Population pharmacokinetics of tacrolimus in pediatric refractory nephrotic syndrome and a summary of other pediatric disease models

Abstract

Different tacrolimus (TAC) population pharmacokinetic (PPK) models have been established in various pediatric disease populations. However, a TAC PPK model for pediatric refractory nephrotic syndrome (PRNS) has not been well characterized. The current study aimed to establish a TAC PPK model in Chinese PRNS and provide a summary of previous literature concerning TAC PPK models in different pediatric diseases. A total of 147 TAC conventional therapeutic drug monitoring (TDM) data from multiple blood samples obtained from 65 Chinese patients with PRNS were characterized using nonlinear mixed-effects modeling. The impacts of demographic features, biological characteristics and drug combination were evaluated. Model validation was assessed using the bootstrap method. A one-compartment model with first-order absorption and elimination was determined to be the most suitable model for TDM data in PRNS. The absorption rate constant (Ka) was set at 4.48 h^-1. The typical values of apparent oral clearance (CL/F) and apparent volume of distribution (V/F) in the final model were 5.46 l/h and 57.1 l, respectively. The inter-individual variability of CL/F and V/F were 22.2 and 0.2%, respectively. The PPK equation for TAC was: CL/F = 5.46 × exponential function (EXP)(0.0323 × age) × EXP(-0.359 × cystatin-C) × EXP(0.148 × daily dose of TAC). No significant effects of covariates on V/F were observed. In conclusion, the current study developed and validated the first TAC PPK model for patients with PRNS. The study also provided a summary of previous literature concerning other TAC PPK models in different pediatric diseases.

Keywords: nonlinear mixed-effects modeling; pediatric refractory nephrotic syndrome; population pharmacokinetics; tacrolimus; therapeutic drug monitoring.

Figure 1.

Population and individual predictions. (A) Observation vs. population predictions and individual predictions in the base model. (B) Observation vs. population predictions and individual predictions in the final model.

Figure 2.

Weighted residuals. (A) Absolute value of weighted residuals vs. population predictions in the base and final model. (B) Weighted residuals vs. time in the base and final model. |WRES|, absolute value ofweighted residuals.