Development of Antibody Immuno-PET/SPECT Radiopharmaceuticals for Imaging of Oncological Disorders—An Update
Autor: | Filipe Elvas, Tim Van den Wyngaert, Christel Vangestel, Karuna Adhikari, Jonatan Dewulf |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Cancer Research
Biodistribution medicine.drug_class pretargeting Review Monoclonal antibody lcsh:RC254-282 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine bioconjugation chemistry antibody medicine positron emission tomography (PET) radiopharmaceuticals Pretargeting biology medicine.diagnostic_test business.industry single-photon emission computed tomography (SPECT) lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens Tumor antigen Oncology Positron emission tomography 030220 oncology & carcinogenesis biology.protein Human medicine Antibody Molecular imaging Nuclear medicine business Emission computed tomography |
Zdroj: | Cancers Cancers, Vol 12, Iss 1868, p 1868 (2020) |
ISSN: | 2072-6694 |
Popis: | Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are molecular imaging strategies that typically use radioactively labeled ligands to selectively visualize molecular targets. The nanomolar sensitivity of PET and SPECT combined with the high specificity and affinity of monoclonal antibodies have shown great potential in oncology imaging. Over the past decades a wide range of radio-isotopes have been developed into immuno-SPECT/PET imaging agents, made possible by novel conjugation strategies (e.g., site-specific labeling, click chemistry) and optimization and development of novel radiochemistry procedures. In addition, new strategies such as pretargeting and the use of antibody fragments have entered the field of immuno-PET/SPECT expanding the range of imaging applications. Non-invasive imaging techniques revealing tumor antigen biodistribution, expression and heterogeneity have the potential to contribute to disease diagnosis, therapy selection, patient stratification and therapy response prediction achieving personalized treatments for each patient and therefore assisting in clinical decision making. |
Databáze: | OpenAIRE |
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