Supernatant from Hepatocyte Cultures with Drugs That Cause Idiosyncratic Liver Injury Activates Macrophage Inflammasomes
Bibliographic record
Abstract
There is increasing evidence that most idiosyncratic drug-induced liver injury (IDILI) is immune mediated, and in most cases, reactive metabolites appear to be responsible for the induction of this immune response. Reactive metabolites can cause cell damage with the release of damage-associated molecular patterns (DAMPs), which is thought to be involved in immune activation. Presumably, the reason that the liver is a common target of idiosyncratic drug reactions is because it is the major site of drug metabolism and reactive metabolite formation. Inflammasomes can be activated by DAMPs, and this may be a common mechanism by which DAMPs initiate an immune response. In this study, we tested the ability of drugs to induce the release of DAMPs that activate inflammasomes. The drugs tested were amodiaquine and nevirapine; both are associated with significant incidences of severe IDILI. The hepatocytes were a human hepatocarcinoma functional liver cell-4 (FLC-4) cell line. For the detection of inflammasome activation, we used the human macrophage cell line, THP-1 cells. We found that the supernatant from the incubation of both drugs with FLC-4 cells for 7 days led to increased caspase-1 activity and production of IL-1β by THP-1 cells. However, amodiaquine alone also directly activated THP-1 cells. This is presumably because the myeloperoxidase in THP-1 cells can bioactivate amodiaquine to a reactive metabolite. In contrast, nevirapine requires cytochromes P450 for reactive metabolite formation and therefore required incubation with hepatocytes. These results support the hypothesis that reactive metabolites of drugs can cause the release of DAMPs, which in turn can activate inflammasomes. Inflammasome activation may be an important step in the activation of the immune system by drugs, which in some patients can lead to IDILI. Our in vitro model is simple and convenient for evaluating inflammasome activation, and this may be a method to screen drugs for IDILI risk.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".