Technology transfer hurdles constantly keep effective medical treatment from healthcare. One prevalent hurdle is that of cost. Regulation from any organization or entity can drive up cost and requires thorough review before implementation. For microspheres specifically, extensive research has been conducted to minimize variation in size. How variation effects drug delivery of microspheres, however, has not been studied in depth. In this study, a preliminary approach to modeling drug delivery in microspheres with a given log-normal distribution is reported. A design of experiment statistical analysis was performed using incremental values of mean and standard deviation. To estimate the rate of drug diffusing from the microspheres, a simplified Fick's second law was used. Various data types were considered and it was found that the shape factors which are related to mean and standard deviation fit the statistical analysis best. Using the shape factor data type, equation characteristics were identified and reported. It was seen that standard deviation has a greater influence on drug delivery than mean. A prediction expression is presented that can be used to identify the time it takes to get to 60% drug delivery and can be used in a scaled manner.

The goal of the works presented in this volume is to develop a magnetic resonance imaging (MRI) probe for non-invasive detection of extracellular matrix (ECM) underlying fenestrated endothelia. The ECM is the scaffold that supports tissue structure in all organs. In fenestrated structures the such as the kidney glomerulus and the hepatic sinusoid the ECM serves a unique role in blood filtration and is directly exposed to blood plasma. An assessment of the ECM in fenestrated organs such as the kidney and liver reports on the organ's ability to filter blood - a process critical to maintaining homeostasis. Unfortunately, clinical assessment of the ECM in most organs requires biopsy, which is focal and invasive. This work will focus on visualizing the ECM underlying fenestrated endothelia with natural nanoparticles and MRI. The superparamagnetic ferritin protein has been proposed as a useful naturally-derived, MRI-detectable nanoparticle due to its biocompatibility, ease of functionalization, and modifiable metallic core. We will show that cationized ferritin (CF) specifically binds to the anionic proteoglycans of the ECM underlying fenestrated endothelia and that its accumulation is MRI-detectable. We will then demonstrate the use of CF and MRI in identifying and measuring all glomeruli in the kidney. We will also explore the toxicity of intravenously injected CF and consider other avenues for its application, including detection of microstructural changes in the liver due to chronic liver disease. This work will show that CF is useful in detected fenestrated microstructures in small animals and humans alike, indicating that CF may find broad application in detecting and monitoring disease in both preclinical and clinical settings.