(230a) Modeling the Effect of Temozolomide On Tumor Progression Using An Agent-Based Lattice-Free Approach

The American Cancer Society estimated that 22,070 patients would be diagnosed with brain and other central nervous system cancers and 12,970 patients with this disease would die in the US during 2009. Glioblastomas, the most common and lethal of these diseases, are intracranial neoplasms with uncontrolled proliferation, generally with a necrotic core, marked angiogenesis, a diffuse infiltration and highly resistance to radio/chemotherapy. In previous efforts, we developed an agent-based model that describes the progression of a brain tumor by capturing the interplay between processes occurring at the intracellular and tissue levels [1]. A major shortcoming of lattice-based models is the difficulty to incorporate mechanical interactions. In addition, proliferation and migration in these models are commonly restricted to cells that have free lattice sites in their neighborhood (i.e., contact inhibition), even though these events can occur in the internal regions of the tumors.

In this work we propose an alternative, lattice-free, approach to alleviate these shortcomings. This is achieved by formulating a large-scale optimization problem as a surrogate of the tumor mechanics, during the evolution of the tumor. The proposed method enables us to account for mechanical interactions determining tumor cell location and allows the proliferation and migration of tumor cells to occur at any location in the tumor. The same approach is also used for vessel-tumor interactions thus enabling us to describe vasculature remodeling through vessel occlusion, vessel dilation, and angiogenesis. Following integration with our previous modeling efforts, the current model describes tumor growth, invasion, and vasculature remodeling as well as the distribution of temozolomide (a chemotherapeutic agent approved by the FDA for the treatment of brain tumors). Based on in vitro and in vivo experiments for temozolomide pharmacokinetics and pharmacodynamics, we simulate the effect of this drug on tumor progression under various treatment strategies (e.g., different drug dosing and/or scheduling). The goal of this modeling base is to establish an in silico approach to provide insight regarding tumor suppression mechanisms of various therapeutics, ultimately to develop more efficacious treatment regimes for this devastating disease.

[1] F. G. Vital-Lopez, A. Armaou, M. Hutnik and C. D. Maranas, Modeling the effect of chemotaxis in glioblastoma tumor progression, (Accepted).