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Evidence for Cross Species Extrapolation of Mammalian-Based High-Throughput Screening Assay Results.

ABSTRACT: High-throughput screening (HTS) and computational technologies have emerged as important tools for chemical hazard identification. The US Environmental Protection Agency (EPA) launched the Toxicity ForeCaster (ToxCast) Program, which has screened thousands of chemicals in hundreds of mammalian-based HTS assays for biological activity. The data are being used to prioritize toxicity testing on those chemicals likely to lead to adverse effects. To use HTS assays in predicting hazard to both humans and wildlife, it is necessary to understand how broadly these data may be extrapolated across species. The US EPA Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS; https://seqapass.epa.gov/seqapass/ ) tool was used to assess conservation of the 484 protein targets represented in the suite of ToxCast assays and other HTS assays. To demonstrate the utility of the SeqAPASS data for guiding extrapolation, case studies were developed which focused on targets of interest to the US Endocrine Disruptor Screening Program and the Organisation for Economic Cooperation and Development. These case studies provide a line of evidence for conservation of endocrine targets across vertebrate species, with few exceptions, and demonstrate the utility of SeqAPASS for defining the taxonomic domain of applicability for HTS results and identifying organisms for suitable follow-up toxicity tests.

SUBMITTER: LaLone CA

PROVIDER: S-EPMC8283686 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Quantitative high throughput screening (qHTS) assays use cells or tissues to screen thousands of compounds in a short period of time. Data generated from qHTS assays are then evaluated using nonlinear regression models, such as the Hill model, and decisions regarding toxicity are made using the estimates of the parameters of the model. For any given compound, the variability in the observed response may either be constant across dose groups (homoscedasticity) or vary with dose (heteroscedasticity). Since thousands of compounds are simultaneously evaluated in a qHTS assay, it is not practically feasible for an investigator to perform residual analysis to determine the variance structure before performing statistical inferences on each compound. Since it is well-known that the variance structure plays an important role in the analysis of linear and nonlinear regression models it is therefore important to have practically useful and easy to interpret methodology which is robust to the variance structure. Furthermore, given the number of chemicals that are investigated in the qHTS assay, outliers and influential observations are not uncommon. In this article we describe preliminary test estimation (PTE) based methodology which is robust to the variance structure as well as any potential outliers and influential observations. Performance of the proposed methodology is evaluated in terms of false discovery rate (FDR) and power using a simulation study mimicking a real qHTS data. Of the two methods currently in use, our simulations studies suggest that one is extremely conservative with very small power in comparison to the proposed PTE based method whereas the other method is very liberal. In contrast, the proposed PTE based methodology achieves a better control of FDR while maintaining good power. The proposed methodology is illustrated using a data set obtained from the National Toxicology Program (NTP). Additional information, simulation results, data and computer code are available online as supplementary materials.

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Evidence for Cross Species Extrapolation of Mammalian-Based High-Throughput Screening Assay Results.

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