Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays

Abstract

The application of in vitro biological assays as new approach methodologies (NAMs) to support grouping of UVCB (unknown or variable composition, complex reaction products, and biological materials) substances has recently been demonstrated. In addition to cell-based phenotyping as NAMs, in vitro transcriptomic profiling is used to gain deeper mechanistic understanding of biological responses to chemicals and to support grouping and read-across. However, the value of gene expression profiling for characterizing complex substances like UVCBs has not been explored. Using 141 petroleum substance extracts, we performed dose-response transcriptomic profiling in human induced pluripotent stem cell (iPSC)-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, as well as cell lines MCF7 and A375. The goal was to determine whether transcriptomic data can be used to group these UVCBs and to further characterize the molecular basis for in vitro biological responses. We found distinct transcriptional responses for petroleum substances by manufacturing class. Pathway enrichment informed interpretation of effects of substances and UVCB petroleum-class. Transcriptional activity was strongly correlated with concentration of polycyclic aromatic compounds (PAC), especially in iPSC-derived hepatocytes. Supervised analysis using transcriptomics, alone or in combination with bioactivity data collected on these same substances/cells, suggest that transcriptomics data provide useful mechanistic information, but only modest additional value for grouping. Overall, these results further demonstrate the value of NAMs for grouping of UVCBs, identify informative cell lines, and provide data that could be used for justifying selection of substances for further testing that may be required for registration.

REACH out-numbered! The future of REACH and animal numbers

“…ECHA has included petroleum substances in the restriction roadmap (pool 2) 8 , which means that test data are critically important. The attempt to reduce the number of tests by grouping through biological similarity by House et al (2022) did not really work out, showing that "transcriptomics data provide … only modest additional value for grouping". In other words, possible additional processing steps such as solvent extraction, hydro-desulfurization, or hydrogenation (McKee et al, 2015).…”

Application of Ion Mobility Spectrometry–Mass Spectrometry for Compositional Characterization and Fingerprinting of a Library of Diverse Crude Oil Samples

“…Machine-learning was used to predict geographic and source rock categories for each oil based on the IMS-MS chemical profiles for all oils tested in each iteration of the analysis as described in previous studies (House et al, 2021(House et al, , 2022Tibshirani et al, 2002). The PAM procedure (Tibshirani et al, 2002), originally developed for tumor classification based on gene expression microarray data, is well-suited for general multi-category classification based on multivariate inputs.…”

“…Because clustering analysis (e.g., unsupervised analysis) with all IMS-MS data was not successful in replicating predetermined groupings, we then investigated whether IMS-MS data could be used to classify samples and whether specific constituents may be more predictive of geographic or geological groupings. For this analysis, we used a supervised prediction algorithm (House et al, 2021(House et al, , 2022Tibshirani et al, 2002). We applied a "leave-one-out" approach whereby a statistical model was trained on the IMS-MS chemical profiles from all tested oils except one; then group classification was predicted for the left-out sample.…”

Cumulative Risk Meets Inter-Individual Variability: Probabilistic Concentration Addition of Complex Mixture Exposures in a Population-Based Human In Vitro Model

“…There has been increasing interest in using so-called New Approach Methodologies (NAMs), in particular, human cell-based in vitro models, to provide empirical data on mixtures for use in cumulative risk assessment [ 27 ]. We have previously demonstrated the feasibility of high-throughput in vitro testing of environmental mixtures [ 28 , 29 ], defined mixtures [ 30 ], and complex substances [ 31 , 32 , 33 ]. In particular, using a diverse set of 42 chemicals from the Agency for Toxic Substances and Disease Registry (ATSDR) substance priority list, we found that in 8 different defined mixtures, CA predictions were typically within an order of magnitude of the effects of defined mixtures, but that the accuracy of additivity assumptions varied greatly by phenotype [ 30 ].…”