Development of a Cationic Mucic Acid Polymer-Based Nanoparticle siRNA Delivery System

Cancer chemotherapy has advanced from highly toxic drugs to more targeted treatments in the last 70 years. Chapter 1 opens with an introduction to targeted therapy for cancer. The benefits of using a nanoparticle to deliver therapeutics are discussed. We move on to siRNA in particular, and why it would be advantageous as a therapy. Specific to siRNA delivery are some challenges, such as nuclease degradation, quick clearance from circulation, needing to enter cells, and getting to the cytosol. We propose the development of a nanoparticle delivery system to tackle these challenges so that siRNA can be effective.

Chapter 2 of this thesis discusses the synthesis and analysis of a cationic mucic acid polymer (cMAP) which condenses siRNA to form a nanoparticle. Various methods to add polyethylene glycol (PEG) for stabilizing the nanoparticle in physiologic solutions, including using a boronic acid binding to diols on mucic acid, forming a copolymer of cMAP with PEG, and creating a triblock with mPEG on both ends of cMAP. The goal of these various pegylation strategies was to increase the circulation time of the siRNA nanoparticle in the bloodstream to allow more of the nanoparticle to reach tumor tissue by the enhanced permeation and retention effect. We found that the triblock mPEG-cMAP-PEGm polymer condensed siRNA to form very stable 30-40 nm particles that circulated for the longest time – almost 10% of the formulation remained in the bloodstream of mice 1 h after intravenous injection.

Chapter 3 explores the use of an antibody as a targeting agent for nanoparticles. Some antibodies of the IgG1 subtype are able to recruit natural killer cells that effect antibody dependent cellular cytotoxicity (ADCC) to kill the targeted cell to which the antibody is bound. There is evidence that the ADCC effect remains in antibody-drug conjugates, so we wanted to know whether the ADCC effect is preserved when the antibody is bound to a nanoparticle, which is a much larger and complex entity. We utilized antibodies against epidermal growth factor receptor with similar binding and pharmacokinetics, cetuximab and panitumumab, which differ in that cetuximab is an IgG1 and panitumumab is an IgG2 (which does not cause ADCC). Although a natural killer cell culture model showed that gold nanoparticles with a full antibody targeting agent can elicit target cell lysis, we found that this effect was not preserved in vivo. Whether this is due to the antibody not being accessible to immune cells or whether the natural killer cells are inactivated in a tumor xenograft remains unknown. It is possible that using a full antibody still has value if there are immune functions which are altered in a complex in vivo environment that are intact in an in vitro system, so the value of using a full antibody as a targeting agent versus using an antibody fragment or a protein such as transferrin is still open to further exploration.

In chapter 4, nanoparticle targeting and endosomal escape are further discussed with respect to the cMAP nanoparticle system. A diboronic acid entity, which gives an order of magnitude greater binding (than boronic acid) to cMAP due to the vicinal diols in mucic acid, was synthesized, attached to 5kD or 10kD PEG, and conjugated to either transferrin or cetuximab. A histidine was incorporated into the triblock polymer between cMAP and the PEG blocks to allow for siRNA endosomal escape. Nanoparticle size remained 30-40 nm with a slightly negative ca. -3 mV zeta potential with the triblock polymer containing histidine and when targeting agents were added. Greater mRNA knockdown was seen with the endosomal escape mechanism than without. The nanoparticle formulations were able to knock down the targeted mRNA in vitro. Mixed effects suggesting function were seen in vivo.

Chapter 5 summarizes the project and provides an outlook on siRNA delivery as well as targeted combination therapies for the future of personalized medicine in cancer treatment.

Microfluidics-based single-cell functional proteomics microchip for portraying protein signal transduction networks within the framework of physicochemical principles, with applications in fundamental and translational cancer research

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing unique opportunities. This thesis describes an emerging microfluidics-based toolkit for single cell functional proteomics, focusing on the development of the single cell barcode chips (SCBCs) with applications in fundamental and translational cancer research.

The microchip designed to simultaneously quantify a panel of secreted, cytoplasmic and membrane proteins from single cells will be discussed at the beginning, which is the prototype for subsequent proteomic microchips with more sophisticated design in preclinical cancer research or clinical applications. The SCBCs are a highly versatile and information rich tool for single-cell functional proteomics. They are based upon isolating individual cells, or defined number of cells, within microchambers, each of which is equipped with a large antibody microarray (the barcode), with between a few hundred to ten thousand microchambers included within a single microchip. Functional proteomics assays at single-cell resolution yield unique pieces of information that significantly shape the way of thinking on cancer research. An in-depth discussion about analysis and interpretation of the unique information such as functional protein fluctuations and protein-protein correlative interactions will follow.

The SCBC is a powerful tool to resolve the functional heterogeneity of cancer cells. It has the capacity to extract a comprehensive picture of the signal transduction network from single tumor cells and thus provides insight into the effect of targeted therapies on protein signaling networks. We will demonstrate this point through applying the SCBCs to investigate three isogenic cell lines of glioblastoma multiforme (GBM).

The cancer cell population is highly heterogeneous with high-amplitude fluctuation at the single cell level, which in turn grants the robustness of the entire population. The concept that a stable population existing in the presence of random fluctuations is reminiscent of many physical systems that are successfully understood using statistical physics. Thus, tools derived from that field can probably be applied to using fluctuations to determine the nature of signaling networks. In the second part of the thesis, we will focus on such a case to use thermodynamics-motivated principles to understand cancer cell hypoxia, where single cell proteomics assays coupled with a quantitative version of Le Chatelier's principle derived from statistical mechanics yield detailed and surprising predictions, which were found to be correct in both cell line and primary tumor model.

The third part of the thesis demonstrates the application of this technology in the preclinical cancer research to study the GBM cancer cell resistance to molecular targeted therapy. Physical approaches to anticipate therapy resistance and to identify effective therapy combinations will be discussed in detail. Our approach is based upon elucidating the signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. Strongly coupled protein-protein interactions constitute most signaling cascades. A physical analogy of such a system is the strongly coupled atom-atom interactions in a crystal lattice. Similar to decomposing the atomic interactions into a series of independent normal vibrational modes, a simplified picture of signaling network coordination can also be achieved by diagonalizing protein-protein correlation or covariance matrices to decompose the pairwise correlative interactions into a set of distinct linear combinations of signaling proteins (i.e. independent signaling modes). By doing so, two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling have been resolved, which in turn allow us to anticipate resistance, and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. We validated our predictions in mouse tumor models and all predictions were borne out.

In the last part, some preliminary results about the clinical translation of single-cell proteomics chips will be presented. The successful demonstration of our work on human-derived xenografts provides the rationale to extend our current work into the clinic. It will enable us to interrogate GBM tumor samples in a way that could potentially yield a straightforward, rapid interpretation so that we can give therapeutic guidance to the attending physicians within a clinical relevant time scale. The technical challenges of the clinical translation will be presented and our solutions to address the challenges will be discussed as well. A clinical case study will then follow, where some preliminary data collected from a pediatric GBM patient bearing an EGFR amplified tumor will be presented to demonstrate the general protocol and the workflow of the proposed clinical studies.

Cell-targeted regulation of gene expression through synthetic RNA devices

The ability to interface with and program cellular function remains a challenging research frontier in biotechnology. Although the emerging field of synthetic biology has recently generated a variety of gene-regulatory strategies based on synthetic RNA molecules, few strategies exist through which to control such regulatory effects in response to specific exogenous or endogenous molecular signals. Here, we present the development of an engineered RNA-based device platform to detect and act on endogenous protein signals, linking these signals to the regulation of genes and thus cellular function.

We describe efforts to develop an RNA-based device framework for regulating endogenous genes in human cells. Previously developed RNA control devices have demonstrated programmable ligand-responsive genetic regulation in diverse cell types, and we attempted to adapt this class of cis-acting control elements to function in trans. We divided the device into two strands that reconstitute activity upon hybridization. Device function was optimized using an in vivo model system, and we found that device sequence is not as flexible as previously reported. After verifying the in vitro activity of our optimized design, we attempted to establish gene regulation in a human cell line using additional elements to direct device stability, structure, and localization. The significant limitations of our platform prevented endogenous gene regulation.

We next describe the development of a protein-responsive RNA-based regulatory platform. Employing various design strategies, we demonstrated functional devices that both up- and downregulate gene expression in response to a heterologous protein in a human cell line. The activity of our platform exceeded that of a similar, small-molecule-responsive platform. We demonstrated the ability of our devices to respond to both cytoplasmic- and nuclear-localized protein, providing insight into the mechanism of action and distinguishing our platform from previously described devices with more restrictive ligand localization requirements. Finally, we demonstrated the versatility of our device platform by developing a regulatory device that responds to an endogenous signaling protein.

The foundational tool we present here possesses unique advantages over previously described RNA-based gene-regulatory platforms. This genetically encoded technology may find future applications in the development of more effective diagnostic tools and targeted molecular therapy strategies.

Spinal cord injury therapy through active learning

Therapy employing epidural electrostimulation holds great potential for improving therapy for patients with spinal cord injury (SCI) (Harkema et al., 2011). Further promising results from combined therapies using electrostimulation have also been recently obtained (e.g., van den Brand et al., 2012). The devices being developed to deliver the stimulation are highly flexible, capable of delivering any individual stimulus among a combinatorially large set of stimuli (Gad et al., 2013). While this extreme flexibility is very useful for ensuring that the device can deliver an appropriate stimulus, the challenge of choosing good stimuli is quite substantial, even for expert human experimenters. To develop a fully implantable, autonomous device which can provide useful therapy, it is necessary to design an algorithmic method for choosing the stimulus parameters. Such a method can be used in a clinical setting, by caregivers who are not experts in the neurostimulator's use, and to allow the system to adapt autonomously between visits to the clinic. To create such an algorithm, this dissertation pursues the general class of active learning algorithms that includes Gaussian Process Upper Confidence Bound (GP-UCB, Srinivas et al., 2010), developing the Gaussian Process Batch Upper Confidence Bound (GP-BUCB, Desautels et al., 2012) and Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) algorithms. This dissertation develops new theoretical bounds for the performance of these and similar algorithms, empirically assesses these algorithms against a number of competitors in simulation, and applies a variant of the GP-BUCB algorithm in closed-loop to control SCI therapy via epidural electrostimulation in four live rats. The algorithm was tasked with maximizing the amplitude of evoked potentials in the rats' left tibialis anterior muscle. These experiments show that the algorithm is capable of directing these experiments sensibly, finding effective stimuli in all four animals. Further, in direct competition with an expert human experimenter, the algorithm produced superior performance in terms of average reward and comparable or superior performance in terms of maximum reward. These results indicate that variants of GP-BUCB may be suitable for autonomously directing SCI therapy.

MEMS for Diabetic Retinopathy

As the worldwide prevalence of diabetes mellitus continues to increase, diabetic retinopathy remains the leading cause of visual impairment and blindness in many developed countries. Between 32 to 40 percent of about 246 million people with diabetes develop diabetic retinopathy. Approximately 4.1 million American adults 40 years and older are affected by diabetic retinopathy. This glucose-induced microvascular disease progressively damages the tiny blood vessels that nourish the retina, the light-sensitive tissue at the back of the eye, leading to retinal ischemia (i.e., inadequate blood flow), retinal hypoxia (i.e., oxygen deprivation), and retinal nerve cell degeneration or death. It is a most serious sight-threatening complication of diabetes, resulting in significant irreversible vision loss, and even total blindness.

Unfortunately, although current treatments of diabetic retinopathy (i.e., laser therapy, vitrectomy surgery and anti-VEGF therapy) can reduce vision loss, they only slow down but cannot stop the degradation of the retina. Patients require repeated treatment to protect their sight. The current treatments also have significant drawbacks. Laser therapy is focused on preserving the macula, the area of the retina that is responsible for sharp, clear, central vision, by sacrificing the peripheral retina since there is only limited oxygen supply. Therefore, laser therapy results in a constricted peripheral visual field, reduced color vision, delayed dark adaptation, and weakened night vision. Vitrectomy surgery increases the risk of neovascular glaucoma, another devastating ocular disease, characterized by the proliferation of fibrovascular tissue in the anterior chamber angle. Anti-VEGF agents have potential adverse effects, and currently there is insufficient evidence to recommend their routine use.

In this work, for the first time, a paradigm shift in the treatment of diabetic retinopathy is proposed: providing localized, supplemental oxygen to the ischemic tissue via an implantable MEMS device. The retinal architecture (e.g., thickness, cell densities, layered structure, etc.) of the rabbit eye exposed to ischemic hypoxic injuries was well preserved after targeted oxygen delivery to the hypoxic tissue, showing that the use of an external source of oxygen could improve the retinal oxygenation and prevent the progression of the ischemic cascade.

The proposed MEMS device transports oxygen from an oxygen-rich space to the oxygen-deficient vitreous, the gel-like fluid that fills the inside of the eye, and then to the ischemic retina. This oxygen transport process is purely passive and completely driven by the gradient of oxygen partial pressure (pO₂). Two types of devices were designed. For the first type, the oxygen-rich space is underneath the conjunctiva, a membrane covering the sclera (white part of the eye), beneath the eyelids and highly permeable to oxygen in the atmosphere when the eye is open. Therefore, sub-conjunctival pO₂ is very high during the daytime. For the second type, the oxygen-rich space is inside the device since pure oxygen is needle-injected into the device on a regular basis.

To prevent too fast or too slow permeation of oxygen through the device that is made of parylene and silicone (two widely used biocompatible polymers in medical devices), the material properties of the hybrid parylene/silicone were investigated, including mechanical behaviors, permeation rates, and adhesive forces. Then the thicknesses of parylene and silicone became important design parameters that were fine-tuned to reach the optimal oxygen permeation rate.

The passive MEMS oxygen transporter devices were designed, built, and tested in both bench-top artificial eye models and in-vitro porcine cadaver eyes. The 3D unsteady saccade-induced laminar flow of water inside the eye model was modeled by computational fluid dynamics to study the convective transport of oxygen inside the eye induced by saccade (rapid eye movement). The saccade-enhanced transport effect was also demonstrated experimentally. Acute in-vivo animal experiments were performed in rabbits and dogs to verify the surgical procedure and the device functionality. Various hypotheses were confirmed both experimentally and computationally, suggesting that both the two types of devices are very promising to cure diabetic retinopathy. The chronic implantation of devices in ischemic dog eyes is still underway.

The proposed MEMS oxygen transporter devices can be also applied to treat other ocular and systemic diseases accompanied by retinal ischemia, such as central retinal artery occlusion, carotid artery disease, and some form of glaucoma.

Delivery of Targeted Nanoparticles Across the Blood-Brain Barrier Using a Detachable Targeting Ligand

Chronic diseases of the central nervous system are poorly treated due to the inability of most therapeutics to cross the blood-brain barrier. The blood-brain barrier is an anatomical and physiological barrier that severely restricts solute influx, including most drugs, from the blood to the brain. One promising method to overcome this obstacle is to use endogenous solute influx systems at the blood-brain barrier to transport drugs. Therapeutics designed to enter the brain through transcytosis by binding the transferrin receptor, however, are restricted within endothelial cells. The focus of this work was to develop a method to increase uptake of transferrin-containing nanoparticles into the brain by overcoming these restrictive processes.

To accomplish this goal, nanoparticles were prepared with surface transferrin molecules bound through various liable chemical bonds. These nanoparticles were designed to shed the targeting molecule during transcytosis to allow increased accumulation of nanoparticles within the brain.

Transferrin was added to the surface of nanoparticles through either redox or pH sensitive chemistry. First, nanoparticles with transferrin bound through disulfide bonds were prepared. These nanoparticles showed decreased avidity for the transferrin receptor after exposure to reducing agents and increased ability to enter the brain in vivo compared to those lacking the disulfide link.

Next, transferrin was attached through a chemical bond that cleaves at mildly acidic pH. Nanoparticles containing a cleavable link between transferrin and gold nanoparticle cores were found to both cross an in vitro model of the blood-brain barrier and accumulate within the brain in significantly higher numbers than similar nanoparticles lacking the cleavable bond. Also, this increased accumulation was not seen when using this same strategy with an antibody to transferrin receptor, indicating that behavior of nanoparticles at the blood-brain barrier varies depending on what type of targeting ligand is used.

Finally, polymeric nanoparticles loaded with dopamine and utilizing a superior acid-cleavable targeting chemistry were investigated as a potential treatment for Parkinson’s disease. These nanoparticles were capable of increasing dopamine quantities in the brains of healthy mice, highlighting the therapeutic potential of this design. Overall, this work describes a novel method to increase targeted nanoparticle accumulation in the brain.