Impact of CYP3A5 genetic polymorphisms on the pharmacokinetics and short‐term remission in patients with ulcerative colitis treated with tacrolimus
Bibliographic record
Abstract
BACKGROUND AND AIM: The pharmacokinetics of tacrolimus (Tac) differ among individuals, and genetic polymorphisms of cytochrome P-450 (CYP) 3A4, CYP3A5, and ABCB1 are thought to be involved. The aim of this study was to clarify whether these genetic polymorphisms affect the pharmacokinetics of Tac in patients with ulcerative colitis. METHODS: The subjects in this study were 45 patients with moderate-to-severe ulcerative colitis who were resistant to other therapies and were treated with Tac. The subjects were tested for genetic polymorphisms of CYP3A4, CYP3A5, and ABCB1, and the relationship between Tac pharmacokinetics and the remission rate was investigated. RESULTS: Of the 45 subjects, 24 (53.3%) were CYP3A5 expressers (Exp), and 21 (46.7%) were non-expressers (Non-Exp). The trough level and the dose-adjusted trough level on days 2-5 were significantly higher in the Non-Exp group than in the Exp group (10.16 ± 5.84 vs 4.47 ± 2.50 ng/mL, P < 0.0001, 139.36 ± 77.43 vs 61.37 ± 41.55 ng/mL per mg/kg/day, P < 0.0001). The percentage of patients achieving the optimal trough level on days 2-5 was significantly higher in the Non-Exp group than in the Exp group (40.0% vs 4.3%, P = 0.01). This trend was also observed on days 7-10. On multivariate analysis, factors associated with achievement of the optimal trough level were food non-intake and Non-Exp of CYP3A5. The remission rate was significantly higher in the Non-Exp group than in the Exp group (47.6% vs 16.7%, P = 0.046). CONCLUSIONS: CYP3A5 genetic polymorphisms affected the pharmacokinetics of Tac, so that the short-term clinical remission rate was different between Exp and Non-Exp of CYP3A5.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".