2.5 Population pharmacokinetic analysis
Analysis of PPK were performed by nonlinear mixed-effects modeling software (NONMEM version 7.4.3 ICON Development Solutions, Ellicott City, MD, USA). The first-order conditional estimation with interaction (FOCE-I) option was chosen to evaluate pharmacokinetic parameters and variability. One- and two-compartment structural kinetic models with first-order and Michaelis–Menten elimination were compared during the base model determination step. An exponential model was chosen to describe the inter-individual variability of voriconazole pharmacokinetic parameters. Furthermore, additive, exponential, proportional and combined residual models were used to evaluate the residual variability.
We initially examined the correlation between pharmacokinetic parameters and potential covariates (associated with voriconazole Cmin in univariate analysis) by visualizing linear plots and box plots. Subsequently, a stepwise forward and backward exclusion approach was used to develop the covariate model. Covariates were considered significant in the case where the following conditions occurred simultaneously: inclusion of covariates resulted in a decrease in the objective function value (OFV) greater than 3.84 (p< 0.05) and exclusion of covariates resulted in an increase in OFV greater than 6.64 (p< 0.01).
Model evaluation was carried out by Goodness of fit plots (GOFs), visual predictive check (VPC), normalized prediction distribution error (NPDE) and nonparametric bootstrapping analysis (Bootstrap). The GOFs were used to evaluate the adequacy of fitting. The VPC test was performed by 1000 simulations based on the final model to assess the predictive performance of the final model. NPDE could estimate the predictive characteristics of the final model by statistical tests and diagnostic charts. Bootstrap is used to evaluate the robustness and stability of the final model by repeating the original data 1000 times with different random samples.