Exposure reconstruction using a physiologically based toxicokinetic model with cumulative amount of metabolite in urine: a case study of trichloroethylene inhalation

Chen, Chu-Chih; Shih, Meng-Chiuan; Wu, Kuen-Yuh

doi:10.1007/s00477-011-0502-8

Cited by 6 publications

(3 citation statements)

References 26 publications

(43 reference statements)

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“…However, the relationships between exposure and biomarkers are not known because of their dependence on physiological parameters, kinetics of metabolism and exposure characteristics (duration, magnitude, frequency, co-exposures) (Chen et al 2004). Non-linear models and physiologically based toxicokinetic and pharmacokinetic models have been applied to describe the stochastic characteristics of exposure characterization using biomarkers (Chen et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Effects of smoking on the levels of urinary biomarkers of aromatic hydrocarbons in oil refinery workers

Chalbot

Vei

Kavouras

et al. 2012

Stoch Environ Res Risk Assess

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Occupational exposures to aromatic hydrocarbons may be overwhelmed by mainstream and secondary smoking exposures. The objective of this study was to evaluate the influence of occupational exposures and smoking on benzene and toluene urinary metabolites. The metabolites were measured in pre and post-work shift urine specimens in oil refinery workers by gas chromatography/ mass spectrometry techniques. Post-shift concentration differences between non-smokers and smokers were statistically significant (at p-value \ 0.05). Pre-and post-shift concentrations of urinary phenol (benzene biomarker) did not vary significantly for both non-smokers and smokers. Occupational exposures to toluene triggered an increase of post-shift levels (compared to pre-shift levels) of urinary cresol for non-smokers and workers who had less than 10 cigarettes during the work shift. For these groups, nonoccupational exposures before and during the work shift did not vary. For smokers who did not smoke during the work shift and for those who had more than ten cigarettes during the shift, the post-shift levels of both cresol and hippuric acid were reduced approximately 30% lower than pre-shift levels. This is due to increased exposures to tobacco smoke before the work shift. The relationships between phenol and (cresol ? hippuric acid) levels for non-smokers and smokers indicated that elevated coexposures due to smoking result in the reduction of all metabolites levels in urine. These findings demonstrated that exposures to tobacco smoke may stochastically interfere with occupational exposures when biological monitoring is used to assess occupational health risks. Factors influencing the magnitude of the interference were specimen collection time (in relation to the timing of occupational exposures and excretion rates of biomarkers), smoking intensity and timing before and during the work shift.

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Section: Introductionmentioning

confidence: 99%

Effects of smoking on the levels of urinary biomarkers of aromatic hydrocarbons in oil refinery workers

Chalbot

Vei

Kavouras

et al. 2012

Stoch Environ Res Risk Assess

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“…More examples of estimation of human exposure based on PBK modeling-facilitated reverse dosimetry of biomonitoring data can be found for other compounds including acrylamide, acrylonitrile, cadmium, carbaryl, chloroform, 2,4-dichlorophenoxyacetic acid, polychlorinated biphenyls, , phthalates, toluene, trihalomethanes, and trichloroethylene. ,,,, …”

Section: Pbk Modeling-based Reverse Dosimetry To Predict Human Exposu...mentioning

confidence: 99%

Use of Physiologically Based Kinetic Modeling-Based Reverse Dosimetry to Predict in Vivo Toxicity from in Vitro Data

Louisse

Beekmann

Rietjens

2016

Chem. Res. Toxicol.

100

101

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The development of reliable nonanimal based testing strategies, such as in vitro bioassays, is the holy grail in current human safety testing of chemicals. However, the use of in vitro toxicity data in risk assessment is not straightforward. One of the main issues is that concentration-response curves from in vitro models need to be converted to in vivo dose-response curves. These dose-response curves are needed in toxicological risk assessment to obtain a point of departure to determine safe exposure levels for humans. Recent scientific developments enable this translation of in vitro concentration-response curves to in vivo dose-response curves using physiologically based kinetic (PBK) modeling-based reverse dosimetry. The present review provides an overview of the examples available in the literature on the prediction of in vivo toxicity using PBK modeling-based reverse dosimetry of in vitro toxicity data, showing that proofs-of-principle are available for toxicity end points ranging from developmental toxicity, nephrotoxicity, hepatotoxicity, and neurotoxicity to DNA adduct formation. This review also discusses the promises and pitfalls, and the future perspectives of the approach. Since proofs-of-principle available so far have been provided for the prediction of toxicity in experimental animals, future research should focus on the use of in vitro toxicity data obtained in human models to predict the human situation using human PBK models. This would facilitate human- instead of experimental animal-based approaches in risk assessment.

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“…Later, Chen, et al . 49 , 50 employed chemical data (i.e. blood sampling and urinary data) in conjunction with Monte-Carlo sampling techniques for reconstructing past exposures.…”

Section: Introductionmentioning

confidence: 99%

Source reconstruction of airborne toxics based on acute health effects information

Argyropoulos

Elkhalifa

Fthenou³

et al. 2018

Sci Rep

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The intentional or accidental release of airborne toxics poses great risk to the public health. During these incidents, the greatest factor of uncertainty is related to the location and rate of released substance, therefore, an information of high importance for emergency preparedness and response plans. A novel computational algorithm is proposed to estimate, efficiently, the location and release rate of an airborne toxic substance source based on health effects observations; data that can be readily available, in a real accident, contrary to actual measurements. The algorithm is demonstrated by deploying a semi-empirical dispersion model and Monte Carlo sampling on a simplified scenario. Input data are collected at varying receptor points for toxics concentrations (C; standard approach) and two new types: toxic load (TL) and health effects (HE; four levels). Estimated source characteristics are compared with scenario values. The use of TL required the least number of receptor points to estimate the release rate, and demonstrated the highest probability (>90%). HE required more receptor points, than C, but with lesser deviations while probability was comparable, if not better. Finally, the algorithm assessed very accurately the source location when using C and TL with comparable confidence, but HE demonstrated significantly lower confidence.

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Exposure reconstruction using a physiologically based toxicokinetic model with cumulative amount of metabolite in urine: a case study of trichloroethylene inhalation

Cited by 6 publications

References 26 publications

Effects of smoking on the levels of urinary biomarkers of aromatic hydrocarbons in oil refinery workers

Effects of smoking on the levels of urinary biomarkers of aromatic hydrocarbons in oil refinery workers

Use of Physiologically Based Kinetic Modeling-Based Reverse Dosimetry to Predict in Vivo Toxicity from in Vitro Data

Source reconstruction of airborne toxics based on acute health effects information

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