Biotransformation of 8:2 Monosubstituted Polyfluoroalkyl Phosphate in Rat & Human Liver, Intestine, and Fecal in Vitro Suspensions
Bibliographic record
Abstract
Polyfluoroalkyl phosphate esters (PAPs) have been used in many commercial and industrial applications due to their grease and water repellency, and surfactant properties.However, these compounds yield transformation products such as fluorotelomer alcohols (FTOHs) and perfluorinated carboxylic acids (PFCAs) that are environmentally persistent, bioaccumulative, and potentially toxic.Given that PAPs metabolize to bioactive products, it is important to understand the sites and kinetics of this metabolism.This research compares the biotransformation of a representative PAP, the 8:2 monosubstituted polyfluoroalkyl phosphate (8:2 monoPAP) in typical host biotransformation sites, the liver and intestine, to the mammalian microbiome.The 8:2 monoPAP was incubated in human and rat (male Sprague-Dawley) liver and intestine S9 fractions, and its immediate hydrolysis products, 8:2 fluorotelomer alcohol (8:2 FTOH) was monitored by GC-MS.Human and rat fecal samples were also collected, used as a surrogate for the gastrointestinal microbiome.Enzyme hydrolysis kinetics were measured and compared.Results show that the rat and human gut phosphatases have 2-fold and 1.3-fold more affinity for 8:2 monoPAP transformation, respectively (KM (rat) = (1.2 ± 0.3) ×10 3 nM; KM (human) = (1.6 ± 0.4) ×10 3 nM) compared to liver (KM (rat) = (4.0 ± 1.5) ×10 3 nM; KM (human) = (4.9± 3.3) ×10 3 nM).Results also show the microbiome contributes to 8:2 monoPAP hydrolysis.While the liver and intestine are the primary sites for metabolism, the microbiome plays a role and should not be overlooked.This may impact the relative risk of PAP exposure, given that levels of bioactive products, including PFCAs, may fluctuate depending on environmental and genetic factors leading to microbial diversity across individuals.
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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) | 0.000 | 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) | 0.001 | 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".