Project ID BE-MI2024_20


Co Supervisor 1A Faculty of Life Sciences & Medicine, School of Biomedical Engineering & Imaging Sciences, Department of Imaging Chemistry & BiologyWebsite

Co Supervisor 1B Faculty of Life Sciences & Medicine, School of Biomedical Engineering & Imaging Sciences, Department of Surgical & Intervention EngineeringWebsite

Biological and computational approaches for targeted delivery of Auger electron-emitting radionuclide theranostic anti-cancer agents

For brain tumours, X-ray radiotherapy is used to reduce tumour bulk, treat inoperable tumours or secondary brain tumours. However, quality of life remains affected, and metastases and relapses still occur. Other, more targeted forms of radiotherapy are now being considered, including radionuclide therapy with Auger electron (AE)-emitters. This exploits the cytotoxicity of short-distance, low energy electrons emitted during radioactive decay; off-target effects are thus extremely unlikely. Also, simultaneous gamma emissions enable a theranostic approach by radionuclide imaging.

This project will carry out research into AE-emitting radionuclides, e.g. thallium-201, for glioblastomas. We showed that thallium-201 was the most lethal to cancer cells amongst AE-emitters; creating traditional tumour-targeting radiopharmaceuticals proved impossible though. We will explore how to target minimally invasive precision drug delivery systems placing multiple catheters into target areas in the brain to deliver AE-emitters directly through infusion. Any catheter placement requires patient/radionuclide-specific planning to maximise targeted delivery, ensuring treatment tumour coverage and minimising implantation-related risks.

1. Determine 2D and 3D spatial distribution requirements for AE-emitters for tumour control in glioblastoma cultures without affecting healthy cells
2. Create a mathematical model to predict treatment volume, i.e. radioactivity coverage
3. Determine from patient radiological scans where and how medical devices should be placed for local AE-emitter release

2D/3D cell culture, radiobiology, immunofluorescence microscopy, flow cytometry, SPECT/CT imaging, mathematical modelling, image processing

Year 1: healthy and cancer cell radioactivity uptake, specificity, and toxicity assays
Year 2: 3D spheroid work, toxicity, localisation methodologies, SPECT/CT imaging
Year 3: Create model to predict treatment volume using AE-emitter parameters including tissue diffusion parameters, toxicity, cell uptake and treatment parameters including catheter numbers and placement, infusion duration
Year 4: Incorporate model into planning using patient images for tumour geometry and patient-specific anatomy to ensure key regions, e.g. vessels, ventricles, are avoided during device placement

Representative Publications

1. ‘1. Rigby A, Firth G, Rivas C, Pham T, Kim J, Phanopoulos A, Wharton L, Ingham A, Li L, Ma MT, Orvig C, Blower PJ, Terry SYA, and Abbate V. Towards bifunctional chelators for thallium-201 for use in nuclear medicine. Bioconjugate Chemistry. Bioconjugate Chem. 2022, 33, 7, 1422–1436. DOI: 10.1021/acs.bioconjchem.2c00284.

2. Costa IM, Siksek N, Volpe A, Man F, Osytek KM, Verger E, Schettino G, Fruhwirth GO and Terry SYA. Relationship of in vitro toxicity of technetium-99m to subcellular localisation and absorbed dose. Int. J. Mol. Sci. 2021. 22(24), 13466.DOI: 10.3390/ijms222413466.

3. Osytek KM, Blower PJ, Costa IM, Smith G, Abbate V and Terry SYA. In vitro proof of concept studies of radiotoxicity from Auger electron-emitter thallium-201 (201Tl). EJNMMI Res. 2021. 11:63. DOI: 10.1186/s13550-021-00802-w.

1. Marcus HJ, Vakharia VN, Sparks R, Rodionov R, Kitchen N, McEvoy AW, Miserocchi A, Thorne L, Ourselin S, Duncan JS. Computer-assisted versus manual planning for stereotactic brain biopsy: a retrospective comparative pilot study. Operative Neurosurgery. 2020 Apr 1;18(4):417-22.

2. Li K, Vakharia VN, Sparks R, França LG, Granados A, McEvoy AW, Miserocchi A, Wang M, Ourselin S, Duncan JS. Optimizing trajectories for cranial laser interstitial thermal therapy using computer-assisted planning: a machine learning approach. Neurotherapeutics. 2019 Jan 15;16:182-91.

3. Pérez-García F, Dorent R, Rizzi M, Cardinale F, Frazzini V, Navarro V, Essert C, Ollivier I, Vercauteren T, Sparks R, Duncan JS. A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections. International Journal of Computer Assisted Radiology and Surgery. 2021 Oct;16:1653-61.