<i>MDR1</i> haplotypes derived from exons 21 and 26 do not affect the steady‐state pharmacokinetics of tacrolimus in renal transplant patients
Bibliographic record
Abstract
AIM: This retrospective study investigated the influence of MDR1 haplotypes derived from the polymorphisms 2677G > T (exon 21) and 3435C > T (exon 26) on the pharmacokinetics of the immunosuppressant drug tacrolimus in 73 renal transplant patients. METHODS: Based on both variants of SNPs 2677 and 3435, four different haplotypes and eight different genotypes were identified in the study sample. Tacrolimus trough concentrations (C(0)) were compared between different SNP variants and genotypes, as well as between carriers and noncarriers of each haplotype. Additionally, CYP3A5 genotype (6956G > A) was determined. RESULTS: No significant differences were observed between groups. Differences in mean tacrolimus C(0) values between carriers and noncarriers of each haplotype ranged from -0.04 microg/litre (95% confidence interval: -0.53 to 0.60) to -23 microg/litre (-1.07 to 1.53). No association was found between CYP3A5*1/*3 genotype and tacrolimus Co concentractions. CONCLUSION: MDR1 haplotypes derived from the SNPs 2677G > T (exon 21) and 3435C > T (exon 26) do not influence the pharmacokinetics of tacrolimus in renal transplant patients.
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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".