From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow

Abstract

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.

Empirical Characterization of False Discovery Rates of Differentially Expressed Genes and Transcriptomic Benchmark Concentrations in Zebrafish Embryos

“…We then attempted to determine the tPOD values from the gene-level BMCs. There are numerous ways in which a tPOD can be derived from gene BMC distribution or gene sets; here we chose to use the 25th gene BMC and lowest median BMC for a gene set (e.g., pathway) as these are the two primary approaches used in our group and within Health Canada, and previous studies have demonstrated that they provide sensitive and consistent approaches for potency ranking. , The latter approach is also referred to as “the lowest pathway BMC” or the “most sensitive pathway”. We determined the FDR by dividing the number of times a tPOD was obtained (i.e., a 25th gene or lowest pathway BMC) by 100 (i.e., the number of simulations run).…”

Systematic comparison of temporal transcriptional responses to hepatotoxicants in primary human hepatocytes and HepaRG cells using concentration-response modelling of gene co-expression networks

“…A. Harrill et al, 2024;Reardon et al, 2023). There is general scientific consensus that pathway based methods are preferred to determine tPOD, which will also provide mechanistic information that could be linked to KEs in AOPs (Barutcu et al, 2023;Basili et al, 2022;National Toxicology Program, 2018;Ramaiahgari et al, 2019;, although others argue to determine tPOD as any concerted molecular change, as being more human health protective (Johnson et al, 2022).…”

Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches

“…Previous studies have used percentile-based methods, particularly the fifth percentile method, to estimate the tPOD ( Farmahin et al, 2017 ; Reardon et al, 2021 ; Reardon et al, 2023 ). The tPOD was defined as the lower fifth or 10th percentile of the distribution of probe BMD values across molecules.…”

“…Regardless of method used, tPOD values were within 10-fold of apical POD values and often less than 3-fold, supporting their conclusion that a variety of approaches can be used to determine a tPOD. Recently, Reardon et al (2023) analyzed seven tPOD methods, four distribution-based methods and three gene set-based methods for in vitro transcriptomic data. Overall, the results demonstrated a high concordance among the different methods.…”

“…Gene set-based results were generated using four different gene sets: 1) gene ontology biological process classifications (GOBP) ( Consortium, 2004 ); 2) BioPlanet pathways ( Huang et al, 2019 ); 3) REACTOME pathways ( Jassal et al, 2020 ); and 4) a gene set derived from a co-expression network built using weighted gene co-expression network analysis (WGCNA) ( Sutherland et al, 2018 ). Transcriptomic POD values derived using these genes sets were compared to tPOD values derived from five distribution-based methods: a method based on the curvature of the accumulation plot of BMD values ( Johnson et al, 2022b ); a method based on the first mode of the BMD distribution ( Page-Lariviere et al, 2019 ); two percentile methods (fifth and 10th percentile of the gene-specific BMD values) ( Farmahin et al, 2017 ; Reardon et al, 2021 ; Reardon et al, 2023 ); and the 25th lowest ranked BMD method ( Reardon et al, 2021 ; Matteo et al, 2023 ; Reardon et al, 2023 ). Results support the hypothesis tested and suggest that distribution-based tPOD derivation is a viable and parsimonious alternative to gene set-based methods.…”