Population pharmacokinetics of ganciclovir and valganciclovir in paediatric solid organ and stem cell transplant recipients
Bibliographic record
Abstract
Aims Ganciclovir (GCV) and its prodrug valganciclovir (VGCV) are first‐line agents to prevent and treat cytomegalovirus in transplant recipients. There is high pharmacokinetic (PK) interindividual variability and PK data are scarce, especially in paediatric stem cell transplant (SCT) recipients. We sought to determine the optimal GCV and VGCV dosing in transplanted children. Methods We conducted a single‐centre retrospective population PK (POPPK) study of IV GCV and enteral VGCV in paediatric solid organ transplant (SOT) and SCT recipients. We included children who were transplanted and had available plasma GCV concentrations, done per standard of care. POPPK analysis was performed using a nonlinear mixed effects modelling approach with NONMEM. Optimal dosing was determined based on the achievement of the surrogate efficacy target: GCV 24 h area under the concentration–time curve (AUC0‐24h) of 40–60 mg.h.L−1. Results Fifty children with a median [range] age of 7.5 years [0.5–17.4] contributed 580 PK samples. A two‐compartment model with first‐order absorption with a lag time and first‐order elimination fit the data well. Creatinine clearance and body weight (WT) were significant covariates for GCV clearance (CL); and WT for the volumes of distribution. IV GCV 15–20 mg.kg−1.day−1 divided every 12 hours achieved the highest probability of target achievement (PTA) (33.0–33.8%). Enteral VGCV 30 and 40 mg.kg−1.day−1 divided every 12 hours in children 0–<6 years, and 6–18 years, respectively, achieved the highest PTA (29.1–33.0%). Conclusion This is the first POPPK model developed in children with either SOT or SCT. Concentration target achievement was low, suggesting a potential benefit for therapeutic drug monitoring to ensure optimal exposure.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".