Prediction of hepatic plasma clearance in vivo from gel-entrapped rat and human hepatocytes
Bibliographic record
Abstract
This paper aimed to evaluate the applicability of gel-entrapped rat and human hepatocytes in the prediction of hepatic plasma clearance (CLh,plasma) in vivo. The in vitro intrinsic clearances (CLint,in vitro) for the selected compounds were determined from the substrate disappearance rate, and further used to predict CLh,plasma using 3 classical mathematical models (well-stirred, parallel-tube, and dispersion) and disregarding drug binding. As a result, the predicted values from gel-entrapped rat hepatocytes were mostly within 2 SE of the literature data with a high correlation coefficient (R(2)) of 0.88-0.91. The predicted data with human hepatocytes also fitted well with the clinical data, indicating a high accuracy in prediction of in-vivo clearance. With respect to the mathematical model for predicting CLh,plasma, the parallel-tube and dispersion models produced a better prediction than the well-stirred model, and we suggest using the parallel-tube model because it is less complex mathematically. In conclusion, gel-entrapped hepatocytes predicted the drug clearance well and seemed to be a useful tool in the process of drug discovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".