Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jul;64(7):1061-1069.
doi: 10.1007/s40262-025-01526-z. Epub 2025 May 24.

Pharmacokinetic/Pharmacodynamic Modelling and Monte Carlo Simulations to Predict Cytomegalovirus Viral Load in Pediatric Transplant Recipients Treated with (val)Ganciclovir

Kévin Koloskoff  1   2 , Bénédicte Franck  3   4 , Sylvain Benito  2 , Julien Welzel  2 , Julie Autmizguine  5   6   7 , Yves Theoret  7 , Annabelle Briand  6 , Philippe Ovetchkine  6 , Jean-Baptiste Woillard  8
Affiliations

Pharmacokinetic/Pharmacodynamic Modelling and Monte Carlo Simulations to Predict Cytomegalovirus Viral Load in Pediatric Transplant Recipients Treated with (val)Ganciclovir

Kévin Koloskoff et al. Clin Pharmacokinet. 2025 Jul.

Abstract

Background and objectives: 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.

Methods: 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.

Results: 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 AUC0-24 ≥ 40 mg·h/L achieved an 85.4% probability of undetectable viral load after 28 days of therapy. An AUC0-24 > 30 mg·h/L provided 80.9% probability of reducing viral loads by - 1 log after 2 weeks. AUC0-24 values > 60 mg·h/L offered minimal incremental benefits.

Conclusion: The pharmacodynamic model accurately predicted observed data. Simulations indicated that maintaining a ganciclovir plasma AUC0-24 around 40-60 mg·h/L maximized antiviral efficacy. An AUC0-24 > 60 mg·h/L might increase the risk of adverse events without providing additional efficacy.

PubMed Disclaimer

Conflict of interest statement

Declarations. Funding: Open access funding provided by Université de Limoges. Kévin Koloskoff received funding from the “association nationale recherche technologie” through a “convention industrielle de formation par la recherche” thesis (n°2021/1440). This research was also supported by ExactCure. Data availability: Supporting information is available in the ESM files, and supporting data and the Mlxtran model code are available from the authors on reasonable request. Conflicts of interest: The authors have no conflicts of interest. Ethics approval: The study protocol was approved by the institutional review board of CHU Sainte-Justine (reference number 2018-1830). Data were completely deidentified in accordance with the European General Data Protection Regulation. Consent to participate: Not applicable. Consent for publication: Not applicable. Authors contributions: KK wrote the manuscript. KK, JBW, and SB designed the research. All authors performed the research. KK, BF, JA, PO, and JBW analyzed the data.

Figures

Fig. 1
Fig. 1
Goodness-of-fit plots of the pharmacodynamic model. (A) observed concentrations (log10 copies/mL) against population predictions (log10 copies/mL), (B) observed concentrations (log10 copies/mL) against individual prediction (IPRED) (log10 copies/mL), (C) individual weighted residuals (IWRES) against time, and (D) IWRES against IPRED. Open circles are data lower limit of quantification
Fig. 2
Fig. 2
Prediction-corrected visual predicted check of the pharmacodynamic model. Dashed lines show the theoretical 10 th, 50 th, and 90 th percentiles; solid lines show the observed 10 th, 50 th, and 90 th percentiles. Grey areas represent the 95% interval of confidence of the corresponding theoretical percentiles. Open circles are data lower limit of quantification
Fig. 3
Fig. 3
Probability of undetectable viral load over time with varying area under the plasma concentration–time curve (AUC) values. Dashed lines show the varying AUCs, from 30 mg·h/L to 70 mg·h/L. The solid line shows the probability of achieving an undetectable viral load in 80% of cases
See this image and copyright information in PMC

References

    1. Ljungman P, Griffiths P, Paya C. Definitions of cytomegalovirus infection and disease in transplant recipients. Clin Infect Dis. 2002;34(8):1094–7. - PubMed
    1. Kotton CN, Kumar D, Caliendo AM, Huprikar S, Chou S, Danziger-Isakov L, et al. The third international consensus guidelines on the management of cytomegalovirus in solid-organ transplantation. Transplantation. 2018;102:900–31. - PubMed
    1. Pescovitz MD, Rabkin J, Merion RM, Paya CV, Pirsch J, Freeman RB, et al. Valganciclovir results in improved oral absorption of ganciclovir in liver transplant recipients. Antimicrob Agents Chemother. 2000;44:2811–5. - PMC - PubMed
    1. Singh N. Preemptive therapy versus universal prophylaxis with ganciclovir for cytomegalovirus in solid organ transplant recipients. Clin Infect Dis. 2001;32:742–51. - PubMed
    1. Franck B, Autmizguine J, Marquet P, Ovetchkine P, Woillard J-B. Pharmacokinetics, pharmacodynamics, and therapeutic drug monitoring of valganciclovir and ganciclovir in transplantation. Clin Pharmacol Ther. 2022;112:233–76. - PubMed

MeSH terms