Effects of intestinal constituents and lipids on intestinal formation and pharmacokinetics of desethylamiodarone formed from amiodarone
Bibliographic record
Abstract
Abstract To model the impact of intestinal components associated with a high fat meal on metabolism of amiodarone, rat everted intestinal sacs were evaluated for their ability to metabolize the drug to its active metabolite (desethylamiodarone) under a variety of conditions. The preparations were obtained from fasted rats or rats pretreated with 1% cholesterol in peanut oil. After isolation of the tissues, the intestinal segments were immersed in oxygenated Krebs Henseleit buffer containing varying concentrations of bile salts, cholesterol, lecithin and lipase with or without soybean oil emulsion as a source of triglycerides. Amiodarone uptake was similar between the five 10-cm segments isolated distally from the stomach. Desethylamiodarone was measurable in all segments. Based on the metabolite-to-drug concentration ratio within the tissues, there was little difference in metabolic efficiency between segments for any of the treatments. Between treatments, however, it appeared that the lowest level of metabolism was noted in rats pretreated with 1% cholesterol in peanut oil. This reduction in metabolic efficiency was not observed in gut sacs from the fasted rats to which soybean oil emulsion was directly added to the incubation media. Despite the apparent reduction in intestinal metabolism, there was no apparent change in the ratio of metabolite-to-drug area under the plasma concentration versus time ratios of fasted rats and those given 1% cholesterol in peanut oil, suggesting that the intestinal presystemic formation of desethylamiodarone is not substantial.
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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) | 0.000 | 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 | 0.001 |
| 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".