The effect of chronic kidney disease on <scp>CYP</scp>2B expression and activity in male Wistar rats
Bibliographic record
Abstract
Abstract Chronic kidney disease (CKD) is characterized by progressive reduction in kidney function over time. CKD affects greater than 10% of the population and its incidence is on the rise due to the growing prevalence of its risk factors. Previous studies demonstrated CKD alters nonrenal clearance of drugs in addition to reducing renal clearance. We assessed the function and expression of hepatic CYP2B enzymes using a rat model of CKD. CKD was induced in Wistar rats by supplementing their chow with adenine and confirmed through the detection of elevated uremic toxins in plasma. Liver enzymes AST and ALT were unchanged by the adenine diet. Bupropion was used as a probe substrate for hepatic CYP2B function using rat liver microsomes. The resulting metabolite, hydroxy‐bupropion, and bupropion were quantified by ultra‐performance liquid chromatography coupled to time‐of‐flight mass spectrometry. Level of mRNA and protein were determined by RT‐PCR and Western blot, respectively. The results of our study demonstrate that CYP2B1 is downregulated in a rat model of CKD. CYP2B1 mRNA level was significantly decreased (88%, P < 0.001) in CKD relative to control. Similarly, maximal enzymatic velocity (Vmax) for CYP2B was decreased by 46% in CKD relative to control (P < 0.0001). Previous studies involving patients with CKD demonstrated altered bupropion pharmacokinetics compared to control. Hence, our results suggest that these alterations may be mediated by attenuated CYP2B hepatic metabolism. This finding may partially explain the alterations in pharmacokinetics and nonrenal drug clearance frequently observed in patients with CKD.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".