Citation:

Reardon, A., R. Farmahin, A. Williams, M. Meier, G. Addicks, C. Yauk, G. Matteo, E. Atlas, J. Harrill, L. Everett, I. Shah, R. Judson, S. Ramaiahgari, S. Ferguson, AND T. Barton-Maclaren. From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow. Frontiers in Toxicology. Frontiers, Lausanne, Switzerland, 5:1194895, (2023). https://doi.org/10.3389/ftox.2023.1194895

Impact/Purpose:

The US EPA Center for Computational Toxicology and Exposure has research programs focused on developing the tools, approaches and data needed to accelerate the pace of chemical risk assessment and foster incorporation of non-traditional toxicity testing data into regulatory decision-making processes. High-throughput transcriptomics (HTTr) with TempO-Seq is a promising technology for comprehensive and cost-effective bioactivity screening of chemicals. In this work, transcriptional points-of-departure (tPODs) from a variety of in vitro models are compared to in vivo apical points-of-departure (aPODs). Multiple methods for calculation of tPODs are compared. Overall, the work demonstrates that for most of the chemicals evaluated tPODs from in vitro HTTr studies approximate or are more conservative than aPODs from in vivo toxicity testing. This information would be of interest to scientists using or contemplating the use of new approach methodologies (NAMs) for next generation risk assessment (NGRA).

Description:

The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications.

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