Development of radiotracers for nuclear imaging of poly(ADP-ribose) polymerase-1 and the translocator protein in glioblastoma.

Glioblastoma (GBM) is a highly aggressive and fatal brain cancer that is associated with a number of diagnostic, therapeutic, and treatment monitoring challenges. At the time of writing, inhibition of a protein called poly (ADP-ribose) polymerase-1 (PARP-1) in combination with chemotherapy was being investigated as a novel approach for the treatment of these tumours. However, human studies have encountered toxicity problems due to sub-optimal PARP-1 inhibitor and chemotherapeutic dosing regiments. Nuclear imaging of PARP-1 could help to address these issues and provide additional insight into potential PARP-1 inhibitor resistance mechanisms. Furthermore, nuclear imaging of the translocator protein (TSPO) could be used to improve GBM diagnosis, pre-surgical planning, and treatment monitoring as TSPO is overexpressed by GBM lesions in good contrast to surrounding brain tissue. To date, relatively few nuclear imaging radiotracers have been discovered for PARP-1. On the other hand, numerous tracers exist for TSPO many of which have been investigated in humans. However, these TSPO radiotracers suffer from either poor pharmacokinetic properties or high sensitivity to human TSPO polymorphism that can affect their binding to TSPO. Bearing in mind the above and the high attrition rates associated with advancement of radiotracers to the clinic, there is a need for novel radiotracers that can be used to image PARP-1 and TSPO. This thesis reports the pre-clinical discovery programme that led to the identification of two potent PARP-1 inhibitors, 4 and 17, that were successfully radiolabelled to generate the potential SPECT and PET imaging agents [123I]-4 and [18F]-17 respectively. Evaluation of these radiotracers in mice bearing subcutaneous human GBM xenografts using ex vivo biodistribution techniques revealed that the agents were retained in tumour tissue due to specific PARP-1 binding. This thesis also describes the pre-clinical in vivo evaluation of [18F]-AB5186, which is a novel radiotracer discovered previously within the research group with potential for PET imaging of TSPO. Using ex vivo autoradiography and PET imaging the agent was revealed to accumulate in intracranial human GBM tumour xenografts in good contrast to surrounding brain tissue, which was due to specific binding to TSPO. The in vivo data for all three radiolabelled compounds warrants further pre-clinical investigations with potential for clinical advancement in mind.

Transmission electron tomography: quality assessment and enhancement for three-dimensional imaging of nanostructures

Nanotechnology has revolutionised humanity's capability in building microscopic systems by manipulating materials on a molecular and atomic scale. Nan-osystems are becoming increasingly smaller and more complex from the chemical perspective which increases the demand for microscopic characterisation techniques. Among others, transmission electron microscopy (TEM) is an indispensable tool that is increasingly used to study the structures of nanosystems down to the molecular and atomic scale. However, despite the effectivity of this tool, it can only provide 2-dimensional projection (shadow) images of the 3D structure, leaving the 3-dimensional information hidden which can lead to incomplete or erroneous characterization. One very promising inspection method is Electron Tomography (ET), which is rapidly becoming an important tool to explore the 3D nano-world. ET provides (sub-)nanometer resolution in all three dimensions of the sample under investigation. However, the fidelity of the ET tomogram that is achieved by current ET reconstruction procedures remains a major challenge. This thesis addresses the assessment and advancement of electron tomographic methods to enable high-fidelity three-dimensional investigations. A quality assessment investigation was conducted to provide a quality quantitative analysis of the main established ET reconstruction algorithms and to study the influence of the experimental conditions on the quality of the reconstructed ET tomogram. Regular shaped nanoparticles were used as a ground-truth for this study. It is concluded that the fidelity of the post-reconstruction quantitative analysis and segmentation is limited, mainly by the fidelity of the reconstructed ET tomogram. This motivates the development of an improved tomographic reconstruction process. In this thesis, a novel ET method was proposed, named dictionary learning electron tomography (DLET). DLET is based on the recent mathematical theorem of compressed sensing (CS) which employs the sparsity of ET tomograms to enable accurate reconstruction from undersampled (S)TEM tilt series. DLET learns the sparsifying transform (dictionary) in an adaptive way and reconstructs the tomogram simultaneously from highly undersampled tilt series. In this method, the sparsity is applied on overlapping image patches favouring local structures. Furthermore, the dictionary is adapted to the specific tomogram instance, thereby favouring better sparsity and consequently higher quality reconstructions. The reconstruction algorithm is based on an alternating procedure that learns the sparsifying dictionary and employs it to remove artifacts and noise in one step, and then restores the tomogram data in the other step. Simulation and real ET experiments of several morphologies are performed with a variety of setups. Reconstruction results validate its efficiency in both noiseless and noisy cases and show that it yields an improved reconstruction quality with fast convergence. The proposed method enables the recovery of high-fidelity information without the need to worry about what sparsifying transform to select or whether the images used strictly follow the pre-conditions of a certain transform (e.g. strictly piecewise constant for Total Variation minimisation). This can also avoid artifacts that can be introduced by specific sparsifying transforms (e.g. the staircase artifacts the may result when using Total Variation minimisation). Moreover, this thesis shows how reliable elementally sensitive tomography using EELS is possible with the aid of both appropriate use of Dual electron energy loss spectroscopy (DualEELS) and the DLET compressed sensing algorithm to make the best use of the limited data volume and signal to noise inherent in core-loss electron energy loss spectroscopy (EELS) from nanoparticles of an industrially important material. Taken together, the results presented in this thesis demonstrates how high-fidelity ET reconstructions can be achieved using a compressed sensing approach.