In vitro biotransformation assays using fish liver cells: Comparing rainbow trout and carp hepatocytes
Bibliographic record
Abstract
Biotransformation assays using primary hepatocytes from rainbow trout, Oncorhynchus mykiss, were validated as a reliable in vitro tool to predict in vivo bioconcentration factors (BCF) of chemicals in fish. Given the pronounced interspecies differences of chemical biotransformation, the present study aimed to compare biotransformation rate values and BCF predictions obtained with hepatocytes from the cold-water species, rainbow trout, to data obtained with hepatocytes of the warm-water species, common carp (Cyprinus carpio). In a first step, we adapted the protocol for the trout hepatocyte assay, including the cryopreservation method, to carp hepatocytes. The successful adaptation serves as proof of principle that the in vitro hepatocyte biotransformation assays can be technically transferred across fish species. In a second step, we compared the in vitro intrinsic clearance rates (CLin vitro, int) of two model xenobiotics, benzo[a]pyrene (BaP) and methoxychlor (MXC), in trout and carp hepatocytes. The in vitro data were used to predict in vivo biotransformation rate constants (kB) and BCFs, which were then compared to measured in vivo kB and BCF values. The CLin vitro, int values of BaP and MXC did not differ significantly between trout and carp hepatocytes, but the predicted BCF values were significantly higher in trout than in carp. In contrast, the measured in vivo BCF values did not differ significantly between the two species. A possible explanation of this discrepancy is that the existing in vitro-in vivo prediction models are parameterized only for trout but not for carp. Therefore, future research needs to develop species-specific extrapolation models.
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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) | 0.000 | 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 | 0.001 |
| 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".