Altered Pharmacokinetics of Daunorubicin in Rats with CCl4-Induced Hepatic Injury
Bibliographic record
Abstract
PURPOSE: The effect of CCl4-induced experimental hepatic injury (CCl4-EHI) on the pharmacokinetics of daunorubicin was investigated systemically in rats, in an attempt to elucidate the major determinants of the effect of CCl4-EHI on the pharmacokinetics of the drug. METHODS: CCl4-EHI was induced in rats by a single intraperitoneal injection of CCl4 (1 mL/kg rat), and a 24 h fasting period. Daunorubicin was administered intravenously to control and EHI rats at a dose of 11.3 mg/mL/kg and the in vivo pharmacokinetics was studied. The in vitro uptake of the drug into isolated hepatocytes and canalicular liver plasma membrane (cLPM) vesicles, as well as the liver microsomal degradation of the drug, were also determined. RESULTS: The area under the plasma concentration-time curve (AUC) of daunorubicin was increased by 1.6 times, resulting in a 34% decrease in the systemic clearance (CL) in rats with CCl4-EHI. The apparent biliary (CLbile) and urinary (CLurine) clearance of the drug were unchanged, whereas the AUC of daunorubicinol, the major metabolite of daunorubicin, was decreased by 66% in rats with CCl4-EHI. EHI seemed to affect the hepatobiliary elimination of the drug in several ways: the in vitro intrinsic sinusoidal uptake clearance was decreased by 20%; the in vitro intrinsic canalicular excretion clearance of the drug was increased by 1.7 times; and the in vitro liver microsomal degradation of daunorubicin was significantly retarded. CONCLUSIONS: CCl4-EHI appears to impair the hepatic metabolism of daunorubicin, thereby decreasing the CL and increasing the AUC of daunorubicin.
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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 | 0.000 |
| 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".