Pharmacogenotyping of <i>CYP3A5</i> in predicting dose-adjusted trough levels of tacrolimus among Malaysian kidney-transplant patients
Bibliographic record
Abstract
Tacrolimus (FK506) is a calcineurin inhibitor with a narrow therapeutic index that exhibits large interindividual variation. Seventy-eight kidney transplant patients treated with tacrolimus were recruited to study the correlation of dose adjusted trough level (level/dose; L/D) of tacrolimus with CYP3A5 and ABCB1 genotypes, as well as the mRNA copy number of ABCB1 in blood. Patients were genotyped for ABCB1 (C1236T, G2677T/A, and C3435T) and CYP3A5 (G6986A), while ABCB1 mRNA transcript copy number was determined by absolute quantification (real-time PCR) in 46 patients. CYP3A5*3 genotypes were found to be a good predictor of tacrolimus L/D in kidney-transplant patients. Significantly higher L/D was observed among non-expressors (2.85, 95%: 2.05-3.70 (ng·mL(-1))/(mg·kg(-1))) as compared with the expressors (1.15, 95%: 0.95-1.80 (ng·mL(-1))/(mg·kg(-1))) of CYP3A5 (Mann-Whitney U test; P < 0.001). No correlation was observed between L/D and the ABCB1 genotypes. A significant inverse correlation of blood ABCB1 mRNA level with L/D was demonstrated (Spearman's Rank Order correlation; P = 0.016, rs = -0.348). However, in multiple regression analysis, only CYP3A5*3 genotype groups were found to be significantly correlated with tacrolimus L/D (P < 0.001). These findings highlight the importance of CYP3A5*3 pharmacogenotyping among kidney-transplant patients treated with tacrolimus, and confirm the role of blood cell P-glycoprotein in influencing the L/D for tacrolimus.
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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) | 0.001 | 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".