Modeling and Predicting Tumor Response in Radioligand Therapy

Area of Science:

• Nuclear Medicine
• Radiopharmaceutical Therapy
• Computational Modeling

Background:

• Prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) is a key treatment for metastatic castration-resistant prostate cancer.
• Predicting treatment response, specifically tumor volume reduction, after RLT is crucial for optimizing patient outcomes.
• Current methods for predicting treatment response often require multiple assessments and may not fully capture individual patient variability.

Purpose of the Study:

• To develop and validate a theranostic method for predicting PSMA-positive tumor volume after ¹⁷⁷Lu-PSMA RLT.
• To integrate pretherapeutic PET/CT imaging with physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling for predictive accuracy.
• To establish a method that relies solely on pretherapy imaging and modeling for volume prediction.

Main Methods:

• Extended a PBPK model for ¹⁷⁷Lu-PSMA I&T RLT to include tumor growth and irradiation effects (linear quadratic model).
• Retrospectively analyzed data from 13 patients with metastatic castration-resistant prostate cancer.
• Simultaneously fitted pharmacokinetic/pharmacodynamic parameters using a Bayesian framework to PET/CT, scintigraphy, and tumor volume data (pre- and post-therapy).
• Validated the predictive model using the leave-one-out Jackknife method.

Main Results:

• The developed method achieved a 1% ± 40% relative deviation in predicting PSMA-positive tumor volume 6 weeks post-therapy, demonstrating acceptable accuracy.
• Determined the radiosensitivity for PSMA-positive patients to be 0.0172 ± 0.0084 Gy^-1.
• The prediction model, based on pretherapy PET/CT and PBPK/PD modeling, showed feasibility and accuracy in internal validation.

Conclusions:

• This study presents the first method to predict PSMA-positive tumor volume after RLT using only pretherapy PET/CT and advanced modeling techniques.
• The internally validated method demonstrates feasibility and acceptable accuracy for predicting treatment outcomes.
• Ongoing work focuses on refining the method and conducting external validation for broader clinical applicability.