Background: Docetaxel is a widely prescribed cytotoxic chemotherapy drug for various cancers and the main dose-limiting toxicity is dose-dependent neutropenia. In China, the current dose regimen for docetaxel frequently results in high inter-individual pharmacokinetic (PK) variability. There is a very urgent need to establish a population PK model of docetaxel for Chinese cancer patients, to predict the area under the curve (AUC) based on the PK model and avoid toxicity, providing optimal drug dose guidance to clinicians. Methods: Docetaxel was administered at a dose of 75 mg/m(2) once every 3 weeks, and at the scheduled time, blood samples were collected to measure the docetaxel concentration. A nonlinear, mixed-effects modelling approach was used to fit the plasma concentration-time data. A two-compartmental model was selected to characterize the in vivo behavior of docetaxel. Using population modelling, various covariates were explored to ascertain their impact on the docetaxel PK. Monte Carlo simulations were performed to derive the optimal individualized dose regimen. Results: A total of 440 patients with 880 concentration data were collected. The covariate selection indicated that age, body mass index (BMI), and body surface area (BSA) had significant correlations with docetaxel clearance (CL). Bootstrap and visual predictive check (VPC) indicated that a robust and reliable PK model had been established. The final population model was effectively used for simulation to determine docetaxel dose regimens for Chinese cancer patients. Aiming an AUC <2.6 mg/L.h, a simple to use dose regimen was derived based on Monte Carlo simulations. Conclusions: A population PK model of docetaxel for Chinese cancer patients was developed and validated, showing that age, BMI, and BSA were significantly associated with CL. A simple to use dose regimen table was created to guide clinicians.