Predictive Dosimetry in PSMA-Targeted Radiopharmaceutical Therapies: A PBPK Modeling and Machine Learning Study

TL;DR

This study develops a three-layer computational framework combining PBPK modeling and machine learning to accurately predict dosimetric outcomes in PSMA-targeted radiopharmaceutical therapy, facilitating personalized treatment planning.

Contribution

It introduces an integrated PBPK-ML approach that leverages virtual patient data and PET imaging features to predict therapy doses, advancing personalized dosimetry in RPT.

Findings

Cu-64 PET yielded the most accurate dose predictions with MAPE as low as 8%.

F-18 PET showed volume-dependent performance, affecting prediction accuracy.

SHAP analysis identified key kinetic features influencing dose prediction.

Abstract

Predictive dosimetry is central to enabling personalized radiopharmaceutical therapy (RPT), particularly in prostate specific membrane antigen (PSMA) targeted theranostics. In this work, we develop a three layer computational framework that integrates physiologically based pharmacokinetic (PBPK) modeling with machine learning (ML) to predict both physical (AUC, absorbed dose) and biological (BED, EQD2) dosimetric endpoints in tumors and major organs. In the first layer, we generated 640 virtual patients using PBPK simulations of F-18, Ga-68, and Cu-64 labeled PSMA PET tracers paired with Lu-177 PSMA therapy, producing 15360 tumor and organ time activity curves (TACs) under realistic biological variability and PET-like noise. In the second layer, TACs were transformed into quantitative kinetic features and mapped to physical and biological dose metrics. In the third layer, ML models (Random Forest, Extra Trees, Ridge, Gradient Boosting, and XGBoost) were trained to predict RPT doses from PET derived features, with performance evaluated using mean absolute percentage error (MAPE) and R2. Cu-64 PSMA-617 based PET yielded the most robust predictions, achieving tumor dose MAPE as low as 8 percent and 10 to 20 percent for normal organs, while F-18 DCFPyL showed volume dependent performance and Ga-68 PSMA-11 exhibited higher variability. SHAP analysis revealed that peak uptake, clearance, and early kinetic features dominated predictive performance across organs and endpoints. This PBPK ML framework enables scalable, physiology informed predictive dosimetry and provides a foundation for trial design and patient specific treatment planning in PSMA targeted RPT. These results demonstrate that pre therapy PET can serve as a reliable surrogate for post therapy dosimetry, enabling scalable personalization of PSMA targeted RPT.