Probabilistic risk assessment – the keystone for the future of toxicology
Article Sidebar
Main Article Content
Abstract
Safety sciences must cope with uncertainty of models and results as well as information gaps. Acknowledging this uncertainty necessitates embracing probabilities and accepting the remaining risk. Every toxicological tool delivers only probable results. Traditionally, this is taken into account by using uncertainty / assessment factors and worst-case / precautionary approaches and thresholds. Probabilistic methods and Bayesian approaches seek to characterize these uncertainties and promise to support better risk assessment and, thereby, improve risk management decisions. Actual assessments of uncertainty can be more realistic than worst-case scenarios and may allow less conservative safety margins. Most importantly, as soon as we agree on uncertainty, this defines room for improvement and allows a transition from traditional to new approach methods as an engineering exercise. The objective nature of these mathematical tools allows to assign each methodology its fair place in evidence integration, whether in the context of risk assessment, systematic reviews, or in the definition of an integrated testing strategy (ITS) / defined approach (DA) / integrated approach to testing and assessment (IATA). This article gives an overview of methods for probabilistic risk assessment and their application for exposure assessment, physiologically-based kinetic modelling, probability of hazard assessment (based on quantitative and read-across based structure-activity relationships, and mechanistic alerts from in vitro studies), individual susceptibility assessment, and evidence integration. Additional aspects are opportunities for uncertainty analysis of adverse outcome pathways and their relation to thresholds of toxicological concern. In conclusion, probabilistic risk assessment will be key for constructing a new toxicology paradigm – probably!
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles are distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is appropriately cited (CC-BY). Copyright on any article in ALTEX is retained by the author(s).
Al-Chalabi, A. and Hardiman, O. (2013). The epidemiology of ALS: A conspiracy of genes, environment and time. Nat Rev Neurol 9, 617-628. doi:10.1038/nrneurol.2013.203
Aldenberg, T. and Jaworska, J. S. (2010). Multiple test in silico weight-of-evidence for toxicological endpoints. Issues Toxicol 7, 558-583.
Apostolakis, G. (1990). The concept of probability in safety assessment of technological systems. Science 50, 1359-1366. doi:10.1126/science.2255906
Aughenbaugh, J. M. and Paredis, C. J. J. (2006). The value of using imprecise probabilities in engineering design. J Mech Des 128, 969-979. doi:10.1115/1.2204976
Barton, H. A., Chiu, W. A., Setzer, R. W. et al. (2007). Characterizing uncertainty and variability in physiologically-based pharmacokinetic (PBPK) models: State of the science and needs for research and implementation. Toxicol Sci 99, 395-402. doi:10.1093/toxsci/kfm100
Basketter, D. A., Clewell, H., Kimber, I. et al. (2012). A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing. ALTEX 29, 3-89. doi:10.14573/altex.2012.1.003
Bessems, J. G., Loizou, G., Krishnan, K. et al. (2014). PBTK modelling platforms and parameter estimation tools to enable animal-free risk assessment: Recommendations from a joint EPAA-EURL ECVAM ADME workshop. Regul Toxicol Pharmacol 68, 119-139. doi:10.1016/j.yrtph.2013.11.008
Blaauboer, B., Bayliss, M. K., Castell, J. et al. (1996). The use of biokinetics and in vitro methods in toxicological risk evaluation. The report and recommendations of ECVAM Workshop 15. Altern Lab Anim 24, 473-497. doi:10.1177/026119299602400408
Bogen, K. T. and Spear, R .C. (1987). Integrating uncertainty and interindividual variability in environmental risk assessment. Risk Anal 7, 427-436. doi:10.1111/j.1539-6924.1987.tb00480.x
Bogen, K. T. and Hall, L. C. (1989). Pharmacokinetics for regulatory risk analysis: The case of 1,1,1-trichloroethane (methyl chloroform). Regul Toxicol Pharmacol 10, 26-50. doi:10.1016/0273-2300(89)90011-1
Bogen, K. T., Cullen, A. C., Frey, H. C. et al. (2009). Probabilistic exposure analysis for chemical risk characterization. Toxicol Sci 109, 4-17. doi:10.1093/toxsci/kfp036
Bokkers, B., Mengelers, M. J., Bakker, M. I. et al. (2017). APROBA-Plus: A probabilistic tool to evaluate and express uncertainty in hazard characterization and exposure assessment of substances. Food Chem Toxicol 110, 408-417. doi:10.1016/j.fct.2017.10.038
Bottini, A. A. and Hartung, T. (2009). Food for thought … on economics of animal testing. ALTEX 26, 3-16. doi:10.14573/altex.2009.1.3
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
Brown, S. A. (2016). Principles for developing patient avatars in precision and systems medicine. Front Genet 6, 365. doi:10.3389/fgene.2015.00365
Browne, P., Judson, R. S., Casey, W. M. et al. (2015). Screening chemicals for estrogen receptor bioactivity using a computational model. Environ Sci Technol 49, 8804-8814. doi:10.1021/acs.est.5b02641
Brozek, J. L., Canelo-Aybar, C., Akl, E. A. et al. (2021). GRADE Guidelines 30: The GRADE approach to assessing the certainty of modeled evidence – An overview in the context of health decision-making. J Clin Epidemiol 129, 138-150. doi:10.1016/j.jclinepi.2020.09.018
Bruynseels, K., Santoni De Sio, F. and van den Hoven, J. (2018). Digital twins in health care: Ethical implications of an emerging engineering paradigm. Front Genet 9, 31. doi:10.3389/fgene.2018.00031
Chesnut, M., Yamada, T., Adams, T. et al. (2018). Regulatory acceptance of read-across: Report from an international satellite meeting at the 56th Annual Meeting of the Society of Toxicology. ALTEX 35, 413-419. doi:10.14573/altex.1805081
Chiu, W. A. and Slob, W. (2015). A unified probabilistic framework for dose-response assessment of human health effects. Environ Health Perspect 123, 1241-1254. doi:10.1289/ehp.1409385
Chou, W.-C. and Lin, Z. (2020). Probabilistic human health risk assessment of perfluorooctane sulfonate (PFOS) by integrating in vitro, in vivo toxicity, and human epidemiological studies using a Bayesian-based dose-response assessment coupled with physiologically based pharmacokinetic (PBPK) modeling approach. Environ Int 137, 105581. doi:10.1016/j.envint.2020.105581
Ciffroy, P., Alfonso, B., Altenpohl, A. et al. (2016). Modelling the exposure to chemicals for risk assessment: a comprehensive library of multimedia and PBPK models for integration, prediction, uncertainty and sensitivity analysis – The MERLIN-Expo tool. Sci Total Environ 568, 770-784. doi:10.1016/j.scitotenv.2016.03.191
Cristea, I. A. and Ioannidis, J. P. A. (2018). P values in display items are ubiquitous and almost invariably significant: A survey of top science journals. PLoS One 13, e0197440. doi:10.1371/journal.pone.0197440
Cullen, A. C. and Frey, H. C. (1999). Probabilistic Techniques in Exposure Assessment. A Handbook for Dealing with Variability and Uncertainty in Models and Inputs. New York, USA: Plenum.
Davison, A. C. and Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge University Press.
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. ALTEX 38, 336-347. doi:10.14573/altex.2101211
Desprez, B., Birk, B., Blaauboer, B. et al. (2019). A mode-of-action ontology model for safety evaluation of chemicals: Outcome of a series of workshops on repeated dose toxicity. Toxicol In Vitro 59, 44-50. doi:10.1016/j.tiv.2019.04.005
Dirven, H., Vist, G. E., Bandhakavi, S. et al. (2021). Performance of preclinical models in predicting drug-induced liver injury in humans: A systematic review. Sci Rep 11, 6403. doi:10.1038/s41598-021-85708-2
Dupuy, J-P. (1982). Ordres et Désordres: Enquête Sur un Nouveau Paradigme. Paris, France: Seuil.
Durand, E., Leroux, C., Perouel, G., Beausoleil, C. et al. (2015). Probabilistic risk assessment of consumer exposure to reproductive and developmental toxicants. J Pharmacol Clin Toxicol 3, 1049.
Efron, B. and Tibshirani, R. (1993). An Introduction to the Bootstrap. Boca Raton, FL, USA: Chapman & Hall/CRC.
EFSA (European Food Safety Authority) and EBTC (Evidence-Based Toxicology Collaboration) (2018). EFSA Scientific Colloquium 23: Evidence integration in risk assessment: The science of combining apples and oranges. EFSA Supporting Publication 16, EN-1396. doi:10.2903/sp.efsa.2018.EN-1396
EFSA Scientific Committee, Benford, D., Halldorsson, T. et al. (2018). Guidance on uncertainty analysis in scientific assessments. EFSA J 16, 5123. doi:10.2903/j.efsa.2018.5123
EPA, U.S. (2014). Probabilistic Risk Assessment to Inform Decision Making: Frequently Asked Questions. https://www.epa.gov/sites/default/files/2014-11/documents/raf-pra-faq-final.pdf
Farhat, N., Tsaioun, K., Saunders-Hastings, P. et al. (2022). Systematic review in evidence-based risk assessment. ALTEX, online ahead of print. doi:10.14573/altex.2004111
Fenton, N. E. and Neil, M. (2014). Decision support software for probabilistic risk assessment using Bayesian networks. IEEE Software 31, 21-26. doi:10.1109/MS.2014.32
Ferrario, D., Brustio, R. and Hartung, T. (2014). Glossary of reference terms for alternative test methods and their validation. ALTEX 31, 319-335. doi:10.14573/altex.1403311
Fisher, J. W., Gearhart, J. M. and Lin, Z. (2020). Physiologically Based Pharmacokinetic (PBPK) Modeling – Methods and Applications in Toxicology and Risk Assessment. Academic Press, Elsevier. doi:10.1016/C2018-0-03297-1
Gilmour, N., Kern, P. S., Alépée, N. et al. (2020). Development of a next generation risk assessment framework for the evaluation of skin sensitisation of cosmetic ingredients. Regul Toxicol Pharmacol 116, 104721. doi:10.1016/j.yrtph.2020.104721
Goodman, S. N. (1999a). Toward evidence-based medical statistics. 1: The p value fallacy. Ann Intern Med 130, 995-1004. doi:10.7326/0003-4819-130-12-199906150-00008
Goodman, S. N. (1999b). Toward evidence-based medical statistics. 2: The Bayes factor. Ann Intern Med 130, 1005-1013. doi:10.7326/0003-4819-130-12-199906150-00019
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.2009.3.155
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. (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., Luechtefeld, T., Maertens, A. et al. (2013). Integrated testing strategies for safety assessments. ALTEX 30, 3-18. doi:10.14573/altex.2013.1.003
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. (2017a). A comprehensive overview of the current status and application of predictive ADMET: Introduction and Overview. In S. Chackalamannil, D. Rotella and S. E. Ward (eds.), Comprehensive Medicinal Chemistry III – Experimental ADME and Toxicology (Chapter 4-08, 150-155). doi:10.1016/B978-0-12-409547-2.12378-9
Hartung, T. (2017b). Thresholds of toxicological concern – Setting a threshold for testing where there is little concern. ALTEX 34, 331-351. doi:10.14573/altex.1707011
Hartung, T. (2017c). Utility of the adverse outcome pathway concept in drug development. Exp Opin Drug Metabol Toxicol 13, 1-3. doi:10.1080/17425255.2017.1246535
Hartung, T. (2018a). Perspectives on in vitro to in vivo extrapolations. J Appl In Vitro Toxicol 4, 305-316. doi:10.1089/aivt.2016.0026
Hartung, T. (2018b). Making big sense from big data. Front Big Data 1, 5. doi:10.3389/fdata.2018.00005
Hartung, T., and Tsatsakis, A. M. (2021). The state of the scientific revolution in toxicology. ALTEX 38, 379-386. doi:10.14573/altex.2106101
Hoffmann, S. and Hartung, T. (2005). Diagnosis: Toxic! – Trying to apply approaches of clinical diagnostics and prevalence in toxicology considerations. Toxicol Sci 85, 422-428. doi:10.1093/toxsci/kfi099
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
Hoffmann, S., Kleinstreuer, N., Alépée, N. et al. (2018). Non-animal methods to predict skin sensitization (I): The Cosmetics Europe database. Crit Rev Toxicol 48, 344-358. doi:10.1080/10408444.2018.1429385
Hrovat, M., Segner, H. and Jeram, S. (2009). Variability of in vivo fish acute toxicity data. Regul Toxicol Pharmacol 54, 294-300. doi:10.1016/j.yrtph.2009.05.013
Ioannidis, J. P. A. (2008). Effect of formal statistical significance on the credibility of observational associations. Am J Epidemiol 168, 374-383. doi:10.1093/aje/kwn156
Ioannidis, J. P. A. (2019). What have we (not) learnt from millions of scientific papers with P values? American Statistician 73, Suppl 1, 20-25. doi:10.1080/00031305.2018.1447512
IOM – Institute of Medicine (2013). Environmental Decisions in the Face of Uncertainty. Washington, DC, USA: The National Academies Press. doi:10.17226/12568
Jacobs, R., van der Voet, H. and Braak, C. J. F. T. (2015). Integrated probabilistic risk assessment for nanoparticles: The case of nanosilica in food. J Nanopart Res 17, 251. doi:10.1007/s11051-015-2911-y
Jager, T., den Hollander, H. A., Janssen, G. B. et al. (2000). Probabilistic risk assessment for new and existing chemicals: Example calculations. RIVM Rapport 679102049. https://www.rivm.nl/bibliotheek/rapporten/679102049.html
Jager, T., Vermeire, T. G., Rikken, M. G. et al. (2001). Opportunities for a probabilistic risk assessment of chemicals in the European Union. Chemosphere 43, 257-264. doi:10.1016/s0045-6535(00)00087-4
Jaworska, J., Dancik, Y., Kern, P. et al. (2013). Bayesian integrated testing strategy to assess skin sensitization potency: From theory to practice. J Appl Toxicol 33, 1353-1364. doi:10.1002/jat.2869
Jaworska, J. S., Natsch, A., Ryan, C. et al. (2015). Bayesian integrated testing strategy (ITS) for skin sensitization potency assessment: A decision support system for quantitative weight of evidence and adaptive testing strategy. Arch Toxicol 89, 2355-2383. doi:10.1007/s00204-015-1634-2
Jensen, U. (2002). Probabilistic risk analysis: Foundations and methods. J Am Stat Assoc 97, 925. doi:10.1198/016214502760301264
Judson, R., Houck, K., Friedman, K. P. et al. (2020). Selecting a minimal set of androgen receptor assays for screening chemicals. Regul Toxicol Pharmacol 117, 104764. doi:10.1016/j.yrtph.2020.104764
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.
Kaplan, S. and Garrick, B. J. (1981). On the quantitative definition of risk. Risk Anal 1, 11-27. doi:10.1111/j.1539-6924.1981.tb01350.x
Keisler, J. M., Collier, Z. A., Chu, E. et al. (2013). Value of information analysis: The state of application. Environ Syst Decis 34, 3-23. doi:10.1007/s10669-013-9439-4
Kirchsteiger, C. (1999). On the use of probabilistic and deterministic methods in risk analysis. J Loss Prevention Process Ind 12, 399-419. doi:10.1016/S0950-4230(99)00012-1
Kleensang, A., Maertens, A., Rosenberg, M. et al. (2014). Pathways of toxicity. ALTEX 31, 53-61. doi:10.14573/altex.1309261
Kleinstreuer, N. C., Browne, P., Chang, X. et al. (2018a). Evaluation of androgen assay results using a curated Hershberger database. Reprod Toxicol 81, 272-280. doi:10.1016/j.reprotox.2018.08.017
Kleinstreuer, N. C., Hoffmann, S., Alépée, N. et al. (2018b). Non-animal methods to predict skin sensitization (II): An assessment of defined approaches. Crit Rev Toxicol 48, 359-374. doi:10.1080/10408444.2018.1429386
Klinke, A. and Renn, O. (2002). A new approach to risk evaluation and management: Risk-based, precaution-based, and discourse-based strategies. Risk Anal 22, 1071-1094. doi:10.1111/1539-6924.00274
Krewski, D., Westphal, M., Andersen, M. E. et al. (2014). A framework for the next generation of risk science. Environ Health Perspect 122, 796-805. doi:10.1289/ehp.1307260
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
Krewski, D., Saunders-Hastings, P., Baan, R. et al. (2022). Workshop report: Development of an evidence-based risk assessment framework. ALTEX, in press.
Krewski, D., Saunders-Hastings, P., Arzuga, X. et al. (in preparation). Development of a framework for evidence synthesis: Workshop report.
Kurt, W. (2019). Bayesian Statistics the Fun Way. No Starch Press.
Last, J. M. (2001). A Dictionary of Epidemiology. Fourth Edition. Oxford University Press
Leist, M., Hasiwa, N., Rovida, C. et al. (2014). Consensus report on the future of animal-free systemic toxicity testing. ALTEX 31, 341-356. doi:10.14573/altex.1406091
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
Leung, H. W. (1991). Development and utilization of physiologically based pharmacokinetic models for toxicological applications. J Toxicol Environ Health 32, 247-267. doi:10.1080/15287399109531480
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., Spendiff, M., Barton, H. A. et al. (2008). Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment: The first steps. Regul Toxicol Pharmacol 50, 400-411. doi:10.1016/j.yrtph.2008.01.011
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). Analysis of publically available skin sensitization data from REACH registrations 2008-2014. ALTEX 33, 135-148. doi:10.14573/altex.1510055
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
Madsen, H. O., Krenk, S. and Lind, N. (1986). Method of Structure Safety. Prentice-Hall.
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
McLanahan, E. D., El-Masri, H. A., Sweeney, L. M. et al. (2012). Physiologically based pharmacokinetic model use in risk assessment – Why being published is not enough. Toxicol Sci 126, 5-15. doi:10.1093/toxsci/kfr295
McNally, K., Hogg, A. and Loizou, G. (2018). A computational workflow for probabilistic quantitative in vitro to in vivo extrapolation. Front Pharmacol 9, 441. doi:10.3389/fphar.2018.00508
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
Melchers, R. E. (1999). Structure Reliability Analysis and Prediction. Ellis Horwood Ltd.
Mlodinow, L. (2008). The Drunkard’s Walk: How Randomness Rules Our Lives. Knopf Doubleday Publishing Group.
Modarres, M. (2008). Probabilistic risk assessment. In K. B. Misra (ed.), Handbook of Performability Engineering. London, UK: Springer.
Monticello, T. M., Jones, T. W., Dambach, D. M. et al. (2017). Current nonclinical testing paradigm enables safe entry to first-in-human clinical trials: The IQ consortium nonclinical to clinical translational database. Toxicol Appl Pharmacol 334, 100-109. doi:10.1016/j.taap.2017.09.006
Motet, G. and Bieder, C. (eds.) (2017). The Illusion of Risk Control. Springer Briefs in Applied Sciences and Technology. Cham, Switzerland: Springer.
Njå, O., Solberg, Ø. and Braut, G. S. (2017). Uncertainty – Its ontological status and relation to safety. In G. Motet, G. and C. Bieder (eds), The Illusion of Risk Control (5-21). Springer Briefs in Applied Sciences and Technology. Cham, Switzerland: Springer. doi:10.1007/978-3-319-32939-0_2
NRC – National Research Council (2007). Toxicity Testing in the 21st Century: A Vision and a Strategy. Washington, DC, USA: The National Academies Press.
NRC (2009). Science and Decisions: Advancing Risk Assessment. https://www.nap.edu/read/12209/
OECD (2014). The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins. OECD Series on Testing and Assessment, No. 168. OECD Publishing, Paris. doi:10.1787/9789264221444-en
OECD (2016). Guidance Document on the Reporting of Defined Approaches to be used within Integrated Approaches to Testing and Assessment. Series on Testing and Assessment, No. 256. OECD Publishing, Paris. doi:10.1787/9789264274822-en
OECD (2018a). Test No. 442D: In Vitro Skin Sensitisation: ARE-Nrf2 Luciferase Test Method. OECD Guidelines for the Testing of Chemicals, Section 4. OECD Publishing, Paris. doi:10.1787/9789264229822-en
OECD (2018b). Test No. 442E: In Vitro Skin Sensitisation Assays Addressing the Key Event on Activation of Dendritic Cells on the Adverse Outcome Pathway for Skin Sensitisation. OECD Guidelines for the Testing of Chemicals, Section 4. OECD Publishing, Paris. doi:10.1787/9789264264359-en
OECD (2020). Test No. 442C: In Chemico Skin Sensitisation Assays Addressing the Adverse Outcome Pathway, Key Event on Covalent Binding to Proteins. OECD Guidelines for the Testing of Chemicals, Section 4. OECD Publishing, Paris. doi:10.1787/9789264229709-en
Olson, H., Betton, G., Robinson, D. et al. (2000). Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32, 56-67. doi:10.1006/rtph.2000.1399
Ostrom, L. T. and Wilhelmsen, C. A. (2012). Probabilistic Risk Assessment. In L. T. Ostrom and C. A. Wilhelmsen (eds.), Risk Assessment (Chapter 15). John Wiley & Sons. doi:10.1002/9781118309629.ch15
Paini, A., Joossens, E., Bessems, J. et al. (2017). EURL ECVAM Workshop on new generation of physiologically-based kinetic models in risk assessment. European Union, 2017. JRC108231, EUR 28794 EN. doi:10.2760/619902
Pardo, O., Beser, M. I., Yusà, V. et al. (2014). Probabilistic risk assessment of the exposure to polybrominated diphenyl ethers via fish and seafood consumption in the region of Valencia (Spain). Chemosphere 104, 7-14. doi:10.1016/j.chemosphere.2013.12.084
Pariès, J. (2017). Recognizing complexity in risk management: The challenge of the improbable. In G. Motet and C. Bieder (eds.), The Illusion of Risk Control (41-55). Springer Briefs in Applied Sciences and Technology. Cham, Switzerland: Springer. doi:10.1007/978-3-319-32939-0_4
Parkin, R. T. and Morgan, M. G. (2006). Examples of potential benefits of probabilistic risk analysis. Cover letter and attachment to Stephen L. Johnson, Administrator, U.S. Environmental Protection Agency, from the EPA Science Advisory Board, Washington, DC, December 6.
Partosch, F., Mielke, H. and Stahlmann, R. et al. (2015). Internal threshold of toxicological concern values: Enabling route-to-route extrapolation. Arch Toxicol 89, 941-948. doi:10.1007/s00204-014-1287-6
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Representation and Reasoning Series (2nd printing edition). San Francisco, California, USA: Morgan Kaufmann. doi:10.1016/C2009-0-27609-4
Pendse, S., Clewell, R., Efremenko, A. et al. (2017). PLETHEM - An interactive open-source platform for bridging the source-to-outcome continuum. Toxicol Lett 280, S288. doi:10.1016/j.toxlet.2017.07.807
Rosling, H., Rosling Rönnlund, A. and Rosling, O. (2018). Factfulness: Ten Reasons We’re Wrong About the World – And Why Things Are Better Than You Think. New York, USA: Flatiron Books, Macmillan Publishers.
Rossi, R. J. (2018). Mathematical Statistics: An Introduction to Likelihood Based Inference (p. 227). New York; USA: John Wiley & Sons.
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
Rovida, C., Barton-Maclaren, T., Benfenati, E. et al. (2020). Internationalisation of read-across as a validated new approach method (NAM) for regulatory toxicology. ALTEX 37, 579-606. doi:10.14573/altex.1912181
Rysavy, M. (2013). Evidence-based medicine: A science of uncertainty and an art of probability. Virtual Mentor 15, 4-8. doi:10.1001/virtualmentor.2013.15.1.fred1-1301
Sala Benito, J. V., Paini, A., Richarz, A. N. et al. (2017). Automated workflows for modelling chemical fate, kinetics and toxicity. Toxicol In Vitro 45, 249-257. doi:10.1016/j.tiv.2017.03.004
Saltelli, A., Ratto, M., Andres, T. et al. (2008). Global Sensitivity Analysis. The Primer. Chichester, UK: John Wiley & Sons Ltd, The Atrium, Southern Gate. https://bit.ly/3q69Q6O
Sanderson, H. (2003). Probabilistic hazard assessment of environmentally occurring pharmaceuticals toxicity to fish, daphnids and algae by ECOSAR screening. Toxicol Lett 144, 383-395. doi:10.1016/s0378-4274(03)00257-1
Santín, E. P., Solana, R. R., García, M. G. et al. (2021). Toxicity prediction based on artificial intelligence: A multidisciplinary overview. Wiley Interdiscip Rev Comput Mol Sci, e1516. doi:10.1002/wcms.1516
Scheringer, M., Steinbach, D., Escher, B. et al. (2002). Probabilistic approaches in the effect assessment of toxic chemicals – What are the benefits and limitations? Environ Sci Pollut Res Int 9, 307-314. doi:10.1065/espr2001.09.091
Schleier, J. J., Marshall, L. A., Davis, R. S. et al. (2015). A quantitative approach for integrating multiple lines of evidence for the evaluation of environmental health risks. PeerJ 3, e730. doi:10.7717/peerj.730
Schowanek, D., David, H., Francaviglia, R. et al. (2007). Probabilistic risk assessment for linear alkylbenzene sulfonate (LAS) in sewage sludge used on agricultural soil. Regul Toxicol Pharmacol 49, 245-259. doi:10.1016/j.yrtph.2007.09.001
Shah, I., Liu, J., Judson, R. S. et al. (2016). Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information. Regul Toxicol Pharmacol 79, 12-24. doi:10.1016/j.yrtph.2016.05.008
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
Slob, W., Bakker, M. I., te Biesebeek, J. D. et al. (2014). Exploring the uncertainties in cancer risk assessment using the integrated probabilistic risk assessment (IPRA) approach. Risk Anal 34, 1401-1422. doi:10.1111/risa.12194
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
Solomon, K., Giesy, J. and Jones, P. (2000). Probabilistic risk assessment of agrochemicals in the environment. Crop Protection 19, 649-655. doi:10.1016/S0261-2194(00)00086-7
Sterne, J. A., Hernán, M. A., Reeves, B. C. et al. (2016). ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355, i4919. doi:10.1136/bmj.i4919
Szucs, D. and Ioannidis, J. P. A. (2017). When null hypothesis significance testing is unsuitable for research: a reassessment. Front Hum Neurosci 11, 390. doi:10.3389/fnhum.2017.00390
Taleb, N. N. (2004). Fooled by Randomness – The Hidden Role of Chance in Life and the Markets. London, UK: Penguin. (2nd edition).
Taleb, N. N. (2007). The Black Swan – The Impact of the Highly Improbable. New York, USA: The Random House Publishing Group.
Tollefsen, K. E., Scholz, S., Cronin, M. T. et al. (2014). Applying adverse outcome pathways (AOPs) to support integrated approaches to testing and assessment (IATA). Regul Toxicol Pharmacol 70, 629-640. doi:10.1016/j.yrtph.2014.09.009
Tomasetti, C., Li, L. and Vogelstein, B. (2017). Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355, 1330-1334. doi:10.1126/science.aaf9011
Tralau, T., Oelgeschläger, M., Gürtler, R. et al. (2015). Regulatory toxicology in the twenty-first century: Challenges, perspectives and possible solutions. Arch Toxicol 89, 823-850. doi:10.1007/s00204-015-1510-0
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
Tsaioun, K. et al. (in preparation). Advancing the application of evidence-based methods to construct mechanistic frameworks for the development and use of non-animal toxicity tests.
van der Voet, H. and Slob, W., (2007). Integration of probabilistic exposure assessment and probabilistic hazard characterization. Risk Anal 27, 351-371. doi:10.1111/j.1539-6924.2007.00887.x
Verdonck, F. A. M., Jaworska, J., Janssen C. R. et al. (2002). Probabilistic ecological risk assessment framework for chemical substances. In Integrated Assessment and Decision Support. Proceedings of the First Biennial Meeting of the International Environmental Modelling and Software Society (IEMSS). https://former.iemss.org/sites/iemss2002/proceedings/pdf/volume%20uno/24_verdonck.pdf
Verdonck, F. A. M., Aldenberg, T., Jaworska, J. et al. (2003). Limitations of current risk characterization methods in probabilistic environmental risk assessment. Environ Toxicol Chem 22, 2209-2213. doi:10.1897/02-435
Vesely, W. E. (2011). Probabilistic Risk Assessment. In S. B. Johnson, T. J. Gormley, S. S. Kessler et al. (eds.), System Health Management: With Aerospace Applications (Chapter 15). John Wiley & Sons. doi:10.1002/9781119994053.ch15
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
Vlek, C. (2010). Judicious management of uncertain risks: I. Criticisms and developments of risk analysis and precautionary reasoning. II. Simple rules and more intricate models for precautionary decision-making. J Risk Res 13, 517-543. doi:10.1080/13669871003629887
Vose, D. (2008). Risk Analysis: A Quantitative Guide. 3rd edition. Chichester, UK: John Wiley & Sons.
Wagner, C., Zhao, P., Pan, Y. et al. (2015). Application of physiologically based pharmacokinetic (PBPK) modeling to support dose selection: Report of an FDA public workshop on PBPK. CPT: Pharmacometrics Syst Pharmacol 4, 226-230. doi:10.1002/psp4.33
Walker, W. E., Harremoës, P., Rotmans, J. et al. (2003). Defining uncertainty, a conceptual basis for uncertainty management in model-based decision support. Integr Assess 4, 5-17. https://doi.org10.1076/iaij.4.1.5.16466
Wambaugh, J. F., Wetmore, B. A., Pearce, R. et al. (2015). Toxicokinetic triage for environmental chemicals. Toxicol Sci 147, 55-67. doi:10.1093/toxsci/kfv118
Wang, B. and Gray, G. (2015). Concordance of noncarcinogenic endpoints in rodent chemical bioassays. Risk Anal 35, 1154-1166. doi:10.1111/risa.12314
Wang, Y.-H. (2010). Confidence assessment of the Simcyp time-based approach and a static mathematical model in predicting clinical drug-drug interactions for mechanism-based CYP3A inhibitors. Drug Metab Dispos 38, 1094-1104. doi:10.1124/dmd.110.032177
Wheelan, C. (2013). Naked Statistics: Stripping the Dread from the Data. W.W. Norton & Company.
Xie, J., Marano, K. M., Wilson, C. L. et al. (2012). A probabilistic risk assessment approach used to prioritize chemical constituents in mainstream smoke of cigarettes sold in China. Regul Toxicol Pharmacol 62, 355-362. doi:10.1016/j.yrtph.2011.10.017
Young, B., Tulve, N., Egeghy, P. et al. (2012). Comparison of four probabilistic models (CARES®, Calendex™, ConsExpo, and SHEDS) to estimate aggregate residential exposures to pesticides. J Expo Sci Environ Epidemiol 22, 522-532. doi:10.1038/jes.2012.54
Zhang, L., Zhang, H., Ai, H. et al. (2018). Applications of machine learning methods in drug toxicity prediction. Curr Top Med Chem 18, 987-997. doi:10.2174/1568026618666180727152557
Zhang, P. (2010). Probabilistic methods used in environmental risk evaluation for groundwater protection. Dissertation, Faculty of Mathematics and Natural Sciences Department of Geosciences, University of Oslo. https://www.duo.uio.no/bitstream/handle/10852/12317/Zhang-avhandling-publ.pdf?sequence=3&isAllowed=y
Zhao, F., Li, L., Chen, Y., Huang, Y. et al. (2021). Risk-based chemical ranking and generating a prioritized human exposome database. Environ Health Perspect 129, 47014. doi:10.1289/ehp7722
Zhu, H., Bouhifd, M., Kleinstreuer, N. et al. (2016). Supporting read-across using biological data. ALTEX 33, 167-182. doi:10.14573/altex.1601252