Sr. Principal Pharmacometrician Novartis Pharmaceutical Corporation Northborough, Massachusetts, United States
Disclosure(s):
Karthik Lingineni, PhD: No financial relationships to disclose
Objectives: Prostate cancer (PC) is the second leading cause of cancer-related death among men in the US and the third leading cause of cancer-related death in Europe. Pluvicto® (177Lu-PSMA-617) is an FDA approved prostate-specific membrane antigen (PSMA) targeted radioligand therapy (RLT) in patients with metastatic castration resistant prostate cancer patients (mCRPC). However, there are still many unknowns regarding the physiological, PK or dosimetry parameters that drive the efficacy of RLTs. This remains the primary challenge for dose optimization across the prostate cancer programs in the RLT space. The integrated model developed based on VISION study (CAAA617A12301 Ph-III trial) enables us to evaluate the complex dynamic interplay between physiological parameters, Lu-PSMA-617 dose, tumor dosimetry and efficacy of Lu-PSMA-617 in patients with mCRPC.
Methods: In the VISION substudy (Ph-III trial), tumor dosimetry i.e, time activity curves (TACs) from SPECT/CT images were available for 29 patients with a total of 104 delineated tumor lesions. Further, longitudinal prostate specific antigen (PSA) levels and overall survival (OS) data were available for these patients after ~5 years long-term follow-up. Model development was done in a sequential manor: First, a compartmental model was developed to describe the dynamics of radioactivity uptake into tumor lesions using TACs after administration of Lu-PSMA-617. Model predicted individual lesion TAC were then normalized by respective tumor lesion volumes, which resulted in high correlation between TAC and total absorbed dose of radioactivity into tumors. Indirect response model was then used to characterize the relationship between normalized TACs and changes in PSA. Finally, a joint model of PSA-OS was developed using a parametric, Weibull, time to event model.
Results: A two-compartment model with a sigmoid Imax function to explain time-varying decline in radioactivity uptake into tumor lesions was selected. Final model included longitudinal tumor volume and baseline SUVmax (maximum standardized uptake value: a measurement used in PET (positron emission tomography) scans to quantify the level of radiotracer uptake) as covariates. Result indicates that increase of SUVmax at baseline would result in a higher uptake of radioactivity into tumors. Also, time-varying tumor volume showed that a decrease in tumor volume would lead to a lower uptake of radioactivity into tumors. Furthermore, baseline PSA was incorporated as a covariate on the rates of synthesis (positive correlation) and degradation (negative correlation) in an indirect response model. Finally, baseline LDH was found to be a significant covariate on the hazard function of OS. Visual predictive checks showed a good agreement between observations and model predictions.
Conclusions: The developed integrated model of Lu-PSMA-617 dose, tumor TAC, PSA and OS was able to explain the dynamic interplay between each of these observations. In particular, efficacy (reduction in PSA and prolongation of OS) is demonstrated when there was decreasing radioactivity in tumors after each subsequent dose. This modeling framework may be extended to other RLTs and can be used to conduct simulations that help in dose optimization.
Citations: NA
Keywords: Radio ligand therapy, Pluvicto, PSA, mCRPC, overall survival
