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Updated: May 5, 2026

Automated Cell Enrichment of Cytomegalovirus-specific T cells for Clinical Applications using the Cytokine-capture System
Published on: October 5, 2015
Optimal sparse sampling for estimating ganciclovir/valganciclovir AUC in solid organ transplant patients using NONMEN
Ariadna Padullés Caldés1, Helena Colom, Anna Caldes
1*Pharmacy Department, IDIBELL, Hospital Universitari de Bellvitge, L'hospitalet de Llobregat; †Department of Biopharmaceutics and Pharmacokinetics, School of Pharmacy, University of Barcelona; and ‡Nephrology Department, IDIBELL, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.
Background:
Ganciclovir and valganciclovir (GCV/VGCV) are used for the treatment and prophylaxis of cytomegalovirus in solid organ transplant (SOT) patients. An area under the time-concentration curve of 40-50 μg × h/mL is related to efficacy. Therapeutic drug monitoring could prevent suboptimal drug exposure and adverse events, but obtaining full concentration profiles is not feasible. Sampling optimization by developing a reliable and clinically applicable limited sampling strategy (LSS) may simplify dose adjustment.
Methods:
An LSS was developed using an original pharmacokinetic (PK) data set of 40 full profiles from 20 adult SOT patients. The LSS was developed based on population and Bayesian prediction approaches. Population PK parameters from a previous model were used for simulation or as priors (NONMEM version 7.2). Median percentage of prediction error and median of absolute percentage prediction error were calculated for plasma clearance (CL) and central compartment distribution volume (V(2)). Bias and precisions were compared using 1-way analysis of variance (SPSSv19.0).
Results:
Sampling windows were designed according to the PK profile previously observed with the entire set of data. The 4 windows selected were distributed from 0.5 to 1.5 hours, 2 to 3 hours, 4 to 5 hours, and 6 to 8 hours. Predose and concentrations beyond 8 hours were not considered in any case because simulated negative concentrations occurred in both cases. Predicted exposure using 3 sampling times (0.5-1.5, 4-5, and 6-8 hours) showed the best predictive performance, by either the population or Bayesian approaches. Bias and imprecision for CL and V(2) were 0 and 0.60%, and -0.78% and 0.78%, respectively.
Conclusions:
GCV/VCG area under the time-concentration curve in SOT patients could be predicted with acceptable accuracy for clinical management and dose individualization using LSS. The estimator of GCV/VGC, using 3 concentrations measured at 0.5-1.5, 4-5, and 6-8 hours after drug intake, could be used for dose adjustment.
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