Abstract 3352: Validation of a predictive modeling approach to demonstrate the relative efficacy of three different schedules of the AKT inhibitor AZD5363.

Abstract

Abstract Introduction: Simeoni et al., Cancer Res 64, 1094-1101 (2004) published a mathematical model, widely used pre-clinically to describe the growth and inhibition by anti-cancer agents of xenografted models. Ribba et al. Eur. J. Cancer, 47, 479-490 (2011) demonstrated the utility of a mechanistic model that characterizes the tumor xenograft in terms of non-hypoxic, hypoxic, and necrotic. This poster demonstrates modeling approaches that have been utilized when the simplifying assumptions in the Simeoni model are invalid. We will also extend the model presented by Ribba et al., to incorporate the spatial features of a tumor in an attempt to better describe the tumor micro-environment. Here, focused experimentation coupled with PKPD modeling elucidates the schedule dependence of anti-tumor efficacy in a xenograted breast cancer cell line (BT474c). Methods: The Simeoni and Ribba models were adapted to incorporate features that describe: (1) the utility of biomarkers in cell signaling pathways as a driver for growth inhibition; (2) multiple mechanisms of drug action on sub-populations of cells that drive tumor growth. The model incorporates the concepts of a “shell” of cells, sufficiently proximal to blood vessels to be cycling; a hypoxic layer with the potential to cycle; a necrotic core. The model was calibrated using data from a dose fractionation experiment where similar weekly doses were delivered either twice daily continuously or a higher dose once daily 4 days of a week. The efficacy for untested doses and schedules were then simulated to identify intermittent doses that would result in the same efficacy as a continuous twice daily schedule. Further efficacy studies were then carried out to validate the prediction. Results: The model described biomarker and tumor volume time series well for a range of doses. It was found that the model predicted that a 30% dose increase was required for the 4 days a week schedule and a 70% increase was required for 2 days per week schedule. Further efficacy studies validated these predictions with the observed efficacy of three different schedules having no statistically significant differences. Conclusions: We demonstrate that adding mechanistic features to a descriptive model of drug-induced tumor growth inhibition makes it more representative of the disease biology and drug action. Modeling has enabled us to elucidate the relative balance of the anti-proliferative and pro-apoptotic consequences of AKT inhibition in a xenografted breast cancer cell line. A focused experiment-model-predict approach has achieved this in an efficient manner, with experimental validation adding credence to model predictions that will facilitate decision making for the clinical development program. Citation Format: James W. Yates, Phillipa Dudley, Barry Davies. Validation of a predictive modeling approach to demonstrate the relative efficacy of three different schedules of the AKT inhibitor AZD5363. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3352. doi:10.1158/1538-7445.AM2013-3352