RESUMO
PURPOSE: Peptide receptor radionuclide therapy (PRRT) is commonly performed in the treatment of neuroendocrine tumors (NET), where somatostatin analogs (DOTATATE) are radiolabeled with (90)Y, (68)Ga or (111)In for pre-therapeutic and therapeutic purposes. Quantitative evaluation of the biokinetic data can be performed by using physiologically based pharmacokinetic (PBPK) models. Knowledge about the biodistribution in a pre-clinical setting would allow optimizing the translation from bench to bedside. The aim of this study was to develop a PBPK model to describe the biodistribution of a novel sst2-targeting radiotracer. METHODS: Biokinetic data of six mice after injection of (18)F-SiFAlin-Asp3-PEG1-TATE were investigated using two PBPK models. The PBPK models describe the biodistribution of the tracer in the tumor, kidneys, liver, remainder and whole body via blood flow to these organs via absorption, distribution, metabolism and excretion. A recently published sst2 PBPK model for humans (model 1) was used to describe the data. Physiological information in this model was adapted to that of a mouse. Model 1 was further modified by implementing receptor-mediated endocytosis (model 2). Model parameters were fitted to the biokinetic data of each mouse. Model selection was performed by calculating Akaike weights wi using the corrected Akaike Information Criterion (AICc). RESULTS: The implementation of receptor-mediated endocytosis considerably improved the description of the biodistribution (Akaike weights w1=0% and w2=100% for model 1 and 2, respectively). The resulting time-integrated activity coefficients determined by model 2 were for tumor (0.05 ± 0.02) h, kidneys (0.11 ± 0.01) h and liver (0.02 ± 0.01) h. CONCLUSION: Simply downscaling a human PBPK model does not allow for an accurate description of (18)F-SiFAlin-Asp3-PEG1-TATE in mice. Biokinetics of this tracer can be accurately and adequately described using a physiologically based pharmacokinetic model including receptor-mediated endocytosis. Thus, an optimized translation from bench to bedside is possible.
