Objective:Icotinib is a potent small-molecule inhibitor of epidermal growth factor receptor (EGFR)-tyrosine kinase (TKI), which was designed for the treatment of non-small cell lung cancer (NSCLC).It is the first novel anti-cancer drug developed by Chinese pharmaceutical industry and has been approved to market in China in 2009.Up to date, the influence factor affecting PK profile was not well characterized, which is the basis for population PK/PD investigation in Chinese cancer patients.In order to support population PK/PD study in patients, we developed a population PK model in healthy subjects.Methods:The icotinib PK concentration data received from phase Ⅰ study conducted in Phase Ⅰ Unit of PUMCH in 2007.The clinical trials contained 2 studies: a three-cross study in which 12 healthy subjects received 100mg, 350mg and 600mg icotinib crossly and food-effect study in which 10 healthy subjects received 400mg icotinib with and without high-fat diet.Firstly, basic pharmacokinetic model with saturated absorption and fnrst order elimination was developed using na(i)ve pooled method by ADAPT5.The model was captured further applied PK profiles in both studies well.Then, the data and this model were used to develop a nonlinear mixed-effects model as population PK model using NONMEN software interfaced with PsN.The covariates, including participants age, weight, ALT, ALB, BSA, TP, LBW, TBIL and DBIL etc., were analysed in a stepwise fashion to identify their influence on icotinib pharmacokinetics.Results and Discussion:The basic two-compartmental pharmacokinetic model was constructed to describe the inter-individual variability of icotinib in healthy Chinese subjects with saturated absorption and first order elimination.The model provided a good description of food-effect when predicted concentration data multiply by a coefficient factor Fa and CLd multiply by a coefficient factor Fc, which illustrated that high-fat diet would increase the bioavailability of icotinib and increase clearance of distribution.Population pharmacokinetic analysis results showed that BSA, ALB and LBW were the influential covariate for clearance: the clearance increased when the three covariates decreased.The three covariates could obviously change in cancer patients, which implied its meaningful and informative for population pharmacokinetic model in Chinese cancer patients.The error model was optimized to use additional plus proportional model and population pharrnacokinetic model was validated by VPC.Conclusion:The model can well describe the population pharmacokinetic character in Chinese healthy subjects and it will be developed for the further population PK model in Chinese non-small cell lung cancer patients and optimize individual dosage regimens.