Citation:

Stanfield, Z. AND J. Wambaugh. Tools that allow Interpreting Biomonitoring Data to Characterize, Quantify, and Remediate Chemical Exposures. ISES, Chicago, IL, August 27 - 31, 2023.

Impact/Purpose:

N/A

Description:

Biomonitoring data provides invaluable insight into real-world exposures to chemicals. As a result of biomonitoring studies, hundreds of xenobiotic metabolites have been detected in humans across the world. Depending on the study design, these data can characterize different classes of chemicals and allow investigation into specific population groups to assess potential exposure disparities. Biomonitoring studies can also be used to capture trends over time, which provides a means to track exposures to chemicals of concern or identify chemicals of potential risk based on increasing prevalence in study populations. Chemical concentrations measured in body tissues and fluids reflect aggregate exposure across all pathways, sources, and routes. However, exposure reconstruction from biomonitoring is a challenging problem. Careful consideration is needed to apportion the specific analyte(s) measured to possible parent chemical exposures, given the potential for metabolic transformation by the body. Analyzed metabolites and parent chemicals do not necessarily have a one-to-one relationship. Parent chemicals may undergo multiple transformations and/or result in multiple metabolites. Additionally, multiple parent chemicals may be transformed into the same metabolite. Further consideration is needed to apportion aggregate exposure to various sources and pathways, potentially differing in magnitude and time since exposure. One approach to overcoming these challenges involves collection of various data, including chemical use information, presence of compounds in different media, and metabolite half-life/elimination kinetics, used with innovative computational approaches. Biomonitoring data are used to understand risk, evaluate or hypothesize approaches to remediation, and to evaluate mathematical models that predict exposure for chemicals without monitoring data. Increasingly, biomonitoring data cover larger numbers of chemicals. As a result, the panel of metabolites screened in a biomonitoring study can now have a wide variety of chemical properties and span multiple classes of chemicals. This makes standardized tools that can be applied uniformly across a range of chemicals more and more useful. Abstracts of interest to this session would describe tools for inference of chemical exposure from biomonitoring data and case studies applying these tools, including 1) linking biomonitored analytes to parent chemical identities; 2) inference of quantitative exposure from biomarker data; 3) inference of exposure route from chemical properties and use; 4) inference of exposure magnitude and patterns of exposure; 5) insight into exposure trends from longitudinal biomonitoring data. The views expressed in this session description are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA.

Record Details: