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.
