# Non-conventional radionuclides in personalised medicine

- Institution: CERN
- Department: Academic Training Lecture Regular Programme,2022-2023
- Speaker: Thierry Stora
- Year: 2023
- Level: Graduate
- Language: English
- Subjects: Medicine and health
- Source: [CERN](https://videos.cern.ch/record/3017208)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Abstract:

Nuclear Medicine, and more particularly the so-called theranostics approach based on the combination of diagnostics and treatment drugs, has seen recent breakthroughs, originating from radionuclides made newly available notably for European academic and industrial R&D scientists.

A striking example is the use of targeted radiopharmaceuticals directed to the Prostate Specific Membrane Antigen (PSMA) and Somatostatin Receptor targeted therapy with 177Lu beta-emitter. Alpha-emitting radionuclides have also been applied successfully in research and clinic. The clinical approval of Xofigo® (223RaCl2) represented an important milestone in the translation and application of alpha emitter based radiopharmaceuticals.

Improved access to a portfolio of selected radionuclides, bioconjugates and radiopharmaceuticals is an important requirement for preclinical evaluations, clinical trials, and ultimately their translation as new drugs.

The production of these radionuclides is complex and sometimes requires lengthy irradiations using powerful reactors, cyclotrons in combination with isotope mass separation facilities.

During the lecture, I will introduce some of the recent challenges and developments that were witnessed over the past few years in the field, and show how the development of ion beam production techniques has allowed the production and test of new radiotherapeutics, notably with a new coordinated approach involving major large scale facilities in Europe. .

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## Lectures

1. [Non-conventional radionuclides in personalised medicine](https://invisiblenetwork.ai/learn/cern/3017208/151ffa8c53f2), 1 h 13 min (CERN's player)
