This thesis presents the development, evaluation, and application of a generalized toxicokinetic model for mixtures of chemicals. Humans are exposed to mixtures of chemicals that are found together in multiple exposure media (soil, food, and air), and at levels that have been shown to cause adverse effects due to toxic interactions. Although several physiologically based toxicokinetic (PBTK) models exist
for different environmental chemicals, using them in assessing risks to co-occurring contaminants is often impractical.
This is especially true for the case of toxic metals, where half-lives in the human body span days (e.g. arsenic), months (e.g. methylmercury), and decades (e.g. lead, cadmium). Several differences in the formulation of these models exist with respect to (a) physiological structure (e.g. body tissue volumes and blood flow ratios), (b) general modeling assumptions (e.g. for transport and transformation of the chemicals within the body), and (c) exposure-relevant parameters. Since assumptions made for one metal or metal compound can be incompatible with the assumptions made for another metal, current formulations are inadequate for use in assessing health risks from mixtures of toxic metals. Further complications arise when assessing risks of both metals and nonmetals, which also interact at the toxicokinetic and toxicodynamic levels. The issues of consistent representation of physiology and chemical interactions across different classes of chemicals such as mixtures of metals and mixtures of metals and organics are addressed through the development of a Generalized Toxicokinetic Model for Mixtures of chemicals (GTMM).
The GTMM resolves inconsistencies by standardizing the physiology across all models, and by allowing simulations of different models to be done simultaneously. It has been implemented as a set of modules in Matlab and as a user-oriented graphical interface in Matlab-Simulink. The GTMM has been evaluated with multiple existing PBTK models for individual chemicals, and the results demonstrate that the GTMM produces identical results as the original published formulations. Subsequently, the GTMM has been applied to different problems relevant to population risk assessment.Ph.D.Includes bibliographical references (p. 209-237)by Alan F. Sass