Gene delivery for the treatment of prostate cancer

Prostate cancer is one of the most common cancers diagnosed in men. Whilst treatments for early-stage disease are largely effective, current therapies for metastatic prostate cancer, particularly for bone metastasis, offer only a few months increased lifespan at best. Hence new treatments are urgently required. Small interfering RNA (siRNA) has been investigated for the treatment of prostate cancer where it can ‘silence’ specific cancer-related genes. However the clinical application of siRNA-based gene therapy is limited due to the absence of an optimised gene delivery vector. The optimisation of such gene delivery vectors is routinely undertaken in vitro using 2D cell culture on plastic dishes which does not accurately simulate the in vivo bone cancer metastasis microenvironment. The goal of this thesis was to assess the potential of two different targeted delivery vectors (gold or modified β-cyclodextrin derivatives) to facilitate siRNA receptor-mediated uptake into prostate cancer cells. Furthermore, this project aimed to develop a more physiologically relevant 3D in vitro cell culture model, to mimic prostate cancer bone metastasis, which is suitable for evaluating the delivery of nanoparticulate gene therapeutics. In the first instance, cationic derivatives of gold and β-cyclodextrin were synthesized to complex anionic siRNA. The delivery vectors were targeted to prostate cancer cells using the anisamide ligand which has high affinity for the sigma receptor that is overexpressed by prostate cancer cells. The gold nanoparticle demonstrated high levels of uptake into prostate cancer PC3 cells and efficient gene silencing when transfection was performed in serum-free media. However, due to the absence of a poly(ethylene glycol) (PEG) stabilising group, the formulation was unsuitable for use in serum-containing conditions. Conversely, the modified β-cyclodextrin formulation demonstrated enhanced stability in the presence of serum due to the inclusion of a PEG chain onto which the anisamide ligand was conjugated. However, the maximum level of gene silencing efficacy from three different prostate cancer cell lines (DU145, VCaP and PC3 cells) was 30 %, suggesting that further optimisation of the formulation would be required prior to application in vivo. In order to develop a more physiologically-relevant in vitro model of prostate cancer bone metastasis, prostate cancer cells (PC3 and LNCaP cells) were cultured in 3D on collagenbased scaffolds engineered to mimic the bone microenvironment. While the model was suitable for assessing nanoparticle-mediated gene knockdown, prostate cancer cells demonstrated a phenotype with lower invasive potential when grown on the scaffolds relative to standard 2D cell culture. Hence, prostate cancer cells (PC3 and LNCaP cells) were subsequently co-cultured with bone osteoblast cells (hFOB 1.19 cells) to enhance the physiological relevance of the model. Co-cultures secreted elevated levels of the MMP9 enzyme, a marker of prostate cancer metastasis, relative to prostate cancer cell monocultures (2D and 3D) indicating enhanced physiological relevance of the model. Furthermore, the coculture model proved suitable for investigating nanoparticle-mediated gene silencing. In conclusion, the work outlined in this thesis identified two different sigma receptor-targeted gene delivery vectors with potential for the treatment of prostate cancer. In addition, a more physiologically relevant model of prostate cancer bone metastasis was developed with the capacity to help optimise gene delivery vectors for the treatment of prostate cancer.

Statistical properties of first-order bang-bang Pll with nonzero loop delay

A method to solve the stationary state probability is presented for the first-order bang-bang phase-locked loop (BBPLL) with nonzero loop delay. This is based on a delayed Markov chain model and a state How diagram for tracing the state history due to the loop delay. As a result, an eigenequation is obtained, and its closed form solutions are derived for some cases. After obtaining the state probability, statistical characteristics such as mean gain of the binary phase detector and timing error variance are calculated and demonstrated.

An assessment of the influence of the industry distribution chain on the oxygen levels in commercial modified atmosphere packaged cheddar cheese using non-destructive oxygen sensor technology

The establishment and control of oxygen levels in packs of oxygen-sensitive food products such as cheese is imperative in order to maintain product quality over a determined shelf life. Oxygen sensors quantify oxygen concentrations within packaging using a reversible optical measurement process, and this non-destructive nature ensures the entire supply chain can be monitored and can assist in pinpointing negative issues pertaining to product packaging. This study was carried out in a commercial cheese packaging plant and involved the insertion of 768 sensors into 384 flow-wrapped cheese packs (two sensors per pack) that were flushed with 100% carbon dioxide prior to sealing. The cheese blocks were randomly assigned to two different storage groups to assess the effects of package quality, packaging process efficiency, and handling and distribution on package containment. Results demonstrated that oxygen levels increased in both experimental groups examined over the 30-day assessment period. The group subjected to a simulated industrial distribution route and handling procedures of commercial retailed cheese exhibited the highest level of oxygen detected on every day examined and experienced the highest rate of package failure. The study concluded that fluctuating storage conditions, product movement associated with distribution activities, and the possible presence of cheese-derived contaminants such as calcium lactate crystals were chief contributors to package failure.