Use of an animal model to test whether non-alcoholic fatty liver disease increases the risk of idiosyncratic drug-induced liver injury
Bibliographic record
Abstract
Clinical evidence suggests that most idiosyncratic drug-induced liver injury (IDILI) is immune-mediated. The danger hypothesis suggests that liver injury and inflammation would increase the risk of an immune response leading to IDILI. Therefore, a reasonable hypothesis would be that an underlying chronic liver disease such as non-alcoholic steatohepatitis (NASH) would increase the risk of developing IDILI due to inflammation and release of danger signals from damaged cells. In order to test this hypothesis, mice were fed a methionine-/choline-deficient (MCD) diet that produces a consistent NASH phenotype, along with amodiaquine (AQ) – a drug known to cause IDILI in humans. This study employed both wild-type C57BL/6 mice and PD-1−/− mice co-treated with anti-CTLA-4 antibodies. The PD-1−/− + anti-CTLA-4 model produces an immune-mediated liver injury very similar to the idiosyncratic liver injury observed in humans. The liver injury observed in the present experiment was dominated by the injury caused by the MCD diet; there was no significant difference between mice treated with the MCD diet alone and those also treated with AQ, whether in wild-type mice of the PD-1−/− model. Therefore, the MCD diet, which results in a state that mimics NASH, did not appear to increase the liver injury associated with AQ treatment. Ultimately, an animal model is just that – only a model, and cannot provide a definitive answer to clinical questions. However, given the difficulty of performing clinical studies with appropriate control populations, the present results provide important evidence to support a general clinical finding that underlying liver injury does not usually increase the risk of IDILI.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".