A physiologically-based pharmacokinetic model for sst2 targeting [Pb-212]Pb labelled ligands in mice

Ziel/Aim Alpha emitter-based peptide receptor radionuclide therapy (a-PRRT) is a promising therapeutic option for neuroendocrine tumours (NETs). Cytotoxic alpha particles reduce the nephrotoxicity and overcome the radioresistance of NETs to beta emitters in PRRT. Mathematical modelling helps performing cost- and animal-free investigations to study the pharmacokinetics of in vivo alpha generators targeting somatostatin receptor type 2 (sstr2). Therefore, a physiologically-based pharmacokinetic (PBPK) model was developed to describe the pharmacokinetics of in vivo alpha generators for xenograft mice in a-PRRT.

Methodik/Methods A whole-body compartmental model was developed using the modelling software SAAM II (v2.3). The model describes main physiological mechanisms (blood flow, diffusion, specific and non-specific uptakes and excretion) and physicochemical properties (physical decay and labelling stability) with parameter values from the literature. Model parameters in a virtual mouse were fitted to [Pb-212]Pb-DOTAMTATE biodistribution data obtained after intravenous administration of 0.0013 nmol (0.169 MBq) of [Pb-212]Pb-DOTAMTATE [1].

Ergebnisse/Results The developed model could successfully describe the experimental data. The fitted curves were good by visual inspection. The tumour plasma flow-rate were 0.12 ± 0.21 ml/min/g. The specific receptor densities in tumour, kidneys, liver, pancreas, spleen and lung were 5.8 ± 4.6, 2.2 ± 0.4, 0.10 ± 0.02, 3.2 ± 1.2, 0.4 ± 0.1, 1.0 ± 0.1 nmol/l, respectively.

Schlussfolgerungen/Conclusions The developed Pb-212-PBPK model allows for simulating the biokinetics of in vivo alpha particle generators targeting sstr2. The ability of the model to estimate important physiological parameters and subsequently predict optimal dosing regimens will reduce the required time for translation from bench to bedside. Also, the model allows for generating hypotheses for experiments leading to improve a-PRRT for NET.

Publication History

Article published online:
08 April 2021

• Literatur/References:

• 1 Stallons et al., 18 (05) 1012-1021