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15.06.2017 | Original Research Article | Ausgabe 3/2018 Open Access

Clinical Pharmacokinetics 3/2018

Population Pharmacokinetic Modeling of Olaratumab, an Anti-PDGFRα Human Monoclonal Antibody, in Patients with Advanced and/or Metastatic Cancer

Clinical Pharmacokinetics > Ausgabe 3/2018
Gary Mo, John R. Baldwin, Debra Luffer-Atlas, Robert L. Ilaria Jr., Ilaria Conti, Michael Heathman, Damien M. Cronier
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1007/​s40262-017-0562-0) contains supplementary material, which is available to authorized users.
Ilaria Conti: Former employee of Eli Lilly and Company.


Background and Objectives

Olaratumab is a recombinant human monoclonal antibody that binds to platelet-derived growth factor receptor-α (PDGFRα). In a randomized phase II study, olaratumab plus doxorubicin met its predefined primary endpoint for progression-free survival and achieved a highly significant improvement in overall survival versus doxorubicin alone in patients with advanced or metastatic soft tissue sarcoma (STS). In this study, we characterize the pharmacokinetics (PKs) of olaratumab in a cancer patient population.


Olaratumab was tested at 15 or 20 mg/kg in four phase II studies (in patients with nonsmall cell lung cancer, glioblastoma multiforme, STS, and gastrointestinal stromal tumors) as a single agent or in combination with chemotherapy. PK sampling was performed to measure olaratumab serum levels. PK data were analyzed by nonlinear mixed-effect modeling techniques using NONMEM®.


The PKs of olaratumab were best described by a two-compartment PK model with linear clearance (CL). Patient body weight was found to have a significant effect on both CL and central volume of distribution (V1), whereas tumor size significantly affected CL. A small subset of patients developed treatment-emergent anti-drug antibodies (TE-ADAs); however, TE-ADAs did not have any effect on CL or PK time course of olaratumab. There was no difference in the PKs of olaratumab between patients who received olaratumab as a single agent or in combination with chemotherapy.


The PKs of olaratumab were best described by a model with linear disposition. Patient body weight and tumor size were found to be significant covariates. The PKs of olaratumab were not affected by immunogenicity or chemotherapeutic agents.

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Fig. S1 Goodness-of-Fit Plots of the Final Olaratumab Population Pharmacokinetic Model. Observed versus predicted olaratumab serum concentration and conditional weighted residuals for the final olaratumab PK model. Color points are used to identify data from the different tumor types. (PDF 67 kb)
Fig. S2 Inter-patient Variability versus Weight at Study Entry and Tumor Size. Inter-patient variability (η) for CL (upper two plots) and V1(lower two plots) are plotted against tumor size (left two plots) and weight at study entry (right two plots). Blue line is the loess regression analysis to show trend in data. (PDF 19 kb)
Fig. S3 Visual Predictive Check of the Final Olaratumab Population Pharmacokinetic Model by Tumor Type. Data from each tumor type are overlaid with model simulation from the full popPK model. Black circles indicate observed data, the dashed lines depict the observed 5th, 50th, and 95th percentiles, and the blue shaded areas define 90% confidence intervals of the 5th, 50th and 95th percentiles of the stimulated model predictions. Actual time from dose was rounded to the nearest 200 hours to facilitate percentage calculation. (PDF 174 kb)
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