Predicting Hepatic Clearance of Psychotropic Drugs in Isolated Perfused Fish Livers Using a Combination of Two <i>In Vitro</i> Assays
Bibliographic record
Abstract
In vitro biotransformation assays with primary trout hepatocytes (RT-HEP) or liver subcellular fractions (RT-S9) have been proposed as valuable tools to help scientists and regulators better understand the toxicokinetics of chemicals. While both assays have been applied successfully to a diversity of neutral organic chemicals, only the RT-S9 assay has been applied to a large number of ionizable organic chemicals. Here, a combination of an in vitro biotransformation assay with RT-HEP with an active transport assay based on the permanent rainbow trout liver cell line RTL-W1 was used to qualitatively predict the potential hepatic clearance of nine psychotropic drugs with various degrees of ionization. Predictions were compared with rates of clearance measured in isolated perfused rainbow trout livers, and the importance of active transport was verified in the presence of the active transport inhibitor cyclosporin A. For the first time, it was demonstrated that a combination of biotransformation and active transport assays is powerful for the prediction of rates of hepatic clearance of ionizable chemicals. Ultimately, it is expected that this approach will allow for use of fewer animals while at the same time improving our confidence in the use of data from in vitro assays in chemical risk assessment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".