Direct Oxidation and Covalent Binding of Isoniazid to Rodent Liver and Human Hepatic Microsomes: Humans Are More Like Mice than Rats
Bibliographic record
Abstract
Isoniazid (INH) is associated with serious liver injury and autoimmunity. Classic studies in rats indicated that a reactive metabolite of acetylhydrazine is responsible for the covalent binding and toxicity of INH. Studies in rabbits suggested that hydrazine might be the toxic species. However, these models involved acute toxicity with high doses of INH, and INH-induced liver injury in humans has very different features than such animal models. In this study, we demonstrated that a reactive metabolite of INH itself can covalently bind in the liver of mice and also to human liver microsomes. Covalent binding also occurred in rats, but it was much less than that in mice. We were able to trap the reactive metabolite of INH with N-α-acetyl-l-lysine in incubations with human liver microsomes. This suggests that the reactive intermediate of INH that leads to covalent binding is a diazohydroxide rather than a radical or carbocation because those reactive metabolites would be too reactive to trap in this way. Treatment of mice or rats with INH for up to 5 weeks did not produce severe liver injury. The alanine transaminase assay (ALT) is inhibited by INH, and other assays such as glutamate and sorbitol dehydrogenase (SDH) were better biomarkers of INH-induced liver injury. High doses of INH (200 and 400 mg/kg/day) for one week produced steatosis in rats and an increase in SDH, which suggests that it can cause mitochondrial injury. However, steatosis was not observed when INH was given at lower doses for longer periods of time to either mice or rats. We propose that covalent binding of the parent drug can contribute to INH-induced hepatotoxicity and autoimmunity. We also propose that these are immune-mediated reactions, and there are clinical data to support these hypotheses.
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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) | 0.000 | 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) | 0.002 | 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".