[en] Chemical risk assessment is evolving from traditional deterministic approaches to embrace probabilistic methodologies, where risk of hazard manifestation is understood as a more or less probable event depending on exposure, individual factors, and stochastic processes. This is driven by advancements in human stem cells, complex tissue engineering, high-performance computing, and cheminformatics, and is more recently facilitated by large-scale artificial intelligence models. These innovations enable a more nuanced understanding of chemical hazards, capturing the complexity of biological responses and variability within populations. However, each technology comes with its own uncertainties impacting on the estimation of hazard probabilities. This shift addresses the limitations of point estimates and thresholds that oversimplify hazard assessment, allowing for the integration of kinetic variability and uncertainty metrics into risk models. By leveraging modern technologies and expansive toxicological data, probabilistic approaches offer a comprehensive evaluation of chemical safety. This paper summarizes a workshop held in 2023 and discusses the technological and data-driven enablers, and the challenges faced in their implementation, with particular focus on perturbation of biology as the basis of hazard estimates. The future of toxicological risk assessment lies in the successful integration of these probabilistic models, promising more accurate and holistic hazard evaluations. [en] Understanding chemical risks is key to public health. Traditional risk assessments rely on fixed safety margins and animal tests, which can miss complex human responses. Probabilistic risk assessment uses advanced tools – human stem cells, organ‑on‑chip systems, and AI – to estimate the likelihood of harm across different scenarios. By modeling individual variability (genetics, exposures) and quantifying uncertainty, it provides nuanced risk estimates rather than binary “safe/unsafe” labels. This approach increases transparency, shows confidence intervals, and reduces animal testing by integrating human‑relevant data. Challenges include defining harm thresholds, integrating diverse datasets, and gaining regulatory acceptance. Workshops like the 2023 CAAT-ONTOX meeting in Italy highlighted how measuring biological perturbations (e.g., molecular or cellular changes) informs probability of adverse outcomes. As technologies and data improve, probabilistic methods promise more realistic, protective chemical safety evaluations that reflect real‑world human diversity.
Disciplines :
Human health sciences: Multidisciplinary, general & others
Author, co-author :
Maertens, Alexandra; Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering, Baltimore, MD, USA
Kincaid, Breanne; Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering, Baltimore, MD, USA
Bridgeford, Eric; Dept. of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
Brochot, Celine; Certara, Canterbury, Kent, UK
de Carvalho E Silva, Arthur; University of Birmingham and Michabo Health Science Ltd, Birmingham, UK
Dorne, Jean-Lou C M; European Food Safety Authority (EFSA), Parma, Italy
Geris, Liesbet ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique ; Mechanical Engineering, KU Leuven, Leuven, Belgium
Husøy, Trine; Food Safety/ Centre for Sustainable Diets, Norwegian Institute of Public Health (NIPH), Oslo, Norway
Kleinstreuer, Nicole; National Institutes of Health, National Institute for Environmental Health Sciences, DTT/NICEATM, Durham, NC, USA
Russo, Giulia; Drug and Health Sciences, University of Catania, Catania, Italy
Thayer, Kris; Chemical & Pollutant Assessment Division (CPAD), US Environmental Protection Agency (EPA), Durham, NC, USA
Hartung, Thomas; Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering, Baltimore, MD, USA ; Doerenkamp-Zbinden Chair for Evidence-based Toxicology, Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering, Baltimore, MD, USA ; CAAT-Europe, University of Konstanz, Konstanz, Germany
Alexandrov, L. B., Kim, J., Haradhvala, N. J. et al. (2020). The repertoire of mutational signatures in human cancer. Nature 578, 94-101. doi:10.1038/s41586-020-1943-3
Alhossary, A., Handoko, S. D., Mu, Y. et al. (2015). Fast, accurate, and reliable molecular docking with QuickVina 2. Bioinformatics 31, 2214-2216. doi:10.1093/bioinformatics/btv082
Ames, B. N. and Gold, L. S. (1990). Misconceptions on pollution and the causes of cancer. Angew Chem Int Ed Engl 29, 1197-1208. doi:10.1002/anie.199011971
Ankley, G. T., Bennett, R. S., Erickson, R. J. et al. (2010). Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 29, 730-741. doi:10.1002/etc.34
Aranda-Anzaldo, A. and Dent, M. A. R. (2018). Landscaping the epigenetic landscape of cancer. Prog Biophys Mol Biol 140, 155-174. doi:10.1016/j.pbiomolbio.2018.06.005
Bachman, J. A., Gyori, B. M. and Sorger, P. K. (2023). Automated assembly of molecular mechanisms at scale from text mining and curated databases. Mol Syst Biol 19, e11325. doi:10.15252/ msb.202211325
Basketter, D. A., Clewell, H., Kimber, I. et al. (2012). A road-map for the development of alternative (non-animal) methods for systemic toxicity testing. ALTEX 29, 3-89. doi: 10.14573/ altex.2012.1.003
Bjørk, M.-H., Zoega, H., Leinonen, M. K. et al. (2022). Association of prenatal exposure to antiseizure medication with risk of autism and intellectual disability. JAMA Neurol 79, 672-681. doi:10.1001/jamaneurol.2022.1269
Blum, A., Behl, M., Birnbaum, L. et al. (2019). Organophosphate ester flame retardants: Are they a regrettable substitution for polybrominated diphenyl ethers? Environ Sci Technol Lett 6, 638-649. doi:10.1021/acs.estlett.9b00582
Bønnelykke, K., Sparks, R., Waage, J. et al. (2015). Genetics of allergy and allergic sensitization: Common variants, rare mutations. Curr Opin Immunol 36, 115-126. doi:10.1016/j.coi.2015.08.002
Bordukova, M., Makarov, N., Rodriguez-Esteban, R. et al. (2024). Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. Expert Opin Drug Discov 19, 33-42. doi:10.1080/17460441.2023.2273839
Bouhifd, M., Hogberg, H. T., Kleensang, A. et al. (2014). Mapping the human toxome by systems toxicology. Basic Clin Pharmacol Toxicol 115, 1-8. doi:10.1111/bcpt.12198
Bouhifd, M., Andersen, M. E., Baghdikian, C. et al. (2015). The human toxome project. ALTEX 32, 112-124. doi:10.14573/ altex.1502091
Brazma, A., Hingamp, P., Quackenbush, J. et al. (2001). Minimum information about a microarray experiment (MIAME)toward standards for microarray data. Nat Genet 29, 365-371. doi:10.1038/ng1201-365
Calabrese, E. J. (2005). Paradigm lost, paradigm found: The reemergence of hormesis as a fundamental dose response model in the toxicological sciences. Environ Pollut 138, 379-411. doi:10.1016/j.envpol.2004.10.001
Celardo, I., Aschner, M., Ashton, R. S., et al. (2025). Developmental neurotoxicity (DNT): A call for implementation of new approach methodologies for regulatory purposes: Summary of the 5th International Conference on DNT Testing. ALTEX 42, 323-349. doi:10.14573/altex.2503191
Chandrasekaran, S. N., Cimini, B. A., Goodale, A. et al. (2024). Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations. Nat Methods 21, 1114-1121. doi:10.1038/s41592-024-02241-6
Chernoff, J. (2021). The two-hit theory hits 50. Mol Biol Cell 32, rt1. doi:10.1091/mbc.e21-08-0407
Christensen, J., Grønborg, T. K., Sørensen, M. J. et al. (2013). Prenatal valproate exposure and risk of autism spectrum disorders and childhood autism. Surv Anesthesiol 57, 292-293. doi:10.1097/01. sa.0000435536.89926.0a
Cöllen, E., Bartmann, K., Blum, J. et al. (2025). Mapping out strategies to further develop human-relevant, new approach methodology (NAM)-based developmental neurotoxicity (DNT) testing. ALTEX 42, 204-223. doi:10.14573/altex.2410112
Committee on the Design and Evaluation of Safer Chemical Substitutions (2014). Overview of the GHS Classification Scheme in Hazard Classification. Washington, DC, USA: National Academies Press.
Corradi, M., Luechtefeld, T., de Haan, A. M. et al. (2024). The application of natural language processing for the extraction of mechanistic information in toxicology. Front Toxicol 6, 1393662. doi:10.3389/ftox.2024.1393662
Coupland, C. A. C., Hill, T., Dening, T. et al. (2019). Anticholinergic drug exposure and the risk of dementia: A nested case-control study. JAMA Intern Med 179, 1084-1093. doi:10.1001/ jamainternmed.2019.0677
Cozzini, P., Cavaliere, F., Spaggiari, G. et al. (2022). Computational methods on food contact chemicals: Big data and in silico screening on nuclear receptors family. Chemosphere 292, 133422. doi:10.1016/j.chemosphere.2021.133422
Craig, M., Gevertz, J. L., Kareva, I. et al. (2023) A practical guide for the generation of model-based virtual clinical trials. Front Syst Biol 3, 1174647. doi:10.3389/fsysb.2023.1174647
Dankovic, D. A., Naumann, B. D., Maier, A. et al. (2015). The scientific basis of uncertainty factors used in setting occupational exposure limits. J Occup Environ Hyg 12, Suppl 1, S55-68. doi: 10.1080/15459624.2015.1060325
Davoren, M. J. and Schiestl, R. H. (2018). Glyphosate-based herbicides and cancer risk: A post-IARC decision review of potential mechanisms, policy and avenues of research. Carcinogenesis 39, 1207-1215. doi:10.1093/carcin/bgy105
de Bruyn Kops, C., Šícho, M., Mazzolari, A. et al. (2021). GLORYx: Prediction of the metabolites resulting from phase 1 and phase 2 biotransformations of xenobiotics. Chem Res Toxicol 32, 286-299. doi:10.1021/acs.chemrestox.0c00224
de Vries, R. B. M., Angrish, M., Browne, P. et al. (2021). Applying evidence-based methods to the development and use of adverse outcome pathways construct mechanistic frameworks for the development and use of non-animal toxicity tests. ALTEX 38, 336-347. doi:10.14573/altex.2101211
Debad, S. J., Aungst, J., Carstens, K. et al. (2025). State of the science on assessing developmental neurotoxicity using new approach methods. ALTEX 42, 121-144. doi:10.14573/altex.2410231
Dehaan, R. L. (1971). Toxicity of tissue culture media exposed to polyvinylchlorideplastic.NatNewBiol231,85-86.doi:10.1038/ newbio231085a0
Djoumbou-Feunang, Y., Fiamoncini, J., Gil-de-la-Fuente, A. et al. (2019). BioTransformer: A comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J Cheminform 11, 2. doi:10.1186/s13321-018-0324-5
Donovan, K. A., An, J., Nowak, R. P. et al. (2018). Thalidomide promotes degradation of SALL4, a transcription factor implicated in Duane Radial Ray syndrome. Elife 7, e38430. doi:10.7554/ eLife.38430
EFSA (2009). Scientific opinion of the panel on contaminants in the food chain on a request from the European Commission on cadmium in food. EFSA J 7, 980. doi:10.2903/j.efsa.2009.980
EFSA (2021). Development of integrated approaches to testing and assessment (IATA) case studies on developmental neurotoxicity (DNT) risk assessment. EFSA J 19, 6599. doi:10.2903/j. efsa.2021.6599
Flynn, D. (2019). No cancer warnings required on coffee sold in California after all. Food Safety News.
Gao, Y., Mughal, Z., Jaramillo-Villegas, J. A. et al. (2024). BioBricks.Ai: A versioned data registry for life sciences data assets. ArXiv. doi:10.48550/arXiv.2408.17320
Gaylor, D. W. (2005). Are tumor incidence rates from chronic bio-assays telling us what we need to know about carcinogens? Regul ToxicolPharmacol41, 128-133. doi:10.1016/j.yrtph.2004.11.001
Golden, E., Ukaegbu, D. C., Ranslow, P. et al. (2023). The good, the bad, and the perplexing: Structural alerts and read-across for predicting skin sensitization using human data. Chem Res Toxicol 36, 734-746. doi:10.1021/acs.chemrestox.2c00383
Golden, E., Allen, D., Amberg, A. et al. (2024). Toward implementing virtual control groups in nonclinical safety studies. ALTEX 41, 282-301. doi:10.14573/altex.2310041
Gottmann, E., Kramer, S., Pfahringer, B. et al. (2001). Data quality in predictive toxicology: Reproducibility of rodent carcinogenicity experiments. Environ Health Perspect 109, 509-514. doi:10.1289/ehp.01109509
Grabowska, M. E., Chun, B., Moya, R. et al. (2021). Computational model of cardiomyocyte apoptosis identifies mechanisms of tyrosine kinase inhibitor-induced cardiotoxicity. J Mol Cell Cardiol 155, 66-77. doi:10.1016/j.yjmcc.2021.02.014
Grimstein, M., Yang, Y., Zhang, X. et al. (2019). Physiologically based pharmacokinetic modeling in regulatory science: An update from the U.S. food and Drug Administration’s Office of Clinical Pharmacology. J Pharm Sci 108, 21-25. doi:10.1016/j. xphs.2018.10.033
Gyori, B. M., Bachman, J. A., Subramanian, K. et al. (2017). From word models to executable models of signaling networks using automated assembly. Mol Syst Biol 13, 954. doi:10.15252/ msb.20177651
Hanahan, D. (2022). Hallmarks of cancer: New dimensions. Cancer Discov 12, 31-46. doi:10.1158/2159-8290.cd-21-1059
Hartung, T. and McBride, M. (2011). Food for thought… on mapping the human toxome. ALTEX 28, 83-93. doi:10.14573/ altex.2011.2.083
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., 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). A call for a human exposome project. ALTEX 40, 4-33. doi:10.14573/altex.2301061
Hartung, T. (2023b). 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. (2023c). Artifical intelligence as the new frontier in chemical risk assessment. Front Artif Intell 6, 1269932. doi:10.3389/frai.2023.1269932
Hartung, T. and Kleinstreuer, N. (2025). Challenges and opportunities for validation of AI-based new approach methods. ALTEX 42, 3-21. doi:10.14573/altex.2412291
Hartung, T. and Smirnova, L. (2025). A path forward advancing microphysiological systems. ALTEX 42, 183-203. doi:10.14573/ altex.2504091
Hemedan, A. A., Schneider, R. and Ostaszewski, M. (2023). Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses. Front Bioinform 3, 1189723. doi:10.3389/fbinf.2023.1189723
Hemedan,A.A.,Satagopam,V.,Schneider,R.etal.(2024).Cohort-specific boolean models highlight different regulatory modules during Parkinson’s disease progression. iScience 27, 110956. doi:10.1016/j.isci.2024.110956
Hoffmann, S., de Vries, R. B. M., Stephens, M. L. et al. (2017). A primer on systematic reviews in toxicology. Arch Toxicol 91, 2551-2575. doi:10.1007/s00204-017-1980-3
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
Jumper, J., Evans, R., Pritzel, A. et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596, 583-589. doi:10.1038/s41586-021-03819-2
Kafka, P. (1998). Observations on risk management policies, focusing on experiences from their implementation and use in the field of nuclear technology. In: Proceedings of ESA Risk Management Workshop, Noordwijk (85-100). European Space Agency, ESA-ESTEC.
Kincaid, B., Piechota, P., Golden, E. et al. (2023). Using in silico tools to predict flame retardant metabolites for more informative exposomics-based approaches. Front Toxicol 5, 1216802. doi:10.3389/ftox.2023.1216802
King, C., Fowler, J. C., Abnizova, I. et al. (2023). Somatic mutations in facial skin from countries of contrasting skin cancer risk. Nat Genet 55, 1440-1447. doi:10.1038/s41588-023-01468-x
Kleensang, A., Maertens, A., Rosenberg, M. et al. (2014). t4 workshop report: Pathways of toxicity. ALTEX 31, 53-61. doi:10.14573/altex.1309261
Kleensang, A., Vantangoli, M. M., Odwin-DaCosta, S. et al. (2016). Genetic variability in a frozen batch of MCF-7 cells invisible in routine authentication affecting cell function. Sci Rep 6, 28994. doi:10.1038/srep28994
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
Knudson, A. G. (1996). Hereditary cancer: Two hits revisited. J Cancer Res Clin Oncol 122, 135-140. doi:10.1007/BF01366952
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:10.1080/10937404.201 0.483176
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
Letort, G., Montagud, A., Stoll, G. et al. (2019). PhysiBoSS: A multi-scale agent-based modelling framework integrating physical dimension and cell signalling. Bioinformatics 35, 1188-1196. doi:10.1093/bioinformatics/bty766
Liao, Z., You, R., Huang, X. et al. (2019). DeepDock: Enhancing ligand-protein interaction prediction by a combination of ligand and structure information. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 311-317. doi:10.1109/bibm47256.2019.8983365
Linkov, I., Massey, O., Keisler, J. et al. (2015). From “weight of evidence” to quantitative data integration using multicriteria decision analysis and Bayesian methods. ALTEX 32, 3-8. doi:10.14573/altex.1412231
Loizou, G. D. (2016). Animal-free chemical safety assessment. Front Pharmacol 7, 218. doi:10.3389/fphar.2016.00218
Luechtefeld, T., Maertens, A., McKim, J. M. et al. (2015). Probabilistic hazard assessment for skin sensitization potency by dose-response modeling using feature elimination instead of quantitative structure-activity relationships. J Appl Toxicol 35, 1361-1371. doi:10.1002/jat.3172
Luechtefeld, T., Maertens, A., Russo, D. P. et al. (2016a). Analysis of Draize eye irritation testing and its prediction by mining publicly available 2008-2014 REACH data. ALTEX 33, 123-134. doi:10.14573/altex.1510053
Luechtefeld, T., Maertens, A., Russo, D. P. et al. (2016b). Global analysis of publicly available safety data for 9,801 substances registered under REACH from 2008-2014. ALTEX 33, 95-109. doi:10.14573/altex.1510052
Luechtefeld, T. and Hartung, T. (2017). Computational approaches to chemical hazard assessment. ALTEX 34, 459-478. doi:10.14573/altex.1710141
Luechtefeld, T., Marsh, D., Rowlands, C. et al. (2018a). 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
Luechtefeld, T., Rowlands, C. and Hartung, T. (2018b). Big-data and machine learning to revamp computational toxicology and its use in risk assessment. Toxicol Res 7, 732-744, doi:10.1039/ c8tx00051d
Maertens, A., Luechtefeld, T., Kleensang, A. et al. (2015). MPTP’s pathway of toxicity indicates central role of transcription factor SP1. Arch Toxicol 89, 743-755. doi:10.1007/s00204-015-1509-6
Maertens, A., Bouhifd, M., Zhao, L. et al. (2017). Metabolomic network analysis of estrogen-stimulated MCF-7 cells: A comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis. Arch Toxicol 91, 217-230. doi:10.1007/s00204-016-1695-x
Maertens, A., Golden, E., Luechtefeld, T. H. et al. (2022). Probabilistic risk assessment – The keystone for the future of toxicology. ALTEX 39, 3-9. doi:10.14573/altex.2201081
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
Marx, U., Beken, S., Chen, Z. et al. (2025). Biology-inspired dynamic microphysiological system approaches to revolutionize basic research, healthcare and animal welfare. ALTEX 42, 204-223. doi:10.14573/altex.2410112
McNally, K. and Loizou, G. D. (2015). A probabilistic model of human variability in physiology for future application to dose reconstruction and QIVIVE. Front Pharmacol 6, 213. doi:10.3389/ fphar.2015.00213
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
Montagud, A., Béal, J., Tobalina, L. et al. (2022). Patient-specific Boolean models of signalling networks guide personalised treatments. Elife 11, e72626 doi:10.7554/eLife.72626.sa2
Musuamba, F. T., Skottheim Rusten, I., Lesage, R. et al. (2021). Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility. CPT Pharmacometrics Syst Pharmacol 10, 804-825. doi:10.1002/psp4.12669
Niarakis, A., Ostaszewski, M., Mazein, A. et al. (2023). Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches. Front Immunol 14, 1282859. doi:10.3389/fimmu.2023.1282859
O’Hara, T., Virág, L., Varró, A. et al. (2011). Simulation of the undiseased human cardiac ventricular action potential: Model formulation and experimental validation. PLoS Comput Biol 7, e1002061. doi:10.1371/journal.pcbi.1002061
Osimitz, T. G., Droege, W., Boobis, A. R. et al. (2013). Evaluation of the utility of the lifetime mouse bioassay in the identification of cancer hazards for humans. Food Chem Toxicol 60, 550-562. doi:10.1016/j.fct.2013.08.020
Paller, C. J., Tukachinsky, H., Maertens, A. et al. (2024). Pan-cancer interrogation of MUTYH variants reveals biallelic inactivation and defective base excision repair across a spectrum of solid tumors. JCO Precis Oncol 8, e2300251. doi:10.1200/PO.23.00251
Pamies, D. and Hartung, T. (2017). 21st century cell culture for 21st century toxicology. Chem Res Toxicol 30, 43-52. doi:10.1021/ acs.chemrestox.6b00269
Parreno, V., Loubiere, V., Schuettengruber, B. et al. (2024). Transient loss of Polycomb components induces an epigenetic cancer fate. Nature 629, 688-696. doi:10.1038/s41586-024-07328-w
Passini, E., Britton, O. J., Lu, H. R. et al. (2017). Human in silico drug trials demonstrate higher accuracy than animal models in predicting clinical pro-arrhythmic cardiotoxicity. Front Physiol 8, 668. doi:10.3389/fphys.2017.00668
Place, T. L., Domann, F. E. and Case, A. J. (2017). Limitations of oxygen delivery to cells in culture: An underappreciated problem in basic and translational research. Free Radic Biol Med 113, 311-322. doi:10.1016/j.freeradbiomed.2017.10.003
Reynolds, E. H. and Green, R. (2020). Valproate and folate: Congenital and developmental risks. Epilepsy Behav 108, 107068. doi:10.1016/j.yebeh.2020.107068
Ross, B. C., Boguslav, M., Weeks, H. et al. (2018). Simulating heterogeneous populations using Boolean models. BMC Syst Biol 12, 64. doi:10.1186/s12918-018-0591-9
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. 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
Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t. Penguin.
Sluka, J. P., Fu, X., Swat, M. et al. (2016). A liver-centric multiscale modeling framework for xenobiotics. PLoS One 11, e0162428. doi:10.1371/journal.pone.0162428
Smirnova, L., Hogberg, H. T., Leist, M. and Hartung, T. (2014). Deve-lopmental neurotoxicity – challenges in the 21st century and in vitro opportunities. ALTEX 31, 129-156. doi:10.14573/ altex.1403271
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
Smirnova, L., Hogberg, H. T., Leist, M. and Hartung, T. (2024). Revolutionizing developmental neurotoxicity testing – a journey from animal models to advanced in vitro systems. ALTEX 41, 152-178. doi:10.14573/altex.2403281
Suciu, I., Pamies, D., Peruzzo, R. et al. (2023). G × E interactions as a basis for toxicological uncertainty. Arch Toxicol 97, 2035-2049. doi:10.1007/s00204-023-03500-9
Sun, J. X., He, Y., Sanford, E. et al. (2018). A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal. PLoS Comput Biol 14, e1005965. doi:10.1371/ journal.pcbi.1005965
Tomasetti, C. and Vogelstein, B. (2015). Cancer etiology. Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science 347, 78-81. doi:10.1126/ science.1260825
Tomczak, K., Czerwińka, P. and Wiznerowicz, M. (2015). The cancer genome atlas (TCGA): An immeasurable source of knowledge. Contemp Oncol 19, A68-77. doi:10.5114/wo.2014.47136
Tran, V., Kim, R., Maertens, M. et al. (2021). Similarities and differences in gene expression networks between the breast cancer cell line Michigan cancer foundation-7 and invasive human breast cancer tissues. Front Artif Intell 4, 674370. doi:10.3389/ frai.2021.674370
Trosko, J. E. and Upham, B. L. (2005). The emperor wears no clothes in the field of carcinogen risk assessment: Ignored concepts in cancer risk assessment. Mutagenesis 20, 81-92. doi:10.1093/mutage/gei017
Trott, O. and Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31, 455-461. doi:10.1002/jcc.21334
Vertrees, R. A., Jordan, J. M., Solley, T. et al. (2009). Tissue culture models. In T. C. Allen and P. T. Cagle (eds), Basic Concepts of Molecular Pathology (159-82). Boston, MA, USA: Springer US. doi:10.1007/978-0-387-89626-7_18
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
Weindling, P. (1985). The Social History of Occupational Health. Croom Helm.
Williams, G. M., Aardema, M., Acquavella, J. et al. (2016). A review of the carcinogenic potential of glyphosate by four independent expert panels and comparison to the IARC assessment. Crit Rev Toxicol 46, 3-20. doi:10.1080/10408444.2016.1214677
