Establishment and validation of a population pharmacokinetic model for apatinib in patients with tumors.

Objective: This study aimed to develop a population pharmacokinetic (PPK) model for oral apatinib in Chinese oncology patients and investigate the factors influencing the pharmacokinetics of apatinib.

Methods: We gathered 199 blood concentration monitoring data points from 91 inpatient oncology participants receiving oral apatinib at the Third Affiliated Hospital of Soochow University. Covariates, such as age, gender, body weight, and indices of liver and renal function, were examined to assess their influence on the pharmacokinetic parameters of apatinib. The PPK model was developed using the nonlinear mixed-effects modeling procedure (NONMEM), and model validation was conducted using the bootstrap method and normalized prediction distribution error (NPDE) method.

Results: The structural model adopted a one-compartment structure with first-order elimination. Notably, aspartate aminotransferase (AST) and the co-administered drug type emerged as primary covariates affecting apatinib clearance (CL/F). The finalized model was expressed as CL/F (L/h) = 56.7 × (AST/26.6)^-0.298 × θ, when apatinib was combined with monoclonal antibodies, θ was 1; when combined with paclitaxel, θ was 0.58; when combined with other drugs (e.g., platinum, capecitabine, or the combination of tegafur, gimeracil, and oteracil potassium), θ was 1.60; When used as monotherapy, θ was 1.38. V/F = 674 L, and the absorption rate constant (Ka) was fixed at 0.08 h^-1. Bootstrap results affirmed the model's reliability and stability, while NPDE outcomes attested to the model's fit.

Conclusion: Our study successfully established a PPK model for apatinib in oncology patients, revealing that liver function status and co-administered drug types significantly impacted apatinib CL/F. This finding underscored the potential necessity for dose adjustments to optimize efficacy, particularly in patients undergoing different chemotherapy regimens involving apatinib.