The Effects of Immune Modulators on Amodiaquine-Induced Liver Injury
Bibliographic record
Abstract
If idiosyncratic drug-induced liver injury (IDILI) is immune mediated, then it is logical that immune modulators may be able to affect liver injury caused by a drug. We have previously shown that modulating the immune system by impairing programmed cell death protein (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) signaling, both receptors involved in immune tolerance, was capable of producing an animal model of amodiaquine (AQ) IDILI with characteristics very similar to IDILI in humans. Other immune modulators may also increase liver injury caused by drugs that cause IDILI in humans. In this study, myeloid derived suppressor cells (MDSCs), transforming growth factor beta (TGF-β), and lymphocyte-activation gene 3 (LAG3) were targeted with antibodies, with and without PD-1 and CTLA-4 impairment. We found that anti-Gr1 antibodies used to deplete MDSCs led to a significant increase in AQ-induced liver injury in wild-type mice; however, the injury was actually less in PD-1 –/– mice, with or without anti-CTLA-4, and it was less than we have previously observed in PD-1 –/– mice combined with anti-CTLA-4 without anti-Gr1. Addition of anti-LAG3 or anti-TGF-β antibodies produced a small increase ALT in AQ-treated wild-type mice. There was a significant increase in ALT in PD-1 –/– mice co-treated with anti-LAG3 or anti-TGF-β relative to AQ-treated wild-type mice. In the case of TGF-β, this was further increased by the addition of anti-CTLA-4, but if anything, there appeared to be a paradoxical decrease when anti-CTLA-4 was combined with anti-LAG3. Overall, the results from this study were not always as expected, and they highlight the complexity of the immune response, in particular immune tolerance, which appears to be the dominant immune response to drugs that cause 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".