A randomized clinical trial of age and genotype‐guided tacrolimus dosing after pediatric solid organ transplantation
Bibliographic record
Abstract
BACKGROUND: Tacrolimus pharmacokinetics are influenced by age and CYP3A5 genotype with CYP3A5 expressors (CYP3A5*1/*1 or *1/*3) being fast metabolizers. However, the benefit of genotype-guided dosing in pediatric solid organ transplantation has been understudied. OBJECTIVE: To determine whether age and CYP3A5 genotype-guided starting dose of tacrolimus result in earlier attainment of therapeutic drug concentrations. SETTING: Single hospital-based transplant center. METHODS: This was a randomized, semi-blinded, 30-day pilot trial. Between 2012 and 2016, pediatric patients listed for solid organ transplant were consented and enrolled into the study. Participants were categorized as expressors, CYP3A5*1/*1 or CYP3A5*1/*3, and nonexpressors, CYP3A5*3/*3. Patients were stratified by age (≤ or > 6 years) and randomized (2:1) after transplant to receive genotype-guided (n = 35) or standard (n = 18) starting dose of tacrolimus for 36-48 hours and were followed for 30 days. RESULTS: Median age at transplant in the randomized cohort was 2.1 (0.75-8.0) years; 24 (45%) were male. Participants in the genotype-guided arm achieved therapeutic concentrations earlier at a median (IQR) of 3.4 (2.5-6.6) days compared to those in the standard dosing arm of 4.7 (3.5-8.6) days (P = 0.049), and had fewer out-of-range concentrations [OR (95% CI) = 0.60 (0.44, 0.83), P = 0.002] compared to standard dosing, with no difference in frequency of adverse events between the two groups. CONCLUSIONS: CYP3A5 genotype-guided dosing stratified by age resulted in earlier attainment of therapeutic tacrolimus concentrations and fewer out-of-range concentrations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".