Effects of ertiprotafib on hepatic cytochrome P450 and peroxisomal enzymes in rats and dogs, and in rat and human primary hepatocytes
Bibliographic record
Abstract
Ertiprotafib (ERTI) significantly increased liver weights in male and female rats, and moderately increased liver weights in male dogs after treatment for 28 days. The present study tested the hypotheses that the organ weight increases were associated with peroxisome proliferation in rats and induction of hepatic enzymes in rats and dogs, and would have limited impacts on humans. At a dosage of 200 mg kg-1 day-1, CYP4A was induced by tenfold in male rats and 2.4-fold in female rats. In male rats, CYP2B was induced by 1.2-fold and CYP3A was induced by 1.7-fold. Palmitoyl CoA oxidase was induced by 5.1-fold in male rats and 2.9-fold in female rats; carnitine acetyltransferase was induced by 10.4-fold in male rats and 5.2-fold in female rats. CYP3A, CYP4A and peroxisomal enzymes were not induced in dogs at 150/200 mg kg-1 day-1. ERTI at 50 microM markedly induced the mRNA level of CYP4A by up to fivefold in rat hepatocytes, but not in human primary hepatocytes. In conclusion, the liver weight increases observed in rats treated with ERTI appears to be due to rodent-specific peroxisome proliferation and the substantial induction of CYP4A1. ERTI is not a potent P450 inducer in dogs or in human hepatocytes. Therefore, ERTI is not expected to exert any significant effects on hepatic drug-metabolizing enzymes in humans.
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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 | 0.000 |
| 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".