Effects of immunization and checkpoint inhibition on amodiaquine-induced liver injury
Bibliographic record
Abstract
If idiosyncratic drug-induced liver injury (IDILI) is immune-mediated, it is possible that an individual’s prior exposure to antigens may affect their susceptibility to IDILI. An individual’s repertoire of memory immune cells is shaped by every past exposure to antigens. Subsequent drug-induced adverse drug reactions may therefore involve an immune cell’s cross reactivity between a prior antigen and resulting drug-modified proteins. Therefore in this experiment, mice were immunized with amodiaquine (AQ)-modified hepatic proteins to mimic a previous exposure; treated with a RIBI adjuvant and anti-CD40 antibodies to stimulate an immune response; and, treated with anti-PD1 and anti-CTLA-4 antibodies prior to AQ treatment in order to overcome immune tolerance. This treatment led to greater liver injury than treatment with AQ alone. However, the mice did not develop serious liver injury. PD1−/− mice were then immunized and treated with AQ and anti-CTLA-4 antibodies so that immune tolerance would be impaired, both during immunization and also during AQ treatment. However, even this did not result in liver failure, and the liver injury was not significantly increased relative to un-immunized PD1−/− mice treated with anti-CTLA-4 and AQ. From these results we conclude that, although previous antigen exposure may affect the risk of IDILI, it appears that a very strong stimulus is required, and impairing immune tolerance remains the most effective method for producing an animal model 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".