Predictive Dosimetry in PSMA-Targeted Radiopharmaceutical Therapies: A PBPK Modeling and Machine Learning Study
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".