Abstract 3777: Applying mechanistic pharmacokinetic-pharmacodynamic models (PKPD) to describe the growth and inhibition of xenograft tumors in rats and mice by targeted anticancer agents

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL Introduction: M. Simeoni et al., Cancer Res 64, 1094-1101 (2004) published a mathematical model, which is now widely used in pre-clinical experimentation to describe the growth and inhibition by anti-cancer agents of mouse and rat xenograft models. It models drug-concentration induced cell damage, and uses a series of transit-compartments to introduce delay into the system and empirically describe populations of cells undergoing subsequent stages of cell damage and death. The model involves a number of simplifying assumptions: All healthy cells are equally susceptible to drug treatment at all times; drug action is linearly related to drug concentration; drug action causes cell damage and death with a single rate of progress through a series of empirical transit compartments; DNA damage repair mechanisms are assumed not to exist. B. Ribba et al. Eur. J. Cancer, 47, 479-490 (2011) recently demonstrated the utility of a mechanistic model that characterizes the tumor xenograft in terms of non-hypoxic, hypoxic, and necrotic cells and the drug action on these sub-populations of cells. In this poster we will illustrate several 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. Methods: We have adapted the Simeoni and Ribba models to be more mechanistic by incorporating features that describe: (1) the utility of biomarkers proximal to the target 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. Pharmacokinetic, biomarker and tumor growth data for example compounds have been used to demonstrate the utility of these mechanistic models. Results: Incorporating non-linearity between drug exposure, biomarker response, and tumor growth inhibition allows observed differences between dosing schedules to be explained. Replacing drug exposure with a biomarker as the driver for tumor growth inhibition provides a more meaningful surrogate for pharmacological action, particularly in the situation where biomarker response to drug is significantly delayed compared to the pharmacokinetics. Modeling multiple mechanisms of action on sub-populations of cells can allow an accurate representation of the drug effect on the disease biology. 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. This offers the opportunity for modeling targeted drug action, which was not possible with the original model. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3777. doi:1538-7445.AM2012-3777