A Pharmacometric Framework for Axitinib Exposure, Efficacy, and Safety in Metastatic Renal Cell Carcinoma Patients

Abstract

The relationships between exposure, biomarkers (vascular endothelial growth factor (VEGF), soluble VEGF receptors (sVEGFR)-1, -2, -3, and soluble stem cell factor receptor (sKIT)), tumor sum of longest diameters (SLD), diastolic blood pressure (dBP), and overall survival (OS) were investigated in a modeling framework. The dataset included 64 metastatic renal cell carcinoma patients (mRCC) treated with oral axitinib. Biomarker timecourses were described by indirect response (IDR) models where axitinib inhibits sVEGFR-1, -2, and -3 production, and VEGF degradation. No effect was identified on sKIT. A tumor model using sVEGFR-3 dynamics as driver predicted SLD data well. An IDR model, with axitinib exposure stimulating the response, characterized dBP increase. In a time-to-event model the SLD timecourse predicted OS better than exposure, biomarker- or dBP-related metrics. This type of framework can be used to relate pharmacokinetics, efficacy, and safety to long-term clinical outcome in mRCC patients treated with VEGFR inhibitors. (ClinicalTrial.gov identifier NCT00569946.).

© 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Figures

Figure 1

Schematic representation of the modeling framework for axitinib in metastatic renal cell carcinoma (mRCC). Axitinib daily area under the curve (AUCdaily) was used as a driver of the timecourses of biomarkers (the vascular endothelial growth factor VEGF and its soluble receptors sVEGFR‐1, ‐2, and ‐3) and diastolic blood pressure (dBP). Biomarker timecourses were described by indirect response models where axitinib inhibits the loss of VEGF response and the production of sVEGFR‐1, ‐2, and ‐3 responses. sKIT was not affected by axitinib. The model describing tumor size (sum of longest diameters, SLD) included an exponential growth and an effect of the relative change in sVEGFR‐3 from baseline over time (sVEGFR‐3rel(t)) that induces tumor size reduction and washes out over time. The SLD timecourse (SLD(t)) was predictive of overall survival. KG, first‐order growth rate constant; kout, first‐order rate constant for the degradation or loss of response; ksVEGFR‐3, tumor size reduction rate constant related to sVEGFR‐3 response; λ, tumor resistance/regrowth appearance rate constant; Rin, zero‐order rate constant for the production of response. Dashed arrows represent relationships identified as significant.

Figure 2

Prediction‐corrected visual predictive checks of the final biomarker models based on 500 simulations. Median (solid line), 5th, and 95th percentiles (dashed lines) of the observed data (solid circles) are compared to the 95% confidence intervals (shaded areas) for the median, 5th, and 95th percentiles of the simulated data. VEGF, vascular endothelial growth factor; sVEGFR‐1, ‐2, ‐3, soluble VEGF receptor 1, 2, 3.

Figure 3

Visual predictive checks of the final sum of longest diameters (SLD, left) and diastolic blood pressure (dBP, right) models based on 500 simulations. Median (solid line), 5th, and 95th percentiles (dashed lines) of the observed data (solid circles) are compared to the 95% CIs (shaded areas) for the median, 5th, and 95th percentiles of the simulated data. Prediction‐correction was used for dBP. For the SLD model, dropout was taken into account in the simulations.

Figure 4

Kaplan–Meier visual predictive checks for the final overall survival model driven by the sum of longest diameters timecourse. The observed Kaplan–Meier curve (black line) is compared to the 95% CI (shaded area) derived from model simulations (200 samples). Vertical black lines represent censored observations.