Chimeric rodents with humanized liver: bridging the preclinical/clinical trial gap in ADME/toxicity studies
Bibliographic record
Abstract
1. Immunocompromised mice with humanized livers were developed in the mid-1990s to allow the study of human hepatotropic viruses, which normally replicate only in higher primates. The production of the uPA/SCID mouse was the vanguard of these models and remains the most widely worked upon model for an ever increasing range of applications. 2. Since toxicology is conducted in laboratory animal species with the implicit intent of predicting the outcome of accidental, or intentional, human exposure, the potential for using an in vivo model with a humanised metabolism opens up the possibility of better predicting the human response following exposure to drugs and industrial chemicals. Chimeric humanised mice provide the tool for bridging between the non-clinical laboratory safety and metabolism studies, carried out in rodent and non-rodent species, and the first in man clinical trials. 3. Chimeric mice carrying a human liver have now been validated against a wide range of different drugs and chemical classes, and have been shown to clearly differentiate metabolically from the recipient mouse, and to show metabolic pathways more similar to those expected from human liver. 4. This review critically appraises the available animal models carrying human livers and where future developments would improve the existing systems.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".