Citation:

Everett, L. Integration and Analysis of High-Throughput Assays in Next Generation Risk Assessment (NGRA). Society of Toxicology 2021 Annual Meeting, Orlando, FL, March 14 - 18, 2021.

Impact/Purpose:

Abstract submitted to the Society of Toxicology Annual Meeting March 2021. The ambition to conduct human health risk assessments without generating new animal data has resulted in intense efforts over the past few decades from industry, academia and regulatory bodies to develop and apply new approach methodologies (NAMs) that can form the basis of Next Generation Risk Assessment (NGRA). This abstract is part of a symposium proposal for SOT 2021 that aims to address important aspects of next generation toxicology, which are needed to bridge emerging innovations and NAMs into practical application for the chemical, cosmetic and pharmaceutical sectors, and ultimately improve the safety assessments for human health, while reducing and replacing the need for animal testing. To achieve this, we will bring experts together to exemplify, through a series of talks, state-of-the-art science that underpins NGRA, and explore quantitative methods for relative characterization and translation of biological responses to test substances towards NGRA. The session will feature several presentations on innovative computational approaches, and in particular the presentation proposed in this abstract will focus on high-throughput transcriptomics (HTTr) data analysis.

Description:

Recent technological advancements have led to the development of new high-throughput profiling methods, such as transcriptomics, that can be used to infer potential hazards across thousands of chemicals. Decreasing costs have made it feasible to profile all protein-coding genes across thousands of samples, allowing for broad evaluation of many target pathways and modes of action in a single screening assay. Similarly, it is now possible to apply high-content imaging across many different chemical exposures to capture a variety of changes in cell morphology. Such methods have been used to rapidly screen chemicals in vitro, and the resulting data can be used for both hazard prediction and potency estimation, thereby informing risk assessments and prioritizing chemicals for further testing. Assessing the reliability and reproducibility of these screening platforms is critical to their utility in regulatory applications. While these platforms often have lower signal-to-noise compared to individual targeted assays, the resulting data is also high-dimensional, allowing for the analysis of consistent trends across many molecular endpoints. For example, analysis of coordinate changes across multiple genes in the same pathway has yielded more accurate results compared to analysis of individual genes in high-throughput transcriptomic data. Advanced analysis methods are also needed to link gene expression or cell morphological changes to organism-level hazards. Large databases of gene signatures, such as those associated with specific human disease processes, can be used to scan high-throughput profiling data and thereby associate the most responsive or potent signatures associated with a particular chemical exposure. Alternatively, “connectivity mapping” techniques can be used to identify chemicals eliciting similar responses, and subsequently infer hazard based on existing characterization of these “connected” chemicals. The landscape of computational methods and best practices for reliable analysis of high-throughput profiling data in a variety of assessment contexts will be discussed. This abstract does not necessarily reflect US EPA policy. Company or product names do not constitute endorsement by US EPA.

Record Details: