The Combination of Anti-CTLA-4 and PD1–/– Mice Unmasks the Potential of Isoniazid and Nevirapine To Cause Liver Injury
Bibliographic record
Abstract
Our laboratory recently reported what we believe is the first valid animal model of idiosyncratic drug-induced liver injury (IDILI) by treating PD1-/- mice with an anti-CTLA-4 antibody and amodiaquine (AQ). PD1 and CTLA-4 are important immune checkpoint receptors that are involved in inducing immune tolerance. This model was able to produce significant liver injury that looks very similar to the liver injury seen in humans. Although this model was shown to work with AQ, the question becomes whether blocking immune tolerance would unmask the potential of other drugs to cause IDILI. In this study, we tested isoniazid and nevirapine, both drugs with significant histories of causing IDILI in humans even though they do not cause significant injury in animals with doses that result in therapeutic blood levels. Both drugs in combination with these immune checkpoint inhibitors caused mild but significant delayed onset liver injury, which is similar to the mild injury that they can cause in humans. INH-induced liver injury in this model was associated with an increase in NK cells, while NVP-induced liver injury was associated with a greater increase in CD8 T cells. Although the liver injury caused by these drugs in this model was mild, these results suggest that impairing immune tolerance may be a general method for unmasking the potential of drugs to cause IDILI and therefore provide a screening tool for drug development.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".