Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability

Area of Science:

• Environmental Health Sciences
• Toxicology
• Computational Biology

Background:

• Thousands of environmental chemicals lack sufficient toxicokinetic (TK) data for human health risk assessment.
• Existing methods for prioritizing chemicals often use generalized population parameters, potentially misrepresenting risk for specific subgroups.
• Limited in vitro TK data and biomonitoring data are available for a fraction of environmental chemicals.

Purpose of the Study:

• To develop and apply an open-source, high-throughput (HT) toxicokinetic (TK) modeling framework incorporating inter-individual variability for chemical risk prioritization.
• To create a population simulation reflecting modern U.S. demographics using National Health and Nutrition Examination Survey (NHANES) data.
• To identify specific chemicals and demographic groups at higher risk from environmental exposures.

Main Methods:

• Developed a virtual population using NHANES demographic and anthropometric data, incorporating inter-individual variability via a Monte Carlo approach.
• Integrated physiological variability into an open-source HT TK modeling framework.
• Prioritized 50 chemicals using in vitro HT screening assays (Tox21, ToxCast) and reverse dosimetry to estimate bioactive equivalent doses, compared against NHANES biomonitoring data for exposure assessment.

Main Results:

• The inclusion of NHANES-derived inter-individual variability decreased predicted bioactive equivalent doses by 12% on average compared to previous methods.
• For specific chemical and demographic group combinations, the margin of safety was reduced by up to 75%.
• The TK modeling framework successfully identified targeted risks for specific demographic groups and potentially sensitive subpopulations.

Conclusions:

• Physiologically-based population simulations significantly improve the accuracy of chemical risk prioritization by accounting for human variability.
• This framework enables targeted identification of high-risk chemicals and populations, guiding future research and regulatory efforts.
• The approach facilitates a more precise understanding of chemical risks across diverse U.S. population segments.