A Comprehensive Library of Lifetime Physiological Equations for PBK Models: Enhancing Dietary Exposure Modeling with Mercury as a Case Study.

Abstract

Dietary risk assessment of food contaminants requires a well-established understanding of the exposure in a heterogeneous population. There are many methods for estimating human exposure to food contaminants, such as intake calculations and internal biomarkers of exposure measured in individuals. However, those methods are expensive, partly invasive, and often provide a momentary exposure snapshot. Physiologically Based Kinetic (PBK) modelling is increasingly used to overcome those challenges that traditional human exposure methods encounter. Still, PBK models are often restricted to certain life stages (e.g., children, adolescents, adults). This study outlines a strategy for implementing nonlinear organ growths in age-specific PBK models to enhance dietary risk assessment from lifetime exposure. To this end, lifetime physiological equations calculating organ growth for both sexes were inventoried from literature and a library was established for 24 organs. We then assessed total lifelong mercury exposure via foodstuff by combining two existing age-specific PBK models for methylmercury (MeHg) and inorganic mercury (iHg) that simulated internal exposure to total mercury, the speciation typically measured in hair and urine. We implemented a set of physiological equations in the PBK model that fitted best the total mercury measured in individuals' organs, hair, and urine from heterogeneous populations. For refined dietary risk assessment, we ultimately estimated total mercury concentration in hair and urine based on i) maximum limits defined by the regulation for MeHg in seafood, ii) the health-based guidance values for MeHg and iHg, and iii) realistic intakes considering French demographic parameters and food consumption data. These exposure scenarios demonstrated that total mercury concentrations in hair and urine estimated from realistic intakes are below critical effect level measures at all ages. The result of this study is the creation of easily accessible tools in Excel and R that facilitate the implementation of physiological equations in Next Generation PBK models.