Pérez García, Victor M.. Fernández Calvo, Gabriel. García Otero, José . Bordel Vozmediano, Silvia. Italia, Matteo. 

Radiopharmaceutical therapy (RPT) has rapidly evolved into a key precision-oncology modality, with radioligands now approved or in late-stage development for multiple solid tumors, including neuroendocrine and prostate cancers. RPT with [177Lu]Lu-PSMA (Prostate-Specific Membrane Antigen) has recently been approved as a life-prolonging treatment for metastatic castration-resistant prostate cancer (mCRPC), but its clinical use still relies on non-personalized, empirically chosen fixed schedules. Here, we develop a mechanistic, patient-personalizable mathematical model simulating mCRPC response and organ-at-risk toxicity during [177Lu]Lu-PSMA RPT. The model integrates tumor growth dynamics, radiobiological response, and organ-resolved pharmacokinetics inferred from mass data and standardized uptake values obtained from positron emission tomography studies. Parameters were derived from the literature, although the framework allows personalization by fitting to patient-specific data such as imaging and prostate-specific antigen levels. Using virtual patient (VP) cohorts generated via stochastic parameter sampling, we conducted in silico trials and validated the model by comparing simulated outcomes with published dosimetry and survival data for [177Lu]Lu-PSMA trials. We then explored dosing and scheduling strategies to optimize efficacy-toxicity trade-offs. Consolidated regimens with fewer, higher-activity injections improved overall survival (OS) in silico but increased toxicity, especially in kidneys. Cycle length had a weaker influence on OS within a 2–9 week window, while it clearly affected toxicity, whereas excessive delays (>12 weeks) markedly reduced efficacy. Global sensitivity analysis identified tumor growth, uptake, and radiosensitivity parameters as key drivers of interpatient variability, and convergence testing confirmed robustness with respect to VP cohort size. These methodological findings illustrate how mechanistic modeling and in silico trials can inform the design and personalization of RPT regimens.