Development of population pharmacokinetics model of icotinib with non-linear absorption characters in healthy Chinese volunteers to assess the CYP2C19 polymorphism and food-intake effect.

Abstract

Icotinib is a potent and selective inhibitor of epidermal growth factor receptors (EGFR) approved to treat non-small cell lung cancer (NSCLC). However, its high variability may impede its application. The objectives of this analysis were to assess plasma pharmacokinetics and identify covariates that may explain variability in icotinib absorption and/or disposition following single dose of icotinib in healthy volunteers. Data from two clinical studies (n = 22) were analyzed. One study was designed as three-period and Latin-squared (six sequence) trial to evaluate dose proportionality, and the other one was designed as two-way crossover trial to evaluate food effect on pharmacokinetics (PK) characters. Icotinib concentrations in plasma were analyzed using non-linear mixed-effects model (NONMEM) method. The model was used to assess influence of food, demographic characteristics, measurements of blood biochemistry, and CYP2C19 genotype on PK characters of icotinib in humans. The final model was diagnosed by goodness-of-fit plots and evaluated by visual predictive check (VPC) and bootstrap methods. A two-compartment model with saturated absorption character was developed to capture icotinib pharmacokinetics. Typical value of clearance, distribution clearance, central volume of distribution, maximum absorption rate were 29.5 L/h, 24.9 L/h, 18.5 L, 122.2 L and 204,245 μg/h, respectively. When icotinib was administrated with food, bioavailability was estimated to be increased by 48%. Inter-occasion variability was identified to affect on maximum absorption rate constant in food-effect study. CL was identified to be significantly influenced by age, albumin concentration (ALB), and CYP2C19 genotype. No obvious bias was found by VPC and bootstrap methods. The developed model can capture icotinib pharmacokinetics well in healthy volunteers. Food intake can increase icotinib exposure. Three covariates, age, albumin concentration, and CYP2C19 genotype, were identified to significantly affect icotinib PK profiles in healthy subjects.