Pharmacokinetic/Pharmacodynamic Modelling and Monte Carlo Simulations to Predict Cytomegalovirus Viral Load in Pediatric Transplant Recipients Treated with (val)Ganciclovir
Bibliographic record
Abstract
Cytomegalovirus (CMV) infection poses significant challenges in pediatric transplant recipients. Ganciclovir and its prodrug valganciclovir are primary treatments because of their potent antiviral effects. Balancing efficacy and toxicity is particularly critical in children. This study aimed to develop a pharmacokinetic/pharmacodynamic (PK/PD) model for (val)ganciclovir and assess the relationship between area under the concentration–time curve (AUC) and CMV viral loads via Monte Carlo simulations. We conducted a retrospective analysis including 184 viral load samples from 36 transplanted children treated with ganciclovir/valganciclovir. We developed a population pharmacodynamic model using Monolix and performed Monte Carlo simulations to assess viral load decline with varying AUCs. Internal validation was performed using goodness-of-fit plots and bootstraps. We used a viral turnover model with stimulated degradation to model the pharmacodynamic data. Model validation showed no bias or misspecification. Simulations indicated that maintaining an AUC 0-24 ≥ 40 mg·h/L achieved an 85.4% probability of undetectable viral load after 28 days of therapy. An AUC 0-24 > 30 mg·h/L provided 80.9% probability of reducing viral loads by − 1 log after 2 weeks. AUC 0-24 values > 60 mg·h/L offered minimal incremental benefits. The pharmacodynamic model accurately predicted observed data. Simulations indicated that maintaining a ganciclovir plasma AUC 0-24 around 40–60 mg·h/L maximized antiviral efficacy. An AUC 0-24 > 60 mg·h/L might increase the risk of adverse events without providing additional efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".