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.