Abstract A28: Imaged-based computational predictions of imaging agent efficacy in pancreatic tumors expressing TLR2

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Abstract The current therapies continuously fail to provide successful results for pancreatic adenocarcinoma, one of the most deadly cancers with only 6% overall 5-year survival rate. The improvement of techniques for early detection, predicting of treatment efficacy and monitoring of tumor response during and after therapy remain under focused research. By combining computational modeling with intravital fluorescence microscopy we developed a novel method to assess intratumoral and intracellular distribution of imaging agents targeted to pancreatic cancer cells. Our long-standing goal is to provide a tool for optimizing efficacy of targeted agents based on individual patient data. Our team reported previously that, in addition to the important role the toll-like receptor 2 (TLR2) plays in the immune system response, TLR2 is also a bona fide cell-surface marker for targeting pancreatic cancer cells. TLR2 recognizes a vast number of biomolecules, including lipopeptides, such as a novel TLR2 ligand designed in our laboratory (TLR2L). We used intravital fluorescence microscopy methods for the real time in vivo imaging of TLR2L conjugated to near-infrared fluorescent dye, Cyanine 5 (TLR2L-Cy5), and its distribution in the tissue of pancreatic adenocarcinoma tumor xenografts in mice with endogenous expression of TLR2. In order to quantify the space- and time-dependent dynamics of TLR2L we combined intravital dorsal window chamber experiments with computational modeling of TLR2L-Cy5 diffusion and internalization following its intravenous administration. Our computational model is calibrated to the tissue histology of a particular tumor and accounts for explicitly defined individual tumor cells and expression of cell membrane receptors, as well as the extracellular matrix interpenetrated by the interstitial fluid, and tumor vasculature. The fluorescent imaging agent is also modeled as individual discrete molecules that extravasate via influx from blood capillaries, move through the tumor and become internalized by the cells. Our microscopic level computational model allowed for quantitative assessment of targeted imaging agent extravasation, interstitial diffusion and intracellular accumulation on a cell-to-cell basis. When accounting for various values of diffusion and binding affinity, we identified a cross-dependence between these physical characteristics of targeted agents. Our computational results revealed that agents of distinct affinities can reach similar efficacies of binding and internalization. Moreover, we identified the optimized dose-response protocols appropriate for any targeted agent with a priori known physical and biochemical properties. Finally, when considering different extravasation schemes of the agent we also showed that the time of crossing the capillary wall by the ligand plays an important role in effective binding and saturation of cell receptors and intracellular distribution. In addition to improvement in drug delivery schemes, our model predicts which biochemical and physical properties of targeted agents could be tuned for the maximum effect in patient-specific extracellular matrix environments, tumor topologies and receptor expression levels. We present an interdisciplinary approach to quantify diffusion and cellular uptake of an imaging agent targeted to pancreatic cancer cell lines expressing the TLR2 receptor. This integrated method can be used for optimizing the administration schedules and time points for data collection from individual human tumor xenografts in order to personalize the treatment and improve its efficacy. In the future, the model can be extended other targeted imaging or therapeutic agents, to other solid tumors, and to an individual patient's tumor histology. Citation Format: Aleksandra Karolak, Veronica Estrella, Tingan Chen, Amanda Huynh, David Morse, Katarzyna Rejniak. Imaged-based computational predictions of imaging agent efficacy in pancreatic tumors expressing TLR2. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A28.