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Computational modeling of targeted prostate cancer treatments, specifically ¹⁷⁷Lu-PSMA drug delivery, holds great promise for the optimization of therapies. This is particularly significant in the context of heterogeneous and drug-resistant prostate cancers. Given the intricacies involved in delivering ¹⁷⁷Lu-PSMA to solid tumors and the complexities of the tumor microenvironment, employing modeling based on partial differential equations proves to be an effective tool, towards identifying factors that hinder effective drug delivery and proposing enhancements. Unlike conventional compartmental modeling methods, such modeling comprehensively considers various parameters, including interstitial velocity, pressure, and tissue permeability, influencing the distribution of radiopharmaceuticals. Moreover, it can incorporate the heterogeneities inherent in the tumor microenvironment, encompassing …