Can the CEIBA Cocktail Designed for Human Cytochrome P450 Enzymes be Used in the Rat for Drug Interaction Studies?
Bibliographic record
Abstract
Purpose - The CEIBA cocktail consisting of caffeine (CAF), omeprazole (OZ), dextromethorphan (DM) and losartan (LOS) was previously proposed for the clinical phenotyping of five major human cytochrome P450 (CYP) isoenzymes. This work aimed to assess the usefulness of CEIBA cocktail to study non-clinical drug interactions in the rat. Methods - Wistar rats were divided into five groups to receive a single-oral dose of each probe drug (CAF, OZ, LOS, DM), individually or in combination as a cocktail. Plasma concentrations of the probe drugs and their metabolites [paraxanthine (1,7-X), 5-hydroxyomeprazole (5-OZ), losartan carboxylic acid (E-3174), dextrorphan (DX) and 3-methoxymorphinan (3-MM)] were determined by LC-MS/MS, and the corresponding pharmacokinetic parameters were estimated by non-compartmental analysis. The AUC0-t and Cmax drug/metabolite ratios (phenotypic metrics) were calculated for each probe drug and compared (probe alone versus cocktail). Results - The primary analysis of the pharmacokinetic data suggested the occurrence of pharmacokinetic-based drug interactions when the probe drugs were concurrently administered; such interactions were documented for CAF, 1,7-X, DX and E-3174. Nevertheless, except for the LOS/E-3174 probe drug-metabolite pair (p<0.05), there was little evidence that the probe drugs interacted metabolically as the metabolic ratios calculated were similar in both approaches. Moreover, no evidence was found for relevant pharmacodynamic interactions. Conclusion - CEIBA cocktail seems to be a useful tool to investigate drug interactions involving CYP isoenzymes in the rat, particularly at the level of CYP1A2, CYP2D1/2 and CYP2D2 isoforms using the CAF/1,7-X, OZ/5-OZ and DM/DX metabolic ratios, respectively. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".