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A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species
Citation:
Dawson, D., Chris Lau, P. Pradeep, R. Sayre, R. Judson, R. Tornero-Velez, AND J. Wambaugh. A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species. Toxics. MDPI, Basel, Switzerland, 11(2):98, (2023). https://doi.org/10.3390/toxics11020098
Impact/Purpose:
This machine learning model synthesize the limited available TK data for per- and polyfluoroalkyl substances (PFAS) in order to estimate half-lives and clearance. These predictions will allow for tenative extrapolation from in vitro concentrations to human doses and also allow for exposure inferences from biomonitoring data. This model tentatively groups thousands of PFAS into one of four half-life categories ranging from < 12 hours to > 2 months.
Description:
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.
URLs/Downloads:
DOI: A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species
https://pubmed.ncbi.nlm.nih.gov/36850973/