A Pre-Therapeutic Dose Prediction Workflow for Lu-177-PSMA Therapy: Integrating PBPK Modeling with Monte Carlo Simulation
Abstract
Purpose
To explore a comprehensive pre-therapeutic dose prediction workflow for Lu‑177‑PSMA radiopharmaceutical therapy by combining a physiologically based pharmacokinetic (PBPK) model with Monte Carlo simulation.
Methods
The PBPK model for Lu‑177‑PSMA, implemented in SimBiology, was used to generate time‑activity curves (TACs) for defined target volumes and critical organs. Input data included administration protocol, along with patient‑specific anatomic and physiological parameters from pre-therapeutic PET/CT images. The PBPK model outputs (activity over time per region) were then converted into source terms and imported into Monte Carlo simulation platform to calculate the corresponding cumulative absorbed dose distributions. To evaluate the workflow, we compared the predicted cumulative absorbed doses with the actual dose verification from a series of post-injection SPECT/CT scans within a retrospective patient cohort.
Results
An integrated technical workflow combining the Lu‑177‑PSMA PBPK model with Monte Carlo dose calculation was successfully implemented, effectively bridging pharmacokinetic modeling and actual therapeutic dose prediction. Using this workflow, predicted cumulative absorbed doses to target regions and critical radiosensitive organs were obtained in the selected patient cohort. And evaluation results against real post‑therapy dosimetry data showed good agreement. These predictions potentially provide a clinical reference and can support the design of patient‑specific administered activities.
Conclusion
The established Lu‑177‑PSMA dose prediction workflow addresses a current gap in pre‑therapeutic dose prediction and offers a reliable tool for pre‑injection dose optimization. This systematic approach shows strong potential for clinical translation toward personalized, precise dosing in radiopharmaceutical therapy.