Bridging Sunitinib Exposure to Time‐to‐Tumor Progression in Hepatocellular Carcinoma Patients With Mathematical Modeling of an Angiogenic Biomarker

Abstract

Hepatocellular carcinoma (HCC) is third in cancer‐related causes of death worldwide and its treatment is a significant unmet medical need. Sunitinib is a selective tyrosine kinase inhibitor of the angiogenic biomarker: soluble vascular endothelial growth factor receptor‐2 (sVEGFR2). Sunitinib failed its primary overall survival endpoint in patients with advanced HCC in a phase III trial compared to sorafenib. In the present study, pharmacokinetic‐pharmacodynamic modeling was used to link drug‐exposure to tumor‐growth‐inhibition (TGI) and time‐to‐tumor progression (TTP) through sVEGFR2 dynamics. The results suggest that 1) active drug concentration (i.e., sunitinib and its metabolite) inhibits the release of sVEGFR2 and that such inhibition is associated with TGI, and 2) daily sVEGFR2 exposure is likely a reliable predictor for the TTP in HCC patients. Moreover, the model quantitatively links the dynamics of an angiogenesis biomarker to TTP and accurately predicts observed literature‐reported results of placebo treatment.

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

“…The biomarker timecourses were successfully characterized by IDR models where axitinib inhibited VEGF degradation and sVEGFR-1, -2, and -3 production. These model structures are consistent with previously published models in sunitinib-treated healthy volunteers, 36 GIST, 21 hepatocellular carcinoma (HCC), 37 and metastatic colon cancer (mCC). 38 The mechanisms behind these biomarker modulations following VEGFR inhibitor administration have not been fully elucidated.…”

“…21 In sunitinibtreated HCC, sVEGFR-2 absolute change from baseline was used as a driver for SLD response in a tumor growth inhibition model. 37 It should be noted that sVEGFR-3 was not evaluated in that study. Our results add evidence to the fact that changes in PD biomarkers, which can easily be measured in plasma, can help to better understand and forecast tumor response in several cancer types treated with VEGFR inhibitors.…”

“…In sunitinib‐treated GIST, sVEGFR‐3 and sKIT reduction, as well as larger sunitinib AUC, were predictive of SLD decreases . In sunitinib‐treated HCC, sVEGFR‐2 absolute change from baseline was used as a driver for SLD response in a tumor growth inhibition model . It should be noted that sVEGFR‐3 was not evaluated in that study.…”

Population Modeling Integrating Pharmacokinetics, Pharmacodynamics, Pharmacogenetics, and Clinical Outcome in Patients With Sunitinib‐Treated Cancer

“…The developed models adequately describe plasma concentration‐time profiles of sunitinib, its active metabolite SU12662, sVEGFR‐2, and s‐VEGFR‐3, as well as clinical outcome in both tumor types. Similar models (but without pharmacogenetics) were published in patients with GIST and recently hepatocellular carcinoma, but there is no model with integrated outcome data yet published for the tumor entities investigated here.…”

Mathematical investigation of antiangiogenic monotherapy effect on heterogeneous tumor progression

“…Однако практически все такие работы рассматривают моноклональную опухоль, то есть в них предполагается, что поведение всех злокачественных клеток в каждом из состояний описывает-ся одними и теми же уравнениями с одинаковыми параметрами, что не позволяет моделировать прогрессию опухоли, то есть эволюцию ее свойств по мере роста. Существует всего несколько моделей, в которых исследуется прогрессия опухоли под влиянием ангиогенеза и противоопухо-левой терапии [Ait-Oudhia, 2016;Saut, 2014], однако в основном в таких работах под прогрессией М. Б. Кузнецов, А. В. Колобов понимают только изменение общих характеристик, например размера опухоли или доли ее де-лящихся клеток. Таким образом, существует необходимость в создании математической модели опухолевого роста, которая рассматривала бы вариацию параметров злокачественных клеток, то есть учитывала гетерогенность опухоли по клональному составу.…”