Physiological modeling of toxicokinetic interactions: implications for mixture risk assessment

Area of Science:

• Toxicology and Pharmacology
• Computational Biology
• Risk Assessment

Background:

• Existing chemical interaction data primarily comes from animal studies with high doses and exposure routes dissimilar to human scenarios.
• Accurate extrapolation of chemical interaction data from animal models to human health risk assessment is challenging.

Purpose of the Study:

• To introduce and validate physiological modeling as a mechanistic approach for extrapolating chemical interactions.
• To enable dose, scenario, species, and route extrapolations for toxic interactions.
• To facilitate risk assessment of complex chemical mixtures for human exposures.

Main Methods:

• Development of mathematical models describing toxicokinetic and toxicodynamic interrelationships.
• Incorporation of binary interaction data into physiologically based models to predict mixture toxicokinetics.
• Simulation of tissue doses and risk calculations using dose-response curves for mixture components.

Main Results:

• Demonstrated feasibility of predicting toxicokinetic changes in complex chemical mixtures using physiologically based models.
• Physiological modeling allows extrapolation of interactions from binary to multichemical mixtures.
• Method enables risk assessment by simulating tissue doses and utilizing established dose-response relationships.

Conclusions:

• Physiological modeling provides a robust, mechanistic framework for evaluating chemical interactions across different exposure conditions.
• This approach enhances the accuracy of risk assessment for low-level human exposures to complex chemical mixtures.
• Mechanistic understanding through physiological modeling is crucial for relevant and reliable chemical safety evaluations.