Baiqing Sun

Background/Objectives: Accurate absorbed dose estimation is essential for optimising targeted radionuclide therapy (TRT) in metastatic castration-resistant prostate cancer, where kidney toxicity is dose-limiting. [¹⁷⁷Lu]Lu-rhPSMA-10.1 is a novel PSMA-targeted radioligand with favourable tumour-to-kidney uptake ratios; however, inter-patient pharmacokinetic variability can lead to differences in organ and tumour absorbed doses under fixed-activity administration. Personalised dosimetry offers a means to address this variability. This work aims to create mouse PBPK model-based digital twins for [¹⁷⁷Lu]Lu-rhPSMA-10.1 to test the model’s resistance to noise and evaluate its impact on accuracy and absorbed dose calculations. Methods: Five CB-17 SCID mice bearing LNCaP tumour xenografts received 2.6–3.1 MBq [¹⁷⁷Lu]Lu-rhPSMA-10.1 intravenously. Biodistribution was assessed 24 h post-injection by organ weighing and gamma counting. The PBPK model, implemented in MATLAB SimBiology (R2023a), was fitted to individual biodistribution data using mouse-specific physiological parameters. Digital twins—combining the model with fitted parameters—were used to generate time–activity curves (TACs) for kidneys, tumours, and the whole body. Gaussian noise ($\sigma$ = 0–0.35) was added to TACs to simulate measurement error. The model was refitted, and absorbed doses from time-integrated activities (TIAs) were compared to digital twin references. Results: The digital twin approach reproduced experimental data with physiologically plausible parameters. Absorbed dose estimates remained consistent and robust, deviating by <2.3% in kidneys and <1.0% in tumours. Conclusions: PBPK-based digital twins enable reliable, individualised dosimetry, even under substantial measurement uncertainty.