Perfluorooctane Sulfonate (PFOS) Precursors Can Be Metabolized Enantioselectively: Principle for a New PFOS Source Tracking Tool
Bibliographic record
Abstract
Perfluorooctane sulfonate (PFOS) is the most prominent perfluoroalkyl substance found in the serum of humans and wildlife, yet the major routes by which exposure occurs are not clear. An important issue facing both the scientific and chemical regulatory communities is the extent to which PFOS concentrations in biota are attributable to direct exposure versus metabolism of PFOS-precursors (higher molecular weight derivatives that can be biotransformed to PFOS). Given that certain branched PFOS-precursors are chiral, we hypothesized that nonracemic proportions of PFOS isomers in biological samples could be used as a marker of significant exposure to PFOS-precursors. In this proof-of-principle study we examined the enantiomer-specific biotransformation of a high-purity model PFOS-precursor isomer: C(6)F(13)C*F(CF(3))SO(2)N(H)CH(2)(C(6)H(4))OCH(3) (named 1m-PreFOS hereafter, and whereby * indicates the chiral carbon center). A method for the enantiospecific separation of a compound with a long perfluoroalkyl chain and a chiral center was developed and applied to evaluate the enantioselectivity of 1m-PreFOS biotransformation in human liver microsomes. Gradient elution in reversed-phase mode on a Chiralpak IC column permitted the near-baseline separation of the two enantiomers (E1 and E2, nomenclature based on retention order) in 65 min. Microsome incubations demonstrated that E1 and E2 were metabolized at significantly different rates; k(E1) = 6.5(+/-0.3) x 10(-2) min(-1) (half-life = 10.6 min) and k(E2) = 5.2(+/-0.3) x 10(-2) min(-1) (half-life = 13.3 min), respectively. These results suggest that tracking of PFOS exposure sources by enantiomeric fractionation is feasible, and that new analytical methods for the enantioselective analysis of PFOS isomers in human and environmental samples should be developed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".