<i>CYP3A4*22</i> and <i>CYP3A</i> Combined Genotypes Both Correlate With Tacrolimus Disposition in Pediatric Heart Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Tacrolimus metabolism depends on CYP3A4 and CYP3A5. We aimed to determine the relationship between the CYP3A4*22 polymorphism and combined CYP3A genotypes with tacrolimus disposition in pediatric heart transplant recipients. METHODS: Sixty pediatric heart transplant recipients were included. Tacrolimus doses and trough concentrations were collected in the first 14 days post-transplantation. CYP3A phenotypes were defined as extensive (CYP3A5*1 + CYP3A4*1/*1 carriers), intermediate (CYP3A5*3/*3 + CYP3A4*1/*1 carriers) or poor (CYP3A5*3/*3 + CYP3A4*22 carriers) metabolizers. RESULTS: CYP3A4*22 carriers needed 30% less tacrolimus (p = 0.016) to reach similar target concentrations compared with CYP3A4*1/*1 (n = 56) carriers. Poor CYP3A metabolizers required 17% (p = 0.023) less tacrolimus than intermediate and 48% less (p < 0.0001) than extensive metabolizers. Poor metabolizers showed 18% higher dose-adjusted concentrations than intermediate (p = 0.35) and 193% higher than extensive metabolizers (p < 0.0001). CONCLUSION: Analysis of CYP3A4*22, either alone or in combination with CYP3A5*3, may help towards individualization of tacrolimus therapy in pediatric heart 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) | 0.003 | 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".