Comparative Evaluation of Individual and Population Based Fitting in [177Lu]Lu-PSMA-617 PK Modeling Raden Ayu Nurfadhillah Rifqah (a*), Mohammad Sidik Cahyana (a), Bisma Barron Patrianesha (a), Deni Hardiansyah (a)
Medical Physics and Biophysics Research Group, Physics Department, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
Abstract
A minimal physiologically based pharmacokinetic (mPBPK) model was implemented to evaluate the predictive performance of individual and population based fitting approaches for estimating the Area Under the Curve (AUC) of time activity curves in 177Lu PSMA 617 therapy. Biokinetic data of kidney and tumour from 6 patients with metastatic hormone sensitive prostate cancer (mHSPC), where each cycle had 5 time points (2h, 20h, 44h, 68h, and 165h), received two cycles of 177Lu PSMA 617 injections. The second injection was administered 8 weeks after cycle 1. Subsequently, model fitting was performed using two approaches: individual for each patient, and population based fitting was performed across all patients by using a nonlinear mixed effect model. Both fitting approaches were performed using proportional error model. Model parameters, including receptor density kidney, tumour serum flow density, and filtrated fraction of blood flow were fitted to biokinetic data. Model performance was considered acceptable based on goodness of fit criteria, including visual inspection of the fitted graphs and parameter precision with coefficients of variation <50%. Simulations were then performed to obtain the AUC values. Relative Deviation (RD), which served as the basis for Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) was used to validate the predictive performance of individual results toward the population based model. The mPBPK model demonstrated good visual agreement for population based fitting, with coefficients of variation ranging from 8% to 28%. Simulation results of individuals towards population based fitting showed mean %RD values of 3% for kidneys and 12% for tumour. Model accuracy evaluation showed MAPE values 19.10% and 23.4%% for kidneys and tumor, with RMSE values 26% and 29%. The high RMSE observed in tumour fitting results indicates substantial deviation between individuals toward population based fitting, likely reflecting low performance of individual fitting in this study
Keywords: [177Lu]Lu-PSMA-617, mPBPK, PK Modelling, Population Based Fitting
