Volume 20, Issue 1 (2023)
ioh 2023, 20(1): 46-62 |
Back to browse issues page
Research code: 50578-99-3-99
Ethics code: IR.TUMS.SPH.REC.1399.221
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mozaffari S, Bayatian M, Hsieh N, Khadem M, Abbasi Garmaroudi A, Ashrafi K et al . DEVELOPMENT OF A PHYSIOLOGICALLY BASED TOXICOKINETIC MODEL FOR HUMAN EXPOSURE RISK ASSESSMENT OF METHYLENE DIPHENYL DIISOCYANATE(MDI). ioh 2023; 20 (1) :46-62
URL: http://ioh.iums.ac.ir/article-1-3272-en.html
URL: http://ioh.iums.ac.ir/article-1-3272-en.html
Sajjad Mozaffari 
, Majid Bayatian , Nan-Hung Hsieh , Monireh Khadem , Amir Abbasi Garmaroudi , Khosro Ashrafi , Jamaleddin Shahtaheri *
, Majid Bayatian , Nan-Hung Hsieh , Monireh Khadem , Amir Abbasi Garmaroudi , Khosro Ashrafi , Jamaleddin Shahtaheri *
Department of Occupational Health Engineering,School of Public Health, Tehran University of Medical Sciences, Tehran,Iran , shahtaheri@tums.ac.ir
Abstract: (932 Views)
Abstract
Background and aims: Given the lack of a developed physiologically based toxicokinetic (PBTK) model for human systemic exposure assessment of methylene diisocyanate (MDI) and prediction of its urinary metabolites, this study aims to develop a PBTK model for exposure risk assessment of MDI.
Methods : In this study, to assess the potential exposure to the MDI, a PBTK model was constructed with parameter uncertainty and variability and calibrated using Bayesian analysis via Markov chain Monte Carlo approach. Exposure reconstruction or reverse dosimetry was performed as an occupational exposure risk assessment through time-kinetic urinary elimination of methylenedianiline (MDA), as the biomarker of MDI, in those exposed to unknown exposure scenarios.
Results: Approximately 15 hours after the start of exposure, the amount of MDA excretion peaked. Understanding simulation results of reverse dosimetry for both exposed persons to the unknown concentration of MDI revealed experienced more systemic exposure than NOAEL (NOAEL = 0.2 ug / l), the exposure concentration (±SD) was 1.58 (±0.856) and 1.005 (±0.705) ug/l for person A and B, respectively. Comparison of predicted results with experimental data shows the model can estimate the kinetic elimination closely to experimental data (R2 = 0.9).
Conclusion: Developed model can be performed to estimate the internal dose of body tissues and understand the risk of occupational exposures by comparing the simulation of biological monitoring with acceptable limit values and determining the potential of external exposure.
Background and aims: Given the lack of a developed physiologically based toxicokinetic (PBTK) model for human systemic exposure assessment of methylene diisocyanate (MDI) and prediction of its urinary metabolites, this study aims to develop a PBTK model for exposure risk assessment of MDI.
Methods : In this study, to assess the potential exposure to the MDI, a PBTK model was constructed with parameter uncertainty and variability and calibrated using Bayesian analysis via Markov chain Monte Carlo approach. Exposure reconstruction or reverse dosimetry was performed as an occupational exposure risk assessment through time-kinetic urinary elimination of methylenedianiline (MDA), as the biomarker of MDI, in those exposed to unknown exposure scenarios.
Results: Approximately 15 hours after the start of exposure, the amount of MDA excretion peaked. Understanding simulation results of reverse dosimetry for both exposed persons to the unknown concentration of MDI revealed experienced more systemic exposure than NOAEL (NOAEL = 0.2 ug / l), the exposure concentration (±SD) was 1.58 (±0.856) and 1.005 (±0.705) ug/l for person A and B, respectively. Comparison of predicted results with experimental data shows the model can estimate the kinetic elimination closely to experimental data (R2 = 0.9).
Conclusion: Developed model can be performed to estimate the internal dose of body tissues and understand the risk of occupational exposures by comparing the simulation of biological monitoring with acceptable limit values and determining the potential of external exposure.
Type of Study: Research |
Subject:
Assessment and risk management
Received: 2021/12/13 | Accepted: 2023/04/4 | Published: 2023/03/30
Received: 2021/12/13 | Accepted: 2023/04/4 | Published: 2023/03/30
References
1. Linakis MW, Sayre RR, Pearce RG, Sfeir MA, Sipes NS, Pangburn HA, et al. Development and evaluation of a high throughput inhalation model for organic chemicals. Journal of exposure science & environmental epidemiology. 2020;30(5):866-77.
https://doi.org/10.1038/s41370-020-0248-9 [DOI:10.1038/s41370-020-0238-y] [PMID]
2. Pearce RG, Setzer RW, Strope CL, Sipes NS, Wambaugh JF. httk: R Package for High-Throughput Toxicokinetics. 2017. 2017;79(4):26. [DOI:10.18637/jss.v079.i04]
3. Bois FY. GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models. Bioinformatics. 2009;25(11):1453-4. [DOI:10.1093/bioinformatics/btp162]
4. Hastings WK. Monte Carlo sampling methods using Markov chains and their applications. Biometrika. 1970;57(1):97-109. [DOI:10.1093/biomet/57.1.97]
5. Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Statistical science. 1992;7(4):457-72. [DOI:10.1214/ss/1177011136]
6. Budnik LT, Nowak D, Merget R, Lemiere C, Baur X. Elimination kinetics of diisocyanates after specific inhalative challenges in humans: mass spectrometry analysis, as a basis for biomonitoring strategies. Journal of Occupational Medicine and Toxicology. 2011;6(1):1-8. [DOI:10.1186/1745-6673-6-9]
7. Horsfield K, Dart G, Olson DE, Filley GF, Cumming G. Models of the human bronchial tree. Journal of applied physiology. 1971;31(2):207-17. [DOI:10.1152/jappl.1971.31.2.207]
8. Pauluhn J. Interrelating the acute and chronic mode of action of inhaled methylenediphenyl diisocyanate (MDI) in rats assisted by computational toxicology. Regulatory Toxicology and Pharmacology. 2011;61(3):351-64. [DOI:10.1016/j.yrtph.2011.09.007]
9. Soltani Gerdfaramarzi R. Evaluation of Exposure Methyl diisocyanate and Review of the Respiratory Capacity of the Workers Employed in the Foam Manufacturing Industry. Occupational Medicine Quarterly Journal. 2016;7(4):58-66.
10. Mortasavi S, Jabbari Gharabag M, Asilian H, Khavanin A, Solimanian A. Evaluation of 4, 4-methylene diphenyl diisocyanate effects on foam producing workers of car manufacture. Journal of Inflammatory Disease. 2005;9(1):43-50.
11. Hamada H, Liljelind I, Bruze M, Engfeldt M, Isaksson M, Jönsson B, et al. Assessment of dermal uptake of diphenylmethane-4, 4'-diisocyanate using tape stripping and biological monitoring. European Journal of Dermatology. 2018;28(2):143-8. [DOI:10.1684/ejd.2018.3247]
12. Tan Y-M, Conolly R, Chang DT, Tornero-Velez R, Goldsmith MR, Peterson SD, et al. Computational toxicology: application in environmental chemicals. Computational Toxicology: Springer; 2012. p. 9-19. [DOI:10.1007/978-1-62703-050-2_2]
13. Sager JE, Yu J, Ragueneau-Majlessi I, Isoherranen N. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metabolism and Disposition. 2015;43(11):1823-37. [DOI:10.1124/dmd.115.065920]
14. Krishnan K, Peyret T. Physiologically based toxicokinetic (PBTK) modeling in ecotoxicology. Ecotoxicology modeling: Springer; 2009. p. 145-75. [DOI:10.1007/978-1-4419-0197-2_6]
15. Cooper AB, Aggarwal M, Bartels MJ, Morriss A, Terry C, Lord GA, et al. PBTK model for assessment of operator exposure to haloxyfop using human biomonitoring and toxicokinetic data. Regulatory Toxicology and Pharmacology. 2019;102:1-12. [DOI:10.1016/j.yrtph.2018.12.004]
16. Organization WH. Characterization and application of physiologically based pharmacokinetic models in risk assessment. World Health Organization, International Programme on Chemical Safety, Geneva, Switzerland. 2010.
17. Schmitt W. General approach for the calculation of tissue to plasma partition coefficients. Toxicology in Vitro. 2008;22(2):457-67. [DOI:10.1016/j.tiv.2007.09.010]
18. Poulin P, Theil FP. A priori prediction of tissue: plasma partition coefficients of drugs to facilitate the use of physiologically‐based pharmacokinetic models in drug discovery. Journal of pharmaceutical sciences. 2000;89(1):16-35.
https://doi.org/10.1002/(SICI)1520-6017(200001)89:1<16::AID-JPS3>3.0.CO;2-E [DOI:10.1002/(SICI)1520-6017(200001)89:13.0.CO;2-E]
19. Poulin P, Theil FP. Prediction of Pharmacokinetics Prior to In Vivo Studies. 1. Mechanism‐Based Prediction of Volume of Distribution. Journal of Pharmaceutical Sciences. 2002;91(1):129-56. [DOI:10.1002/jps.10005]
20. EPA. Distributed Structure-Searchable Toxicity (DSSTox) Database [Available from: https://www.epa.gov/chemical-research/distributed-structure-searchable-toxicity-dsstox-database.
21. Watanabe R, Esaki T, Kawashima H, Natsume-Kitatani Y, Nagao C, Ohashi R, et al. Predicting fraction unbound in human plasma from chemical structure: improved accuracy in the low value ranges. Molecular pharmaceutics. 2018;15(11):5302-11. [DOI:10.1021/acs.molpharmaceut.8b00785]
22. Luttringer O, Theil FP, Poulin P, Schmitt‐Hoffmann AH, Guentert TW, Lavé T. Physiologically based pharmacokinetic (PBPK) modeling of disposition of epiroprim in humans. Journal of pharmaceutical sciences. 2003;92(10):1990-2007. [DOI:10.1002/jps.10461]
23. Bois FY. Bayesian inference. Computational toxicology. 2013:597-636. [DOI:10.1007/978-1-62703-059-5_25]
24. Feron V, Kittel B, Kuper C, Ernst H, Rittinghausen S, Muhle H, et al. Chronic pulmonary effects of respirable methylene diphenyl diisocyanate (MDI) aerosol in rats: combination of findings from two bioassays. Archives of toxicology. 2001;75(3):159-75. [DOI:10.1007/s002040100223]
25. Reuzel P, Arts J, Lomax L, Kuijpers M, Kuper C, Gembardt C, et al. Chronic inhalation toxicity and carcinogenicity study of respirable polymeric methylene diphenyl diisocyanate (polymeric MDI) aerosol in rats. Toxicological Sciences. 1994;22(2):195-210.
https://doi.org/10.1093/toxsci/22.2.186 [DOI:10.1093/toxsci/22.2.195]
26. Gledhill A, Wake A, Hext P, Leibold E, Shiotsuka R. Absorption, distribution, metabolism and excretion of an inhalation dose of [14C] 4, 4'-methylenediphenyl diisocyanate in the male rat. Xenobiotica. 2005;35(3):273-92. [DOI:10.1080/00498250500057591]
27. Sabbioni G, Dongari N, Kumar A. Determination of a new biomarker in subjects exposed to 4,4′-methylenediphenyl diisocyanate. Biomarkers. 2010;15(6):508-15. [DOI:10.3109/1354750X.2010.490880]
28. Sabbioni G, Wesp H, Lewalter J, Rumler R. Determination of isocyanate biomarkers in construction site workers. Biomarkers. 2007;12(5):468-83. [DOI:10.1080/13547500701395636]
29. Bello A, Xue Y, Gore R, Woskie S, Bello D. Assessment and control of exposures to polymeric methylene diphenyl diisocyanate (pMDI) in spray polyurethane foam applicators. International journal of hygiene and environmental health. 2019;222(5):804-15. [DOI:10.1016/j.ijheh.2019.04.014]
30. CREELY KS, Hughson GW, Cocker J, Jones K. Assessing isocyanate exposures in polyurethane industry sectors using biological and air monitoring methods. The Annals of occupational hygiene. 2006;50(6):609-21.
31. Kääriä K, Hirvonen A, Norppa H, Piirilä P, Vainio H, Rosenberg C. Exposure to 4, 4′-methylenediphenyl diisocyanate (MDI) during moulding of rigid polyurethane foam: determination of airborne MDI and urinary 4, 4′-methylenedianiline (MDA). Analyst. 2001;126(4):476-9. [DOI:10.1039/b009549o]
32. Robert A, Ducos P, Francin J, Marsan P. Biological monitoring of workers exposed to 4, 4′-methylenediphenyl diisocyanate (MDI) in 19 French polyurethane industries. International archives of occupational and environmental health. 2007;80(5):412-22. [DOI:10.1007/s00420-006-0150-3]
33. Tebby C, van der Voet H, de Sousa G, Rorije E, Kumar V, de Boer W, et al. A generic PBTK model implemented in the MCRA platform: Predictive performance and uses in risk assessment of chemicals. Food and Chemical Toxicology. 2020;142:111440. [DOI:10.1016/j.fct.2020.111440]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
