Validation of Methods for <i>in Vitro</i>–<i>in Vivo</i> Extrapolation Using Hepatic Clearance Measurements in Isolated Perfused Fish Livers
Bibliographic record
Abstract
In vitro biotransformation assays using hepatocytes or liver subcellular fractions, combined with in vitro–in vivo extrapolation (IVIVE) models, have been proposed as an alternative to live fish bioconcentration studies. The uncertainty associated with IVIVE approaches to date has been attributed to assay protocols, model assumptions, or variability of in vivo data. An isolated perfused trout liver model that measures hepatic clearance has been proposed for validating IVIVE predictions in the absence of other confounding factors. Here, we investigated the hepatic clearances of five chemicals (pyrene, phenanthrene, 4- n -nonlyphenol, deltamethrin, and methoxychlor) in this model and compared measured rates to values predicted from published in vitro intrinsic clearances for validation of IVIVE models. Additionally, we varied protein concentrations in perfusates to test binding assumptions of these models. We found that measured and predicted hepatic clearances were in very good agreement (root mean squared error 16.8 mL h –1 g –1 ) across three levels of protein binding and across a more diverse chemical space than previously studied within this system. Our results show that current IVIVE methods can reliably predict in vivo clearance rates and indicate that discrepancies from measured bioconcentration factors might be driven by other processes, such as extrahepatic biotransformation, etc., and help streamline optimization efforts to the processes that truly matter.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".