Considerations for strategic use of high-throughput transcriptomics chemical screening data in regulatory decisions

Abstract

Recently, numerous organizations, including governmental regulatory agencies in the U.S. and abroad, have proposed using data from New Approach Methodologies (NAMs) for augmenting and increasing the pace of chemical assessments. NAMs are broadly defined as any technology, methodology, approach or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals. High-throughput transcriptomics (HTTr) is a type of NAM that uses gene expression profiling as an endpoint for rapidly evaluating the effects of large numbers of chemicals on in vitro cell culture systems. As compared to targeted high-throughput screening (HTS) approaches that measure the effect of chemical X on target Y, HTTr is a non-targeted approach that allows researchers to more broadly characterize the integrated response of an intact biological system to chemicals that may affect a specific biological target or many biological targets under a defined set of treatment conditions (time, concentration, etc.). HTTr screening performed in concentration-response mode can provide potency estimates for the concentrations of chemicals that produce perturbations in cellular response pathways. Here, we discuss study design considerations for HTTr concentration-response screening and present a framework for the use of HTTr-based biological pathway-altering concentrations (BPACs) in a screening-level, risk-based chemical prioritization approach. The framework involves concentration-response modeling of HTTr data, mapping gene level responses to biological pathways, determination of BPACs, in vitro-to-in vivo extrapolation (IVIVE) and comparison to human exposure predictions.

Phenotypic Profiling of Reference Chemicals across Biologically Diverse Cell Types Using the Cell Painting Assay

“…As compared to traditional HTS assays, positive controls and reference chemicals are more difficult to identify for high-throughput profiling assays due to the multiplexed nature of the assay readout. 35 Previously, we used a set of four chemicals (i.e., berberine chloride, Ca-074-Me, etoposide, and rapamycin) as reference chemicals for the Cell Painting assay in the U-2 OS model. 14 These chemicals produced robust profiles of phenotypic effects that were dissimilar across the chemical set (spanning nearly every category measured in the Cell Painting assay) but highly reproducible for each chemical among the many assay plates used in the study.…”

Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data

“…In the regulatory science arena, it has been proposed that HTP assays can be used to rapidly screen chemicals for the purpose of hazard identification and identification of bioactive concentrations. 9,12 However, at present, there are no widely accepted standard practices for identifying hits or potency estimates from imaging-based HTP assays. 15 The lack of standardized approaches for data analysis, including demonstration of reliable approaches for classification of chemicals as inactive or bioactive with some accompanying estimation of potency, represents a barrier to broader use of imaging-based HTP data for application to regulatory decision making.…”

“…The starting point for the comparative analysis was category-level aggregation of BMDExpress fitted feature-level data, as described in Nyffeler et al 26 This approach was adapted from a standard approach used in transcriptomics research for concentration-response modeling of high-dimensional data that also provides biological context for interpretation of chemical effects by mapping to gene sets. 12,29,39,40 Phenotypic category-based analysis (similar to gene set–based analysis in transcriptomics) facilitates biological interpretation of high-dimensional feature data by aiding in identification of effects on organelles that may be associated with chemical bioactivity or toxicity. Feature-level fitting with BMDExpress was time-consuming (~20 min per chemical for modeling four curve shapes on a computer with 20 processing cores) and documentation of and access to the underlying model executables was limited within the confines of the R computing environment.…”

“…The high-content nature of profiling assays introduces additional challenges to hit identification 10,12 as compared to targeted high-throughput screening (HTS) assays. Targeted HTS assays are designed to measure one (or a few) specific endpoints, and response thresholds for hit identification are based on either the use of well-characterized negative and positive control treatments or defined based on the separation of true signal from statistically characterized baseline activity (i.e., noise).…”

Drivers of and Obstacles to the Adoption of Toxicogenomics for Chemical Risk Assessment: Insights from Social Science Perspectives

“…Also, we acknowledge that the need for toxicogenomics researchers to make their tools and data sets more readily and easily usable by regulators and other end users has been identified in recent research ( Farmahin et al. 2017 ; Harrill et al. 2019 ; Thomas et al.…”