Model Evaluation and Simulation
. The predictive performance of the final model was assessed by prediction-corrected visual predictive check (pcVPC) method using 1000 trial replicates stratified by study. The observed data were plotted against the median, the 5th and 95th percentiles of predicted concentrations. The model was considered to be precise if the observed data were evenly distributed around the median prediction and within the 90% predicted intervals. In addition, a bootstrap procedure (n = 1000) sampling with replacement from the original data was used to further test the robustness of the final model.
Individual empirical Bayes estimates PK parameters from the final model were used to predict the steady state exposure of yimitasvir. The simulated dosing regimen was yimitasvir 100 mg once daily for 12 consecutive weeks. The sensitive plot was plotted to present the effect of a significant covariate on yimitasvir exposure [steady state area under curve (AUCss), steady state minimum concentration (Ctrough,ss) and steady state maximum concentration (Cmax,ss)]. The steady state exposure was calculated using PK parameters with incorporation of the isolated effect from the covariate and with other unaffected PK parameters fixed to the typical value. The overall exposure variability of the population was compared with the variability from those significant covariates.