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A generalized physiologically-based toxicokinetic modeling system for chemical mixtures containing metals.

Sasso AF, Isukapalli SS, Georgopoulos PG - Theor Biol Med Model (2010)

Bottom Line: Interaction effects of complex mixtures can be directly incorporated into the GTMM.The application of GTMM to different individual metals and metal compounds showed that it explains available observational data as well as replicates the results from models that have been optimized for individual chemicals.The GTMM provides a central component in the development of a "source-to-dose-to-effect" framework for modeling population health risks from environmental contaminants.

View Article: PubMed Central - HTML - PubMed

Affiliation: Environmental and Occupational Health Sciences Institute, A joint institute of UMDNJ-Robert Wood Johnson Medical School and Rutgers University, Piscataway, New Jersey, USA.

ABSTRACT

Background: Humans are routinely and concurrently exposed to multiple toxic chemicals, including various metals and organics, often at levels that can cause adverse and potentially synergistic effects. However, toxicokinetic modeling studies of exposures to these chemicals are typically performed on a single chemical basis. Furthermore, the attributes of available models for individual chemicals are commonly estimated specifically for the compound studied. As a result, the available models usually have parameters and even structures that are not consistent or compatible across the range of chemicals of concern. This fact precludes the systematic consideration of synergistic effects, and may also lead to inconsistencies in calculations of co-occurring exposures and corresponding risks. There is a need, therefore, for a consistent modeling framework that would allow the systematic study of cumulative risks from complex mixtures of contaminants.

Methods: A Generalized Toxicokinetic Modeling system for Mixtures (GTMM) was developed and evaluated with case studies. The GTMM is physiologically-based and uses a consistent, chemical-independent physiological description for integrating widely varying toxicokinetic models. It is modular and can be directly "mapped" to individual toxicokinetic models, while maintaining physiological consistency across different chemicals. Interaction effects of complex mixtures can be directly incorporated into the GTMM.

Conclusions: The application of GTMM to different individual metals and metal compounds showed that it explains available observational data as well as replicates the results from models that have been optimized for individual chemicals. The GTMM also made it feasible to model toxicokinetics of complex, interacting mixtures of multiple metals and nonmetals in humans, based on available literature information. The GTMM provides a central component in the development of a "source-to-dose-to-effect" framework for modeling population health risks from environmental contaminants. As new data become available on interactions of multiple chemicals, the GTMM can be iteratively parameterized to improve mechanistic understanding of human health risks from exposures to complex mixtures of chemicals.

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Comparisons of GTMM predictions with measured human data from (A) autopsy measurements of kidney cadmium levels [36-38]and (B) urinary cadmium measurements from the National Health and Nutrition Examination Survey (NHANES) [39]. Estimates for population exposure were obtained from Choudhury et al. (2001) [34]. All data points represent median values.
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Figure 3: Comparisons of GTMM predictions with measured human data from (A) autopsy measurements of kidney cadmium levels [36-38]and (B) urinary cadmium measurements from the National Health and Nutrition Examination Survey (NHANES) [39]. Estimates for population exposure were obtained from Choudhury et al. (2001) [34]. All data points represent median values.

Mentions: The GTMM was evaluated by applying estimates from the cadmium intake model by Choudhury et al. (2001) [34,35], and comparing to available population data. Figure 3 (A) shows comparisons to autopsy data [36-38]. Predictions were made using the median and 95th percentiles for dietary cadmium intake [34]. Data from Friis et al. (1998) [36] consist of 58 nonsmokers, while data from Lyon et al. (1999) [37] and Benedetti et al. (1999) [38] each consist of approximately 300 smokers and nonsmokers. The Benedetti data are for cadmium concentration in the whole kidney, while all other data and model predictions are for concentration in the kidney cortex. Figure 3 (B) compares model predictions to urinary data from over 12,000 individuals of the National Health and Nutrition Examination Survey (NHANES) [39]. Predictions were made assuming constant cadmium intake of 0.4 μg/kg/day, and differences between males and females are attributed to higher fractional cadmium absorption in females.


A generalized physiologically-based toxicokinetic modeling system for chemical mixtures containing metals.

Sasso AF, Isukapalli SS, Georgopoulos PG - Theor Biol Med Model (2010)

Comparisons of GTMM predictions with measured human data from (A) autopsy measurements of kidney cadmium levels [36-38]and (B) urinary cadmium measurements from the National Health and Nutrition Examination Survey (NHANES) [39]. Estimates for population exposure were obtained from Choudhury et al. (2001) [34]. All data points represent median values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2903511&req=5

Figure 3: Comparisons of GTMM predictions with measured human data from (A) autopsy measurements of kidney cadmium levels [36-38]and (B) urinary cadmium measurements from the National Health and Nutrition Examination Survey (NHANES) [39]. Estimates for population exposure were obtained from Choudhury et al. (2001) [34]. All data points represent median values.
Mentions: The GTMM was evaluated by applying estimates from the cadmium intake model by Choudhury et al. (2001) [34,35], and comparing to available population data. Figure 3 (A) shows comparisons to autopsy data [36-38]. Predictions were made using the median and 95th percentiles for dietary cadmium intake [34]. Data from Friis et al. (1998) [36] consist of 58 nonsmokers, while data from Lyon et al. (1999) [37] and Benedetti et al. (1999) [38] each consist of approximately 300 smokers and nonsmokers. The Benedetti data are for cadmium concentration in the whole kidney, while all other data and model predictions are for concentration in the kidney cortex. Figure 3 (B) compares model predictions to urinary data from over 12,000 individuals of the National Health and Nutrition Examination Survey (NHANES) [39]. Predictions were made assuming constant cadmium intake of 0.4 μg/kg/day, and differences between males and females are attributed to higher fractional cadmium absorption in females.

Bottom Line: Interaction effects of complex mixtures can be directly incorporated into the GTMM.The application of GTMM to different individual metals and metal compounds showed that it explains available observational data as well as replicates the results from models that have been optimized for individual chemicals.The GTMM provides a central component in the development of a "source-to-dose-to-effect" framework for modeling population health risks from environmental contaminants.

View Article: PubMed Central - HTML - PubMed

Affiliation: Environmental and Occupational Health Sciences Institute, A joint institute of UMDNJ-Robert Wood Johnson Medical School and Rutgers University, Piscataway, New Jersey, USA.

ABSTRACT

Background: Humans are routinely and concurrently exposed to multiple toxic chemicals, including various metals and organics, often at levels that can cause adverse and potentially synergistic effects. However, toxicokinetic modeling studies of exposures to these chemicals are typically performed on a single chemical basis. Furthermore, the attributes of available models for individual chemicals are commonly estimated specifically for the compound studied. As a result, the available models usually have parameters and even structures that are not consistent or compatible across the range of chemicals of concern. This fact precludes the systematic consideration of synergistic effects, and may also lead to inconsistencies in calculations of co-occurring exposures and corresponding risks. There is a need, therefore, for a consistent modeling framework that would allow the systematic study of cumulative risks from complex mixtures of contaminants.

Methods: A Generalized Toxicokinetic Modeling system for Mixtures (GTMM) was developed and evaluated with case studies. The GTMM is physiologically-based and uses a consistent, chemical-independent physiological description for integrating widely varying toxicokinetic models. It is modular and can be directly "mapped" to individual toxicokinetic models, while maintaining physiological consistency across different chemicals. Interaction effects of complex mixtures can be directly incorporated into the GTMM.

Conclusions: The application of GTMM to different individual metals and metal compounds showed that it explains available observational data as well as replicates the results from models that have been optimized for individual chemicals. The GTMM also made it feasible to model toxicokinetics of complex, interacting mixtures of multiple metals and nonmetals in humans, based on available literature information. The GTMM provides a central component in the development of a "source-to-dose-to-effect" framework for modeling population health risks from environmental contaminants. As new data become available on interactions of multiple chemicals, the GTMM can be iteratively parameterized to improve mechanistic understanding of human health risks from exposures to complex mixtures of chemicals.

Show MeSH
Related in: MedlinePlus