How Many Urine Samples Are Needed to Accurately Assess Exposure to Non-Persistent Chemicals? The Biomarker Reliability Assessment Tool (BRAT) for Scientists, Research Sponsors, and Risk Managers

In epidemiologic and exposure research, biomonitoring is often used as the basis for assessing human exposure to environmental chemicals. Studies frequently rely on a single urinary measurement per participant to assess exposure to non-persistent chemicals. However, there is a growing consensus that...

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Bibliographic Details
Published in:International journal of environmental research and public health 2020-12, Vol.17 (23), p.9102
Main Authors: Verner, Marc-André, Salame, Hassan, Housand, Conrad, Birnbaum, Linda S, Bouchard, Maryse F, Chevrier, Jonathan, Aylward, Lesa L, Naiman, Daniel Q, LaKind, Judy S
Format: Article
Language:English
Subjects:
Biomarkers
Biomonitoring
Bisphenol A
Chemicals
Classification
Decision making
Dosimetry
Environmental Exposure - analysis
Environmental Monitoring
Environmental Pollutants - analysis
Epidemiology
Exposure
Humans
Monte Carlo Method
Normal distribution
Ordinary differential equations
Pharmacokinetics
Population
Reliability analysis
Reproducibility of Results
Risk Assessment
Risk management
Simulation
Software
Urinalysis - standards
Urine
User interface
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Summary:In epidemiologic and exposure research, biomonitoring is often used as the basis for assessing human exposure to environmental chemicals. Studies frequently rely on a single urinary measurement per participant to assess exposure to non-persistent chemicals. However, there is a growing consensus that single urine samples may be insufficient for adequately estimating exposure. The question then arises: how many samples would be needed for optimal characterization of exposure? To help researchers answer this question, we developed a tool called the Biomarker Reliability Assessment Tool (BRAT). The BRAT is based on pharmacokinetic modeling simulations, is freely available, and is designed to help researchers determine the approximate number of urine samples needed to optimize exposure assessment. The BRAT performs Monte Carlo simulations of exposure to estimate internal levels and resulting urinary concentrations in individuals from a population based on user-specified inputs (e.g., biological half-life, within- and between-person variability in exposure). The BRAT evaluates-through linear regression and quantile classification-the precision/accuracy of the estimation of internal levels depending on the number of urine samples. This tool should guide researchers towards more robust biomonitoring and improved exposure classification in epidemiologic and exposure research, which should in turn improve the translation of that research into decision-making.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph17239102