Differential Effects of Chrysin on Nitrofurantoin Pharmacokinetics Mediated by Intestinal Breast Cancer Resistance Protein in Rats and Mice
Bibliographic record
Abstract
PURPOSE: The activities of breast cancer resistance protein (Bcrp/ABCG2) as well as P-glycoprotein (P-gp) and drug-metabolizing enzymes can be inhibited by several flavonoids or drugs in rats. However, the species, gender and regional differences of effects of flavonoids on Bcrp/ABCG2 in rats and mice remain unclear, although Bcrp, like P-gp, is also important in controlling drug absorption and disposition. METHODS: We used chrysin as a model flavonoid because it possesses anti-inflammatory and antioxidative properties and is used as a dietary supplement. We examined the pharmacokinetics of nitrofurantoin, a specific Bcrp substrate, in rats and mice treated with chrysin. Bcrp mRNA levels were measured in liver, kidney, duodenum, jejunum and ileum in rats and mice. RESULTS: Plasma concentrations of nitrofurantoin were increased in rats, but not mice, treated with oral chrysin, compared with untreated controls. Intraperitoneal injection of chrysin into rats or mice had little effect on the elimination of nitrofurantoin, compared with untreated animals. CONCLUSIONS: These results suggest that chrysin-nitrofurantoin interactions occur in the small intestine in rats, but not in mice, possibly due to the higher levels of Bcrp expression in the small intestine in rats, compared with those in mice.
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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".