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

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John S. House, Fabian A. Grimm, William D. Klaren, Abigail Dalzell, Srikeerthana Kuchi, Shu-Dong Zhang, Klaus Lenz, Peter J. Boogaard, Hans B. Ketelslegers, Timothy W. Gant, Ivan Rusyn, Fred A. Wright
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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.

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House, J. S., Grimm, F. A., Klaren, W. D., Dalzell, A., Kuchi, S., Zhang, S.-D., Lenz, K., Boogaard, P. J., Ketelslegers, H. B., Gant, T. W., Rusyn, I. and Wright, F. A. (2022) “Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays”, ALTEX - Alternatives to animal experimentation, 39(3), pp. 388–404. doi: 10.14573/altex.2107051.

ASTM International (2014). Standard Test Method for Determining Carcinogenic Potential of Virgin Base Oils in Metalworking Fluids. E1687-10. ASTM International. doi:10.1520/E1687-10 (accessed 01.07.2019).

Berggren, E., Amcoff, P., Benigni, R. et al. (2015). Chemical safety assessment using read-across: Assessing the use of novel testing methods to strengthen the evidence base for decision making. Environ Health Perspect 123, 1232-1240. doi:10.1289/ehp.1409342

Bopp, S. K., Kienzler, A., Richarz, A. N. et al. (2019). Regulatory assessment and risk management of chemical mixtures: Challenges and ways forward. Crit Rev Toxicol 49, 174-189. doi:10.1080/10408444.2019.1579169

Burnett, S. D., Blanchette, A. D., Grimm, F. A. et al. (2019). Population-based toxicity screening in human induced pluripotent stem cell-derived cardiomyocytes. Toxicol Appl Pharmacol 381, 114711. doi:10.1016/j.taap.2019.114711

Chen, Z., Liu, Y., Wright, F. A. et al. (2020). Rapid hazard characterization of environmental chemicals using a compendium of human cell lines from different organs. ALTEX 37, 623-638. doi:10.14573/altex.2002291

Chen, Z., Lloyd, D., Zhou, Y. H. et al. (2021). Risk characterization of environmental samples using in vitro bioactivity and polycyclic aromatic hydrocarbon concentrations data. Toxicol Sci 179, 108-120. doi:10.1093/toxsci/kfaa166

Chiu, W. A., Guyton, K. Z., Martin, M. T. et al. (2018). Use of high-throughput in vitro toxicity screening data in cancer hazard evaluations by IARC monograph working groups. ALTEX 35, 51-64. doi:10.14573/altex.1703231

Clark, C. R., McKee, R. H., Freeman, J. J. et al. (2013). A GHS-consistent approach to health hazard classification of petroleum substances, a class of UVCB substances. Regul Toxicol Pharmacol 67, 409-420. doi:10.1016/j.yrtph.2013.08.020

CONCAWE (1994). The use of the dimethyl sulphoxide (DMSO) extract by the IP 346 method as an indicator of the carcinogenicity of lubricant base oils and distillate aromatic extracts. European Petroleum Refiners Association – Concawe Division. (accessed 01.05.2020).

CONCAWE (2019). REACH Roadmap for Petroleum Substances. European Petroleum Refiners Association – Concawe Division. (accessed 15.05.2020).

CONCAWE (2020). Hazard Classification and Labelling of Petroleum Substances in the European Economic Area – 2020. 22/20. C. C. a. L. T. F. (STF-23). (accessed 25.06.2021).

De Abrew, K. N., Kainkaryam, R. M., Shan, Y. K. et al. (2016). Grouping 34 chemicals based on mode of action using connectivity mapping. Toxicol Sci 151, 447-461. doi:10.1093/toxsci/kfw058

De Abrew, K. N., Shan, Y. K., Wang, X. et al. (2019). Use of connectivity mapping to support read across: A deeper dive using data from 186 chemicals, 19 cell lines and 2 case studies. Toxicology 423, 84-94. doi:10.1016/j.tox.2019.05.008

Drakvik, E., Altenburger, R., Aoki, Y. et al. (2020). Statement on advancing the assessment of chemical mixtures and their risks for human health and the environment. Environ Int 134, 105267. doi:10.1016/j.envint.2019.105267

ECHA – European Chemical Agency (2017). Read-Across Assessment Framework (RAAF). Considerations on multi-constituent substances and UVCBs. (accessed 25.08.2020).

ECHA (2020). Testing Proposal Decision on Substance EC 295-332-8 “extracts (petroleum), deasphalted vacuum residue solvent”. (accessed 02.09.2020).

Escher, S. E., Kamp, H., Bennekou, S. H. et al. (2019). Towards grouping concepts based on new approach methodologies in chemical hazard assessment: The read-across approach of the EU-ToxRisk project. Arch Toxicol 93, 3643-3667. doi:10.1007/s00204-019-02591-7

Fang, H., Knezevic, B., Burnham, K. L. et al. (2016). XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits. Genome Med 8, 129. doi:10.1186/s13073-016-0384-y

Ganter, B., Tugendreich, S., Pearson, C. I. et al. (2005). Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. J Biotechnol. 119, 219-244. doi:10.1016/j.jbiotec.2005.03.022

Goedtke, L., Sprenger, H., Hofmann, U. et al. (2020). Polycyclic aromatic hydrocarbons activate the aryl hydrocarbon receptor and the constitutive androstane receptor to regulate xenobiotic metabolism in human liver cells. Int J Mol Sci 22, 372. doi:10.3390/ijms22010372

Grimm, F. A., Iwata, Y., Sirenko, O. et al. (2015). High-content assay multiplexing for toxicity screening in induced pluripotent stem cell-derived cardiomyocytes and hepatocytes. Assay Drug Dev Technol 13, 529-546. doi:10.1089/adt.2015.659

Grimm, F. A., Iwata, Y., Sirenko, O. et al. (2016). A chemical-biological similarity-based grouping of complex substances as a prototype approach for evaluating chemical alternatives. Green Chem 18, 4407-4419. doi:10.1039/c6gc01147k

Grimm, F. A., Russell, W. K., Luo, Y. S. et al. (2017). Grouping of petroleum substances as example UVCBs by ion mobility-mass spectrometry to enable chemical composition-based read-across. Environ Sci Technol 51, 7197-7207. doi:10.1021/acs.est.6b06413

Grimm, F. A., House, J. S., Wilson, M. R. et al. (2019). Multi-dimensional in vitro bioactivity profiling for grouping of glycol ethers. Regul Toxicol Pharmacol 101, 91-102. doi:10.1016/j.yrtph.2018.11.011

Hammershoj, R., Birch, H., Sjoholm, K. K. et al. (2020). Accelerated passive dosing of hydrophobic complex mixtures-controlling the level and composition in aquatic tests. Environ Sci Technol 54, 4974-4983. doi:10.1021/acs.est.9b06062

Harrill, J., Shah, I., Setzer, R. W. et al. (2019). Considerations for strategic use of high-throughput transcriptomics chemical screening data in regulatory decisions. Curr Opin Toxicol 15, 64-75. doi:10.1016/j.cotox.2019.05.004

Harrill, J. A., Everett, L. J., Haggard, D. E. et al. (2021). High-throughput transcriptomics platform for screening environmental chemicals. Toxicol Sci 181, 68-89. doi:10.1093/toxsci/kfab009

Herrmann, K., Pistollato, F. and Stephens, M. L. (2019). Beyond the 3Rs: Expanding the use of human-relevant replacement methods in biomedical research. ALTEX 36, 343-352. doi:10.14573/altex.1907031

House, J. S., Grimm, F. A., Jima, D. D. et al. (2017). A pipeline for high-throughput concentration response modeling of gene expression for toxicogenomics. Front Genet 8, 168. doi:10.3389/fgene.2017.00168

House, J. S., Grimm, F. A., Klaren, W. D. et al. (2021). Grouping of UVCB substances with new approach methodologies (NAMs) data. ALTEX 38, 123-137. doi:10.14573/altex.2006262

Hsieh, N. H., Chen, Z., Rusyn, I. et al. (2021). Risk characterization and probabilistic concentration-response modeling of complex environmental mixtures using new approach methodologies (NAMs) data from organotypic in vitro human stem cell assays. Environ Health Perspect 129, 17004. doi:10.1289/EHP7600

Joseph, P. (2017). Transcriptomics in toxicology. Food Chem Toxicol 109, 650-662. doi:10.1016/j.fct.2017.07.031

Kavlock, R. J., Bahadori, T., Barton-Maclaren, T. S. et al. (2018). Accelerating the pace of chemical risk assessment. Chem Res Toxicol 31, 287-290. doi:10.1021/acs.chemrestox.7b00339

Leuraud, K. and Benichou, J. (2001). A comparison of several methods to test for the existence of a monotonic dose-response relationship in clinical and epidemiological studies. Stat Med 20, 3335-3351. doi:10.1002/sim.959

Liu, Z., Huang, R., Roberts, R. et al. (2019). Toxicogenomics: A 2020 vision. Trends Pharmacol Sci 40, 92-103. doi:10.1016/

Love, M. I., Huber, W. and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550. doi:10.1186/s13059-014-0550-8

Low, Y., Uehara, T., Minowa, Y. et al. (2011). Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chem Res Toxicol 24, 1251-1262. doi:10.1021/tx200148a

McKee, R. H., Adenuga, M. D. and Carrillo, J. C. (2015). Characterization of the toxicological hazards of hydrocarbon solvents. Crit Rev Toxicol 45, 273-365. doi:10.3109/10408444.2015.1016216

McKee, R. H., Tibaldi, R., Adenuga, M. D. et al. (2018). Assessment of the potential human health risks from exposure to complex substances in accordance with REACH requirements. “White spirit” as a case study. Regul Toxicol Pharmacol 92, 439-457. doi:10.1016/j.yrtph.2017.10.015

Onel, M., Beykal, B., Ferguson, K. et al. (2019). Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and visualization. PLoS One 14, e0223517. doi:10.1371/journal.pone.0223517

Paul Friedman, K., Gagne, M., Loo, L. H. et al. (2020). Utility of in vitro bioactivity as a lower bound estimate of in vivo adverse effect levels and in risk-based prioritization. Toxicol Sci 173, 202-225. doi:10.1093/toxsci/kfz201

Phillips, J. R., Svoboda, D. L., Tandon, A. et al. (2019). BMDExpress 2: Enhanced transcriptomic dose-response analysis workflow. Bioinformatics 35, 1780-1782. doi:10.1093/bioinformatics/bty878

Roman-Hubers, A. T., McDonald, T. J., Baker, E. S. et al. (2021). A comparative analysis of analytical techniques for rapid oil spill identification. Environ Toxicol Chem 40, 1034-1049. doi:10.1002/etc.4961

Roy, T. A., Johnson, S. W., Blackburn, G. R. et al. (1988). Correlation of mutagenic and dermal carcinogenic activities of mineral oils with polycyclic aromatic compound content. Fundam Appl Toxicol 10, 466-476. doi:10.1016/0272-0590(88)90293-x

Salvito, D., Fernandez, M., Jenner, K. et al. (2020). Improving the environmental risk assessment of substances of unknown or variable composition, complex reaction products, or biological materials. Environ Toxicol Chem 39, 2097-2108. doi:10.1002/etc.4846

Samet, J. M., Chiu, W. A., Cogliano, V. et al. (2020). The IARC monographs: Updated procedures for modern and transparent evidence synthesis in cancer hazard identification. J Natl Cancer Inst 112, 30-37. doi:10.1093/jnci/djz169

Schultz, T. W., Amcoff, P., Berggren, E. et al. (2015). A strategy for structuring and reporting a read-across prediction of toxicity. Regul Toxicol Pharmacol 72, 586-601. doi:10.1016/j.yrtph.2015.05.016

Tibshirani, R., Hastie, T., Narasimhan, B. et al. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A 99, 6567-6572. doi:10.1073/pnas.082099299

Trac, L. N., Sjo Holm, K. K., Birch, H. et al. (2021). Passive dosing of petroleum and essential oil UVCBs-whole mixture toxicity testing at controlled exposure. Environ Sci Technol 55, 6150-6159. doi:10.1021/acs.est.1c00343

Uehara, T., Ono, A., Maruyama, T. et al. (2010). The Japanese toxicogenomics project: Application of toxicogenomics. Mol Nutr Food Res 54, 218-227. doi:10.1002/mnfr.200900169

Waters, M., Stasiewicz, S., Merrick, B. A. et al. (2008). CEBS – Chemical effects in biological systems: A public data repository integrating study design and toxicity data with microarray and proteomics data. Nucleic Acids Res 36, D892-D900. doi:10.1093/nar/gkm755

Williams, A. J., Grulke, C. M., Edwards, J. et al. (2017). The CompTox chemistry dashboard: A community data resource for environmental chemistry. J Cheminform 9, 61. doi:10.1186/s13321-017-0247-6

Yauk, C. L., Harrill, A. H., Ellinger-Ziegelbauer, H. et al. (2020). A cross-sector call to improve carcinogenicity risk assessment through use of genomic methodologies. Regul Toxicol Pharmacol 110, 104526. doi:10.1016/j.yrtph.2019.104526

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