A modeling framework to simulate motesanib efficacy in thyroid cancer

Abstract

e14553 Background: Motesanib is a highly selective, oral inhibitor of VEGF receptors 1, 2, and 3; PDGFR, and Kit with antiangiogenic and direct antitumor activity. A modeling framework that simulates clinical endpoints, including objective response rate (ORR; per RECIST) and progression-free survival (PFS), was developed to support clinical development of motesanib. This study evaluated the framework using results from a trial of motesanib in thyroid cancer (TC). Methods: Models for tumor growth inhibition (J Clin Oncol 24[18S]:abstract 6025, 2006) with drug effect driven by area under the curve (AUC) (as predicted by a population pharmacokinetic model), overall survival, and probability and duration of dose reductions were developed based on data from 93 differentiated TC (DTC) and 91 medullary TC patients who received motesanib monotherapy (125 mg once daily [QD]) in a phase 2 study (Horm Res 68[suppl 3]:28–9, 2007; NEJM 359:31–42, 2008). The full simulation framework was assessed in predicting dose intensity (starting dose of 125 mg QD), tumor size over time, ORR, and PFS. Dose-response simulations were performed in DTC patients. Results: Survival times followed a Weibull distribution with ECOG performance status, baseline tumor size, and change in tumor size from baseline at week 7 as predictors. The probability of dose reductions was dependent on time and AUC. Time to event Weibull models predicted the duration of dose reductions and dose interruptions. The models correctly predicted median daily exposure intensities up to week 24. The predicted ORR in DTC patients was 15.0% (95% prediction interval [PI], 7.5%-23.7%) compared with the observed ORR of 14.0%. Predicted median PFS was 40 weeks (95% PI, 32–49 wk) compared with the observed median PFS of 40 weeks. Dose- response simulations confirmed the appropriateness of 125-mg QD dosing in DTC: the modeling framework predicted no clinically relevant improvement in PFS would be obtained by dose intensification. Conclusions: This modeling framework (dose reduction/tumor growth inhibition/survival) will be an important tool to simulate clinical response and support clinical development decisions. Further evaluation of the model using additional datasets will be required. [Table: see text]