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: Lutetium-177 Prostate Specific Membrane Antigen (177Lu-PSMA) is a radiopharmaceutical, currently under investigation in phase 3 trials, that shows promising results in the treatment of prostate cancer patients. This radionuclide labeled peptide binds to the PSMA receptor on cell membranes and after internalization the emission of beta radiation leads to cell death. Before patients receive 177Lu-PSMA therapy, diagnosis of PSMA expressing prostate cancer tumors is performed using 68Ga-PSMA PET/CT [1,2]. Although studies on clinical use of 177Lu-PSMA are promising, research mainly focusses on individual absorbed radiation doses to the tumors and organs, resulting in a lack of information regarding pharmacokinetics (PK) of 177Lu-PSMA and variability within the population. Insights in population variability are needed, since it might explain differences in treatment response This study focused on variability in systemic PK of 177Lu-PSMA-617 in low volume metastatic prostate cancer (mPC) patients as a first step in optimizing therapy. Eventually, the developed model will be used to explore the possibilities of using data derived from SPECT/CT scans for population PK modeling.
Methods: Data of 10 patients receiving two cycles of 177Lu-PSMA-617 were available from a study in the Radboud hospital in Nijmegen (NCT03828838). Patients received two intravenous injections of ~3 GBq and ~6 GBq with an interval of 8 weeks and 9 blood samples were acquired after each administration. A total of 180 blood activity sample concentrations (MBq/L) were included and activity was corrected for decay to time of injection. An initial model using blood sample data was developed using NONMEM (version 7.4). Model evaluation was based on objective function value (OFV) and visual assessment of goodness-of-fit plots.
Results: A three compartment model with a combined proportional and (fixed) additive residual error model fitted the data best. Allometric scaling was added to all parameters. Population parameter estimations were 2.83 L/h for systemic clearance (CL) (relative standard error (RSE) 5.8%) and 10.9 L (RSE 4.2%), 14.7 L (RSE 7.8%) and 10.3 L (RSE 8.7%) for volume of distribution in compartment (V1), V2 and V3, respectively. Inter-compartmental clearance (Q) clearly differed between both compartments (Q2=15.8 L/h (RSE 9.5%) and Q3=0.372 L/h (RSE 15.7%)). Although data of only 10 patients were used for model development, results showed a remarkably low variability on CL (between subject variability (BSV) of 11.5% (RSE 33%), while BSV was slightly higher for Q2 (29.8% (RSE 76%)).
Based on these results, treatment response differences are probably explained by variability on parameters other than CL. Addition of scan data to compartments might improve insights in this variability and this will be performed in future model optimization. Furthermore, model predictions will be optimized by including more patient data using blood activity data based on SPECT/CT scans. But still, goodness-of-fit plots of this model confirmed that PK and low variability of this patient group were well captured by the model.
Conclusions: A three compartment 177Lu-PSMA-617 population PK model was developed based on activity blood sample data of mPC patients. This first population PK model adequately described systemic PK and the remarkably low variability within the data. This model was a first step to characterize 177Lu-PSMA-617 PK variability and will be further optimized by increasing data input based on SPECT/CT scan data.
References:
[1] Fendler WP, Rahbar K, Herrmann K, Kratochwil C, Eiber M. 177Lu-PSMA Radioligand Therapy for Prostate Cancer. J Nucl Med. 2017;58:1196–200.
[2] Kratochwil C, Fendler WP, Eiber M, Baum R, Bozkurt MF, Czernin J, et al. EANM procedure guidelines for radionuclide therapy with 177Lu-labelled PSMA-ligands (177Lu-PSMA-RLT). Eur J Nucl Med Mol Imaging. 2019;46:2536–2544.
Reference: PAGE 29 (2021) Abstr 9667 [www.page-meeting.org/?abstract=9667]
Poster: Drug/Disease Modelling - Oncology