The effect of ageing on isoniazid pharmacokinetics and hepatotoxicity in Fischer 344 rats
Bibliographic record
Abstract
Isoniazid is the first-line treatment for tuberculosis; however, its use is limited by hepatotoxicity. Age-related differences in isoniazid pharmacokinetics and hepatotoxicity are uncertain. We aimed to investigate these in young (3 ± 0 months, n = 26) and old (23.0 ± 0.2 months, n = 27) male Fischer 344 rats following a low- or high-dose toxic regimen of isoniazid or vehicle (4 doses/day over 2 days; low: 100, 75, 75, 75 mg/kg; high: 150, 105, 105, 105 mg/kg i.p. every 3 h). Fifteen hours after the last dose, animals were euthanized and sera and livers were prepared for analysis. Isoniazid treatment increased serum hepatotoxicity markers (alanine and aspartate transaminase) in young animals but not in old animals, and only reached significance with the high dose in young animals. Isoniazid treatment caused a trend towards an increase in necrosis in young animals with both doses. In contrast, microvesicular steatosis was increased in old isoniazid-treated animals, reaching significance only with the low dose (steatosis prevalence in old: vehicle 1/9, isoniazid 4/5; P < 0.05). Among isoniazid-treated animals, concentrations of toxic intermediates acetylhydrazine and hydrazine were higher in old than young animals (P < 0.05). With both doses, hepatic cytochrome P450 2E1 activity was higher in young animals compared with old (P < 0.05). There were no other age effects seen on any of the other measured enzymes involved in isoniazid metabolism (N-acetyl transferase, amidase, glutathione-S-transferase). These results show age-related changes in isoniazid pharmacokinetics may contribute towards differential patterns of toxicity and confirm that standard hepatotoxicity markers do not detect isoniazid-induced microvesicular steatosis.
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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 | 0.001 | 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".