Isomer-Specific Binding Affinity of Perfluorooctanesulfonate (PFOS) and Perfluorooctanoate (PFOA) to Serum Proteins
Bibliographic record
Abstract
Perfluorooctanesulfonate (PFOS) and perfluorooctanoate (PFOA) are among the most prominent contaminants in human serum, and these were historically manufactured as technical mixtures of linear and branched isomers. The isomers display unique pharmacokinetics in humans and in animal models, but molecular mechanisms underlying isomer-specific PFOS and PFOA disposition have not previously been studied. Here, ultrafiltration devices were used to examine (i) the dissociation constants (Kd) of individual PFOS and PFOA isomers with human serum albumin (HSA) and (ii) relative binding affinity of isomers in technical mixtures spiked to whole calf serum and human serum. Measurement of HSA Kd's demonstrated that linear PFOS (Kd=8(±4)×10(-8) M) was much more tightly bound than branched PFOS isomers (Kd range from 8(±1)×10(-5) M to 4(±2)×10(-4) M). Similarly, linear PFOA (Kd=1(±0.9)×10(-4) M) was more strongly bound to HSA compared to branched PFOA isomers (Kd range from 4(±2)×10(-4) M to 3(±2)×10(-4) M). The higher binding affinities of linear PFOS and PFOA to total serum protein were confirmed when both calf serum and human serum were spiked with technical mixtures. Overall, these data provide a mechanistic explanation for the longer biological half-life of PFOS in humans, compared to PFOA, and for the higher transplacental transfer efficiencies and renal clearance of branched PFOS and PFOA isomers, compared to the respective linear isomer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".