Evaluation of semi-generic PBTK modeling for emergency risk assessment after acute inhalation exposure to volatile hazardous chemicals. (August 2015)

• Record Type: Journal Article
• Authors: Olie, J. Daniël N.; Bessems, Jos G.; Clewell, Harvey J.; Meulenbelt, Jan; Hunault, Claudine C.
• Is Part Of: Chemosphere. Volume 132(2015)
• Page Start: 47
• Page End: 55
• Publication Date: 2015-08
• Subjects: 2-P Isopropanol -- AEGL Acute Exposure Guideline Level -- BNZ Benzene -- DCM Dichloromethane (Methylene Chloride) -- ERPG-3 Emergency Response Planning Guideline, Level 3 -- MeOH Methanol -- PBTK Model Physiologically Based Toxicokinetic Model -- PCE Tetrachloroethylene (Perchloroethylene) -- STY Styrene -- TCE Trichloroethylene -- TOL Toluene -- XYL Xylene; Hazardous chemicals -- Acute exposure -- PBTK -- Emergency -- Risk assessment; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2015.02.048
• Languages: English
• ISSNs: 0045-6535

Abstract

Highlights: Simpler, user-friendlier PBTK models can equal complex ones. 8 out of 9 chemicals tested were calculated adequately. Simple PBTK-models could be applicable in acute risk assessment. Abstract: Background: Physiologically Based Toxicokinetic Models (PBTK) may facilitate emergency risk assessment after chemical incidents with inhalation exposure, but they are rarely used due to their relative complexity and skill requirements. We aimed to tackle this problem by evaluating a semi-generic PBTK model built in MS Excel for nine chemicals that are widely-used and often released in a chemical incident. Material & methods: The semi-generic PBTK model was used to predict blood concentration–time curves using inhalation exposure scenarios from human volunteer studies, case reports and hypothetical exposures at Emergency Response Planning Guideline, Level 3 (ERPG-3) levels. 2 Predictions using this model were compared with measured blood concentrations from volunteer studies or case reports, as well as blood concentrations predicted by chemical-specific models. The performances of the semi-generic model were evaluated on biological rationale, accuracy, and ease of use and range of application. Results: Our results indicate that the semi-generic model can be easily used to predict blood levels for eight out of nine parent chemicals (dichloromethane, benzene, xylene, styrene, toluene, isopropanol trichloroethylene and tetrachloroethylene). However, for methanol, 2-propanol and dichloromethane the semi-generic model could not cope with the endogenous production of methanol and of acetone (being a metabolite of 2-propanol) nor could it simulate the formation of HbCO, which is one of the toxic end-points of dichloromethane. The model is easy and intuitive to use by people who are not so familiar with toxicokinetic models. Conclusion: A semi-generic PBTK modeling approach can be used as a 'quick-and-dirty' method to get a crude estimate of the exposure dose.