitative high throughput screening data.

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  • 1Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina 27709, USA.



The ability of a substance to induce a toxicological response is better understood by analyzing the response profile over a broad range of concentrations than at a single concentration. In vitro quantitative high throughput screening (qHTS) assays are multiple-concentration experiments with an important role in the National Toxicology Program's (NTP) efforts to advance toxicology from a predominantly observational science at the level of disease-specific models to a more predictive science based on broad inclusion of biological observations.


We developed a systematic approach to classify substances from large-scale concentration-​response data into statistically supported, toxicologically relevant activity categories.


The first stage of the approach finds active substances with robust concentration-response profiles within the tested concentration range. The second stage finds substances with activity at the lowest tested concentration not captured in the first stage. The third and final stage separates statistically significant (but not robustly statistically significant) profiles from responses that lack statistically compelling support (i.e., "inactives"). The performance of the proposed algorithm was evaluated with simulated qHTS data sets.


The proposed approach performed well for 14-point-concentration-response curves with typical levels of residual error (σ ≤ 25%) or when maximal response (|RMAX|) was > 25% of the positive control response. The approach also worked well in most cases for smaller sample sizes when |RMAX| ≥ 50%, even with as few as four data points.


The three-stage classification algorithm performed better than one-stage classification approaches based on overall F-tests, t-tests, or linear regression.

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