Concentration dependence of biotransformation in fish liver S9: Optimizing substrate concentrations to estimate hepatic clearance for bioaccumulation assessment
Bibliographic record
Abstract
In vitro bioassays to estimate biotransformation rate constants of contaminants in fish are currently being investigated to improve bioaccumulation assessments of hydrophobic contaminants. The present study investigates the relationship between chemical substrate concentration and in vitro biotransformation rate of 4 environmental contaminants (9-methylanthracene, pyrene, chrysene, and benzo[a]pyrene) in rainbow trout (Oncorhynchus mykiss) liver S9 fractions and methods to determine maximum first-order biotransformation rate constants. Substrate depletion experiments using a series of initial substrate concentrations showed that in vitro biotransformation rates exhibit strong concentration dependence, consistent with a Michaelis-Menten kinetic model. The results indicate that depletion rate constants measured at initial substrate concentrations of 1 μM (a current convention) could underestimate the in vitro biotransformation potential and may cause bioconcentration factors to be overestimated if in vitro biotransformation rates are used to assess bioconcentration factors in fish. Depletion rate constants measured using thin-film sorbent dosing experiments were not statistically different from the maximum depletion rate constants derived using a series of solvent delivery-based depletion experiments for 3 of the 4 test chemicals. Multiple solvent delivery-based depletion experiments at a range of initial concentrations are recommended for determining the concentration dependence of in vitro biotransformation rates in fish liver fractions, whereas a single sorbent phase dosing experiment may be able to provide reasonable approximations of maximum depletion rates of very hydrophobic substances.
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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 | 0.000 |
| 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".