Alteration of Pharmacokinetics of Grepafloxacin in Type 2 Diabetic Rats
Bibliographic record
Abstract
PURPOSE: Patients with type 2 diabetes are generally treated with various pharmacological compounds and are exposed to a high risk of drug-drug interactions. However, alterations of pharmacokinetics in a type 2 diabetes model have been obscure. The present study was undertaken to investigate the effects of type 2 diabetes on the pharmacokinetics of the fluoroquinolone grepafloxacin (GPFX) and the expression level of P-glycoprotein (P-gp), one of the drug efflux transporters. METHODS: We used Goto-Kakizaki (GK) rats, a lean model of type 2 diabetes. Plasma concentration and intestinal, renal, and biliary clearance of GPFX were measured after intravenous and intraintestinal administration in Wistar and GK rats. Real-time PCR and Western blotting were used to assess mRNA and protein expression levels. RESULTS: We found a significant increase in the plasma concentrations of GPFX at 90, 120 and 240 minutes after intraintestinal administration in GK rats compared with the concentrations in Wistar rats but not after intravenous administration. The increase in plasma GPFX concentration was associated with reduction in jejunal clearance of GPFX caused by a decrease in secretory transport of GPFX. However, there was no correlation between the decrease in secretory transport of GPFX and P-gp expression level. CONCLUSION: Type 2 diabetic conditions alter P-gp function as well as expression level and correlate poorly with each other.
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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 | 0.000 | 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".