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Exposure Prioritization (Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool.

Heidi F Hubbard¹, Caroline L Ring², Tao Hong¹

¹ICF International, 2635 Meridian Parkway, Durham, NC 27713, USA.

|October 26, 2022

Summary

This summary is machine-generated.

Ex Priori is a new Excel-based model that helps prioritize chemical exposures from consumer products. It visualizes exposure rankings and models changes in consumer use patterns, aiding chemical risk assessment.

Keywords:

consumer habits and practices consumer products high-throughput exposure modeling

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Area of Science:

Environmental Health
Toxicology
Computational Chemistry

Background:

Chemical risk assessment requires tools to prioritize exposures for data-limited chemicals.
Consumer products represent a significant exposure pathway, influenced by usage patterns.

Purpose of the Study:

To develop and present Ex Priori, a screening-level exposure model for visualizing and ranking chemical exposures from consumer products.
To enable rapid modeling of consumer use pattern variations and their impact on chemical exposure prioritization.

Main Methods:

Ex Priori is an Excel-based dashboard model incorporating 7 exposure routes for 1108 chemicals across 228 product types.
The model includes toxicokinetic considerations for body burden estimation and a framework for modeling changes in consumer use patterns.
It was used to model shifts in consumer behavior during the COVID-19 pandemic.

Main Results:

The model rapidly visualizes exposure rankings based on consumer product use.
Sensitivity analysis identified the air emissions rate as a key factor influencing chemical exposure.
Ex Priori effectively models changes in chemical exposure rankings due to altered consumer use patterns.

Conclusions:

Ex Priori provides a flexible tool for dynamic exploration of consumer use patterns and their effect on chemical exposure prioritization.
The model serves as a valuable visualization and modeling resource for both novice and experienced exposure modelers.
It complements existing, more computationally intensive population-based exposure models.