PhD | A whole-body physiologically based pharmacokinetic model for personalized 161Tb-PRRT
Position: Temporary (4 years), fulltime (40 h/week)
Institution: SCK CEN
Location: Mol, Belgium
Application closes at: Mar 24th, 2023 00:00
Peptide receptor radionuclide therapy (PRRT) has been proven to be a safe and effective treatment of somatostatin receptor expressing neuroendocrine tumours (NETs). So far, standard activities are usually injected, although it has been shown that the variable tumour burden between patients can have a high impact on the biologically effective doses (BEDs) to normal tissues and tumour tissue with large tumour loads leading to a considerably lower uptake in dose-limiting organs. Therefore, patients with a large tumour burden will presumably receive a less effective treatment when a standard activity and amount of peptide are used. To avoid under treatment, we should move towards personalized treatment with optimized therapeutic activities based on tumour burden, body size, and renal function. In PRRT the kidneys are often a major organ at risk and can be a dose-limiting organ due to radiation-induced nephrotoxicity. The distribution of radiopharmaceuticals (RPs) is usually not uniform in kidney tissues due to a differential uptake of the radiopharmaceutical along distinctive nephron substructures and this can lead to a corresponding non-uniform absorbed dose distribution across the renal tissue regions. A better understanding of the impact of a non-uniform dose distribution on kidney response will be essential to advance the implementation of treatment optimization by facilitating the interpretation of clinical response data and the selection of optimal treatment options. Yet this requires knowing the microscopic distribution of RPs in human kidney tissues, which is challenging (if not impossible) to obtain using emission tomography modalities (SPECT and PET) clinically used for biodistribution assessment in patients because their spatial resolution is limited to a few millimetres.
To overcome the above-mentioned limitations, physiologically based pharmacokinetic models (PBPK) with specific compartments dedicated to relevant tissues and substructures have been proposed to compute the time-dependent (microscopic) distribution of radiopharmaceuticals in human tissues. Such biokinetic models can be developed, calibrated, refined, and validated using experimental data from biodistribution studies in animals and humans.
Therefore, the aim of this project is to develop a whole-body pharmacokinetic model that considers the individual patient physiology of the organs at risk (OARs) and the pathophysiology of the tumour based on individually estimated kinetic parameters between relevant compartments (blood, tumour, red marrow, liver, kidneys, and spleen) as well as sub-organ compartments (for kidney and tumour). As PRRT has evolved with 161Tb being considered as an emerging beta emitter for targeted radionuclide therapy and associated with heterogeneous activity distribution, the PBPK model will be developed for 161Tb-labeled compounds. To estimate the corresponding kinetic parameters between the different compartments, we will perform in vivo and ex vivo preclinical biokinetic studies under controlled conditions but anticipating on the clinical translatability of the PBPK model. We will investigate the influence of differences in SSTR2 target expression of the tumour on the outcome of the model. Experimental techniques, such as autoradiography and fluorescence microscopy will be optimised and used to determine the variability in SSTR2 expression among mice models. We will test the suitability of the model for different SSTR2-targeting radiolabelled peptides of clinical interest, such as 161Tb-DOTATATE and 161Tb-JR11. Finally, its clinical use will be evaluated by using biodistribution data from patients treated with 177Lu-DOTATATE.
The PBPK model will allow a prediction of the activity concentration in OARs and tumour tissue such that the optimal systemically administered therapeutic dose can be estimated for personalized PRRT. We expect to provide clear evidence on some of the relevant input parameters for the model, which is very useful for clinical translatability.