Exploring Indirect Sources of Human Exposure to Perfluoroalkyl Carboxylates (PFCAs): Evaluating Uptake, Elimination, and Biotransformation of Polyfluoroalkyl Phosphate Esters (PAPs) in the Rat
Bibliographic record
Abstract
BACKGROUND: Perfluorinated carboxylic acids (PFCAs) are ubiquitous in human sera worldwide. Biotransformation of the polyfluoroalkyl phosphate esters (PAPs) is a possible source of PFCA exposure, because PAPs are used in food-contact paper packaging and have been observed in human sera. OBJECTIVES: We determined pharmacokinetic parameters for the PAP monoesters (monoPAPs) and PAP diesters (diPAPs), as well as biotransformation yields to the PFCAs, using a rat model. METHODS: The animals were dosed intravenously or by oral gavage with a mixture of 4:2, 6:2, 8:2, and 10:2 monoPAP or diPAP chain lengths. Concentrations of the PAPs and PFCAs, as well as metabolic intermediates and phase II metabolites, were monitored over time in blood, urine, and feces. RESULTS: The diPAPs were bioavailable, with bioavailability decreasing as the chain length increased from 4 to 10 perfluorinated carbons. The monoPAPs were not absorbed from the gut; however, we found evidence to suggest phosphate-ester cleavage within the gut contents. We observed biotransformation to the PFCAs for both monoPAP and diPAP congeners. CONCLUSIONS: Using experimentally derived biotransformation yields, perfluorooctanoic acid (PFOA) sera concentrations were predicted from the biotransformation of 8:2 diPAP at concentrations observed in human serum. Because of the long human serum half-life of PFOA, biotransformation of diPAP even with low-level exposure could over time result in significant exposure to PFOA. Although humans are exposed directly to PFCAs in food and dust, the pharmacokinetic parameters determined here suggest that PAP exposure should be considered a significant indirect source of human PFCA contamination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".