Magnetic Drug Targeting: Developing the Basics

Focusing medicine to disease locations is a needed ability to treat a variety of pathologies. During chemotherapy, for example, typically less than 0.1% of the drugs are taken up by tumor cells, with the remaining 99.9% going into healthy tissue. Physicians often select the dosage by how much a patient can physically withstand rather than by how much is needed to kill all the tumor cells. The ability to actively position medicine, to physically direct and focus it to specific locations in the body, would allow better treatment of not only cancer but many other diseases. Magnetic drug targeting (MDT) harnesses therapeutics attached to magnetizable particles, directing them to disease locations using magnetic fields. Particles injected into the vasculature will circulate throughout the body as the applied magnetic field is used to attempt confinement at target locations. The goal is to use the reservoir of particles in the general circulation and target a specific location by pulling the nanoparticles using magnetic forces. This dissertation adds three main advancements to development of magnetic drug targeting. Chapter 2 develops a comprehensive ferrofluid transport model within any blood vessel and surrounding tissue under an applied magnetic field. Chapter 3 creates a ferrofluid mobility model to predict ferrofluid and drug concentrations within physiologically relevant tissue architectures established from human autopsy samples. Chapter 4 optimizes the applied magnetic fields within the particle mobility models to predict the best treatment scenarios for two classes of chemotherapies for treating future patients with hepatic metastatic breast cancer microtumors

Nanoporous surfaces enable high throughput specific cell capture

Thesis (Ph. D. in Medical and Electrical Engineering)--Harvard-MIT Program in Health Sciences and Technology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 108-114).Adhesion-based cell capture on surfaces in microfluidic devices forms the basis of numerous biomedical diagnostics and in vitro assays. Solid surface microfluidic platforms have been widely explored for biomedical diagnostics since samples can be precisely and reproducibly manipulated under well-defined physicochemical conditions. However, at these small length scales, the fluid dynamics are dominated by the high surface-to-volume ratio and interfacial phenomena limiting device performance at high flow rates. In contrast, cell homing to porous vasculature is highly effective in vivo during inflammation; stem cell trafficking and cancer metastasis. In this work, we demonstrate that fluid-permeable surface functionalized with cell-specific antibodies can promote efficient and selective cell capture in vitro. This architecture might be advantageous due to enhanced transport due to fluid field modification leading to diverted streamlines towards the surface. Moreover, specific cell-surface interactions can be promoted due to reduced shear, allowing gentle cell rolling and arrest. Together, these synergistic effects enable highly effective target cell capture at flow rates over an order of magnitude larger than existing devices with solid surfaces. Additionally, in this study, we overcome a major limitation relevant to porous surfaces due to formation of stagnant layers of cells from non-target background population. These stagnant layers are detrimental to device performance as they act to reduce interaction of the cells with the reactive surface thereby reducing capture efficiency. We theoretically and experimentally understand the mechanisms for formation of the stagnant bioparticle layer in microfluidic devices and define a parameter space for optimal operation of the device over long periods of time. Key insights from these studies, collectively allow us to design a spatially modified microfluidic devices that allow us to isolate cancer lines as low as 5 cells/mL spiked into buffy coat.by Sukant Mittal.Ph.D.in Medical and Electrical Engineerin

The emergence of biofilms:Computational and experimental studies

The response of biofilms to any external stimuli is a cumulative response aggregated from individual bacteria residing within the biofilm. The organizational complexity of biofilm can be studied effectively by understanding bacterial interactions at cell level. The overall aim of the thesis is to explore the complex evolutionary behaviour of bacterial biofilms. This thesis is divided into three major studies based on the type of perturbation analysed in the study. The first study analyses the physics behind the development of mushroom-shaped structures from the influence of nutrient cues in biofilms. Glazier-Graner-Hogeweg model is used to simulate the cell characteristics. From the study, it is observed that chemotaxis of bacterial cells towards nutrient source is one of the major precursors for formation of mushroom-shaped structures. The objective of the second study is to analyse the impact of environmental conditions on the inter-biofilm quorum sensing (QS) signalling. Using a hybrid convection-diffusion-reaction model, the simulations predict the diffusivity of QS molecules, the spatiotemporal variations of QS signal concentrations and the competition outcome between QS and quorum quenching mutant bacterial communities. The mechanical effects associated with the fluid-biofilm interaction is addressed in the third study. A novel fluid-structure interaction model based on fluid dynamics and structural energy minimization is developed in the study. Model simulations are used to analyse the detachment and surface effects of the fluid stresses on the biofilm. In addition to the mechanistic models described, a separate study is carried out to estimate the computational efficiency of the biofilm simulation models

Towards human-relevant preclinical models: fluid-dynamics and three-dimensionality as key elements

The activity of research of this thesis focuses on the relevance that appropriate in vitro fully humanized models replicating physiological microenvironments and cues (e.g., mechanical and fluidic) are essential for improving human biology knowledge and boosting new compound testing. In biomedical research, the high percentage of the low rate of successful translation from bench to bedside failure is often attributed to the inability of preclinical models in generating reliable results. Indeed, it is well known that 2D models are far from being representative of human complexity and, on the other side, although animal tests are currently required by regulatory organizations, they are commonly considered unpredictive. As a matter of fact, there is a growing awareness that 3D human tissue models and fluid-dynamic scenarios are better reproducers of the in vivo context. Therefore, during this PhD, I have worked to model and validate technologically advanced fluidic platforms, where to replicate biological processes in a systemic and dynamic environment to better assess the pharmacokinetics and the pharmacodynamics of drug candidates, by considering different case studies. First, skin absorption assays have been performed accordingly to the OECD Test Guidelines 428 comparing the standard diffusive chamber (Franz Diffusion Cell) to a novel fluidic commercially available organ on chip platform (MIVO), demonstrating the importance of emulating physiological fluid flows beneath the skin to obtain in vivo-like transdermal penetration kinetics. On the other hand, after an extensive research analysis of the currently available intestinal models, which resulted insufficient in reproducing chemicals and food absorption profiles in vivo, a mathematical model of the intestinal epithelium as a novel screening strategy has been developed. Moreover, since less than 8% of new anticancer drugs are successfully translated from preclinical to clinical trials, breast, and ovarian cancer, which are among the 5 most common causes of death in women, and neuroblastoma, which has one of the lowest survival rates of all pediatric cancers, have been considered. For each, I developed and optimized 3D ECM-like tumor models, then cultured them under fluid-dynamic conditions (previously predicted by CFD simulations) by adopting different (customized or commercially available) fluidic platforms that allowed to mimic u stimuli (fluid velocity and the fluid flow-induced shear stress) and investigate their impact on tumor cells viability and drug response. I provided evidence that such an approach is pivotal to clinically reproduce the complexity and dynamics of the cancer phenomenon (onset, progression, and metastasis) as well as to develop and validate traditional (i.e., platin-based drugs, caffein active molecule) or novel treatment strategies (i.e., hydroxyapatite nanoparticles, NK cells-based immunotherapies)

Nature-inspired soft robotics: On articial cilia and magnetic locomotion

Inspired by micro-organisms in nature, people imagined using micro-scale soft robots to work inside the human body for therapeutic drug delivery, minimally invasive surgery, or diagnostic biochemical sensing. To create these robots is challenging due to their small size, viscosity environment, and soft constituting materials. In addition, the mechanisms of operation are quite different from the conventional rigid macro-scale robots that we are familiar with. In this PhD project, we focused on the computational analysis and design of micro-scale soft robots. Working closely with experimental groups, we studied artificial cilia and micro-swimmers that can realize particle manipulation, fluid transport, fluid mixing, or magnetic locomotion. Various cilia systems are considered, including soft inflatable cilia which could be controlled individually and programmable magnetic cilia featuring phase shifts and collective metachronal patterns. We also analyze micro-swimmers that are soft and adaptive in confined spaces. Driven by different external magnetic fields, the swimmer's motion can be changed between undulation crawling, undulation swimming, and helical crawling. By using computational modeling, we analyze the transport mechanisms of the soft robots and study the effect of different parameters to provide guidelines for the design of the robots in specific applications. By studying the physical mechanisms of micro-organisms in nature, we are not only able to understand more clearly their functional behaviour, it also opens the possibility of biomimetic design of soft robotic cilia and micro-swimmers

Development of a Multiscale Numerical Model with Two Human Pulmonary Health Applications

Determination of the site-specific dosimetry and clearance of deposited aerosols in the human airways is critical in health risk assessment studies such as toxicant exposure evaluation and inhaled medication delivery with pulmonary topical or systemic actions. However, comprehensive evaluation still lacks informative data, i.e., high-resolution local dosimetry of inhaled aerosols in airways and systemic regions, due to the limited imaging resolutions, restricted operational flexibilities, and invasive nature of experimental and clinical examinations. Computational simulations, on the other hand, can provide a detailed explanation for the chemical dynamics in the respiratory system, intrapulmonary and extrapulmonary tissues, and systemic regions using multiscale platforms. In this study, two experimentally validated multiscale numerical analyses were developed for the post-deposition calculation of the respirable aerosols, which expands the application of mathematical models in the respiratory system to the health endpoint. First, computational fluid-particle dynamics (CFPD) is coupled with a physiologically based toxicokinetic (PBTK) model to predict the in tissue translocation and systemic disposition of inhaled volatile organic compound and toxicant constituents in an electronic cigarette (EC). The proposed framework can be used as a benchmark to identify drug or toxicant dynamics in the human body, significantly applicable in the fields of pharmacokinetics and toxicokinetics. Second, an epidemiological computational approach was programmed and optimized by connecting CFPD and host cell dynamics (HCD) models to simulate the transport and deposition of low-strain influenza A virus (IAV)-laden droplets in subject-specific human lung airways and to predict the regional responses of targeted host cells to IAV infection. Furthermore, the hygroscopic growth and shrinkage of multicomponent droplets were considered by examining the thermodynamic equilibrium between the phases. These frameworks overcome the limitation of the experimental studies by connecting levels of biological dynamics that are not measurable using clinical studies. The influence of repetitive exposure incidents on the post-deposition dynamics was determined, which is valuable for assessing the chronic health effects of inhaled airborne particles.Chemical Engineerin

Nanotechnology and microfluidics:formulation design and on-chip manufacture of nanoparticles

Nanoparticles offer an ideal platform for the delivery of small molecule drugs, subunit vaccines and genetic constructs. Besides the necessity of a homogenous size distribution, defined loading efficiencies and reasonable production and development costs, one of the major bottlenecks in translating nanoparticles into clinical application is the need for rapid, robust and reproducible development techniques. Within this thesis, microfluidic methods were investigated for the manufacturing, drug or protein loading and purification of pharmaceutically relevant nanoparticles. Initially, methods to prepare small liposomes were evaluated and compared to a microfluidics-directed nanoprecipitation method. To support the implementation of statistical process control, design of experiment models aided the process robustness and validation for the methods investigated and gave an initial overview of the size ranges obtainable in each method whilst evaluating advantages and disadvantages of each method. The lab-on-a-chip system resulted in a high-throughput vesicle manufacturing, enabling a rapid process and a high degree of process control. To further investigate this method, cationic low transition temperature lipids, cationic bola-amphiphiles with delocalized charge centers, neutral lipids and polymers were used in the microfluidics-directed nanoprecipitation method to formulate vesicles. Whereas the total flow rate (TFR) and the ratio of solvent to aqueous stream (flow rate ratio, FRR) was shown to be influential for controlling the vesicle size in high transition temperature lipids, the factor FRR was found the most influential factor controlling the size of vesicles consisting of low transition temperature lipids and polymer-based nanoparticles. The biological activity of the resulting constructs was confirmed by an invitro transfection of pDNA constructs using cationic nanoprecipitated vesicles. Design of experiments and multivariate data analysis revealed the mathematical relationship and significance of the factors TFR and FRR in the microfluidics process to the liposome size, polydispersity and transfection efficiency. Multivariate tools were used to cluster and predict specific in-vivo immune responses dependent on key liposome adjuvant characteristics upon delivery a tuberculosis antigen in a vaccine candidate. The addition of a low solubility model drug (propofol) in the nanoprecipitation method resulted in a significantly higher solubilisation of the drug within the liposomal bilayer, compared to the control method. The microfluidics method underwent scale-up work by increasing the channel diameter and parallelisation of the mixers in a planar way, resulting in an overall 40-fold increase in throughput. Furthermore, microfluidic tools were developed based on a microfluidics-directed tangential flow filtration, which allowed for a continuous manufacturing, purification and concentration of liposomal drug products

Coupling solid and fluid stresses with brain tumour growth and white matter tract deformations in a neuroimaging-informed model

Brain tumours are among the deadliest types of cancer, since they display a strong ability to invade the surrounding tissues and an extensive resistance to common therapeutic treatments. It is therefore important to reproduce the heterogeneity of brain microstructure through mathematical and computational models, that can provide powerful instruments to investigate cancer progression. However, only a few models include a proper mechanical and constitutive description of brain tissue, which instead may be relevant to predict the progression of the pathology and to analyse the reorganization of healthy tissues occurring during tumour growth and, possibly, after surgical resection. Motivated by the need to enrich the description of brain cancer growth through mechanics, in this paper we present a mathematical multiphase model that explicitly includes brain hyperelasticity. We find that our mechanical description allows to evaluate the impact of the growing tumour mass on the surrounding healthy tissue, quantifying the displacements, deformations, and stresses induced by its proliferation. At the same time, the knowledge of the mechanical variables may be used to model the stress-induced inhibition of growth, as well as to properly modify the preferential directions of white matter tracts as a consequence of deformations caused by the tumour. Finally, the simulations of our model are implemented in a personalized framework, which allows to incorporate the realistic brain geometry, the patient-specific diffusion and permeability tensors reconstructed from imaging data and to modify them as a consequence of the mechanical deformation due to cancer growth