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

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.

Figure 1

A schematic depiction of PBTK model structures for two common toxic metals (cadmium [33]and lead [45]), and a toxic metal compound (methylmercury [56]), as they have been implemented in the literature. The different physicochemical properties of the toxicants of concern have resulted in different structures (i.e. representations of the physiology) in the three models, thus limiting the usefulness of these formulations in assessing cumulative and/or comparative exposures and risks.

Figure 2

A schematic depiction of major compartments considered in the generalized PBTK modeling framework (adapted from Georgopoulos, 2008) [4].

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.

Figure 4

Comparisons of GTMM predictions with measured data of cumulative urinary arsenic from a volunteer human study in which individual males ingested (A) a single 100 μg As^V oral dose (Lee, 1999 [42]), and (B) multiple 250 μg As ^III oral doses (Buchet et al., 1981 [43]). Data legend: Total arsenic (black diamond), total inorganic arsenic (blue square), total MMA (green triangle), total DMA (red circle)

Figure 5

Comparisons of GTMM predictions with measured human data of (A) tracer blood lead for a male absorbing 17.5 μg/day lead-204 for 104 days (Rabinowitz et al., 1976 [46]), and (B) blood lead for a subgroup of children from the Cincinnati Prospective Lead Study (Bornschein et al., 1985 [49]), using the O'Flaherty lead exposure model to characterize ingestion and inhalation intakes [48]. The Cincinnati data represent the median blood lead measurements of individuals monitored from birth to early childhood, and only include children whose highest blood lead concentration did not exceed 15 μg/dL.

Figure 6

Comparisons of GTMM predictions with measured human data from the volunteer study by Kerger et al. (1996) [53]in which an individual male ingested a 5 mg oral dose of Cr^VI. Results are shown for (A) Cr^III plasma concentration and (B) Cr^III urinary elimination.

Figure 7

Comparisons of GTMM predictions with measured human methylmercury (MeHg) data for (A) a male consuming approximately 3 μg/kg/day MeHg for 96 days (Hislop et al., 1983) [57], and (B) a pregnant woman consuming 42 μg/kg/day MeHg for 108 days (Amin-Zaki et al., 1976 [58]). Data legend: hair (blue square), blood (red circle), fetal blood (purple triangle).

Figure 8

Hypothetical inhibition of benzene (BNZ) metabolism in the liver by cadmium (Cd), lead (Pb), methylmercury (MeHg), total arsenic (tot As), and toluene (TOL). Metal intakes were increased by 40% of the original intakes at day 500. A: Metal and VOC liver concentrations for the base-case (no interactions). B: Benzene liver concentrations under different interaction assumptions.