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Abstract
Lutetium-177 prostate-specific membrane antigen (177Lu-PSMA)-targeted radiopharmaceutical therapy is a clinically approved treatment for patients with metastatic castration-resistant prostate cancer (mCRPC). Even though common practice reluctantly follows “one size fits all” approach, medical community believes there is significant room for deeper understanding and personalization of radiopharmaceutical therapies. To pursue this aim, we present a 3-dimensional spatiotemporal radiopharmaceutical delivery model based on clinical imaging data to simulate pharmacokinetic of 177Lu-PSMA within the prostate tumors. The model includes interstitial flow, radiopharmaceutical transport in tissues, receptor cycles, association/dissociation with ligands, synthesis of PSMA receptors, receptor recycling, internalization of radiopharmaceuticals, and degradation of receptors and drugs. The model was studied for a range of values for injection amount (100–1000 nmol), receptor density (10–500 nmol•l–1), and recycling rate of receptors (10–4 to 10–1 min–1). Furthermore, injection type, different convection-diffusion-reaction mechanisms, characteristic time scales, and length scales are discussed. The study found that increasing receptor density, ligand amount, and labeled ligands improved radiopharmaceutical uptake in the tumor. A high receptor recycling rate (0.1 min–1) increased radiopharmaceutical concentration by promoting repeated binding to tumor cell receptors. Continuous infusion results in higher radiopharmaceutical concentrations within tumors compared to bolus administration. These insights are crucial for advancing targeted therapy for prostate cancer by understanding the mechanism of radiopharmaceutical distribution in tumors. Furthermore, measures of characteristic length and advection time scale were computed. The presented spatiotemporal tumor transport model can analyze different physiological parameters affecting 177Lu-PSMA delivery.
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1 K. N. Toosi University of Technology, Department of Mechanical Engineering, Tehran, Iran (GRID:grid.411976.c) (ISNI:0000 0004 0369 2065)
2 University of Aberdeen, School of Engineering, King’s College, Aberdeen, UK (GRID:grid.7107.1) (ISNI:0000 0004 1936 7291)
3 Indian Institute of Technology (Indian School of Mines), Biofluids Research Lab, Department of Mechanical Engineering, Dhanbad, India (GRID:grid.417984.7) (ISNI:0000 0001 2184 3953)
4 Institute of Nuclear Medicine, Department of Computational Nuclear Oncology, Bethesda, USA (GRID:grid.417984.7); BC Cancer Research Institute, Department of Integrative Oncology, Vancouver, Canada (GRID:grid.417984.7)
5 BC Cancer Research Institute, Department of Integrative Oncology, Vancouver, Canada (GRID:grid.417984.7); University of British Columbia, Departments of Radiology and Physics, Vancouver, Canada (GRID:grid.17091.3e) (ISNI:0000 0001 2288 9830)
6 K. N. Toosi University of Technology, Department of Mechanical Engineering, Tehran, Iran (GRID:grid.411976.c) (ISNI:0000 0004 0369 2065); BC Cancer Research Institute, Department of Integrative Oncology, Vancouver, Canada (GRID:grid.411976.c); University of Waterloo, Department of Electrical and Computer Engineering, Waterloo, Canada (GRID:grid.46078.3d) (ISNI:0000 0000 8644 1405); University of Waterloo, Centre for Biotechnology and Bioengineering (CBB), Waterloo, Canada (GRID:grid.46078.3d) (ISNI:0000 0000 8644 1405)