Population Pharmacokinetics of Radotinib in Healthy Volunteers and Patients with Chronic Myeloid Leukemia

雷多替尼在健康志愿者和慢性粒细胞白血病患者中的群体药代动力学

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Abstract

Background/Objectives: Radotinib is a second-generation tyrosine kinase inhibitor (TKI) that has been used for treatment of chronic myeloid leukemia (CML). This study was performed for the first time to characterize the pharmacokinetics of radotinib, identify the factors contributing to pharmacokinetic variabilities and explore alternative dosing regimens. Methods: A total of 640 plasma concentration-time datapoints obtained from 47 participants were evaluated using nonlinear mixed-effects modeling to estimate pharmacokinetic parameters and evaluate covariate effects. The study population comprised 23 healthy volunteers (HVs) who received a single, oral dose of 400 mg radotinib and 24 CML patients who repeatedly received 300 mg twice daily. Based on the final population pharmacokinetic model, alternative dosing regimens to the current every 12 h regimen were explored using Monte Carlo simulations. Results: A two-compartment model with first-order absorption through transit compartments and first-order elimination incorporating a circadian rhythm effect best described radotinib pharmacokinetics. Disease status significantly affected apparent clearance; it was slower by 39.2% in CML patients compared with HVs (23.0 L/h versus 37.9 L/h), resulting in a longer terminal half-life (28.8 h versus 17.5 h). Age was negatively associated with volume of distribution in the central compartment, with an estimated slope of -0.0129 L/year. A 400 mg once-daily regimen was predicted to provide comparable systemic exposures to those of other TKIs with similar physiochemical and pharmacological properties to radotinib, and a 36% lower exposure than that of the current 300 mg twice-daily regimen. Conclusions: The model developed in this study adequately describes the population pharmacokinetics of radotinib and provides a basis for optimal, individualized radotinib therapy for patients with CML.

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