The probable future of toxicology – probabilistic risk assessment

Main Article Content

Alexandra Maertens, Eric Antignac, Emilio Benfenati, Denise Bloch, Ellen Fritsche, Sebastian Hoffmann, Joanna Jaworska, George Loizou, Kevin McNally, Przemyslaw Piechota, Erwin L. Roggen, Marc Teunis, Thomas Hartung
[show affiliations]

Abstract

Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated artificial intelligence (AI) models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges, and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.


Plain language summary
This workshop report discusses the future of toxicology and how probabilistic risk assessment can help address uncertainties in assessing chemical risks. Experts emphasize the importance of quantitative assessment in toxicology and the need for a deeper understanding of how chemicals affect our health. By incorporating probabilistic risk assessment, we can better evaluate the potential risks posed by chemicals and make more informed decisions to protect human health and the environment. Embracing new technologies like artificial intelligence and natural language processing can enhance data analysis and improve the accuracy of risk assessments in toxicology.

Article Details

How to Cite
Maertens, A. (2024) “The probable future of toxicology – probabilistic risk assessment”, ALTEX - Alternatives to animal experimentation, 41(2), pp. 273–281. doi: 10.14573/altex.2310301.
Section
Articles
References

Arkes, H. R., Aberegg, S. K. and Arpin, K. A. (2022). Analysis of physicians’ probability estimates of a medical outcome based on a sequence of events. JAMA Netw Open 5, e2218804. doi:10.1001/jamanetworkopen.2022.18804

Baxter, A. L., BenZvi, S. Y., Bonivento, W. et al. (2022). Collaborative experience between scientific software projects using agile scrum development. Softw Pract Exp 52, 2077-2096. doi:10.1002/spe.3120

Blackburn, K. L., Ellison, C. A., Stuard, S. B. et al. (2019). Dosimetry considerations for in vivo and in vitro test data and a novel surrogate ITTC approach for read-across based on metabolites. Comput Toxicol 10, 145-157. doi:10.1016/j.comtox.2018.08.005

Cofield, S. S., Corona, R. V. and Allison, D. B. (2010). Use of causal language in observational studies of obesity and nutrition. Obes Facts 3, 353-356. doi:10.1159/000322940

Corradi, M. P. F., de Haan, A. M., Staumont, B. et al. (2022). Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies. Biomater Biosyst 7, 100061. doi:10.1016/j.bbiosy.2022.100061

Crevel, R. W., Baumert, J. L., Baka, A. et al. (2014). Development and evolution of risk assessment for food allergens. Food Chem Toxicol 67, 262-276. doi:10.1016/j.fct.2014.01.032

Di Guardo, A., Gouin, T., MacLeod, M. et al. (2018). Environmental fate and exposure models: Advances and challenges in 21st century chemical risk assessment. Environ Sci 20, 58-71. doi:10.1039/c7em00568g

Dourson, M., Ewart, L., Fitzpatrick, S. C. et al. (2022). The future of uncertainty factors with in vitro studies using human cells. Toxicol Sci 186, 12-17. doi:10.1093/toxsci/kfab134

Eaton, C. D., Lamar, M. D. and McCarthy, M. L. (2020). 21st century reform efforts in undergraduate quantitative biology education: Conversations, initiatives, and curriculum change in the United States of America. Lett Biomath 7, 55. https://scarab.bates.edu/faculty_publications/345/

EFSA Scientific Committee, More, S. J., Bampidis, V. et al. (2019). Guidance on the use of the threshold of toxicological concern approach in food safety assessment. EFSA J 17, e05708. doi:10.2903/j.efsa.2019.5708

Ellison, C. A., Api, A. M., Becker, R. A. et al. (2021). Internal threshold of toxicological concern (iTTC): Where we are today and what is possible in the near future. Front Toxicol 2, 621541. doi:10.3389/ftox.2020.621541

EC – European Commission, Directorate-General for Research and Innovation, Valdes, C. et al. (2017). Evaluation of research careers fully acknowledging Open Science practices: Rewards, incentives and/or recognition for researchers practicing Open Science. Publications Office of the European Union. https://data.europa.eu/doi/10.2777/75255

European Commission (2020). Chemicals Strategy for Sustainability – Towards a toxic-free environment communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. COM/2020/667 final. European Commission, Brussels, Belgium. https://eur-lex.europa.eu/legal-content/en/txt/pdf/?uri=celex:52020dc0667

Gao, J., Aksoy, B. A., Gross, B. et al. (2014). Abstract 4271: The CBioPortal for cancer genomics as a clinical decision support tool. Cancer Res 74, Suppl, 4271. doi:10.1158/1538-7445.am2014-4271

Gentleman, R. C., Carey, V. J., Bates, D. M. et al. (2004). Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol 5, R80. doi:10.1186/gb-2004-5-10-r80

Hartung, T. (2013). Look back in anger – What clinical studies tell us about preclinical work. ALTEX 30, 275-291. doi:10.14573/altex.2013.3.275

Hartung, T. (2017). Thresholds of toxicological concern – Setting a threshold for testing below which there is little concern. ALTEX 34, 331-351. doi:10.14573/altex.1707011

Heidari, S., Mostafaei, S., Razazian, N. et al. (2022). The effect of lead exposure on IQ test scores in children under 12 years: A systematic review and meta-analysis of case-control studies. Syst Rev 11, 106. doi:10.1186/s13643-022-01963-y

Herzler, M., Marx-Stoelting, P., Pirow, R. et al. (2021). The “EU chemicals strategy for sustainability” questions regulatory toxicology as we know it: Is it all rooted in sound scientific evidence? Arch Toxicol 95, 2589-2601. doi:10.1007/s00204-021-03091-3

Hoover, R. N., Hyer, M., Pfeiffer, R. M. et al. (2011). Adverse health outcomes in women exposed in utero to diethylstilbestrol. N Engl J Med 365, 1304-1314. doi:10.1056/nejmoa1013961

Houben, G. F., Baumert, J. L., Blom, W. M. et al. (2020). Full range of population eliciting dose values for 14 priority allergenic foods and recommendations for use in risk characterization. Food Chem Toxicol 146, 111831. doi:10.1016/j.fct.2020.111831

Houck, K. A., Richard, A. M., Judson, R. S. et al. (2013). ToxCast: Predicting toxicity potential through high-throughput bioactivity profiling. In P. Steinberg (ed.), High-Throughput Screening Methods in Toxicity Testing (1-31). John Wiley & Sons, Inc. doi:10.1002/9781118538203.ch1

Ioannidis J. P. (2005). Why most published research findings are false. PLoS Med 2, e124. doi:10.1371/journal.pmed.0020124

Jentink, J., Loane, M. A., Dolk, H. et al. (2010). Valproic acid monotherapy in pregnancy and major congenital malformations. N Engl J Med 362, 2185-2193. doi:10.1056/nejmoa0907328

Kleensang, A., Maertens, A., Rosenberg, M. et al. (2014). Pathways of toxicity. ALTEX 31, 53-61. doi:10.14573/altex.1309261

Krewski, D., Acosta, D., Jr, Andersen, M. et al. (2010). Toxicity testing in the 21st century: A vision and a strategy. J Toxicol Environ Health B Crit Rev 13, 51-138. doi:10.1080/10937404.2010.483176

Kroes, R., Kleiner, J. and Renwick, A. (2005). The threshold of toxicological concern concept in risk assessment. Toxicol Sci 86, 226-230. doi:10.1093/toxsci/kfi169

Krug, H. F., Bohmer, N., Kühnel, D. et al. (2018). The DaNa2.0 knowledge base nanomaterials – An important measure accompanying nanomaterials development. Nanomaterials 8, 204. doi:10.3390/nano8040204

Lucas, R. M. and McMichael, A. J. (2005). Association or causation: Evaluating links between “environment and disease”. Bull World Health Organ 83, 792-795.

Luechtefeld, T., Marsh, D., Rowlands, C. et al. (2018). Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol Sci 165, 198-212. doi:10.1093/toxsci/kfy152

Luo, R., Sun, L., Xia, Y. et al. (2022). BioGPT: Generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform 23, bbac409. doi:10.1093/bib/bbac409

Maertens, A., Tran, V., Kleensang, A. et al. (2018). Weighted gene correlation network analysis (WGCNA) reveals novel transcription factors associated with bisphenol A dose-response. Front Genet 9, 508. doi:10.3389/fgene.2018.00508

Maertens, A., Golden, E. and Hartung, T. (2021). Avoiding regrettable substitutions: Green toxicology for sustainable chemistry. ACS Sustain Chem Eng 9, 7749-7758. doi:10.1021/acssuschemeng.0c09435

Maertens, A., Golden, E., Luechtefeld, T. H. et al. (2022). Probabilistic risk assessment – The keystone for the future of toxicology. ALTEX 39, 3-29. doi:10.14573/altex.2201081

McDonald, J. A., Goyal, A. and Terry, M. B. (2013). Alcohol intake and breast cancer risk: Weighing the overall evidence. Curr Breast Cancer Rep 5, 208-221. doi:10.1007/s12609-013-0114-z

McNamara, C., Rohan, D., Golden, D. et al. (2007). Probabilistic modelling of European consumer exposure to cosmetic products. Food Chem Toxicol 45, 2086-2096. doi:10.1016/j.fct.2007.06.037

Middleton, A. M., Reynolds, J., Cable, S. et al. (2022). Are non-animal systemic safety assessments protective? A toolbox and workflow. Toxicol Sci 189, 124-147. doi:10.1093/toxsci/kfac068

Munro, I. C., Ford, R. A., Kennepohl, E. et al. (1996). Correlation of structural class with no-observed-effect levels: A proposal for establishing a threshold of concern. Food Chem Toxicol 34, 829-867. doi:10.1016/s0278-6915(96)00049-x

Najjar, A., Ellison, C. A., Gregoire, S. et al. (2023). Practical application of the interim internal threshold of toxicological concern (iTTC): A case study based on clinical data. Arch Toxicol 97, 155-164. doi:10.1007/s00204-022-03371-6

Peng, R. D. and Hicks, S. C. (2021). Reproducible research: A retrospective. Ann Rev Publ Health 42, 79-93. doi:10.1146/annurev-publhealth-012420-105110

Ruffle, B., Henderson, J., Murphy-Hagan, C. et al. (2018). Application of probabilistic risk assessment: Evaluating remedial alternatives at the Portland Harbor Superfund Site, Portland, Oregon, USA. Integr Environ Assess Manag 14, 63-78. doi:10.1002/ieam.1999

Rusyn, I. and Chiu, W. A. (2022). Decision-making with new approach methodologies: Time to replace default uncertainty factors with data. Toxicol Sci 189, 148-149. doi:10.1093/toxsci/kfac033

Safford, B., Api, A. M., Barratt, C. et al. (2017). Application of the expanded Creme RIFM consumer exposure model to fragrance ingredients in cosmetic, personal care and air care products. Regul Toxicol Pharmacol 86, 148-156. doi:10.1016/j.yrtph.2017.02.021

Savage, S. L. and Markowitz, H. M. (2009). The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty. John Wiley & Sons. https://play.google.com/store/books/details?id=2lsLAQi0LlcC

Schickore, J. (2010). Trying again and again: Multiple repetitions in early modern reports of experiments on snake bites. Early Sci Med 15, 567-617. http://www.jstor.org/stable/20787431

Sherman, M. (2009). Vinyl chloride and the liver. J Hepatology 51, 1074-1081. doi:10.1016/j.jhep.2009.09.012

Sillé, F. C. M., Karakitsios, S., Kleensang, A. et al. (2020). The exposome – A new approach for risk assessment. ALTEX 37, 3-23. doi:10.14573/altex.2001051

Spinu, N., Cronin, M. T. D., Enoch, S. J. et al. (2020). Quantitative adverse outcome pathway (qAOP) models for toxicity prediction. Arch Toxicol 94, 1497-1510. doi:10.1007/s00204-020-02774-7

Viscusi, W. K., Hamilton, J. T. and Dockins, P. C. (1997). Conservative versus mean risk assessments: Implications for superfund policies. J Environ Econ Manage 34, 187-206. doi:10.1006/jeem.1997.1012

Voigt, C. A. (2020). Synthetic biology 2020-2030: Six commercially-available products that are changing our world. Nat Commun 11, 6379. doi:10.1038/s41467-020-20122-2

Most read articles by the same author(s)

<< < 13 14 15 16 17 18