Evolving knowledge about the tumor microenvironment (TME) is driving innovation in designing novel therapies against hard-to-treat breast cancer. Addressing the immune elements within the tumor microenvironment (TME) has emerged as a highly encouraging strategy for treating cancer. Although current immunotherapies have made advancements in reinstating the body's ability to fight tumors, the search for effective cancer treatments to combat tumor evasion remains a formidable challenge. In line with this objective, there is a pressing need to better understand the complex tumor-immune dynamics and crosstalk within the TME. To evaluate the cancer-immune interaction, this study aimed at investigating the crosstalk between naïve macrophages and cytotoxic T cells in driving tumor progression using an organotypic 3D ex vivo tumor on-a-chip model. The presented microfluidic platform consists of two distinct regions namely: The tumor region and the stroma region separated by trapezoidal microposts to ensure interconnectivity between regions thereby incorporating high spatial organization. In the established triculture platform, the complex Tumor Immune Microenvironment was successfully recapitulated by incorporating naïve macrophage and T cells within an appropriate 3D matrix. Through invasion and morphometric analyses, definitive outcomes were obtained that underscore the significant contribution of macrophages in facilitating tumor progression. Furthermore, the inclusion of T cells led to a notable decrease in the migratory speed of cancer cells and macrophages, underscoring the reciprocal communication between these two immune cell populations in the regulation of tumor advancement. Overall, this study highlights the complexity of TME and underscores the critical role of immune cells in regulating cancer progression.

Adoptive cell therapies such as chimeric antigen receptor (CAR) modified immune cells are revolutionizing cancer treatment. These innovative immunotherapies have a promising outlook for liquid cancers, but lack robustness against solid tumors due to complex variables introduced by the tumor microenvironment (TME). Additionally, existing CAR-T cell treatments are commonly accompanied by toxic side effects. However, by grafting a CAR construct onto macrophages, a professional phagocytic innate cell which are actively recruited by solid tumors, the efficacy of this treatment is hoped to be extended beyond hematological malignancies. Moreover, the introduction of energy metabolite-based polymers (EMPs) to provide a sustained release of activating F16BP-poly(I:C) microparticles could address the toxicity complications that arise from CAR treatments. It was determined that PLGA-F16BP-poly(I:C) microparticles allow for CAR-macrophage activation in vitro, though not in a sustained manner. Moreover, F16BP-poly(I:C) microparticles were better geared toward reducing cytokine related toxicity in vitro, with in vivo results remaining inconclusive. These findings suggest prioritization between macrophage activation or cytokine storm reduction would be required at this time, though additional future studies to explore variables such as CAR-macrophage sensitivity and the positive control could help refine this immunotherapy.

Autoimmunity develops when the immune system targets self-antigens within the body. Rheumatoid arthritis (RA) is a common autoimmune disease, and its progression is characterized by pro-inflammatory immune cells rapidly proliferating, migrating, and infiltrating joint tissue to provoke inflammation. In order to fulfill this taxing autoreactive response, an increase in energy metabolism is required by immune cells, such as dendritic cells (DCs). Therefore, a shift in DC energy reliance from the Krebs cycle toward glycolysis occurs. This metabolic shift phenotypically transitions DCs from anti-inflammatory properties toward an aggressive pro-inflammatory phenotype, in turn activating pro-inflammatory T cells and promoting RA pathogenesis. If the disease persists uncontrollably, further complications and eventual joint dysfunction can occur. Although, clinically approved drugs can prevent RA progression, they require frequent administration for temporary symptom relief. Furthermore, current approved biological products for RA are not known to have a direct modulatory effect on immunometabolism. Given that cellular metabolism controls immune cell function, this work aims to harness perturbations within RA immune cell energy metabolism and utilizes it as a therapeutic target by reprogramming immune cell metabolism via the delivery of metabolite-based particles. The two-time delivery of these particles reduced RA inflammation in a RA collagen-induced arthritis (CIA) mouse model and generated desired responses with long-term effects. Specifically, this work was achieved by: Aim 1 – developing and delivering metabolite-based polymeric microparticles synthesized from the Krebs cycle metabolite, alpha-ketoglutarate (aKG; termed paKG MPs) to DCs to modulate their energy metabolism and promote anti-inflammatory properties (in context of RA). Aim 2 – exploiting the encapsulation ability of paKG MPs to inhibit DC glycolysis in the presence of the CIA self-antigen (collagen type II (bc2)) for the treatment of RA in CIA mice. Herein, paKG MPs encapsulating a glycolytic inhibitor and bc2 induce an anti-inflammatory DC phenotype in vitro and generate suppressive bc2-specific T cell responses and reduce paw inflammation in CIA mice.

Drug delivery has made a significant contribution to cancer immunotherapy and can have a tremendous impact on modulating immunometabolism, thereby affecting cancer outcomes. Notably, the science of delivery of cancer vaccines and immunotherapeutics, modulating immune cell functions has inspired development of several successful companies and clinical products. For example, cancer vaccines require activation of dendritic cells (DCs) and tumour associated Mɸs (TAMs) through modulation of their energy metabolism (e.g., glycolysis, glutaminolysis, Krebs cycle). Similar to activated immune cells, cancer cells also upregulate glucose and glutamine transporters for proliferation and survival. Cancer cells having accelerated energy metabolism, which has been exploited as a target for various therapeutic studies. In the first strategy, an immunometabolism strategy based on sustained release of succinate from biomaterials, which incorporate succinate in the backbone of the polymer was developed. This study demonstrates that succinate-based polymeric microparticles act as alarmins by modulating the immunometabolism of DCs and Mɸs to generate robust pro-inflammatory responses for melanoma treatment in immunocompetent young as well as aging mice. In the second strategy, a biomaterial-based strategy was developed to deliver metabolites one-step downstream of the node where the glycolytic pathway is inhibited, to specifically rescue DCs from glycolysis inhibition. The study successfully demonstrated for the first time that the glycolysis of DCs can be rescued both in vitro and in vivo using a biomaterial strategy of delivering metabolites downstream of the inhibitory node. Overall, it is believed that advanced drug delivery strategies will play an important role in marrying the fields of immunometabolism and immunotherapy to generate translatable anti-cancer treatments.

In this dissertation, new data-driven techniques are developed to solve three problems related to generating predictive models of the immune system. These problems and their solutions are summarized as follows. The first problem is that, while cellular characteristics can be measured using flow cytometry, immune system cells are often analyzed only after they are sorted into groups by those characteristics. In Chapter 3 a method of analyzing the cellular characteristics of the immune system cells by generating Probability Density Functions (PDFs) to model the flow cytometry data is proposed. To generate a PDF to model the distribution of immune cell characteristics a new class of random variable called Sliced-Distributions (SDs) is developed. It is shown that the SDs can outperform other state-of-the-art methods on a set of benchmarks and can be used to differentiate between immune cells taken from healthy patients and those with Rheumatoid Arthritis. The second problem is that while immune system cells can be broken into different subpopulations, it is unclear which subpopulations are most significant. In Chapter 4 a new machine learning algorithm is formulated and used to identify subpopulations that can best predict disease severity or the populations of other immune cells. The proposed machine learning algorithm performs well when compared to other state-of-the-art methods and is applied to an immunological dataset to identify disease-relevant subpopulations of immune cells denoted immune states. Finally, while immunotherapies have been effectively used to treat cancer, selecting an optimal drug dose and period of treatment administration is still an open problem. In Chapter 5 a method to estimate Lyapunov functions of a system with unknown dynamics is proposed. This method is applied to generate a semialgebraic set containing immunotherapy doses and period of treatment that is predicted to eliminate a patient's tumor. The problem of selecting an optimal pulsed immunotherapy treatment from this semialgebraic set is formulated as a Global Polynomial Optimization (GPO) problem. In Chapter 6 a new method to solve GPO problems is proposed and optimal pulsed immunotherapy treatments are identified for this system.

This work aims to characterize protein-nanoparticle interactions through the application of experimental techniques to aid in controlled nanoparticle production for various applications from manufacturing through medical to defense. It includes multiple steps to obtain purified and characterized protein and then the production of nanoparticles using the protein. This application of protein requires extremely pure homogenous solution of the protein that was achieved using numerous protein separation techniques which were experimented with. Crystallization conditions, protein separation methods and protein characterization methods were all investigated along with the protein-nanoparticle interaction studies. The main protein of study here is GroEL and the inorganic nanoparticle used is platinum. Some studies on MBP producing gold nanoparticles from an ionic gold precursor were also conducted to get a better perspective on nanoparticle formation. Protein purification methods, crystallization conditions, Car-9 tag testing and protein characterization methods were all investigated along with the focus of this work. It was concluded that more Car9 studies need to be carried out before being used as in the form of a loop in the protein. The nanoparticle experiments were successful and platinum nanoparticles were successfully synthesized using GroEL. The direction of further research in protein-nanoparticle studies are outlined towards the end of the thesis.