Abstract B39: Computational modeling of therapy using nanovectors altering macrophage subtypes to treat hypo-perfused tumor lesions
Authors: Leonard Fransisca , Curtis Louis T. , Nassar Elias , Liu Xuewu , Ferrari Mauro , Yokoi Kenji , Godin Biana , Frieboes Hermann B. 
Published: March 2017

Abstract
We hypothesize that an interdisciplinary approach integrating computational modeling with experimental measurements enables rational design of cancer nanotherapy using tumor-associated macrophages targeting hypo-vascularized tumor lesions.
Background: Hypovascularization in liver metastases, typically surrounded by macrophages, has been shown to correlate with poor chemotherapeutic response and higher mortality. Poor prognosis is linked to impaired transport into the lesions of both high- and low-molecular weight drugs as well as high washout rates. Further, macrophage infiltration is known to provide a mix of cancer-suppressing and cancer-promoting signals through the M1 and M2 subtypes, respectively.
Experimental Procedures: In vitro multi-cellular tumor spheroids mimicking hypovascularized lesions were established using magnetic 3D bioprinting. In vivo studies were performed in breast cancer liver metastasis models in mice characterized by low vascularization patterns. We employ a multi-stage nanovector system encapsulating albumin-bound-paclitaxel (nAb-PTX) into nanoporous biocompatible and biodegradable nanoparticles, which promote nAb-PTX retention in macrophages. The M1-M2 transition was assesed following several treatment schedules. The concentration of inflammatory chemokines in the microenvironment of breast cancer liver metastases was measured using MILLIPLEX MAP 25-Plex Mouse Cytokine/Chemokine Magnetic Bead Panel, and the transition between the two macrophage subtypes was assessed using F4/80 antibody to recognize a total number of macrophages and CD204 antibody as M2 macrophage marker. The infiltration of macrophages into the spheroids and their respective subtypes were anlyzed in response to therapy. The reaction-diffusion equation-based mathematical model simulates hypovascularized tumor tissue and the associated intra-tumoral transport barriers, as well as macrophage infiltration and related nanotherapy effects. Viable and necrotic tumor tissue are represented with transport of molecules and macrophages through this tissue, and with structure similar to liver metastasis. Tumor growth is calculated based on the balance of cell proliferation and death. Proliferation depends on adequate cell nutrients, oxygen, and is promoted by M2 macrophages. Death is induced by levels of oxygen below a certain threshold as well as paclitaxel above a certain level of cytotoxicity released from nAb-PTX carried by the macrophages. Death is also induced by interaction with M1 macrophages. We calibrate the model parameters from the experimental data in vitro, and then validate the computational simulation results by comparing with the in vivo experiments.
Results: The longer-acting and spatially focused drug release from macrophages achieves a more pronounced lesion regression over the course of therapy than bolus injection. Balance of M1 (cytotoxic) vs. M2 (tumorigenic) macrophages can be manipulated based on therapeutic modality to maximize tumor cytotoxicity. Fine-tuning of the therapeutic parameters with the mathematical model shows that lesion eradication is attainable, and that it depends on the specific tumor parameters.
Conclusion: An interdisciplinary approach combining experiments with computational simulation enables assessment of tumor-associated macrophage-based nanotherapeutic performance targeting hypo-vascularized tumor lesions, thus providing a platform for potential clinical assessment based on patient tumor-specific parameters.
Citation Format: Fransisca Leonard, Louis T. Curtis, Elias Nassar, Xuewu Liu, Mauro Ferrari, Kenji Yokoi, Biana Godin, Hermann B. Frieboes. Computational modeling of therapy using nanovectors altering macrophage subtypes to treat hypo-perfused tumor lesions. [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 B39.