The use of physiologically based models to integrate diverse data sets and reduce uncertainty in the prediction of perchlorate and iodide kinetics across life stages and species

Abstract

The effects of perchlorate on the incorporation of iodide into thyroid hormones have been studied for more than 40 years in many species and under varying exposure conditions. Nevertheless, the database for this drinking water contaminant is still incomplete, particularly with regard to human developmental risk. A method for integrating the available data and forming meaningful conclusions for risk assessment is needed. To this end, an initial suite of physiologically based pharmacokinetic (PBPK) models has been developed, which incorporates physiological data for the relevant species and life stages and kinetic data for perchlorate and iodide, as well as the interaction between the two anions. The validated models successfully describe perchlorate-induced inhibition of thyroid iodide uptake and perchlorate and iodide kinetics in the male, pregnant, lactating, fetal, and neonatal rats and the adult humans. The relationships of model-predicted internal dose metrics and kinetic parameters allow a direct comparison of internal dose metrics across life stages in rats and humans. By incorporating all the available data, these models provide a framework for species and life stage extrapolation where the lack of specific data sets would otherwise limit predictive capability. This paper demonstrates two approaches for calculating life stage-specific equivalent doses in a risk assessment for perchlorate: the direct combination of validated model predictions, and the development of preliminary PBPK models for the human-sensitive populations based on the relationship of the parameters in the validated rat and human models. Either approach can be used to perform the needed dosimetry. However, the second approach provides the advantage of a preliminary human life stage-specific PBPK model that can be used for identification of key data gaps and estimation of uncertainty.