Effects of Orotic Acid-Induced Non-Alcoholic Fatty Liver on the Pharmacokinetics of Metoprolol and its Metabolites in Rats
Bibliographic record
Abstract
PURPOSE: Preliminary study results have shown that rats with non-alcoholic fatty liver disease (NAFLD) induced by 1% orotic acid-containing diet have decreased hepatic CYP2D activity. This study aims to evaluate the possible pharmacokinetic changes in NAFLD as a result of reduced metabolic activity of CYP2D. METHODS: The pharmacokinetics of metoprolol and its metabolites, O-desmethyl metoprolol (DMM) and α-hydroxy metoprolol (HM), was investigated in NAFLD and control rats following intravenous (1 mg/kg) and oral (2 mg/kg) administration of metoprolol. The hepatic CYP2D expression was also investigated. RESULTS: NAFLD rats had lower CYP2D expression (by 36.6%) and slower intrinsic clearance (CLint) of metoprolol and formation of HM (by 40.1% and 37.2%, respectively). There were no significant changes in the pharmacokinetics of metoprolol and its metabolites following intravenous administration. In contrast, oral administration of metoprolol resulted in significantly increased total area under plasma concentration-time curve (AUC) of metoprolol (by 127%) and decreased metabolite formation ratios (AUCDMM/AUCMetoprolol [by 42.8%], AUCHM/AUCMetoprolol [by 35.0%]) in NAFLD rats. Moreover, these changes were well correlated with severity of steatosis as quantified by hepatic triglyceride contents. CONCLUSIONS: NALFD can lead to a reduction in the hepatic CLint of a drug if it is a substrate of the CYP2D subfamily. The decreased clearance may result in elevated drug concentrations and increased exposure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".