Speaker
Description
Radiopharmaceutical therapy with $^{177}$Lu–PSMA has emerged as an effective treatment for metastatic prostate cancer, yet current clinical protocols rely on empirically fixed and non-personalized schedules. A mechanistic mathematical model based on ordinary differential equations is introduced to integrate tumor growth dynamics, radiation damage and organs pharmacokinetics. Radiopharmaceutical activity in tumor and organs at risk is described through compartment-specific effective decay rates combining physical and biological clearance, while injected activity is distributed according to uptake-weighted mass fractions. The toxicity is quantified using biologically effective dose thresholds, enabling a quantitative assessment of efficacy–toxicity trade-offs \cite{2025RPT}.
Virtual patient cohorts generated through stochastic sampling of biologically grounded parameter ranges allow in silico trials. Model predictions reproduce published dosimetry and survival outcomes from independent clinical studies, showing good agreement in absorbed doses and Kaplan–Meier survival distributions.
Exploration of treatment schedules reveals a clear efficacy–toxicity landscape: consolidated regimens with fewer, higher-activity injections increase median overall survival but raise renal toxicity, whereas excessive cycle delays markedly reduce therapeutic efficacy. Notably, a nine-week cycle preserves survival comparable to the standard six-week protocol while significantly reducing toxicity.
Bibliography
@article{2025RPT,
author = {Italia, Matteo and Bordel-Vozmediano, Silvia and Otero, José García and Calvo, Gabriel F. and Pérez-García, Víctor M.},
title = {Radiopharmaceutical Therapy for Metastatic Prostate Cancer: Insights from Mechanistic Modeling and In Silico Trials},
journal = {bioRxiv},
year = {2025},
note = {Preprint},
}
