[18F] Fluorodeprenyl-D2 PET Imaging of Reactive Astrocytes as Chronic Active Inflammation Marker in Multiple Sclerosis
[18F] Fluorodeprenyl-D2 PET Imaging of Reactive Astrocytes as Chronic Active Inflammation Marker in Multiple Sclerosis
Despite significant advancements in multiple sclerosis (MS) treatment over the past decade, a specific imaging biomarker for predicting disease progression remains lacking. This project is conducted as a non-therapeutic clinical trial with an investigational medicinal product and aims to validate PET imaging with the radiotracer [18F]fluorodeprenyl-D2 ([18F]F-DED) as a diagnostic tool for detecting chronic active lesions in MS and to evaluate its potential as a predictor of disease progression. Chronic astrocyte activation plays a key role in MS pathology, yet current imaging techniques, such as MRI, have limited sensitivity in identifying chronic neuroinflammation. By leveraging PET imaging with [18F]F-DED, which specifically targets activated astrocytes, this study seeks to provide a more precise assessment of chronic inflammatory lesions in MS patients. The project will follow a cross-sectional and longitudinal design: O1) To validate the ability of [18F]F-DED PET to identify chronic active lesions by comparing PET findings with MRI markers, including paramagnetic rings (PRLs) and determine its diagnostic accuracy; O2) To explore the relationship between astrocyte-targeted PET imaging and blood-based biomarkers of astrogliosis ; O3) To assess the predictive value of PET-detected chronic inflammation in disease progression over a three-year follow-up period through regression analysis, integrating clinical parameters such as disability progression (EDSS scores), relapse rates, and MRI changes. A total of 30 MS patients will be recruited from the MS Unit of Hospital del Mar and undergo PET and MRI imaging, following a single intravenous administration of the investigational radiotracer [18F]F-DED, together with MRI imaging with advanced image fusion techniques ensuring precise lesion localization. Machine learning models will be applied to imaging and clinical data to identify biomarkers associated with MS progression. By improving diagnostic accuracy and predicting disease evolution, this clinical trial aims to enhance patient stratification and treatment personalization.
Inclusion Criteria:
agiraldo@researchmar.net933160675