H. Siebinga (1, 2), B.J. de Wit-van der Veen (2), B.M. Privé (3), S.M.B. Peters (3), J. Nagarajah (3), A.D.R. Huitema (1, 4, 5), J.J.M.A. Hendrikx (1, 2)
(1) Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. (2) Department of Nuclear Medicine, The Netherlands Cancer Institute, Amsterdam, The Netherlands. (3) Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands. (4) Department of Clinical Pharmacy, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. (5) Department of Pharmacology, Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
Objectives:
Radioligand therapy with Lutetium-177 Prostate Specific Membrane Antigen (177Lu-PSMA) has showed its favorable therapeutic effect in metastatic prostate cancer (PCa) [1]. Whilst the treatment was implemented with a fixed-dose scheme, it is known that efficacy and toxicity are proportional to absorbed radiation doses in tissues. Dosimetry aims at quantifying radioligand uptake to estimate time activity curves and absorbed radiation doses in tissues. Unfortunately, studies regarding dosimetric treatment evaluation and optimization focus on individual patient data, and little research has been conducted into population trends of 177Lu-PSMA uptake.
Population pharmacokinetic (PK) modeling might be a promising tool to enhance quantification of 177Lu-PSMA uptake based on post treatment scans. However, tissue uptake derived from imaging data are not regularly used as input for such models, and since imaging data generally shows high variability, this might limit the possibility of using it as model input. Therefore, the primary aim of this project was to explore the potential of using uptake profiles derived from SPECT/CT scans as input for a population PK model. In addition, we aimed to develop a population PK dosimetry model to get a better understanding of PK parameters, and assess variability in organ and tumor uptake of 177Lu-PSMA-617 in patients with low volume metastatic PCa.
Methods:
Data of 10 patients receiving two cycles of 177Lu-PSMA-617 (~3 GBq and ~6 GBq with an interval of 8 weeks) were available from a prospective clinical study in the Radboud hospital in Nijmegen (NCT03828838) [2]. After each administration, nine blood samples and five SPECT/CT scans were acquired to quantify radioactivity in tissues. All records regarding radioactivity were corrected for decay to time of injection. An initial model using blood sample and scan data was developed using NONMEM (version 7.4) and was then expanded to a multicompartment model, with extra compartments representing relevant tissues, such as salivary glands, liver, kidney, tumor lesions and a rest compartment. A SIR was performed to assess parameter precision.
Results:
A six compartment model with a combined proportional and additive residual error model per compartment was developed. Blood activity data obtained from SPECT scans was eligible for model input after (estimated) linear correction (Fcorrected = 0.828*Fscan+6.27). Between occasion variability (BOV) on k15 (tumor compartment) improved the model fit and between subject variability (BSV) was added to k10, k12, k13, k14 and k15. Lastly, tumor volume was added as covariate on tumor uptake (k15) and the second treatment cycle as covariate on salivary gland uptake (k12), since salivary gland uptake decreased for most patients in cycle two. This final model adequately predicted uptake in all compartments. Parameter estimates for kin differed between the organ and tumor compartments and were 0.0055 h-1 for k12 (salivary glands), 0.0085 h-1 for k13 (kidney), 0.022 h-1 for k14 (liver) and 0.00022 h-1 for k15 (tumor). In addition, kout was lower for tumor (0.0075 h-1) compared to organs (0.026 h-1, 0.015 h-1 and 0.27 h-1 for salivary glands, kidney and liver, respectively). BOV on tumor uptake was 35.2% and BSV on k10, k13 and k14 was rather small (10%, 17% and 1.2%, respectively). BSV on salivary glands and tumor uptake was higher (45% and 55% for k12 and k15, respectively), which could be explained by high PSMA expression in those compartments, that possibly also show high interpatient variability. SIR results showed RSEs <30% for all PK parameters.
Conclusions:
A six compartment 177Lu-PSMA-617 population PK model was developed based on blood sample and SPECT/CT data of metastatic PCa patients. Blood activity concentrations derived from SPECT/CT scans proved suitable for model development using an estimated linear correction, thus expelling the need for future blood sampling. The final model adequately described uptake into the relevant tissues; salivary glands, kidney, liver and tumor lesions. This study demonstrated that by using a population PK approach based on imaging data, it is possible to obtain information regarding population PK parameters and its variability within the population. Future research based on this model could focus on comparing different PSMA-ligands, extrapolating this model to a patient population with larger total tumor burden or enhancing dose predictions for individual patients.
References:
[1] Sartor O, de Bono J, Chi KN, Fizazi K, Herrmann K, Rahbar K, et al. Lutetium-177-PSMA-617 for Metastatic Castration-Resistant Prostate Cancer. N Engl J Med. 2021;385(12):1091-103.
[2] Prive BM, Peters SMB, Muselaers CHJ, van Oort IM, Janssen MJR, Sedelaar JPM, et al. Lutetium-177-PSMA-617 in Low-Volume Hormone-Sensitive Metastatic Prostate Cancer: A Prospective Pilot Study. Clin Cancer Res. 2021;27(13):3595-601
Reference: PAGE 30 (2022) Abstr 10038 [www.page-meeting.org/?abstract=10038]
Poster: Drug/Disease Modelling - Oncology