Information détaillée

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Titre du projet :

Precision Radiopharmaceutical Therapies with Theranostic Digital Twins

Chercheur principal :

Uribe, Carlos F

Co-chercheurs :

S/O

Directeur(s) de recherche :

S/O

Établisssement payé :

University of British Columbia

Établissement de recherche :

B.C. Cancer Research Centre (Vancouver)

Département :

S/O

Programme :

PRIX - Proj. - AP : Chercheur en début de carrière dans le domaine du cancer

Concours (année/mois) :

202409

CEP désigné :

Physique médicale et imagerie

Institut principal :

Cancer

Thème principal :

Recherche biomédicale

Durée (année/mois) :

1 an 0 mois

Contribution des IRSC :

Donateurs :

Montant :

25 000$

Équipement :

0$

Contribution du partenaire externe :

Nom du partenaire :

S/O

Montant :

S/O

Équipement :

S/O

Partenaire du candidat à l'externe :

Nom du partenaire :

S/O

Montant :

S/O

Équipement :

S/O

Partenaire externe (en nature) :

Nom du partenaire :

S/O

Montant :

S/O

Équipement :

S/O

Mots clés :

Artificial Intelligence; Dosimetry; Dynamic Pet; Molecular Imaging; Personalized Medicine; Physiologically Based Radiopharmacokinetic Models; Quantitative Spect And Pet; Radiopharmaceutical Therapies; Theranostic Digital Twins; Theranostics

Résumé :

Radiopharmaceutical therapies (RPTs) have shown favorable results in the treatment of different cancers, like advanced prostate cancer (PCa) and neuroendocrine tumors. The paradigm of theranostics is an exciting frontier in nuclear medicine (NM). With theranostics, the same molecule can be radiolabeled with an isotope for imaging (e.g., PET imaging) and then another isotope for therapy (i.e., RPTs). We can "treat what we see and see what we treat." However, existing imaging protocols are only used to select patients for therapies, but not to optimize RPTs. Our proposal seeks to change this. We aim to build what we term theranostic digital twins (TDTs) to create a computational version (like a digital avatar) for each patient based on imaging and clinical data. TDTs will enable optimization of RPTs. We will build TDTs adapted to individual patients. This will allow us to optimize and personalize RPTs by simulating injection protocols with acquired data from diagnostic NM scans. We will develop what we call physiologically based radiopharmacokinetic (PBRPK) models for PCa. PBRPK models the uptake of a radiopharmaceutical in different organs and tumors after an injection. We will study how the different parameters of the model change the uptake to reveal the relevance of each of them for patient-specific RPT planning. We will then personalize PBRPK models including use of artificial intelligence (AI) methods to best predict doses delivered to different organs and tumors. We will collect clinical trial data by adding additional imaging to patients undergoing RPTs at our institution. This will allow us to evaluate the reliability of our TDT-based RPTs as we will be able to predict the absorbed radiation doses with our models and verify them with measured data.

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Version :

20250311.1