Cytotoxic Doses of Ketoconazole Affect Expression of a Subset of Hepatic Genes
Bibliographic record
Abstract
Ketoconazole is a widely prescribed antifungal drug, which has also been investigated as an anticancer therapy in both clinical and pre-clinical settings. However, severe hepatic injuries were reported to be associated with the use of ketoconazole, even in patients routinely monitored for their liver functions. Several questions concerning ketoconazole-induced hepatic injury remain unanswered, including (1) does ketoconazole alter cytochrome P450 expression at the transcriptional level?, (2) what types of gene products responsible for cytotoxicity are induced by ketoconazole?, and (3) what role do the major metabolites of ketoconazole play in this pathophysiologic process? A mouse model was employed to investigate hepatic gene expression following hepatotoxic doses of ketoconazole. Hepatic gene expression was analyzed using a toxicogenomic microarray platform, which is comprised of cDNA probes generated from livers exposed to various hepatoxicants. These hepatoxicants fall into five well-studied toxicological categories: peroxisome proliferators, aryl hydrocarbon receptor agonists, noncoplanar polychlorinated biphenyls, inflammatory agents, and hypoxia-inducing agents. Nine genes encoding enzymes involved in Phase I metabolism and one Phase II enzyme (glutathione S-transferase) were found to be upregulated. Serum amyloid A (SAA1/2) and hepcidin were the only genes that were downregulated among the 2364 genes assessed. In vitro cytotoxicity and transcription analyses revealed that SAA and hepcidin are associated with the general toxicity of ketoconazole, and might be usefully explored as generalized surrogate markers of xenobiotic-induced hepatic injury. Finally, it was shown that the primary metabolite of ketoconazole (de-N-acetyl ketoconazole) is largely responsible for the hepatoxicity and the downregulation of SAA and hepcidin.
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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) | 0.001 | 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".