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Physiologically-based toxicokinetic modeling of zearalenone and its metabolites: application to the Jersey girl study.

Mukherjee D, Royce SG, Alexander JA, Buckley B, Isukapalli SS, Bandera EV, Zarbl H, Georgopoulos PG - PLoS ONE (2014)

Bottom Line: Zearalenone (ZEA), a fungal mycotoxin, and its metabolite zeranol (ZAL) are known estrogen agonists in mammals, and are found as contaminants in food.Zeranol, which is more potent than ZEA and comparable in potency to estradiol, is also added as a growth additive in beef in the US and Canada.Metabolic events such as dehydrogenation and glucuronidation of the chemicals, which have direct effects on the accumulation and elimination of the toxic compounds, have been quantified.

View Article: PubMed Central - PubMed

Affiliation: Environmental and Occupational Health Sciences Institute, Rutgers University, Piscataway, New Jersey, United States of America; Department of Environmental and Occupational Medicine, Rutgers University - Robert Wood Johnson Medical School, Piscataway, New Jersey, United States of America; Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey, United States of America.

ABSTRACT
Zearalenone (ZEA), a fungal mycotoxin, and its metabolite zeranol (ZAL) are known estrogen agonists in mammals, and are found as contaminants in food. Zeranol, which is more potent than ZEA and comparable in potency to estradiol, is also added as a growth additive in beef in the US and Canada. This article presents the development and application of a Physiologically-Based Toxicokinetic (PBTK) model for ZEA and ZAL and their primary metabolites, zearalenol, zearalanone, and their conjugated glucuronides, for rats and for human subjects. The PBTK modeling study explicitly simulates critical metabolic pathways in the gastrointestinal and hepatic systems. Metabolic events such as dehydrogenation and glucuronidation of the chemicals, which have direct effects on the accumulation and elimination of the toxic compounds, have been quantified. The PBTK model considers urinary and fecal excretion and biliary recirculation and compares the predicted biomarkers of blood, urinary and fecal concentrations with published in vivo measurements in rats and human subjects. Additionally, the toxicokinetic model has been coupled with a novel probabilistic dietary exposure model and applied to the Jersey Girl Study (JGS), which involved measurement of mycoestrogens as urinary biomarkers, in a cohort of young girls in New Jersey, USA. A probabilistic exposure characterization for the study population has been conducted and the predicted urinary concentrations have been compared to measurements considering inter-individual physiological and dietary variability. The in vivo measurements from the JGS fall within the high and low predicted distributions of biomarker values corresponding to dietary exposure estimates calculated by the probabilistic modeling system. The work described here is the first of its kind to present a comprehensive framework developing estimates of potential exposures to mycotoxins and linking them with biologically relevant doses and biomarker measurements, including a systematic characterization of uncertainties in exposure and dose estimation for a vulnerable population.

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Dietary concentrations.Summary of available dietary concentrations (means and ranges) from published literature of ZEA (in blue) and α-ZAL (in red) in major food groups identified for the Jersey Girl Study (Food groups explained in Table 4); green bars at the bottom represent the percentages of positive detects among the food samples tested (Only the food groups with any positive detects identified from the literature are shown here).
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pone-0113632-g005: Dietary concentrations.Summary of available dietary concentrations (means and ranges) from published literature of ZEA (in blue) and α-ZAL (in red) in major food groups identified for the Jersey Girl Study (Food groups explained in Table 4); green bars at the bottom represent the percentages of positive detects among the food samples tested (Only the food groups with any positive detects identified from the literature are shown here).

Mentions: Exposure assessment of contaminants must consider multiple uncertainties and variabilities associated with contaminant concentrations in relevant media as well as with human behavior patterns. The MENTOR (Modeling ENvironment for TOtal Risk) framework [44] considers a comprehensive source-to-dose analysis of chemicals of concern which requires detailed, case-specific data. For situations where such detailed data are not available, as in the present study, a screening level system, PRoTEGE (Prioritization/Ranking of Toxic Exposures with GIS Extension) [45] that was developed employing a LCA (Life Cycle Analysis) approach along with basic human LSA (Life Stage Analysis) to identify potential exposures to chemicals of current and emerging concern, for which significant information gaps may exist. In the present study, the PRoTEGE system has been implemented for ZEA and α-ZAL using available data on concentrations of ZEA reported in the literature. The analysis can provide an approximate estimate of exposure for a first-tier analysis of the problem at hand. The WHO report of 2000 [10] regarding food additives and contaminants and the European Food Safety Authority (EFSA) report of 2011 [46] included compiled ZEA concentrations in food items from around the world. However, the contamination levels estimated in food vary widely and often have a wide range of values even when taken from the same study. There is large uncertainty in the process, particularly in the variety of geographical origins of different food items. Uncertainty is increased by the fact that the concentration of ZEA in a particular food item is a function of the level of fungal contamination, which in turn is affected by multiple factors such as geographical location, climate (tropical or temperate), availability of storage and transport facilities, duration of storage, use of fungicides, etc. Dietary estimation of ZEA and ZAL has been carried out using scientific literature by prioritizing studies from the continental US and Europe, to ensure similar geographic, climatic, and economic conditions to ones relevant to the JGS. However, since it is very difficult to obtain ZEA concentrations in all the different food items comprising a group, each food group has been represented by a single food item. The contamination levels of ZEA and α-ZAL in various food groups obtained from the literature are summarized in Table S22 in S1 Information. The range of concentrations and percent positive detects among the samples tested are utilized to develop a lognormal distribution of mycotoxin concentration. Fig. 5 shows the mean and range of the levels of ZEA and α-ZAL in the various food groups as obtained from the literature. Other than the mean and range, the percentage of positive detects (shown as colored bars at the bottom in Fig. 5) among the samples tested has also been taken into consideration while estimating dietary exposure. The percent detects value has been used in a probabilistic model to capture the latent uncertainty in the process of selecting random food samples from the market. ZEA amounts in animal tissues, milk, and eggs have been reported widely in the literature [47]–[49] but most studies have focussed on analyzing ZEA concentrations in animals after various doses of ZEA. Coffey et al.[50] recently presented a probabilistic assessment of ZEA levels in milk based on carry over of ZEA from regular feed. the values estimated by Coffey et al. have been used in the exposure model. Kleinova et al.[29] reported ZEA concentrations in muscle and liver of cows fed regular feed to be negligible. Consequently, ZEA concentration in meat from cows raised on regular feed has been assumed to be zero. Concentrations of α-ZAL in beef have been obtained from studies [49] which used doses of α-ZAL comparable to common Ralgro doses used in commercial meat production. However, these studies all found some concentrations of α-ZAL in beef. Exposure to α-ZAL from beef consumption would depend on the time gap between date of application of the Ralgro implant in the particular animal and the date of consumption. Also, organic beef in the market is assumed to be free of synthetic hormones or additives. Accordingly, the market share of organic beef in the US market [51] was used to estimate the probability of not being exposed to α-ZAL due to beef consumption.


Physiologically-based toxicokinetic modeling of zearalenone and its metabolites: application to the Jersey girl study.

Mukherjee D, Royce SG, Alexander JA, Buckley B, Isukapalli SS, Bandera EV, Zarbl H, Georgopoulos PG - PLoS ONE (2014)

Dietary concentrations.Summary of available dietary concentrations (means and ranges) from published literature of ZEA (in blue) and α-ZAL (in red) in major food groups identified for the Jersey Girl Study (Food groups explained in Table 4); green bars at the bottom represent the percentages of positive detects among the food samples tested (Only the food groups with any positive detects identified from the literature are shown here).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256163&req=5

pone-0113632-g005: Dietary concentrations.Summary of available dietary concentrations (means and ranges) from published literature of ZEA (in blue) and α-ZAL (in red) in major food groups identified for the Jersey Girl Study (Food groups explained in Table 4); green bars at the bottom represent the percentages of positive detects among the food samples tested (Only the food groups with any positive detects identified from the literature are shown here).
Mentions: Exposure assessment of contaminants must consider multiple uncertainties and variabilities associated with contaminant concentrations in relevant media as well as with human behavior patterns. The MENTOR (Modeling ENvironment for TOtal Risk) framework [44] considers a comprehensive source-to-dose analysis of chemicals of concern which requires detailed, case-specific data. For situations where such detailed data are not available, as in the present study, a screening level system, PRoTEGE (Prioritization/Ranking of Toxic Exposures with GIS Extension) [45] that was developed employing a LCA (Life Cycle Analysis) approach along with basic human LSA (Life Stage Analysis) to identify potential exposures to chemicals of current and emerging concern, for which significant information gaps may exist. In the present study, the PRoTEGE system has been implemented for ZEA and α-ZAL using available data on concentrations of ZEA reported in the literature. The analysis can provide an approximate estimate of exposure for a first-tier analysis of the problem at hand. The WHO report of 2000 [10] regarding food additives and contaminants and the European Food Safety Authority (EFSA) report of 2011 [46] included compiled ZEA concentrations in food items from around the world. However, the contamination levels estimated in food vary widely and often have a wide range of values even when taken from the same study. There is large uncertainty in the process, particularly in the variety of geographical origins of different food items. Uncertainty is increased by the fact that the concentration of ZEA in a particular food item is a function of the level of fungal contamination, which in turn is affected by multiple factors such as geographical location, climate (tropical or temperate), availability of storage and transport facilities, duration of storage, use of fungicides, etc. Dietary estimation of ZEA and ZAL has been carried out using scientific literature by prioritizing studies from the continental US and Europe, to ensure similar geographic, climatic, and economic conditions to ones relevant to the JGS. However, since it is very difficult to obtain ZEA concentrations in all the different food items comprising a group, each food group has been represented by a single food item. The contamination levels of ZEA and α-ZAL in various food groups obtained from the literature are summarized in Table S22 in S1 Information. The range of concentrations and percent positive detects among the samples tested are utilized to develop a lognormal distribution of mycotoxin concentration. Fig. 5 shows the mean and range of the levels of ZEA and α-ZAL in the various food groups as obtained from the literature. Other than the mean and range, the percentage of positive detects (shown as colored bars at the bottom in Fig. 5) among the samples tested has also been taken into consideration while estimating dietary exposure. The percent detects value has been used in a probabilistic model to capture the latent uncertainty in the process of selecting random food samples from the market. ZEA amounts in animal tissues, milk, and eggs have been reported widely in the literature [47]–[49] but most studies have focussed on analyzing ZEA concentrations in animals after various doses of ZEA. Coffey et al.[50] recently presented a probabilistic assessment of ZEA levels in milk based on carry over of ZEA from regular feed. the values estimated by Coffey et al. have been used in the exposure model. Kleinova et al.[29] reported ZEA concentrations in muscle and liver of cows fed regular feed to be negligible. Consequently, ZEA concentration in meat from cows raised on regular feed has been assumed to be zero. Concentrations of α-ZAL in beef have been obtained from studies [49] which used doses of α-ZAL comparable to common Ralgro doses used in commercial meat production. However, these studies all found some concentrations of α-ZAL in beef. Exposure to α-ZAL from beef consumption would depend on the time gap between date of application of the Ralgro implant in the particular animal and the date of consumption. Also, organic beef in the market is assumed to be free of synthetic hormones or additives. Accordingly, the market share of organic beef in the US market [51] was used to estimate the probability of not being exposed to α-ZAL due to beef consumption.

Bottom Line: Zearalenone (ZEA), a fungal mycotoxin, and its metabolite zeranol (ZAL) are known estrogen agonists in mammals, and are found as contaminants in food.Zeranol, which is more potent than ZEA and comparable in potency to estradiol, is also added as a growth additive in beef in the US and Canada.Metabolic events such as dehydrogenation and glucuronidation of the chemicals, which have direct effects on the accumulation and elimination of the toxic compounds, have been quantified.

View Article: PubMed Central - PubMed

Affiliation: Environmental and Occupational Health Sciences Institute, Rutgers University, Piscataway, New Jersey, United States of America; Department of Environmental and Occupational Medicine, Rutgers University - Robert Wood Johnson Medical School, Piscataway, New Jersey, United States of America; Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey, United States of America.

ABSTRACT
Zearalenone (ZEA), a fungal mycotoxin, and its metabolite zeranol (ZAL) are known estrogen agonists in mammals, and are found as contaminants in food. Zeranol, which is more potent than ZEA and comparable in potency to estradiol, is also added as a growth additive in beef in the US and Canada. This article presents the development and application of a Physiologically-Based Toxicokinetic (PBTK) model for ZEA and ZAL and their primary metabolites, zearalenol, zearalanone, and their conjugated glucuronides, for rats and for human subjects. The PBTK modeling study explicitly simulates critical metabolic pathways in the gastrointestinal and hepatic systems. Metabolic events such as dehydrogenation and glucuronidation of the chemicals, which have direct effects on the accumulation and elimination of the toxic compounds, have been quantified. The PBTK model considers urinary and fecal excretion and biliary recirculation and compares the predicted biomarkers of blood, urinary and fecal concentrations with published in vivo measurements in rats and human subjects. Additionally, the toxicokinetic model has been coupled with a novel probabilistic dietary exposure model and applied to the Jersey Girl Study (JGS), which involved measurement of mycoestrogens as urinary biomarkers, in a cohort of young girls in New Jersey, USA. A probabilistic exposure characterization for the study population has been conducted and the predicted urinary concentrations have been compared to measurements considering inter-individual physiological and dietary variability. The in vivo measurements from the JGS fall within the high and low predicted distributions of biomarker values corresponding to dietary exposure estimates calculated by the probabilistic modeling system. The work described here is the first of its kind to present a comprehensive framework developing estimates of potential exposures to mycotoxins and linking them with biologically relevant doses and biomarker measurements, including a systematic characterization of uncertainties in exposure and dose estimation for a vulnerable population.

Show MeSH
Related in: MedlinePlus