A human next generation PBK model for PFOA.

Area of Science:

• Environmental toxicology and risk assessment
• Computational toxicology and modeling
• Human health risk assessment

Background:

• Assessing human health risks of per- and polyfluoroalkyl substances (PFAS) is complex due to their vast number, structural variety, and limited toxicity data.
• Next Generation Physiologically Based Kinetic (NG-PBK) models offer a mechanistic approach to overcome data gaps and facilitate risk assessment.
• Existing models often rely on animal or human data calibration, limiting their mechanistic application.

Purpose of the Study:

• To develop and validate a Next Generation Physiologically Based Kinetic (NG-PBK) model for perfluorooctanoic acid (PFOA) in humans.
• To parameterize the model exclusively using in vitro and in silico data, avoiding calibration with in vivo observations.
• To demonstrate the model's utility for extrapolation to data-poor PFAS and integration with human biomonitoring (HBM) data for Next Generation Risk Assessment (NGRA).

Main Methods:

• Development of a NG-PBK model incorporating key toxicokinetic processes: protein partitioning, lipid partitioning, entero-hepatic circulation, renal elimination, and menstruation.
• Parameterization of the model using exclusively in vitro and in silico derived data.
• Global sensitivity analysis to identify critical model parameters.

Main Results:

• The developed NG-PBK model for PFOA was highly sensitive to unbound plasma fraction, active transport, and tissue-plasma partition coefficients.
• Model predictions for serum concentrations and half-lives aligned with human volunteer and biomonitoring study findings.
• The model demonstrated mechanistic robustness, achieving equivalence with existing validated PFOA-PBK models without in vivo calibration.

Conclusions:

• The study successfully developed a mechanistic NG-PBK model for PFOA, relying solely on in vitro and in silico data.
• This model accurately predicts PFOA toxicokinetics and serves as a foundation for extrapolating to other PFAS.
• Integration with HBM data will enhance NGRA for PFAS, supporting the 3R principles by reducing reliance on in vivo studies.