Bayesian estimation of human population toxicokinetics of PFOA, PFOS, PFHxS, and PFNA from studies of contaminated drinking water
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Background: Setting health-protective standards for poly- and perfluoroalkyl substances (PFAS) exposure requires estimates of their population toxicokinetics, but existing studies have reported widely varying PFAS half-lives (T½) and volumes of distribution (Vd). Objectives: We combined data from multiple studies to develop harmonized estimates of T½ and Vd, along with their interindividual variability, for four PFAS commonly found in drinking water: perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoic acid (PFNA), and perfluorohexane sulfonate (PFHxS). Methods: We identified published data on PFAS concentrations in human serum with corresponding drinking water measurements, separated into training and testing data sets. We fit training data sets to a one-compartment model incorporating interindividual variability, time-dependent drinking water concentrations, and background exposures. Use of a hierarchical Bayesian approach allowed us to incorporate informative priors at the population level, as well as at the study level. We compared posterior predictions to testing data sets to evaluate model performance. Results: Posterior median (95% CI) estimates of T½ (in years) for the population geometric mean were 3.14 (2.69, 3.73) for PFOA, 3.36 (2.52, 4.42) for PFOS, 2.35 (1.65, 3.16) for PFNA, and 8.30 (5.38, 13.5) for PFHxS, all of which were within the range of previously published values. The extensive individual-level data for PFOA allowed accurate estimation of population variability, with a population geometric standard deviation of 1.57 (95% CI: 1.42, 1.73); data from other PFAS were also consistent with this degree of population variability. Vd estimates ranged from 0.19 to 0.43L/kg across the four PFAS, which tended to be slightly higher than previously published estimates. Discussion: These results have direct application in both risk assessment (quantitative interspecies extrapolation and uncertainty factors for interindividual variability) and risk communication (interpretation of monitoring data). In addition, this study provides a rigorous methodology for further refinement with additional data, as well as application to other PFAS. https://doi.org/10.1289/EHP10103
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Another significant source of uncertainty in half-life estimation could be due to the possible ongoing and/or unaccounted background exposures, and to the possible role of individual determinants (such as sex, age, genetics and general health), which may contribute to interindividual variation.
“Another significant source of uncertainty in half-life estimation could be due to the possible ongoing and/or unaccounted background exposures (Dourson and Gadagbui, 2021), and to the possible role of individual determinants (such as sex, age, genetics and general health), which may contribute to interindividual variation (Bois et al., 2010; Chiu et al., 2022).”
From Estimation of per- and polyfluoroalkyl substances (PFAS) half-lives in human studies: a systematic review and meta-analysis (2024) · cites this paper as Chiu et al., 2022
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Experimental — accuracy not yet measuredMost human studies on PFAS toxicokinetic processes focused on long chain PFAS, showing heterogeneous estimates among chemical compounds and investigated populations.
“Most human studies on PFAS toxicokinetic processes focused on long chain PFAS, showing heterogeneous estimates among chemical compounds and investigated populations (Chiu et al., 2022), potentially due to the variability of exposure settings and concentration levels (Worley et al., 2017).”
From Estimation of per- and polyfluoroalkyl substances (PFAS) half-lives in human studies: a systematic review and meta-analysis (2024) · cites this paper as Chiu et al., 2022
Only studies conducted after the cessation of the most substantial exposure to PFAS were included, because persistent and ongoing exposures can influence half-life estimation, leading to potential overestimation.
“We decided to include only studies that were conducted after the cessation of the most substantial exposure to PFAS (i.e. principally contaminated drinking water or occupational exposure), as persistent and ongoing exposures can influence half-life estimation, leading to a potential overestimation of results (Russell et al., 2015; Chiu et al., 2022).”
From Estimation of per- and polyfluoroalkyl substances (PFAS) half-lives in human studies: a systematic review and meta-analysis (2024) · cites this paper as Chiu et al., 2022
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- Authors
- Chiu, W. A., Lynch, M. T., Lay, C. R., Antezana, A., Malek, P., Sokolinski, S., & Rogers, R. D.
- Affiliations
- Texas A&M University (US)
- Abt Global (United States) (US)
- Centers for Disease Control and Prevention (US)
- Corresponding author
- Lynch, Meghan T.
- Venue
- Environmental Health Perspectives
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-12
- Volume
- 130
- Issue
- 12
- Pages
- 127001-127001
- ISSN
- 0091-6765 (ISSN-L)
- 1552-9924
- PMC
- PMC9714558
- OpenAlex
- W4310700145
- Cited by
- 57
- References
- 40
- Citations by year
- 2026: 17 · 2025: 16 · 2024: 17 · 2023: 7
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Locations
- Environmental Health Perspectives (publishedVersion, public-domain)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Environmental Health Sciences (P30ES029067, P42ES027704)
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Per- and polyfluoroalkyl substances research
- Physical Sciences › Environmental Science › Water Science and Technology › Fluoride Effects and Removal
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Toxic Organic Pollutants Impact
- Keywords
- PFAS, perfluorooctanoic acid, PFOS, PFHxS, perfluorononanoic acid, toxicokinetics, half-life, volume of distribution, interindividual variability, drinking water contamination, Bayesian hierarchical model, one-compartment model
- MeSH terms
- Alkanesulfonates, Bayes Theorem, Caprylates, Fatty Acids, Fluorocarbons, Humans, Alkanesulfonic Acids, Drinking Water, Toxicokinetics
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