A machine learning model to estimate toxicokinetic half-lives of per- and polyfluoro-alkyl substances (PFAS) in multiple species
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Per- and polyfluoroalkyl substances (PFAS) are a diverse group of man-made chemicals that are commonly found in body tissues. The toxicokinetics of most PFAS are currently uncharacterized, but long half-lives (t½) have been observed in some cases. Knowledge of chemical-specific t½ is necessary for exposure reconstruction and extrapolation from toxicological studies. We used an ensemble machine learning method, random forest, to model the existing in vivo measured t½ across four species (human, monkey, rat, mouse) and eleven PFAS. Mechanistically motivated descriptors were examined, including two types of surrogates for renal transporters: (1) physiological descriptors, including kidney geometry, for renal transporter expression and (2) structural similarity of defluorinated PFAS to endogenous chemicals for transporter affinity. We developed a classification model for t½ (Bin 1: <12 h; Bin 2: <1 week; Bin 3: <2 months; Bin 4: >2 months). The model had an accuracy of 86.1% in contrast to 32.2% for a y-randomized null model. A total of 3890 compounds were within domain of the model, and t½ was predicted using the bin medians: 4.9 h, 2.2 days, 33 days, and 3.3 years. For human t½, 56% of PFAS were classified in Bin 4, 7% were classified in Bin 3, and 37% were classified in Bin 2. This model synthesizes the limited available data to allow tentative extrapolation and prioritization.
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The one-compartment first-order model is often adopted in pharmacokinetics, toxicology, and environmental science to estimate parameters such as half-life, clearance, and steady-state concentration.
“This model is often adopted in pharmacokinetics, toxicology, and environmental science to estimate parameters such as half-life, clearance, and steady-state concentration”
From Estimation of per- and polyfluoroalkyl substances (PFAS) half-lives in human studies: a systematic review and meta-analysis (2024) · cites this paper as Dawson et al., 2023
Section 2.4.2 (page unavailable) uses a one-compartment model with predicted PFAS half-lives to calculate elimination rates, clearance, and steady-state concentrations. Section 3.4 calls it a simple approach to toxicokinetic modeling. The paper does not establish that the model is often adopted across pharmacokinetics, toxicology, and environmental science. (LLM, conf=0.84)
Experimental — accuracy not yet measuredA one-compartment model with first-order elimination may oversimplify complex processes but provides a useful approximation for understanding and predicting substance behaviour over time.
“While a onecompartment model with first-order elimination may oversimplify the complex processes occurring in real-world systems, it provides a useful approximation for understanding and predicting the behaviour of substances in terms of their change in concentrations over time.”
From Estimation of per- and polyfluoroalkyl substances (PFAS) half-lives in human studies: a systematic review and meta-analysis (2024) · cites this paper as Dawson et al., 2023
Section 2.4.2 describes a simple one-compartment model that uses an elimination rate constant to predict steady-state PFAS concentrations. Section 3.1 describes multiple renal-elimination processes, consistent with the model being a simplification. The paper does not establish that this model predicts how concentrations change over time; its stated application is steady-state prediction. (LLM, conf=0.85)
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- Authors
- Dawson, D. E., Lau, C., Pradeep, P., Sayre, R. R., Judson, R. S., Tornero-Velez, R., & Wambaugh, J. F.
- Affiliations
- Environmental Protection Agency (US)
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
- Corresponding author
- Wambaugh, John F.
- ORCID
- Venue
- Toxics
- Publisher
- MDPI AG
- Published
- 2023-01-20
- Volume
- 11
- Issue
- 2
- Pages
- 98
- ISSN
- 2305-6304 (ISSN-L)
- PMC
- PMC9962572
- OpenAlex
- W4317810030
- Cited by
- 56
- References
- 120
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 18 · 2023: 7
- FWCI
- 4.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Toxics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- The United States Environmental Protection Agency (EPA)
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Per- and polyfluoroalkyl substances research
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmaceutical Science › Fluorine in Organic Chemistry
- Keywords
- per- and polyfluoroalkyl substances, PFAS, random forest, renal transporters, structural similarity, interspecies extrapolation, exposure reconstruction, chemical prioritization
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