Cyp3A4, Cyp3A5, and MDR-1 genetic influences on tacrolimus pharmacokinetics in renal transplant recipients
Bibliographic record
Abstract
OBJECTIVE: The immunosuppressive drug tacrolimus requires strict therapeutic monitoring due to its narrow therapeutic index and great inter-individual variability. Cytochrome P450 3A4 (Cyp3A4) and Cyp3A5 are the most important contributors to tacrolimus metabolism while the P-glycoprotein pump (MDR-1) modulates its bioavailability. The objective was to investigate the association between Cyp3A4, Cyp3A5, and MDR-1 polymorphisms and tacrolimus pharmacokinetics in the early period after renal transplantation. METHODS: Forty-four renal transplant recipients were genotyped for 8 Cyp3A4, 7 Cyp3A5, and 5 MDR-1 genetic variants affecting the proteins' expression and/or function. Dose-adjusted tacrolimus though levels were determined during the first week after transplantation and correlated with corresponding genotype. RESULTS: We found no correlation between Cyp3A4 polymorphism and tacrolimus pharmacokinetics. Patients who do not carry both Cyp3A5*3 alleles achieved lower mean dose-adjusted tacrolimus blood concentrations (p<0.001) and needed a longer time to reach the target concentration (10-12 ng/ml; p<0.001) compared to Cyp3A5*3 homozygotes. Patients with less than three copies of MDR-1 (T-129C, C3435T and G2677T) polymorphisms, associated with reduced expression of P-glycoprotein, had also lower dose-adjusted tacrolimus blood concentrations compared to patients having equal to or greater than three copies of MDR-1 genetic variants (P=0.003). There was no difference in the rate of biopsy-confirmed acute rejection among groups during the first 3 months after transplantation. CONCLUSION: The complete absence of Cyp3A5*3 allele and the accumulation of less than three copies of MDR-1 (T-129C, C3435T and G2677T) polymorphisms are associated with lower tacrolimus blood levels identifying these genotypes as markers for patients requiring higher tacrolimus doses.
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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 | 0.001 |
| 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) | 0.002 | 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".