For treatment planning in radioimmunotherapy (RIT), the accurate estimation of time-integrated activity coefficients (TIACs) is essential. To estimate the TIACs in RIT using (90)Y-labeled anti-CD66 antibodies, physiologically based pharmacokinetic (PBPK) models are advantageous. Further optimization in predicting therapeutic TIACs may be achieved by including population-specific parameters. Therefore, the aims of this work were (1) to estimate population parameters and (2) to show the effect of these parameters on prediction accuracy of therapeutic biodistributions. To estimate population values, a PBPK model was fitted to pretherapeutic (gamma camera and serum) and therapeutic (serum) measurements simultaneously using the standard two-stage (STS) and iterated two-stage (ITS) algorithms. Including the estimated population values as Bayesian information, the model parameters of each patient were fitted to pretherapeutic data only (simulating therapeutic TIACs). To validate the prediction accuracy of the therapeutic serum curve, the simulated and fitted TIACs were compared. Prediction accuracy expressed as relative deviation (RD) improved from RD=8%±16% to RD=0%±10% for STS and ITS, respectively. The authors demonstrated a method to estimate and apply population values for RIT using a PBPK model and population fitting. For (90)Y-labeled anti-CD66 antibodies, the prediction accuracy was substantially improved.
Keywords: Bayes; PBPK modeling; anti-CD66; leukemia; population kinetics; radioimmunotherapy.