Pharmacokinetics of Amiodarone in hyperlipidemic and simulated high fat‐meal rat models
Bibliographic record
Abstract
The objective of this study was to examine the effect of a high fat meal and hyperlipidemia on the pharmacokinetic behavior of amiodarone. To evaluate these effects, single doses of amiodarone were administered to rats i.v. (25 mg/kg) or orally (50 mg/kg). Some rats were rendered hyperlipidemic by intraperitoneal doses of poloxamer 407 followed by amiodarone i.v. In other normolipidemic rats, amiodarone was administered i.v. in a fasted state or after the administration of 1% cholesterol in peanut oil. Amiodarone plasma concentrations were considerably (>11-fold) increased in hyperlipidemia. Substantial decreases were noted in the clearance, volume of distribution and unbound fraction (11.6, 23 and 24.7-fold, respectively) in plasma of hyperlipidemic rats. Oral lipid caused a significant increase in plasma AUC(0-infinity) (1.38-fold) and a significant decrease in clearance (1.5-fold) of amiodarone after intravenous doses. Oral consumption of 1% cholesterol in peanut oil significantly increased the plasma AUC (1.83-fold) and bioavailability of amiodarone (1.31-fold) after oral doses. In determining oral bioavailability of lipophilic drugs such as amiodarone in food effect studies, in addition to the increase in absorption of drugs, other factors such as a decrease in clearance due to increases in lipoprotein levels should be taken into account.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".