Protectiveness of NAM-based hazard assessment – which testing scope is required?

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Walter Zobl , Annette Bitsch, Jonathan Blum, Jan J. W. A. Boei, Liliana Capinha, Giada Carta, Jose V. Castell, Enrico Davoli, Christina Drake, Ciaran P. Fisher, Muriel M. Heldring, Barira Islam, Paul Jennings, Marcel Leist, Damiano Pellegrino-Coppola, Johannes P. Schimming, Kirsten E. Snijders, Laia Tolosa, Bob van de Water, Barbara M. A. van Vugt-Lussenburg, Paul Walker, Matthias M. Wehr, Lukas S. Wijaya, Sylvia E. Escher
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Hazard assessment requires toxicity tests to allow deriving protective points of departure (PoDs) for risk assessment irrespective of a compound’s mode of action (MoA). The scope of in vitro test batteries (ivTB) needed to assess systemic toxicity is still unclear. We explored the protectiveness regarding systemic toxicity of an ivTB with a scope that was guided by previous findings from rodent studies, where examining six main targets, including liver and kidney, was sufficient to predict the guideline scope-based PoD with high probability. The ivTB comprises human in vitro models representing liver, kidney, lung, and the neuronal system covering transcriptome, mitochondrial dysfunction, and neuronal outgrowth. Additionally, 32 CALUXR- and 10 HepG2 BAC-GFP reporters cover a broad range of disturbance mechanisms. Eight compounds were chosen for causing adverse effects such as immunotoxicity or anemia in vivo, i.e., effects not directly covered by assays in the ivTB. PoDs derived from the ivTB and from oral repeated dose studies in rodents were extrapolated to maximum unbound plasma concentrations for comparison. The ivTB-based PoDs were one to five orders of magnitude lower than in vivo PoDs for six of eight compounds, implying that they were protective. The extent of in vitro response varied across test compounds. Especially for hematotoxic substances, the ivTB showed either no response or only cytotoxicity. Assays better capturing this type of hazard would be needed to complement the ivTB. This study highlights the potentially broad applicability of ivTBs for deriving protective PoDs of compounds with unknown MoA.

Plain language summary
Animal tests are used to determine how much of a chemical is toxic (threshold of toxicity) and which organs are affected. In principle, the threshold can also be derived solely from tests with cultured cells. However, only a limited number of cell types can practically be tested, so one challenge is to determine how many and which types shall be tested. In animal tests, only few organs including liver and kidney are regularly among those most sensitively affected. We explored whether a cell-based test battery representing these sensitive organs and covering important mechanisms of toxicity can be used to derive protective human thresholds. To challenge this approach, eight chemicals were tested that primarily cause effects in organs not directly represented in our test battery. Results provided protective thresholds for most of the investigated compounds and gave indications how to further improve the approach towards a full-fledged replacement of animal tests.

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Zobl, W. (2024) “Protectiveness of NAM-based hazard assessment – which testing scope is required?”, ALTEX - Alternatives to animal experimentation, 41(2), pp. 302–319. doi: 10.14573/altex.2309081.

Abdel-Dayem, M. A., Elmarakby, A. A., Abdel-Aziz, A. A. et al. (2014). Valproate-induced liver injury: Modulation by the omega-3 fatty acid DHA proposes a novel anticonvulsant regimen. Drugs R D 14, 85-94. doi:10.1007/s40268-014-0042-z

Anonymous (1992). Toxicology and carcinogenesis studies of ethylene thiourea (CAS No. 96-45-7) in F344 rats and B6C3F1 mice (feed studies). Natl Toxicol Program Tech Rep Ser 388, 1-256.

Anonymous (1999). NTP toxicity studies of methyl ethyl ketoxime administered in drinking water to F344/N rats and B6C3F1 mice (CAS No. 96-29-7). Toxic Rep Ser 51, 1-F9.

Anonymous (2012). N-Butylzinnverbindungen [MAK value documentation in German language, 2008]. In The MAK-Collection for Occupational Health and Safety. doi:10.1002/3527600418.mb68873verd0044

Aschauer, L., Gruber, L. N., Pfaller, W. et al. (2013). Delineation of the key aspects in the regulation of epithelial monolayer formation. Mol Cell Biol 33, 2535-2550. doi:10.1128/mcb.01435-12

Baltazar, M. T., Cable, S., Carmichael, P. L. et al. (2020). A next-generation risk assessment case study for coumarin in cosmetic products. Toxicol Sci 176, 236-252. doi:10.1093/toxsci/kfaa048

Batke, M., Aldenberg, T., Escher, S. et al. (2013). Relevance of non-guideline studies for risk assessment: The coverage model based on most frequent targets in repeated dose toxicity studies. Toxicol Lett 218, 293-298. doi:10.1016/j.toxlet.2012.09.002

Bitsch, A., Jacobi, S., Melber, C. et al. (2006). Repdose: A database on repeated dose toxicity studies of commercial chemicals – A multifunctional tool. Regul Toxicol Pharmacol 46, 202-210. doi:10.1016/j.yrtph.2006.05.013

Callegaro, G., Schimming, J. P., Piñero González, J. et al. (2023). Identifying multiscale translational safety biomarkers using a network-based systems approach. iScience 26, 106094. doi:10.1016/j.isci.2023.106094

Chamorro-Garcia, R., Sahu, M., Abbey, R. J. et al. (2013). Transgenerational inheritance of increased fat depot size, stem cell reprogramming, and hepatic steatosis elicited by prenatal exposure to the obesogen tributyltin in mice. Environ Health Perspect 121, 359-366. doi:10.1289/ehp.1205701

Chamorro-Garcia, R., Shoucri, B. M., Willner, S. et al. (2018). Effects of perinatal exposure to dibutyltin chloride on fat and glucose metabolism in mice, and molecular mechanisms, in vitro. Environ Health Perspect 126, 057006. doi:10.1289/EHP3030

Chan, P. C. (2004). NTP technical report on the toxicity studies of 2- and 4-methylimidazole (CAS No. 693-98-1 and 822-36-6) administered in feed to F344/N rats and B6C3F1 mice. Toxic Rep Ser, 1-G12.

Delp, J., Cediel-Ulloa, A., Suciu, I. et al. (2021). Neurotoxicity and underlying cellular changes of 21 mitochondrial respiratory chain inhibitors. Arch Toxicol 95, 591-615. doi:10.1007/s00204-020-02970-5

Dent, M., Amaral, R. T., Da Silva, P. A. et al. (2018). Principles underpinning the use of new methodologies in the risk assessment of cosmetic ingredients. Comput Toxicol 7, 20-26. doi:10.1016/j.comtox.2018.06.001

EFSA – European Food Safety Authority (2021). EFSA Strategy 2027: Science, safe food, sustainability. Publications Office of the European Union. doi:10.2805/886006

EMA – European Medicines Agency (2020). EMA Regulatory Science to 2025.

Escher, S. E., Tluczkiewicz, I., Batke, M. et al. (2010). Evaluation of inhalation TTC values with the database repdose. Regul Toxicol Pharmacol 58, 259-274. doi:10.1016/j.yrtph.2010.06.009

Escher, S. E., Mangelsdorf, I., Hoffmann-Doerr, S. et al. (2020). Time extrapolation in regulatory risk assessment: The impact of study differences on the extrapolation factors. Regul Toxicol Pharmacol 112, 104584. doi:10.1016/j.yrtph.2020.104584

Escher, S. E., Aguayo-Orozco, A., Benfenati, E. et al. (2022a). Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action. Toxicol In Vitro 79, 105269. doi:10.1016/j.tiv.2021.105269

Escher, S. E., Partosch, F., Konzok, S. et al. (2022b). Development of a roadmap for action on new approach methodologies in risk assessment. EFSA Support Publ 19, 7341E. doi:10.2903/sp.efsa.2022.en-7341

Farmahin, R., Williams, A., Kuo, B. et al. (2017). Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment. Arch Toxicol 91, 2045-2065. doi:10.1007/s00204-016-1886-5

Fisher, C., Siméon, S., Jamei, M. et al. (2019). VIVD: Virtual in vitro distribution model for the mechanistic prediction of intracellular concentrations of chemicals in in vitro toxicity assays. Toxicol In Vitro 58, 42-50. doi:10.1016/j.tiv.2018.12.017

Gant, T. W., Auerbach, S. S., Von Bergen, M. et al. (2023). Applying genomics in regulatory toxicology: A report of the ECETOC workshop on omics threshold on non-adversity. Arch Toxicol 97, 2291-2302. doi:10.1007/s00204-023-03522-3

Gaulton, A., Hersey, A., Nowotka, M. et al. (2017). The ChEMBL database in 2017. Nucleic Acids Res 45, D945-D954. doi:10.1093/nar/gkw1074

Gumy, C., Chandsawangbhuwana, C., Dzyakanchuk, A. A. et al. (2008). Dibutyltin disrupts glucocorticoid receptor function and impairs glucocorticoid-induced suppression of cytokine production. PLoS One 3, e3545. doi:10.1371/journal.pone.0003545

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

Jamei, M., Turner, D., Yang, J. et al. (2009). Population-based mechanistic prediction of oral drug absorption. AAPS J 11, 225-237. doi:10.1208/s12248-009-9099-y

Kamburov, A., Stelzl, U., Lehrach, H. et al. (2012). The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Res 41, D793-D800. doi:10.1093/nar/gks1055

Kamentsky, L., Jones, T. R., Fraser, A. et al. (2011). Improved structure, function and compatibility for cellprofiler: Modular high-throughput image analysis software. Bioinformatics 27, 1179-1180. doi:10.1093/bioinformatics/btr095

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

Kawasaki, Y., Umemura, T., Saito, M. et al. (1998). Toxicity study of a rubber antioxidant, 2-mercaptobenzimidazole, by repeated oral administration to rats. J Toxicol Sci 23, 53-68. doi:10.2131/jts.23.53

Klimisch, H. J., Andreae, M. and Tillmann, U. (1997). A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul Toxicol Pharmacol 25, 1-5. doi:10.1006/rtph.1996.1076

Krug, A. K., Balmer, N. V., Matt, F. et al. (2013). Evaluation of a human neurite growth assay as specific screen for developmental neurotoxicants. Arch Toxicol 87, 2215-2231. doi:10.1007/s00204-013-1072-y

Lobell, M. and Sivarajah, V. (2003). In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values. Mol Divers 7, 69-87. doi:10.1023/b:modi.0000006562.93049.36

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

Mahalingaiah, P. K., Palenski, T. and Van Vleet, T. R. (2018). An in vitro model of hematotoxicity: Differentiation of bone marrow-derived stem/progenitor cells into hematopoietic lineages and evaluation of lineage-specific hematotoxicity. Curr Protoc Toxicol 76, e45. doi:10.1002/cptx.45

Maranghi, F., De Angelis, S., Tassinari, R. et al. (2013). Reproductive toxicity and thyroid effects in Sprague Dawley rats exposed to low doses of ethylenethiourea. Food Chem Toxicol 59, 261-271. doi:10.1016/j.fct.2013.05.048

Norford, D. C., Meuten, D. J., Cullen, J. M. et al. (1993). Pituitary and thyroid gland lesions induced by 2-mercaptobenzimidazole (2-MBI) inhalation in male fischer-344 rats. Toxicol Pathol 21, 456-464. doi:10.1177/019262339302100505

Noyes, P. D., Friedman, K. P., Browne, P. et al. (2019). Evaluating chemicals for thyroid disruption: Opportunities and challenges with in vitro testing and adverse outcome pathway approaches. Environ Health Perspect 127, 95001. doi:10.1289/ehp5297

NTP – National Toxicology Program (2018). NTP Research Report on National Toxicology Program Approach to Genomic Dose-Response Modeling. NTP RR 5. Research Triangle Park, NC. National Toxicology Program, 1-44. doi:10.22427/ntp-rr-5

Palmen, N. G. M. and Evelo, C. T. A. (1998). Oxidative effects in human erythrocytes caused by some oximes and hydroxylamine. Arch Toxicol 72, 270-276. doi:10.1007/s002040050501

Paul Friedman, K., Watt, E. D., Hornung, M. W. et al. (2016). Tiered high-throughput screening approach to identify thyroperoxidase inhibitors within the ToxCast phase I and II chemical libraries. Toxicol Sci 151, 160-180. doi:10.1093/toxsci/kfw034

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

Ramaiahgari, S. C., Auerbach, S. S., Saddler, T. O. et al. (2019). The power of resolution: Contextualized understanding of biological responses to liver injury chemicals using high-throughput transcriptomics and benchmark concentration modeling. Toxicol Sci 169, 553-566. doi:10.1093/toxsci/kfz065

Reardon, A. J. F., Farmahin, R., Williams, A. et al. (2023). From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow. Front Toxicol 5, 1194895. doi:10.3389/ftox.2023.1194895

Rodgers, T., Leahy, D. and Rowland, M. (2005). Physiologically based pharmacokinetic modeling 1: Predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci 94, 1259-1276. doi:10.1002/jps.20322

Rodgers, T. and Rowland, M. (2006). Physiologically based pharmacokinetic modelling 2: Predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci 95, 1238-1257. doi:10.1002/jps.20502

Sakuratani, Y., Zhang, H. Q., Nishikawa, S. et al. (2013). Hazard evaluation support system (HESS) for predicting repeated dose toxicity using toxicological categories. SAR QSAR Environ Res 24, 351-363. doi:10.1080/1062936x.2013.773375

Schimming, J. P., ter Braak, B., Niemeijer, M. et al. (2019). System microscopy of stress response pathways in cholestasis research. In M. Vinken (ed.), Experimental Cholestasis Research. New York, NY, USA: Springer New York. doi:10.1007/978-1-4939-9420-5_13

Scholz, D., Pöltl, D., Genewsky, A. et al. (2011). Rapid, complete and large-scale generation of post-mitotic neurons from the human luhmes cell line. J Neurochem 119, 957-971. doi:10.1111/j.1471-4159.2011.07255.x

Stiegler, N. V., Krug, A. K., Matt, F. et al. (2011). Assessment of chemical-induced impairment of human neurite outgrowth by multiparametric live cell imaging in high-density cultures. Toxicol Sci 121, 73-87. doi:10.1093/toxsci/kfr034

Thomas, R. S., Wesselkamper, S. C., Wang, N. C. Y. et al. (2013). Temporal concordance between apical and transcriptional points of departure for chemical risk assessment. Toxicol Sci 134, 180-194. doi:10.1093/toxsci/kft094

Thomas, R. S., Bahadori, T., Buckley, T. J. et al. (2019). The next generation blueprint of computational toxicology at the U.S. Environmental Protection Agency. Toxicol Sci 169, 317-332. doi:10.1093/toxsci/kfz058

Tisdel, M. (1985). Chronic toxicity study of rotenone in rats: Final report: Study No. 6115-100. Unpublished study prepared by Hazleton Laboratories America, Inc.

Tolosa, L., Donato, M. T., Pérez-Cataldo, G. et al. (2012). Upgrading cytochrome P450 activity in HepG2 cells co-transfected with adenoviral vectors for drug hepatotoxicity assessment. Toxicol In Vitro 26, 1272-1277. doi:10.1016/j.tiv.2011.11.008

US EPA (2021a). Estimation Programs Interface Suite™ for Microsoft® Windows V 4.1. United States Environmental Protection Agency, Washington, DC, USA.

US EPA (2021b). New Approach Methods Work Plan (V2). U.S. Environmental Protection Agency, Washington, DC. EPA/600/X-21/209.

US FDA (2021). Advancing New Alternative Methodologies at FDA.

van der Burg, B., van der Linden, S., Man, H.-Y. et al. (2013). A panel of quantitative Calux® reporter gene assays for reliable high-throughput toxicity screening of chemicals and complex mixtures. In P. Steinberg (ed.), High-Throughput Screening Methods in Toxicity Testing. John Wiley & Sons, Inc. doi:10.1002/9781118538203.ch28

van der Stel, W., Carta, G., Eakins, J. et al. (2020). Multiparametric assessment of mitochondrial respiratory inhibition in HepG2 and RPTEC/TERT1 cells using a panel of mitochondrial targeting agrochemicals. Arch Toxicol 94, 2707-2729. doi:10.1007/s00204-020-02792-5

Webster, A. F., Chepelev, N., Gagne, R. et al. (2015). Impact of genomics platform and statistical filtering on transcriptional benchmark doses (BMD) and multiple approaches for selection of chemical point of departure (PoD). PLoS One 10, e0136764. doi:10.1371/journal.pone.0136764

Wieser, M., Stadler, G., Jennings, P. et al. (2008). hTERT alone immortalizes epithelial cells of renal proximal tubules without changing their functional characteristics. Am J Physiol Renal Physiol 295, F1365-F1375. doi:10.1152/ajprenal.90405.2008

Wink, S., Hiemstra, S., Huppelschoten, S. et al. (2014). Quantitative high content imaging of cellular adaptive stress response pathways in toxicity for chemical safety assessment. Chem Res Toxicol 27, 338-355. doi:10.1021/tx4004038

Wink, S., Hiemstra, S., Herpers, B. et al. (2017). High-content imaging-based BAC-GFP toxicity pathway reporters to assess chemical adversity liabilities. Arch Toxicol 91, 1367-1383. doi:10.1007/s00204-016-1781-0

Yamada, T., Kawamura, T., Tsujii, S. et al. (2022). Formation and evaluation of mechanism-based chemical categories for regulatory read-across assessment of repeated-dose toxicity: A case of hemolytic anemia. Regul Toxicol Pharmacol 136, 105275. doi:10.1016/j.yrtph.2022.105275

Yeakley, J. M., Shepard, P. J., Goyena, D. E. et al. (2017). A trichostatin A expression signature identified by TempO-Seq targeted whole transcriptome profiling. PLoS One 12, e0178302. doi:10.1371/journal.pone.0178302

Zhang, L.-F., Liu, L.-S., Chu, X.-M. et al. (2014). Combined effects of a high-fat diet and chronic valproic acid treatment on hepatic steatosis and hepatotoxicity in rats. Acta Pharmacol Sin 35, 363-372. doi:10.1038/aps.2013.135

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