Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

Nanotoxicology

Volumn 11, Issue 1, 2017, Pages 123-133

  Marvin, Hans J P ^a   Bouzembrak, Yamine ^a   Janssen, Esmée M ^a   van der Zande, Meike ^a   Murphy, Finbarr ^b   Sheehan, Barry ^b   Mullins, Martin ^b   Bouwmeester, Hans ^a  

^a WAGENINGEN UNIVERSITY   (Netherlands)

^b UNIVERSITY OF LIMERICK   (Ireland)

Author keywords

Bayesian networks; expert elicitation; metal nanomaterials; risk assessment; scenario studies

Indexed keywords

CERIUM OXIDE NANOPARTICLE; SILICA NANOPARTICLE; SILVER NANOPARTICLE; TITANIUM DIOXIDE NANOPARTICLE; ZINC OXIDE NANOPARTICLE; CERIC OXIDE; CERIUM; DANGEROUS GOODS; NANOMATERIAL; SILICON DIOXIDE; SILVER; ZINC OXIDE;

ARTICLE; BAYESIAN LEARNING; BIOLOGICAL ACTIVITY; CARDIOVASCULAR DISEASE; CELL TRANSFORMATION; CELL VIABILITY; CONSULTATION; CYTOTOXICITY; ENTROPY; FIBROSIS; GENOTOXICITY; HEALTH HAZARD; HUMAN; INFLAMMATION; LUNG DISEASE; NANOTOXICOLOGY; PARTICLE SIZE; PHYSICAL CHEMISTRY; PREDICTION; PRIORITY JOURNAL; QUESTIONNAIRE; RISK ASSESSMENT; SURFACE AREA; SURFACE CHARGE; BAYES THEOREM; CHEMISTRY; DANGEROUS GOODS; INFORMATION PROCESSING; THEORETICAL MODEL; TOXICITY;

BAYES THEOREM; CERIUM; DATA COLLECTION; HAZARDOUS SUBSTANCES; HUMANS; MODELS, THEORETICAL; NANOSTRUCTURES; RISK ASSESSMENT; SILICON DIOXIDE; SILVER; ZINC OXIDE;

EID: 85010902419     PISSN: 17435390     EISSN: 17435404     Source Type: Journal    
DOI: 10.1080/17435390.2016.1278481     Document Type: Article

References (64)

1. Alameddine I, Cha Y, Reckhow KH., 2011. An evaluation of automated structure learning with Bayesian networks:an application to estuarine chlorophyll dynamics. Environ Modell Softw 26:163–72.
2. Arias J, Martínez-Gómez J, Gámez JA, Seco de Herrera AG, Müller H., 2016. Medical image modality classification using discrete Bayesian networks. Comput Vis Image Underst 151:61–71.
3. Arts JHE, Hadi M, Irfan M-A, Keene AM, Kreiling R, Lyon D, et al. 2015. A decision-making framework for the grouping and testing of nanomaterials (DF4nanoGrouping). Regul Toxicol Pharmacol 71:S1–S27.
4. Arts JHE, Hadi M, Keene AM, Kreiling R, Lyon D, Maier M, et al. 2014. A critical appraisal of existing concepts for the grouping of nanomaterials. Regul Toxicol Pharmacol 70:492–506.
5. Arts JHE, Irfan M-A, Keene AM, Kreiling R, Lyon D, Maier M, et al. 2016. Case studies putting the decision-making framework for the grouping and testing of nanomaterials (DF4nanoGrouping) into practice. Regul Toxicol Pharmacol 76:234–61.
6. Ashcroft T, Simpson JM, Timbrell V., 1988. Simple method of estimating severity of pulmonary fibrosis on a numerical scale. J Clin Pathol 41:467–70.
7. Banko M, Brill E., 2001. Scaling to very very large corpora for natural language disambiguation. Proceedings of the 39th Annual Meeting on Association for Computational Linguistics. Toulouse, France:Association for Computational Linguistics.
8. Beaudequin D, Harden F, Roiko A, Stratton H, Lemckert C, Mengersen K., 2015. Beyond QMRA:modelling microbial health risk as a complex system using Bayesian networks. Environ Int 80:8–18.
9. Bouwmeester H, Lynch I, Marvin HJ, Dawson KA, Berges M, Braguer D, et al. 2011. Minimal analytical characterization of engineered nanomaterials needed for hazard assessment in biological matrices. Nanotoxicology 5:1–11.
10. Bouzembrak Y, Marvin HJP., 2016. Prediction of food fraud type using data from rapid alert system for food and feed (RASFF) and Bayesian network modelling. Food Control 61:180–7.
11. Carriger JF, Martin TM, Barron MG., 2016. A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors. Aquat. Toxicol 180:11–24.
12. Cheng J, Greiner R, Kelly J, Bell D, Liu W., 2002. Learning Bayesian networks from data:an information-theory based approach. Artif Intel 137:43–90.
13. Chuang HC, Juan HT, Chang CN, Yan YH, Yuan TH, Wang JS, et al. 2014. Cardiopulmonary toxicity of pulmonary exposure to occupationally relevant zinc oxide nanoparticles. Nanotoxicology 8:593–604.
14. CoupÉ VMH, Van Der Gaag LC, Habbema JDF., 2000. Sensitivity analysis:an aid for belief-network quantification. Knowl Eng Rev 15:215–32.
15. Cover TM, Thomas JA., (2006). Elements of information theory (Wiley series in telecommunications and signal processing). USA:Wiley-Interscience.
16. Dekkers S, Bouwmeester H, Bos PM, Peters RJ, Rietveld AG, Oomen AG., 2013. Knowledge gaps in risk assessment of nanosilica in food:evaluation of the dissolution and toxicity of different forms of silica. Nanotoxicology 7:367–77.
17. Dekkers S, Krystek P, Peters RJ, Lankveld DP, Bokkers BG, van Hoeven-Arentzen PH, et al. 2011. Presence and risks of nanosilica in food products. Nanotoxicology 5:393–405.
18. Denœux T., 2010. Maximum likelihood from evidential data:an extension of the EM algorithm. Adv Intell Soft Comp 77:181–8.
19. Denœux T., 2011. Maximum likelihood estimation from fuzzy data using the EM algorithm. Fuzzy Set Syst 183:72–91.
20. EPA. 2011. Conditional Registration of HeiQ AGS-20 as a Materials Preservative in Textiles. Environmental Protection Agency, Office of Pesticide Programs, EPA-HQ-OPP-2009-1012-0064.
21. Friedman N, Yakhini Z., 1996. On the sample complexity of learning Bayesian networks. Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence. Portland, OR:Morgan Kaufmann Publishers Inc.
22. Gordon T, Nadziejko C, Schlesinger R, Chi Chen L., 1998. Pulmonary and cardiovascular effects of acute exposure to concentrated ambient particulate matter in rats. Toxicol Lett 96–97:285–8.
23. Heckerman D., 2008. A Tutorial on Learning with Bayesian Networks. In:Holmes DE, Jain LC, eds. Innovations in Bayesian Networks:Theory and Applications. Berlin, Heidelberg:Springer Berlin Heidelberg, 33–82.
24. Heringa MB, Geraets L, van Eijkeren JCH, Vandebriel RJ, de Jong WH, Oomen AG., 2016. Risk assessment of titanium dioxide nanoparticles via oral exposure, including toxicokinetic considerations. Nanotoxicology 10:1515–25.
25. Hiramatsu K, Aoyama H, Zen Y, Aishima S, Kitagawa S, Nakanuma Y., 2006. Proposal of a new staging and grading system of the liver for primary biliary cirrhosis. Histopathology 49:466–78.
26. Hristozov DR, Gottardo S, Critto A, Marcomini A., 2012. Risk assessment of engineered nanomaterials:a review of available data and approaches from a regulatory perspective. Nanotoxicology 6:880–98.
27. Hristozov DR, Zabeo A, Foran C, Isigonis P, Critto A, Marcomini A, Linkov I., 2014. A weight of evidence approach for hazard screening of engineered nanomaterials. Nanotoxicology 8:72–87.
28. Izak-Nau E, Huk A, Reidy B, Uggerud H, Vadset M, Eiden S, et al. 2015. Impact of storage conditions and storage time on silver nanoparticles' physicochemical properties and implications for their biological effects. RSC Adv 5:84172–85.
29. Keisler J, Linkov I., 2014. Environment models and decisions. Environ Syst Decis 34:369–72.
30. Kim KM, Choi MH, Lee JK, Jeong J, Kim YR, Kim MK, et al. 2014. Physicochemical properties of surface charge-modified ZnO nanoparticles with different particle sizes. Int J Nanomed 9:41–56.
31. Kjærulff UB, Madsen AL., (2013). Sensitivity analysis. In:Bayesian networks and influence diagrams:A guide to construction and analysis. New York, NY:Springer New York. p. 303–25.
32. Langseth H, Portinale L., 2007. Bayesian networks in reliability. Reliab Eng Syst Safe 92:92–108.
33. Lauritzen SL., 1995. The EM algorithm for graphical association models with missing data. Comput Stat Data Anal 19:191–201.
34. Lee CJ, Lee KJ., 2006. Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal. Reliab Eng Syst Safe 91:515–32.
35. Linkov I, Anklam E, Collier ZA, DiMase D, Renn O., 2014. Risk-based standards:integrating top–down and bottom–up approaches. Environ Syst Decis 34:134–7.
36. Linkov I, Massey O, Keisler J, Rusyn I, Hartung T., 2015. From “ "weight of evidence" to quantitative data integration using multicriteria decision analysis and Bayesian methods”. Altex 32:3–8.
37. Linkov I, Welle P, Loney D, Tkachuk A, Canis L, Kim JB, Bridges T., 2011. Use of multicriteria decision analysis to support weight of evidence evaluation. Risk Analy 31:1211–25.
38. Low-Kam C, Telesca D, Ji Z, Zhang H, Xia T, Zink JI, Nel AE., 2015. A Bayesian regression tree approach to identify the effect of nanoparticles' properties on toxicity profiles. Ann Appl Stat 9:383–401.
39. Marcot BG, Steventon JD, Sutherland GD, McCann RK., 2006. Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Can J Forest Res 36:3063–74.
40. Marvin HJP, Bouzembrak Y, Janssen EM, van der Fels-Klerx HJ, van Asselt ED, Kleter GA., 2016. A holistic approach to food safety risks:food fraud as an example. Food Res Int. 89:463–70.
41. Money ES, Barton LE, Dawson J, Reckhow KH, Wiesner MR., 2014. Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver. Sci Total Environ 473–474:685–91.
42. Money ES, Reckhow KH, Wiesner MR., 2012. The use of Bayesian networks for nanoparticle risk forecasting:model formulation and baseline evaluation. Sci Total Environ 426:436–45.
43. Morgan K., 2005. Development of a preliminary framework for informing the risk analysis and risk management of nanoparticles. Risk Anal 25:1621–35.
44. Murphy F, Sheehan B, Mullins M, Bouwmeester H, Marvin HJP, Bouzembrak Y, et al. 2016. A tractable method for measuring nanomaterial risk using Bayesian networks. Nanoscale Res Lett 11:503.
45. Nel A, Xia T, Meng H, Wang X, Lin S, Ji Z, Zhang H., 2013. Nanomaterial toxicity testing in the 21st century:use of a predictive toxicological approach and high-throughput screening. Acc Chem Res 46:607–21.
46. Ng C-T, Li JJ, Bay B-H, Yung L-YL., 2010. Current studies into the genotoxic effects of nanomaterials. J Nucl Acids 2010:1–12.
47. Nyberg JB, Marcot BG, Sulyma R., 2006. Using Bayesian belief networks in adaptive management. Can J For Res 36:3104–16.
48. OECD. 2007. OECD guidline for the testing of chemicals:Developmental Neurotoxicity Study. Test no. 426, pp. 1–26.
49. OECD. 2014. Report of the OECD expert meeting on the physical chemical properties of manufactured nanomaterials and test guidelines. OECD Environment, Health, and Safety Publications. Series on the safety of manufactured nanomaterials. 41:15.
50. Oomen A, Bleeker E, Bos P, van Broekhuizen F, Gottardo S, Groenewold M, et al. 2015. Grouping and read-across approaches for risk assessment of nanomaterials. Int J Environ Res Public Health 12:13415.
51. Ostraat ML, Thornburg JW, Malloy QGJ., 2013. Measurement strategies of airborne nanomaterials. Environ Eng Sci 30:126–32.
52. Pollino CA, Woodberry O, Nicholson A, Korb K, Hart BT., 2007. Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environ Model Softw 22:1140–52.
53. Sessa S, D'Urso MG., 2014. Employment of Bayesian networks for risk assessment of excavation process in dense urban areas. Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures. The Netherlands:CRC Press, 3163–3169.
54. Singh KP, Gupta S., 2014. Nano-QSAR modeling for predicting biological activity of diverse nanomaterials. RSC Adv 4:13215–30.
55. Sohn S, Larson DW, Habermann EB, Naessens JM, Alabbad JY, Liu H., 2016. Detection of clinically important colorectal surgical site infection using Bayesian network. J Surg Res 11:9–20.
56. Stone V, Pozzi-Mucelli S, Tran L, Aschberger K, Sabella S, Vogel U, et al. 2014a. ITS-NANO–prioritising nanosafety research to develop a stakeholder driven intelligent testing strategy. Part Fibre Toxicol 11:9.
57. Stone V, Pozzi-Mucelli S, Tran L, Aschberger K, Sabella S, Vogel U, et al. 2014b. ITS-NANO - Prioritising nanosafety research to develop a stakeholder driven intelligent testing strategy. Part Fibre Toxicol 11:1–12.
58. Tantra R, Bouwmeester H, Bolea E, Rey-Castro C, David CA, Dogne JM, et al. 2016. Suitability of analytical methods to measure solubility for the purpose of nanoregulation. Nanotoxicology 10:173–84.
59. Tervonen T, Lahdelma R, Almeida Dias J, Figueira J, Salminen P., 2007. SMAA-TRI. In:Linkov I, Kiker GA, Wenning RJ, eds. Environmental Security in Harbors and Coastal Areas:Management Using Comparative Risk Assessment and Multi-Criteria Decision Analysis. Dordrecht:Springer, the Netherlands, 217–231.
60. WHO. 2012. Guidance for immunotoxicity risk assessment for chemicals. Harmonization Project Document No. 10:1–312.
61. Wiesner MR, Bottero JY., 2011. A risk forecasting process for nanostructured materials, and nanomanufacturing. C R Phys 12:659–68.
62. Wijnhoven SWP, Peijnenburg WJGM, Herberts CA, Hagens WI, Oomen AG, Heugens EHW, et al. 2009. Nano-silver–a review of available data and knowledge gaps in human and environmental risk assessment. Nanotoxicology 3:109–38.
63. Winkler DA, Burden FR, Yan B, Weissleder R, Tassa C, Shaw S, Epa VC., 2014. Modelling and predicting the biological effects of nanomaterials. SAR QSAR Environ Res 25:161–72.
64. Zarei E, Azadeh A, Khakzad N, Mohammadfam I., 2016. Dynamic safety assessment of natural gas stations using Bayesian network. J Hazard Mater 321:830–40.