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Development of a prioritization method for chemical-mediated effects on steroidogenesis using an integrated statistical analysis of high-throughput H295R data
Citation:
Haggard, D., Woodrow Setzer, R. Judson, AND K. Friedman. Development of a prioritization method for chemical-mediated effects on steroidogenesis using an integrated statistical analysis of high-throughput H295R data. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, 109:104510, (2019). https://doi.org/10.1016/j.yrtph.2019.104510
Impact/Purpose:
The HT-H295R assay and analysis approach are being considered for use as an alternative for the low-throughput H295R assay. The USEPA convened a Scientific Advisory Panel to review the performance of the HT-H295R assay and the statistical model that was implemented (USEPA, 2017). There were three main needs identified for technical refinement (USEPA, 2017): demonstration of the robustness and/or reproducibility of the methodology chosen using data simulations (with specific requests to understand the false positive rate and normality of the data); investigation of whether the assay can identify specific mechanisms of disruption; and, demonstration of how the results may have been confounded by mitochondrial function and/or cytotoxicity despite use of a parallel mitochondrial toxicity assay in H295R cells. The objective of the work herein is to provide more context for the use of this statistical model in prioritization or hazard screening by addressing these questions, which may help move this approach further towards regulatory acceptance.
Description:
Synthesis of 11 steroid hormones in human adrenocortical carcinoma cells (H295R) was measured in a high-throughput steroidogenesis assay (HT-H295R) for 656 chemicals in concentration-response as part of the US Environmental Protection Agency’s ToxCast program. This work extends previous analysis of the HT-H295R dataset and model by examining the utility of a novel prioritization metric based on the Mahalanobis distance that reduced these 11-dimensional data to 1-dimension via calculation of a mean Mahalanobis distance (mMd) at each chemical concentration screened for all hormone measures available. Herein, we evaluated the robustness of mMd values, and demonstrate that covariance and variance of the hormones measured appear independent of the chemicals screened and are inherent to the assay; the Type I error rate of the mMd method is less than 1%; and, absolute fold changes (up or down) of 1.5 to 2-fold have sufficient power for statistical significance. As a case study, we examined hormone responses for aromatase inhibitors in the HT-H295R assay and found high concordance with other ToxCast assays for known aromatase inhibitors. Finally, we used mMd and other ToxCast cytotoxicity data to demonstrate prioritization of the most selective and active chemicals as candidates for further in vitro or in silico screening.
URLs/Downloads:
DOI: Development of a prioritization method for chemical-mediated effects on steroidogenesis using an integrated statistical analysis of high-throughput H295R data
https://pubmed.ncbi.nlm.nih.gov/31676319/