Biotransformation Potential of Cationic Surfactants in Fish Assessed with Rainbow Trout Liver S9 Fractions
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Biotransformation may substantially reduce the extent to which organic environmental contaminants accumulate in fish. Presently, however, relatively little is known regarding the biotransformation of ionized chemicals, including cationic surfactants, in aquatic organisms. To address this deficiency, a rainbow trout liver S9 substrate depletion assay (RT-S9) was used to measure in vitro intrinsic clearance rates (CLint; ml min–1 g liver–1) for 22 cationic surfactants that differ with respect to alkyl chain length and degree of methylation on the charged nitrogen atom. None of the quaternary N,N,N-trimethylalkylammonium compounds exhibited significant clearance. Rapid clearance was observed for N,N-dimethylalkylamines, and slower rates of clearance were measured for N-methylalkylamine analogs. Clearance rates for primary alkylamines were generally close to or below detectable levels. For the N-methylalkylamines and N,N-dimethylalkylamines, the highest CLint values were measured for C10–C12 homologs; substantially lower clearance rates were observed for homologs containing shorter or longer carbon chains. Based on its cofactor dependency, biotransformation of C12–N,N-dimethylamine appears to involve one or more cytochrome P450–dependent reaction pathways, and sulfonation. On a molar basis, N-demethylation metabolites accounted for up to 25% of the N,N-dimethylalkylamines removed during the 2-h assay, and up to 55% of the removed N-methylalkylamines. These N-demethylation products possess greater metabolic stability in the RT-S9 assay than the parent structures from which they derive and may contribute to the overall risk of ionizable alkylamines. The results of these studies provide a set of consistently determined CLint values that may be extrapolated to whole trout to inform in silico bioaccumulation assessments. Environ Toxicol Chem 2021;40:3123–3136. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. Abstract
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.008 | 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 it