PKPD-Modeling of VEGF, sVEGFR-1, sVEGFR-2, sVEGFR-3 and tumor size following axitinib treatment in metastatic renal cell carcinoma (mRCC) patients
Emilie Schindler (1), Michael Amantea (2), Peter A. Milligan (2), Mats O. Karlsson (1), Lena E. Friberg (1)
(1) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; (2) Pfizer Global Research and Development
Objectives: Axitinib (Inlyta®) is a multi-targeted tyrosine kinase inhibitor with anti-angiogenic properties, approved for the treatment of metastatic renal cell carcinoma (mRCC). Axitinib inhibits vascular endothelial growth factor (VEGF) receptors 1, 2 and 3. This study aims to characterize the time-course of candidate biomarkers (VEGF and its circulating receptors sVEGFR-1, sVEGFR-2, sVEGFR-3 as well as sKIT) and to investigate potential longitudinal relationships between axitinib dose, AUC, biomarkers and tumor size in patients with metastatic renal cell carcinoma using PKPD models previously developed for sunitinib in patients with gastro-intestinal stromal tumors (GIST) .
Methods: VEGF, VEGFR-1,2,3, sKIT and tumor size (sum of the longest diameters, SLD) measurements were available from 64 Japanese cytokine-refractory mRCC patients treated with axitinib administered orally at a starting dose of 5 mg BID continuously. Biomarker and SLD data were collected for up to 89 and 104 weeks, respectively. Axitinib PK was characterized by individual parameter estimates from a previously developed PK model. Indirect response models were fitted to log-transformed biomarker data. Models for each biomarker were developed separately and finally combined into a joint model to explore correlations. Dose, daily AUC and model-predicted relative change from baseline in the biomarkers were evaluated as drivers for the change in SLD with a longitudinal tumor growth inhibition model [1,2].
Results: Indirect response models adequately described the time-course of VEGF, sVEGFR-1, 2 and 3. Axitinib inhibited the production of sVEGFR-1, 2 and 3 and the degradation of VEGF. A linear disease progression was included in the VEGF model. Axitinib AUC50 values were 348, 1400, 722 and 722 µg/L.h, for VEGF, sVEGFR-1, 2 and 3, respectively. A common typical AUC50 could be estimated for sVEGFR-2 and 3. Individual AUC50 for sVEGFR-1, 2 and 3 were highly correlated (80-99%). No drug effect was identified for sKIT. Longitudinal SLD data were well characterized by a tumor growth inhibition model. The relative change in sVEGFR-3 was found to be the best predictor for SLD time-course, and inclusion of AUC did not result in a further improvement of the model fit.
Conclusions: The time-courses of VEGF, sVEGFR-1, 2 and 3 following axitinib treatment in mRCC patients were successfully characterized by indirect response models. sVEGFR-3 baseline value was lower in mRCC than in GIST (19700 vs 63900 pg/mL), and the mean turnover times were shorter in mRCC; 0.55, 19 and 6.1 days vs 3.8, 23 and 17 days, for VEGF, sVEGFR-2 and sVEGFR-3, respectively. The soluble biomarkers were highly correlated. As for sunitinib in GIST, sVEGFR-3 was the best predictor of change in tumor size in mRCC following axitinib treatment. In a next step, the relationship of biomarkers and SLD to overall survival will be investigated.
Acknowledgements: This work was supported by the DDMoRe (www.ddmore.eu) project.
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