Using exposure prediction tools to link exposure and dosimetry for risk-based decisions: A case study with phthalates

- Evaluation of available exposure prediction tools for source-to-outcome modeling.
- Estimation of the human exposure based on PBPK-reverse dosimetry for comparison to predictions.
- Discussion of the utility and areas for improvement of each tool.
- Implication in risk assessment for prioritization and safe exposure decision.

A few different exposure prediction tools were evaluated for use in the new in vitro-based safety assessment paradigm using di-2-ethylhexyl phthalate (DEHP) and dibutyl phthalate (DnBP) as case compounds. Daily intake of each phthalate was estimated using both high-throughput (HT) prediction models such as the HT Stochastic Human Exposure and Dose Simulation model (SHEDS-HT) and the ExpoCast heuristic model and non-HT approaches based on chemical specific exposure estimations in the environment in conjunction with human exposure factors. Reverse dosimetry was performed using a published physiologically based pharmacokinetic (PBPK) model for phthalates and their metabolites to provide a comparison point. Daily intakes of DEHP and DnBP were estimated based on the urinary concentrations of their respective monoesters, mono-2-ethylhexyl phthalate (MEHP) and monobutyl phthalate (MnBP), reported in NHANES (2011-2012). The PBPK-reverse dosimetry estimated daily intakes at the 50th and 95th percentiles were 0.68 and 9.58 μg/kg/d and 0.089 and 0.68 μg/kg/d for DEHP and DnBP, respectively. For DEHP, the estimated median from PBPK-reverse dosimetry was about 3.6-fold higher than the ExpoCast estimate (0.68 and 0.18 μg/kg/d, respectively). For DnBP, the estimated median was similar to that predicted by ExpoCast (0.089 and 0.094 μg/kg/d, respectively). The SHEDS-HT prediction of DnBP intake from consumer product pathways alone was higher at 0.67 μg/kg/d. The PBPK-reverse dosimetry-estimated median intake of DEHP and DnBP was comparable to values previously reported for US populations. These comparisons provide insights into establishing criteria for selecting appropriate exposure prediction tools for use in an integrated modeling platform to link exposure to health effects.