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      Population pharmacokinetics of the humanised monoclonal antibody, HuHMFG1 (AS1402), derived from a phase I study on breast cancer

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          Abstract

          Background:

          HuHMFG1 (AS1402) is a humanised monoclonal antibody that has undergone a phase I trial in metastatic breast cancer. The aim of this study was to characterise the pharmacokinetics (PKs) of HuHMFG1 using a population PK model.

          Method:

          Data were derived from a phase I study of 26 patients receiving HuHMFG1 at doses ranging from 1 to 16 mg kg −1. Data were analysed using NONMEM software and covariates were included. A limited sampling strategy (LSS) was developed using training and a validation data set.

          Results:

          A linear two-compartment model was shown to be adequate to describe data. Covariate analysis indicated that weight was not related to clearance. An LSS was successfully developed on the basis of the model, in which one sample is collected immediately before the start of an infusion and the second is taken at the end of infusion.

          Conclusion:

          A two-compartment population PK model successfully describes HuHMFG1 behaviour. The model suggests using a fixed dose of HuHMFG1, which would simplify dosing. The model could be used to optimise dose level and dosing schedule if more data on the correlation between exposure and efficacy become available from future studies. The derived LSS could optimise further PK assessment of this antibody.

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          Most cited references21

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          Prediction of creatinine clearance from serum creatinine.

          A formula has been developed to predict creatinine clearance (Ccr) from serum creatinine (Scr) in adult males: (see article)(15% less in females). Derivation included the relationship found between age and 24-hour creatinine excretion/kg in 249 patients aged 18-92. Values for Ccr were predicted by this formula and four other methods and the results compared with the means of two 24-hour Ccr's measured in 236 patients. The above formula gave a correlation coefficient between predicted and mean measured Ccr's of 0.83; on average, the difference predicted and mean measured values was no greater than that between paired clearances. Factors for age and body weight must be included for reasonable prediction.
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            Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: the npde add-on package for R.

            Pharmacokinetic/pharmacodynamic data are often analysed using nonlinear mixed-effect models, and model evaluation should be an important part of the analysis. Recently, normalised prediction distribution errors (npde) have been proposed as a model evaluation tool. In this paper, we describe an add-on package for the open source statistical package R, designed to compute npde. npde take into account the full predictive distribution of each individual observation and handle multiple observations within subjects. Under the null hypothesis that the model under scrutiny describes the validation dataset, npde should follow the standard normal distribution. Simulations need to be performed before hand, using for example the software used for model estimation. We illustrate the use of the package with two simulated datasets, one under the true model and one with different parameter values, to show how npde can be used to evaluate models. Model estimation and data simulation were performed using NONMEM version 5.1.
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              Population pharmacokinetics of trastuzumab in patients with HER2+ metastatic breast cancer.

              To characterize the population pharmacokinetics of trastuzumab in patients with metastatic breast cancer. 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. 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. 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.
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                Author and article information

                Journal
                Br J Cancer
                British Journal of Cancer
                Nature Publishing Group
                0007-0920
                1532-1827
                16 February 2010
                02 March 2010
                02 March 2010
                : 102
                : 5
                : 827-832
                Affiliations
                [1 ]CHU Besançon, Laboratoire de Pharmacologie Clinique, Hôpital Jean Minjoz Besançon 25030, France
                [2 ]INSERM, UMR645 Besançon 25020, France
                [3 ]Université de Franche-Comté IFR133, Besançon, France
                [4 ]EFS Bourgogne Franche-Comté Besançon, France
                [5 ]CIC intégré en Biothérapies 506 CHU Besançon, France
                [6 ]Antisoma Research Limited 566 Chiswick High Road, Chiswick, London W4 5YF, UK
                [7 ]University of Miami Miller School of Medicine 1475 NW 12th Avenue, Miami, FL 33136, USA
                [8 ]MD Anderson Cancer Center 1515 Holcombe Blvd, Houston, TX 77030, USA
                [9 ]CHU Besançon, Service d'oncologie, Hôpital Jean Minjoz Besançon 25030, France
                Author notes
                [* ]Author for correspondence: broyer@ 123456chu-besancon.fr
                Article
                6605560
                10.1038/sj.bjc.6605560
                2833251
                20160731
                d0a711e9-190a-4d8f-a866-291823cd977e
                Copyright 2010, Cancer Research UK
                History
                : 27 October 2009
                : 07 January 2010
                : 11 January 2010
                Categories
                Translational Therapeutics

                Oncology & Radiotherapy
                population pharmacokinetic,linear two-compartment,as1402,breast cancer,antibody,huhmfg1

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