E-validation – Unleashing AI for validation

Main Article Content

Thomas Hartung , Alexandra Maertens, Thomas Luechtefeld
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Abstract

The validation of new approach methods (NAMs) in toxicology faces significant challenges, including the integration of diverse data, selection of appropriate reference chemicals, and lengthy, resource-intensive consensus processes. This article proposes an artificial intelligence (AI)-based approach, termed e-validation, to optimize and accelerate the NAM validation process. E-vali­dation employs advanced machine learning and simulation techniques to systematically design validation studies, select informative reference chemicals, integrate existing data, and provide tailored training. The approach aims to shorten current decade-long validation timelines, using fewer resources while enhancing rigor. Key components include the smart selection of reference chemicals using clustering algorithms, simulation of validation studies, mechanistic validation powered by AI, and AI-enhanced training for NAM education and implementation. A centralized dashboard interface could integrate these components, streamlining workflows and providing real-time decision support. The potential impacts of e-validation are extensive, promising to accel­erate biomedical research, enhance chemical safety assessment, reduce animal testing, and drive regulatory and commercial innovation. While the integration of AI and machine learning offers sig­nificant advantages, challenges related to data quality, complexity of implementation, scalability, and ethical considerations must be addressed. Real-world validation and pilot studies are crucial to demonstrate the practical benefits and feasibility of e-validation. This transformative approach has the potential to revolutionize toxicological science and regulatory practices, ushering in a new era of predictive, personalized, and preventive health sciences.


Plain language summary
Validating new methods to replace traditional animal testing for chemicals can be slow and costly, often taking up to ten years. This article introduces e-validation, an artificial intelligence (AI)- powered approach designed to speed up and improve this process. By using advanced computer techniques, e-validation selects the best chemicals for testing, designs efficient studies, and inte­grates existing data. This approach would cut validation time and use fewer resources. E-validation includes a smart system for choosing test chemicals, virtual simulations to predict study outcomes, and AI tools to understand the biological effects of chemicals. It also provides training in these new methods. E-validation could accelerate medical research, improve chemical safety, reduce the need for animal testing, and help create safer products faster. While promising, this new approach will need real-world testing to prove its benefits and address potential challenges.

Article Details

How to Cite
Hartung, T., Maertens, A. and Luechtefeld, T. (2024) “E-validation – Unleashing AI for validation”, ALTEX - Alternatives to animal experimentation, 41(4), pp. 567–587. doi: 10.14573/altex.2409211.
Section
Food for Thought ...
References

Abbas, I., Rovira, J., Cobo, E. et al. (2006). Simulation models for optimizing the design of clinical trials. Qual Reliab Enging Int 22, 683-691. doi:10.1002/qre.799

Balbus, J. M., Boxall, A. B., Fenske, R. A. et al. (2013). Implications of global climate change for the assessment and management of human health risks of chemicals in the natural environment. Environ Toxicol Chem 32, 62-78. doi:10.1002/etc.2046

Balls, M., Bass, R., Curren, R. et al. (2024). 60 Years of the 3Rs symposium: Lessons learned and the road ahead. ALTEX 41, 179-201. doi:10.14573/altex.2403061

Baranwal, M., Magner, A., Elvati, P. et al. (2020). A deep learning architecture for metabolic pathway prediction. Bioinformatics 36, 2547-2553. doi:10.1093/bioinformatics/btz954

Beilmann, M., Boonen, H., Czich, A. et al. (2019). Optimizing drug discovery by investigative toxicology: Current and future trends. ALTEX 36, 3-17. doi:10.14573/altex.1808181

Bell, S. M., Phillips, J., Sedykh, A. et al. (2017). An integrated chemical environment to support 21st-century toxicology. Environ Health Perspect 125, 054501. doi:10.1289/ehp1759

Bell, S., Abedini, J., Ceger, P. et al. (2020). An integrated chemical environment with tools for chemical safety testing. Toxicol In Vitro 67, 104916. doi:10.1016/j.tiv.2020.104916

Blaauboer, B. J. (2010). Biokinetic modeling and in vitro-in vivo extrapolations. J Toxicol Environ Health B 13, 242-252. doi:10.1080/10937404.2010.483940

Bouvier d’Yvoire, M., Prieto, P., Blaauboer, B. J. et al. (2007). Physiologically-based kinetic modelling (PBK modelling): Meeting the 3Rs agenda. The report and recommendations of ECVAM workshop 63. Altern Lab Anim 35, 661-671. doi:10.1177/026119290703500606

Boxall, A. B., Hardy, A., Beulke, S. et al. (2009). Impacts of climate change on indirect human exposure to pathogens and chemicals from agriculture. Environ Health Perspect 117, 508-514. doi:10.1289/ehp.0800084

Busquet, F. and Hartung, T. (2017). The need for strategic development of safety sciences. ALTEX 34, 3-21. doi:10.14573/altex.1701031

Butera, A., Smirnova, L., Ferrando-May, E. et al. (2023). Deconvoluting gene and environment interactions to develop “epigenetic score meter” of disease. EMBO Mol Med 15, e18208. doi:10.15252/emmm.202318208

Chesnut, M., Paschoud, H., Repond, C. et al. (2021). Human 3D iPSC-derived brain model to study chemical-induced myelin disruption. Int J Mol Sci 22, 9473. doi:10.3390/ijms22179473

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 Biosys 7, 100061. doi:10.1016/j.bbiosy.2022.100061

Corvi, R., Albertini, S., Hartung, T. et al. (2008). ECVAM retrospective validation of in vitro micronucleus test (MNT). Mutagenesis 23, 271-283. doi:10.1093/mutage/gen010

Costello, Z. and Martin, H. G. (2018). A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data. NPJ Syst Biol Appl 4, 19. doi:10.1038/s41540-018-0054-3

Crawford, S. E., Hartung, T., Hollert, H. et al. (2017). Green toxicology: A strategy for sustainable chemical and material development. Environ Sci Eur 29, 16. doi:10.1186/s12302-017-0115-z

Hamon, J., Renner, M., Jamei, M. et al. (2015). Quantitative in vitro to in vivo extrapolation of tissues toxicity. Toxicol In Vitro 30, 203-216. doi:10.1016/j.tiv.2015.01.011

Hartung, T., Bremer, S., Casati, S. et al. (2004). A modular approach to the ECVAM principles on test validity. Altern Lab Anim 32, 467-472. doi:10.1177/026119290403200503

Hartung, T. and Leist, M. (2008). Food for thought … on the evolution of toxicology and phasing out of animal testing. ALTEX 25, 91-96. doi:10.14573/altex.2008.2.91

Hartung, T., van Vliet, E., Jaworska, J. et al. (2012). Systems toxicology. ALTEX 29, 119-128. doi:10.14573/altex.2012.2.119

Hartung, T., Luechtefeld, T., Maertens, A. et al. (2013a). Integrated testing strategies for safety assessments. ALTEX 30, 3-18. doi:10.14573/altex.2013.1.003

Hartung, T., Stephens, M. and Hoffmann, S. (2013b). Mechanistic validation. ALTEX 30, 119-130. doi:10.14573/altex.2013.2.119

Hartung, T. (2017). A comprehensive overview of the current status and application of predictive ADMET. In S. Chackalamannil, D. Rotella and S. E. Ward (eds), Comprehensive Medicinal Chemistry III (Chapter 4.08, 150-155). doi:10.1016/B978-0-12-409547-2.12378-9

Hartung, T., FitzGerald, R., Jennings, P. et al. (2017). Systems toxicology – Real world applications and opportunities. Chem Res Toxicol 30, 870-882. doi:10.1021/acs.chemrestox.7b00003

Hartung, T. (2023a). ToxAIcology – The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science. ALTEX 40, 559-570. doi:10.14573/altex.2309191

Hartung, T. (2023b). Artificial intelligence as the new frontier in chemical risk assessment. Front Artif Intell 6, 1269932. doi:10.3389/frai.2023.1269932

Hartung, T. (2024a). The (misleading) role of animal models in drug development. Front Drug Discovery 4, 1355044. doi:10.3389/fddsv.2024.1355044

Hartung, T. (2024b). The validation of regulatory test methods – Conceptual, ethical, and philosophical foundations. ALTEX 41, 525-544. doi:10.14573/altex.2409271

Hartung, T., King, N. P. M., Kleinstreuer, N. et al. (2024). Lever¬aging biomarkers and translational medicine for preclinical safe¬ty - Lessons for advancing the validation of alternatives to ani¬mal testing. ALTEX 41, 545-566. doi:10.14573/altex.2410011

Hoffmann, S., Edler, L., Gardner, I. et al. (2008). Points of reference in validation – The report and recommendations of ECVAM Workshop. Altern Lab Anim 36, 343-352. doi:10.177/026119290803600311

Kang, H., Goo, S., Lee, H. et al. (2022). Fine-tuning of BERT model to accurately predict drug-target interactions. Pharmaceutics 14, 1710. doi:10.3390/pharmaceutics14081710

Kleinstreuer, N. and Hartung, T. (2024). Artificial intelligence (AI) – It’s the end of the tox as we know it (and I feel fine) – AI for predictive toxicology. Arch Toxicol 98, 735-754. doi:10.1007/s00204-023-03666-2

Leist, M., Hartung, T. and Nicotera, P. (2008). The dawning of a new age of toxicology. ALTEX 25, 103-114. doi:10.14573/altex.2008.2.103

Leist, M., Ghallab, A., Graepel, R. et al. (2017). Adverse outcome pathways: Opportunities, limitations and open questions. Arch Toxicol 31, 221-229. doi:10.1007/s00204-017-2045-3

Lin, Z. and Chou, W-C. (2022). Machine learning and artificial intelligence in toxicological sciences. Toxicol Sci 189, 7-19. doi:10.1093/toxsci/kfac075

Luechtefeld, T. and Hartung, T. (2017). Computational approaches to chemical hazard assessment. ALTEX 34, 459-478. doi:10.14573/altex.1710141

Maertens, A. and Hartung, T. (2018). Green toxicology – Know early about and avoid toxic product liabilities. Toxicol Sci 161, 285-289. doi:10.1093/toxsci/kfx243

Maertens, A. and Hartung, T. (2019). Green toxicology meets nanotoxicology: The process of sustainable nanomaterial development and use. A. Gajewicz and T. Puzyn (eds), Computational Nanotoxicology: Challenges, Pitfalls, and Perspectives (495-506). Jenny Stanford Publishing. doi:10.1201/9780429341373-11

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

Maertens, A., Antignac, E., Benfenati, E. et al. (2024a). The probable future of toxicology – Probabilistic risk assessment. ALTEX 41, 273-281. doi:10.14573/altex.2310301

Maertens, A., Luechtefeld, T. and Hartung, T. (2024b). Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals. ALTEX 41, 3-19. doi:10.14573/altex.2312291

Malikova, M. A. (2016). Optimization of protocol design: A path to efficient, lower cost clinical trial execution. Future Sci OA 2, FSO89. doi:10.4155/fso.15.89

Marx, U., Andersson, T. B., Bahinski, A. et al. (2016). Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing using animals. ALTEX 33, 272-321. doi:10.14573/altex.1603161

Marx, U., Akabane, T., Andersson, T. B. et al. (2020). Biology-inspired microphysiological systems to advance medicines for patient benefit and animal welfare. ALTEX 37, 364-394. doi:10.14573/altex.2001241

Meigs, L., Smirnova, L., Rovida, C. et al. (2018). Animal testing and its alternatives – The most important omics is economics. ALTEX 35, 275-305. doi:10.14573/altex.1807041

Petersen, E. J., Nguyen, A., Brown, J. et al. (2021). Characteristics to consider when selecting a positive control material for an in vitro assay. ALTEX 38, 365-376. doi:10.14573/altex.2102111

Proença, S., Paini, A., Joossens, E. et al. (2019). Insights into in vitro biokinetics using virtual cell based assay simulations. ALTEX 36, 447-461. doi:10.14573/altex.1812101

Rodríguez-Belenguer, P., March-Vila, E., Pastor, M. et al. (2023). Usage of model combination in computational toxicology. Toxicol Lett 389, 34-44. doi:10.1016/j.toxlet.2023.10.013

Roth, A. and MPS-WS Berlin 2019 [Marx, U., Vilén, L., Ewart, L. et al.] (2019). Human microphysiological systems for drug development. Science 373, 1304-1306. doi:10.1126/science.abc3734

Rovida, C., Alépée, N., Api, A. M. et al. (2015). Integrated testing strategies (ITS) for safety assessment. ALTEX 32, 171-181. doi:10.14573/altex.1506201

Sauer, J. M., Hartung, T., Leist, M. et al. (2015). Systems toxicology: The future of risk assessment. Int J Toxicol 34, 346-348. doi:10.1177/1091581815576551

Shah, H. A., Liu, J., Yang, Z. et al. (2021). Review of machine learning methods for the prediction and reconstruction of metabolic pathways. Front Mol Biosci 8, 634141. doi:10.3389/fmolb.2021.634141

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

Sillé, F. and Hartung, T. (2024). Metabolomics in preclinical drug safety assessment: Current status and future trends. Metabolites 14, 98. doi:10.3390/metabo14020098

Sillé, F. C. M., Busquet, F., Fitzpatrick, S. et al. (2024). The implementation moonshot project for alternative chemical testing (IMPACT) toward a human exposome project. ALTEX 41, 344-362. doi:10.14573/altex.2407081

Smirnova, L., Kleinstreuer, N., Corvi, R. et al. (2018). 3S – Systematic, systemic, and systems biology and toxicology. ALTEX 35, 139-162. doi:10.14573/altex.1804051

Suciu, I., Pamies, D., Peruzzo, R. et al. (2023). GxE interactions as a basis for toxicological uncertainty. Arch Toxicol 97, 2035-2049. doi:10.1007/s00204-023-03500-9

Sverdlov, O., Ryeznik, Y. and Wong, W. K. (2019). On optimal designs for clinical trials: An updated review. J Stat Theory Pract 14. 10. doi:10.1007/s42519-019-0073-4

Tetko, I. V., Klambauer, G., Clevert, D. A. et al. (2022). Artificial intelligence meets toxicology. Chem Res Toxicol 35, 1289-1290. doi:10.1021/acs.chemrestox.2c00196

Tonoyan, L. and Siraki, A. G. (2024). Machine learning in toxicological sciences: Opportunities for assessing drug toxicity. Front Drug Discov 4, 1336025. doi:10.3389/fddsv.2024.1336025

Tsaioun, K., Blaauboer, B. J. and Hartung, T. (2016). Evidence-based absorption, distribution, metabolism, excretion and toxicity (ADMET) and the role of alternative methods. ALTEX 33, 343-358. doi:10.14573/altex.1610101

van Ertvelde, J., Verhoeven, A., Maerten, A. et al. (2023). Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach. J Biomed Inform 145, 104465-104465. doi:10.1016/j.jbi.2023.104465

Vinken, M., Benfenati, E., Busquet, F. et al. (2021). Safer chemicals using less animals: Kick-off of the European ONTOX project. Toxicology 458, 152846. doi:10.1016/j.tox.2021.152846

von Aulock, S., Busquet, F., Locke, P. et al. (2022). Engagement of scientists with the public and policymakers to promote alternative methods. ALTEX 39, 543-559. doi:10.14573/altex.2209261

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