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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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Biomarker-to-food correlation.(a) Correlation coefficients of median ZEA concentrations in urine predicted for the JGS cohort with number of servings of food consumed from various food groups and (b) Corresponding p-values for the correlation coefficients (Dotted line in figure (b) corresponds to a significance level of 0.05) (Food groups explained in Table 4).
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pone-0113632-g011: Biomarker-to-food correlation.(a) Correlation coefficients of median ZEA concentrations in urine predicted for the JGS cohort with number of servings of food consumed from various food groups and (b) Corresponding p-values for the correlation coefficients (Dotted line in figure (b) corresponds to a significance level of 0.05) (Food groups explained in Table 4).

Mentions: The PBTK model was implemented for the JGS subjects using physiological parameters obtained from the NHANES database [54] commensurate with the age and the recorded body weight of each subject. The steps in this implementation are summarized schematically in Fig. 6. Daily intakes estimated as discussed in the “Dose estimation” section, were used as inputs to the model as bolus doses. Fig. 9 shows a cumulative probability distribution function of ZEA doses received from various major food groups with comparison to the ADI proposed by WHO [10], showing that the study population might receive a dose greater than the WHO proposed ADI only about 10% of the time. Model simulations were conducted 100 times for each subject of the JGS, to capture variability and uncertainty in dose estimation. It was found that the levels of chemicals in the body became steady after a period of approximately 20 days. Accordingly, the simulations included 20 additional days prior to the urine collection date. A realistic urination frequency was considered [55] including accepted sleeping and waking periods. The predicted urinary concentrations of the chemicals were corrected for urine dilution by specific gravity using the formula by Pearson et al.[56] and using the measured specific gravity in morning void urinary samples of female children [56]. Fig. 10 shows the model predictions for the levels of ZEA metabolites in urine for a span of 20 days, for a virtual subject (with average body weight of the JGS cohort) normalized to levels of the parent compound ZEA. Means of observations from the JGS are represented by horizontal dotted lines for the five metabolites. Correlation coefficients were estimated for ZEA and α-ZAL amounts predicted in urine with number of servings of food consumed by the subjects. Overall, both ZEA and α-ZAL were found to be positively correlated with amount of food consumed (Table 5) with sufficiently low p-values. The positive detects in urine for ZEA and α-ZAL were also found to be positively correlated with the measured observations for corresponding subjects (Table 5), though the value for α-ZAL is associated with a high p-value signifying that the association is probably less significant. Fig. 11(a) shows correlation coefficients of predicted ZEA amounts in urine with food consumption from all the major food groups considered in the JGS. Whole grains and eggs show the highest correlations with increased ZEA in urine, and the high correlations are also accompanied by low (<0.05) p-values as shown in Fig. 11(b). The entire collection of correlation coefficients for different food groups are summarized in detail in Table S23 in S1 Information. Fig. 12 shows probability distribution predictions for ZEA and α-ZAL in urine for the entire cohort of JGS subjects compared with corresponding measurements. ZEA and α-ZAL intake distributions were calculated for each subject separately for three ranges - high, low, and medium. These three intake ranges were used to predict urinary biomarker distributions corresponding to high, low, and medium exposure levels of JGS subjects.


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)

Biomarker-to-food correlation.(a) Correlation coefficients of median ZEA concentrations in urine predicted for the JGS cohort with number of servings of food consumed from various food groups and (b) Corresponding p-values for the correlation coefficients (Dotted line in figure (b) corresponds to a significance level of 0.05) (Food groups explained in Table 4).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0113632-g011: Biomarker-to-food correlation.(a) Correlation coefficients of median ZEA concentrations in urine predicted for the JGS cohort with number of servings of food consumed from various food groups and (b) Corresponding p-values for the correlation coefficients (Dotted line in figure (b) corresponds to a significance level of 0.05) (Food groups explained in Table 4).
Mentions: The PBTK model was implemented for the JGS subjects using physiological parameters obtained from the NHANES database [54] commensurate with the age and the recorded body weight of each subject. The steps in this implementation are summarized schematically in Fig. 6. Daily intakes estimated as discussed in the “Dose estimation” section, were used as inputs to the model as bolus doses. Fig. 9 shows a cumulative probability distribution function of ZEA doses received from various major food groups with comparison to the ADI proposed by WHO [10], showing that the study population might receive a dose greater than the WHO proposed ADI only about 10% of the time. Model simulations were conducted 100 times for each subject of the JGS, to capture variability and uncertainty in dose estimation. It was found that the levels of chemicals in the body became steady after a period of approximately 20 days. Accordingly, the simulations included 20 additional days prior to the urine collection date. A realistic urination frequency was considered [55] including accepted sleeping and waking periods. The predicted urinary concentrations of the chemicals were corrected for urine dilution by specific gravity using the formula by Pearson et al.[56] and using the measured specific gravity in morning void urinary samples of female children [56]. Fig. 10 shows the model predictions for the levels of ZEA metabolites in urine for a span of 20 days, for a virtual subject (with average body weight of the JGS cohort) normalized to levels of the parent compound ZEA. Means of observations from the JGS are represented by horizontal dotted lines for the five metabolites. Correlation coefficients were estimated for ZEA and α-ZAL amounts predicted in urine with number of servings of food consumed by the subjects. Overall, both ZEA and α-ZAL were found to be positively correlated with amount of food consumed (Table 5) with sufficiently low p-values. The positive detects in urine for ZEA and α-ZAL were also found to be positively correlated with the measured observations for corresponding subjects (Table 5), though the value for α-ZAL is associated with a high p-value signifying that the association is probably less significant. Fig. 11(a) shows correlation coefficients of predicted ZEA amounts in urine with food consumption from all the major food groups considered in the JGS. Whole grains and eggs show the highest correlations with increased ZEA in urine, and the high correlations are also accompanied by low (<0.05) p-values as shown in Fig. 11(b). The entire collection of correlation coefficients for different food groups are summarized in detail in Table S23 in S1 Information. Fig. 12 shows probability distribution predictions for ZEA and α-ZAL in urine for the entire cohort of JGS subjects compared with corresponding measurements. ZEA and α-ZAL intake distributions were calculated for each subject separately for three ranges - high, low, and medium. These three intake ranges were used to predict urinary biomarker distributions corresponding to high, low, and medium exposure levels of JGS subjects.

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