PURPOSE: To characterize the population pharmacokinetics of trastuzumab in patients with metastatic breast cancer. METHODS: A nonlinear mixed effect model was based on pharmacokinetic data from phase I, II, and III studies of 476 patients. The phase I study enrolled patients with advanced solid tumors. The phase II and III studies enrolled patients with HER2-positive metastatic breast cancer. Patients in the pivotal phase II and III studies were treated with a 4 mg/kg loading dose of trastuzumab followed by 2 mg/kg weekly for up to 840 days. The model adequately predicted observed trastuzumab concentrations. Model stability and performance were verified using bootstrap simulations. Percentiles, mean, and standard deviation of observed levels were compared with their distributions from 100 replicates of datasets simulated under the model. RESULTS: A two-compartment linear pharmacokinetic model best described the data and accounted for the long-term accumulation observed following weekly administration of trastuzumab. Population estimates from the base model for clearance (CL) and volume of distribution of the central compartment (V1) of trastuzumab were 0.225 L/day, and 2.95 L, respectively. Estimated terminal halflife (t1/2) based on the population estimate was 28.5 days. Interpatient variabilities in clearance and volume were 43 and 29%, respectively. The number of metastatic sites, plasma level of extracellular domain of the HER2 receptor, and patient weight were significant baseline covariates for clearance, volume, or both (P<0.005). However, these covariate effects on trastuzumab exposure were modest and not clinically important in comparison with the large inter-patient variability of CL. Concomitant chemotherapy (anthracycline plus cyclophosphamide, or paclitaxel) did not appear to influence clearance. CONCLUSION: This population pharmacokinetic model can predict trastuzumab exposure in the long-term treatment of patients with metastatic breast cancer and provide comparison of alternative dosage regimens via simulation.
PURPOSE: To characterize the population pharmacokinetics of trastuzumab in patients with metastatic breast cancer. METHODS: A nonlinear mixed effect model was based on pharmacokinetic data from phase I, II, and III studies of 476 patients. The phase I study enrolled patients with advanced solid tumors. The phase II and III studies enrolled patients with HER2-positive metastatic breast cancer. Patients in the pivotal phase II and III studies were treated with a 4 mg/kg loading dose of trastuzumab followed by 2 mg/kg weekly for up to 840 days. The model adequately predicted observed trastuzumab concentrations. Model stability and performance were verified using bootstrap simulations. Percentiles, mean, and standard deviation of observed levels were compared with their distributions from 100 replicates of datasets simulated under the model. RESULTS: A two-compartment linear pharmacokinetic model best described the data and accounted for the long-term accumulation observed following weekly administration of trastuzumab. Population estimates from the base model for clearance (CL) and volume of distribution of the central compartment (V1) of trastuzumab were 0.225 L/day, and 2.95 L, respectively. Estimated terminal halflife (t1/2) based on the population estimate was 28.5 days. Interpatient variabilities in clearance and volume were 43 and 29%, respectively. The number of metastatic sites, plasma level of extracellular domain of the HER2 receptor, and patient weight were significant baseline covariates for clearance, volume, or both (P<0.005). However, these covariate effects on trastuzumab exposure were modest and not clinically important in comparison with the large inter-patient variability of CL. Concomitant chemotherapy (anthracycline plus cyclophosphamide, or paclitaxel) did not appear to influence clearance. CONCLUSION: This population pharmacokinetic model can predict trastuzumab exposure in the long-term treatment of patients with metastatic breast cancer and provide comparison of alternative dosage regimens via simulation.
Authors: Peter L Bonate; Malidi Ahamadi; Nageshwar Budha; Amparo de la Peña; Justin C Earp; Ying Hong; Mats O Karlsson; Patanjali Ravva; Ana Ruiz-Garcia; Herbert Struemper; Janet R Wade Journal: J Pharmacokinet Pharmacodyn Date: 2016-02-02 Impact factor: 2.745
Authors: Carolyn D Britten; Richard S Finn; Linda D Bosserman; Steven G Wong; Michael F Press; Mubashira Malik; Bert L Lum; Dennis J Slamon Journal: Clin Breast Cancer Date: 2009-02 Impact factor: 3.225