Binding of per- and polyfluoroalkyl substances with liver and serum proteins in rats: implications for physiologically based pharmacokinetic modelling
Bibliographic record
Abstract
Understanding the binding between per- and polyfluoroalkyl substances (PFAS) and proteins is essential for elucidating their toxicokinetics and tissue distribution. Here, we quantified the binding affinities of 14 PFAS to rat liver fatty acid-binding protein (rL-FABP) and rat serum albumin (RSA). Results showed that PFAS exhibit strong binding affinities (K a ) to the rL-FABP (10 3 ∼ 10 5 M −1 ), particularly among medium- to long-chain perfluorinated carboxylic acids (PFCAs). The binding affinity of PFAS to RSA ranged from approximately 10 4 to 10 5 M −1 , with 1 to 4 binding sites. Molecular docking results supported that PFAS binding to proteins is an exothermic process driven by van der Waals forces, hydrogen bonding, and electrostatic interactions. Additionally, long-chain PFCAs were shown to adopt a “U”-shaped conformation within the ligand-binding cavities of rL-FABP and RSA. The newly developed physiologically based pharmacokinetic model using measured binding data demonstrates a substantial improvement in the goodness of fit to experimental observations, reducing the prediction error by 20 %∼216 %. Finally, we found that the PFAS liver-blood partition could be mainly explained by the binding affinity ratios of PFAS to liver and blood proteins, which could be further extrapolated from rats to humans, providing useful insights to understand the tissue distribution of PFAS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".