Model Development
Population pharmacokinetic analysis of yimitasvir was performed by
Phoenix NLME software (Version 8.1, Certara) using the first-order
conditional estimation-extended least squares (FOCE-ELS) method.
The structural model was first tested by fitting a one-compartment or
two-compartment model to the log-transformed PK data. Different
absorption models including first-order absorption with or without a lag
time, sequential zero-first order absorption, transit absorption and
saturable Michaelis-Menten absorption model, were also tested. The
optimal structural model was selected based on the Akaike Information
Criteria (AIC), minimization success, visual inspection of
goodness-of-fit plots and individual fit.
Inter-subject variability was estimated by exponential model for PK
parameters as follows (Eq. 1):
θi = θTV × eηi (1)
where θi is the parameter estimation for the i th
individual, θTV is the typical value of the parameter
estimation in the population, ηi is a random variable
which assumed to be normally distributed with a mean of 0 and a variance
of ω2. Proportional error model and proportional plus
additive error model were tested as residual error models.
Following the development of structural model, the dose effect on
bioavailability was evaluated first due to the less than
dose-proportional profile of yimitasvir. Sigmoidal maximum effect (Emax)
(Eq.2) and linear models (Eq.3) were tested to quantify the relationship
between bioavailability and dose:
F = θF – Fmax × (Dose -
100)/(F50 + (Dose - 100)) (2)
where θF is the bioavailability in individuals who
received 100 mg yimitasvir, which was fixed to 1. Fmaxis the maximal reduction in bioavailability and F50 is
the dose associated with a half-maximal reduction in bioavailability.
F = θF – Alpha × (Dose -100)/100 (3)
where Alpha is a slope term determining the relative change in
bioavailability for each 100 mg increase in yimitasvir dose.
Subsequently, different covariates were tested using a stepwise forward
inclusion (a decrease in objective function value [OFV] of
> 6.63, P < 0.01) and a stricter backward
exclusion procedure (an increase in OFV of > 10.83,P < 0.001). The covariates included age, gender, body
weight (BW), body mass index (BMI), baseline haemoglobin (HGB), baseline
aspartate aminotransferase (AST), baseline alanine aminotransferase
(ALT), baseline albumin (ALB), baseline total bilirubin (TBIL), baseline
creatinine clearance (CLcr) calculated by Cockcroft-Gault formula12, co-medication of sofosbuvir, disease status
(healthy volunteers vs patients) and food effect.
The effect of continuous covariates was modeled using a power function
after normalization by the population median (Eq. 4):
θi = θTV ×
(cov i/cov median)θx (4)
The effect of categorical covariates was modeled using exponential
format as follows (Eq. 5):
θi = θTV × eθx cov = k(5)
where cov i and cov medianrepresent covariate values for the i th individual and the
population median, respectively. k is a categorical variable, and
θx is a coefficient used to describe the strength of the
covariate effect.
When two covariates were highly correlated (r2> 0.7) such as ALT versus AST, only the most significant
one was reserved in the model if both covariates were considered to be
significant for the same PK parameter during univariate screen process.