A Spatiotemporal Image-Guided Model of Radiopharmaceutical Transport in Heterogeneous Vasculature Prostate Tumor: A Computational Analysis of Key Parameters in Lutetium-177 PSMA Therapy.

Abstract

PurposeRecent clinical advances highlight 177Lu-PSMA-617 radioligand therapy as a promising option for treating prostate tumors. However, drug transport mechanisms and optimization of delivery strategies remain major challenges. In this study, we developed a multiscale computational model to analyze radioligand dynamics in prostate tumors and provide insights for improving therapeutic efficacy.MethodsA spatiotemporal computational model was constructed using image-based tumor vasculature. The model employed the convection-diffusion-reaction framework to describe transport phenomena in both vascular and interstitial domains. Physiological parameters including vascular permeability, lymphatic drainage, receptor density, ligand-receptor binding affinity, and internalization kinetics were incorporated. Parametric studies were performed to investigate the effects of injected dose, labeling efficiency, ligand affinity, receptor density, blood flow, and tumor size on radioligand uptake and time-integrated activity (TIA).ResultsSimulations revealed pronounced spatial heterogeneity in intravascular and interstitial pressure and velocity fields. Tumor uptake and TIA exhibited nonlinear dependence on injected dose, peaking at 500 nmol, beyond which receptor saturation limited binding. Increasing the proportion of labeled ligand (2-8%) linearly enhanced TIA. Lower dissociation constants and higher internalization rates improved retention, while elevated receptor density increased uptake up to saturation. Blood flow reduction prolonged intratumoral retention, and tumor volume showed a linear relationship with accumulated activity for 10-50 mm3.ConclusionThe results highlight the critical role of vascular architecture and tumor-specific parameters in governing radiopharmaceutical distribution. The developed model provides mechanistic insights for optimizing 177Lu-PSMA therapy and guiding personalized treatment strategies in radiopharmaceutical therapies.