ToxAIcology - The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science

Main Article Content

Thomas Hartung
[show affiliations]

Abstract

Toxicology has undergone a transformation from an observational science to a data-rich discipline ripe for artificial intelligence (AI) integration. The exponential growth in computing power coupled with accumulation of large toxicological datasets has created new opportunities to apply techniques like machine learning and especially deep learning to enhance chemical hazard assessment. This article provides an overview of key developments in AI-enabled toxicology, including early expert systems, statistical learning methods like quantitative structure-activity relationships (QSARs), recent advances with deep neural networks, and emerging trends. The promises and challenges of AI adoption for predictive toxicology, data analysis, risk assessment, and mechanistic research are discussed. Responsible development and application of interpretable and human-centered AI tools through multidisciplinary collaboration can accelerate evidence-based toxicology to better protect human health and the environment. However, AI is not a panacea and must be thoughtfully designed and utilized alongside ongoing efforts to improve primary evidence generation and appraisal.

Article Details

How to Cite
Hartung, T. (2023) “ToxAIcology - The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science ”, ALTEX - Alternatives to animal experimentation, 40(4), pp. 559–570. doi: 10.14573/altex.2309191.
Section
Food for Thought ...
References

Allen, T. E. H., Goodman, J. M., Gutsell, S. et al. (2014). Defining molecular initiating events in the adverse outcome pathway framework for risk assessment. Chem Res Toxicol 27, 2100-2112. doi:10.1021/tx500345j

Angelov, P. P., Soares, E. A., Jiang, R. et al. (2021). Explainable artificial intelligence: An analytical review. Wiley Interdiscip Rev Data Min Knowl Discov 11, e1424. doi:10.1002/widm.1424

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

Benfenati, E. and Gini, G. (1997). Computational predictive programs (expert systems) in toxicology. Toxicology 119, 213-225. doi:10.1016/S0300-483X(97)03488-2

Caloni, F., De Angelis, I. and Hartung, T. (2022). Replacement of animal testing by integrated approaches to testing and assessment (IATA): A call for in vivitrosi. Arch Toxicol 96, 1935-1950. doi:10.1007/s00204-022-03299-x

Chang, H. Y., Jung, C. K., Woo, J. I. et al. (2019). Artificial intelligence in pathology. J Pathol Transl Med 53, 1-12. doi:10.4132/jptm.2018.12.16

Chen, X., Roberts, R., Tong, W. et al. (2021). Tox-GAN: An artificial intelligence approach alternative to animal studies – A case study with toxicogenomics. Toxicol Sci 186, 242-259. doi:10.1093/toxsci/kfab157

Cherkasov, A., Muratov, E. N., Fourches, D. et al. (2014). QSAR modeling: Where have you been? Where are you going to? J Med Chem 57, 4977-5010. doi:10.1021/jm4004285

Dave, T., Athaluri, S. A. and Singh, S. (2023). ChatGPT in medicine: An overview of its applications, advantages, limitations, future prospects, and ethical considerations. Front Artif Intell 6, 1169595. doi:10.3389/frai.2023.1169595

Diamandis, P. H. and Kotler, S. (2012). Abundance: The Future Is Better Than You Think. New York, USA: Free Press.

Esteva, A., Robicquet, A., Ramsundar, B. et al. (2019). A guide to deep learning in healthcare. Nat Med 25, 24-29. doi:10.1038/s41591-018-0316-z

Gilpin, L. H., Bau, D., Yuan, B. Z. et al. (2018). Explaining explanations: An overview of interpretability of machine learning. IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 80-89. doi:10.1109/DSAA.2018.00018

Gunning, D., Vorm, E., Wang, J. Y. et al. (2021). DARPA’s explainable AI (XAI) program: A retrospective. Appl AI Lett 2, e61. doi:10.1002/ail2.61

Hamet, P. and Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism 69, S36-S40. doi:10.1016/j.metabol.2017.01.011

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. and Hoffmann, S. (2009). Food for thought … on in silico methods in toxicology. ALTEX 26, 155-166. doi:10.14573/altex.2009.2.155

Hartung, T. (2016). Making big sense from big data in toxicology by read-across. ALTEX, 33, 83-93. doi:10.14573/altex.1603091

Hartung, T. (2023a). ToxAIcology – AI as the new frontier in chemical risk assessment. Front Artif Intell, 6. in press. doi:10.3389/frai.2023.1269932

Hartung, T. (2023b). A call for a human exposome project. ALTEX 40, 4-33. doi:10.14573/altex.2301061

Hemmerich, J. and Ecker, G. F. (2020). In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways. Wiley Interdiscip Rev Comput Mol Sci 10, e1475. doi:10.1002/wcms.1475

Jia, X., Wang, T. and Zhu, H. (2023). Advancing computational toxicology by interpretable machine learning. Environ Sci Technol, online ahead of print. doi:10.1021/acs.est.3c00653

Judson, R. S., Houck, K. A., Martin, M. T. et al. (2014). in vitro and modelling approaches to risk assessment from the U.S. Environmental Protection Agency ToxCast programme. Basic Clin Pharmacol Toxicol 115, 69-76. doi:10.1111/bcpt.12239

Kavlock, R., Chandler, K., Houck, K. et al. (2012). Update on EPA’s ToxCast program: Providing high throughput decision support tools for chemical risk management. Chem Res Toxicol 25, 1287-1302. doi:10.1021/tx3000939

Kleinstreuer, N., Ceger, P., Watt, E. D. et al. (2016a). Development and validation of a computational model for androgen receptor activity. Chem Res Toxicol 30, 946-964. doi:10.1021/acs.chemrestox.6b00347

Kleinstreuer, N. C., Sullivan, K., Allen, D. et al. (2016b). Adverse outcome pathways: From research to regulation scientific workshop report. Regul Toxicol Pharmacol 76, 39-50. doi:10.1016/j.yrtph.2016.01.007

Krewski, D., Andersen, M., Tyshenko, M. G. et al. (2020). Toxicity testing in the 21st century: Progress in the past decade and future perspectives. Arch Toxicol 94, 1-58. doi:10.1007/s00204-019-02613-4

LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning. Nature 521, 436-444. doi:10.1038/nature14539

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 91, 3477-3505. doi:10.1007/s00204-017-2045-3

Li, T., Tong, W., Roberts, R. et al. (2021). DeepCarc: Deep learning-powered carcinogenicity prediction using model-level representation. Front Artif Intell 4, 757780. doi:10.3389/frai.2021.757780. Erratum in Front Artif Intell 5, 1046668 (2022).

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

Linardatos, P., Papastefanopoulos, V. and Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy 23, 18. doi:10.3390/e23010018

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

Luechtefeld, T., Rowlands, C. and Hartung, T. (2018a). Big-data and machine learning to revamp computational toxicology and its use in risk assessment. Toxicol Res 7, 732-744. doi:10.1039/C8TX00051D

Luechtefeld, T., Marsh, D., Rowlands, C. et al. (2018b). 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

Madabhushi, A. and Lee, G. (2016). Image analysis and machine learning in digital pathology: Challenges and opportunities. Med Image Anal 33, 170-175. doi:10.1016/j.media.2016.06.037

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

Mak, K.-K. and Pichika, M. R. (2019). Artificial intelligence in drug development: Present status and future prospects. Drug Discov Today 24, 773-780. doi:10.1016/j.drudis.2018.11.014

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

Mayr, A., Klambauer, G., Unterthiner, T. et al. (2016). DeepTox: Toxicity prediction using deep learning. Front Environ Sci 3, 80. doi:10.3389/fenvs.2015.00080

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

Minh, D., Wang, H. X., Li, Y. F. et al. (2022). Explainable artificial intelligence: A comprehensive review. Artif Intell Rev 55, 3503-3568. doi:10.1007/s10462-021-10088-y

Niazi, M. K. K., Parwani, A. V. and Gurcan, M. N. (2019). Digital pathology and artificial intelligence. Lancet Oncol 20, e253-e261. doi:10.1016/S1470-2045(19)30154-8

Paul, D., Sanap, G., Shenoy, S. et al. (2021). Artificial intelligence in drug discovery and development. Drug Discov Today 26, 80-93. doi:10.1016/j.drudis.2020.10.010

Pérez Santín, E., Rodríguez Solana, R., González García, M. et al. (2021). Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIREs Comput Mol Sci 11, e1516. doi:10.1002/wcms.1516

Reel, P. S., Reel, S., Pearson, E. et al. (2021). Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv 49, 107739. doi:10.1016/j.biotechadv.2021.107739

Roth, A. and MPS-WS Berlin 2019 (Marx, U., Vilén, L., Ewart, L. et al.) (2021). Human microphysiological systems for drug development. Science 373, 1304-1306. doi:10.1126/science.abc3734

Saranya, A. and Subhashini R. (2023). A systematic review of explainable artificial intelligence models and applications: Recent developments and future trends. Decision Analytics Journal 7, 100230. doi:10.1016/j.dajour.2023.100230

Schölkopf, B., Locatello, F., Bauer, S. et al. (2021). Toward causal representation learning. Proceedings of the IEEE 109, 612-634. doi:10.1109/jproc.2021.3058954

Singh, A. V., Chandrasekar, V., Paudel, N. et al. (2023). Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother 163, 114784. doi:10.1016/j.biopha.2023.114784

Tang, W., Chen, J., Wang, Z. et al. (2018). Deep learning for predicting toxicity of chemicals: A mini review. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 36, 252-271. doi:10.1080/10590501.2018.1537563

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

Thomas, R. S., Philbert, M. A., Auerbach, S. M. et al. (2013). Incorporating new technologies into toxicity testing and risk assessment: Moving from 21st century vision to a data-driven framework. Toxicol Sci 136, 4-18. doi:10.1093/toxsci/kft178

Tizhoosh, H. R. and Pantanowitz, L. (2018). Artificial intelligence and digital pathology: Challenges and opportunities. J Pathol Inform 14, 38. doi:10.4103/jpi.jpi_53_18

Tran, T. T. V., Wibowo, A. S., Tayara, H. et al. (2023). Artificial intelligence in drug toxicity prediction: Recent advances, challenges, and future perspectives. J Chem Inf Model 63, 2628-2643. doi:10.1021/acs.jcim.3c00200

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

Wang, H., Liu, R., Schyman, P. et al. (2019). Deep neural network models for predicting chemically induced liver toxicity endpoints from transcriptomic responses. Front Pharmacol 10, 42. doi:10.3389/fphar.2019.00042

Wang, M. W. H., Goodman, J. M. and Allen, T. E. H. (2021). Machine learning in predictive toxicology: Recent applications and future directions for classification models. Chem Res Toxicol 34, 217-239. doi:10.1021/acs.chemrestox.0c00316

Wetmore, B. A., Allen, B., Clewell, H. J. 3rd et al. (2014). Incorporating population variability and susceptible subpopulations into dosimetry for high-throughput toxicity testing. Toxicol Sci 142, 210-224. doi:10.1093/toxsci/kfu169

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

Williams, A. J., Lambert, J. C., Thayer, K. et al. (2021). Sourcing data on chemical properties and hazard data from the US-EPA CompTox Chemicals Dashboard: A practical guide for human risk assessment. Environ Int 154, 106566. doi:10.1016/j.envint.2021.106566

Zhang, Q., Bhattacharya, S., Conolly, R. B. et al. (2014). Molecular signaling network motifs provide a mechanistic basis for cellular threshold responses. Environ Health Perspect 122, 1261-1270. doi:10.1289/ehp.1408244

Most read articles by the same author(s)

<< < 10 11 12 13 14 15 16 > >>