Física das Radiações e Biologia ao nível celular: rumo à optimização da dosimetria clínica

Cell.DOT's significant challenge, marking its groundbreaking nature, involves imaging subcellular distributions, integrating it into physico-chemical modeling, and modeling the bio-effects from individual cells, followed by scaling to 3D models (resembling a metastatic niche). Thus, Cell.DOT's major goal is to mitigate uncertainties, in TRT, towards the optimization of clinical dosimetry. As described, TRT emerged to amplify radiation damage to tumors while minimizing side effects to healthy tissue. While the benefits of TRT are unquestionable, its optimization, as outlined by the European Council Directive (2013/59/EURATOM), needs the adoption of a personalized dosimetric approach, as the one used in external beam radiation therapy (EBRT). How does this represent a very challenging task? Mainly because unlike EBRT, TRT results in an absorbed dose that is heterogeneously distributed across the tumor, both on a tissue and subcellular level, while the dose rate is protracted, relatively low and variable over time. Furthermore, TRT exploits high and medium LET alpha, beta, or Auger electron radiation, while in conventional EBRT, (low LET) X-rays are used. Considering this, Cell.DOT aims to address this challenge by thoroughly examining cellular models and leveraging the data to scale up to a 3D cellular model (resembling a metastatic niche), using radiopharmaceuticals (RP) (Ra-223 and Lu-177) and radiosensitizers (RS), like AuNPs. To what extent this goes beyond the state-of-the-art? By synergistically exploring the complementarity of different research areas, such as imaging, physics, chemistry and biology. In short, the subcellular distribution of RP and RS can be integrated into MCTS codes to calculate absorbed doses point-to-point in each cellular compartment. Then, we will be able to use its own biophysical models, Local Effect Model (LEM) and Microdosimetric Kinetic Models (MKM) to predict the biological outcome in different irradiation scenarios. The benchmark of the estimated radiobiological outcome will be performed using assays allowing to assess the effects on proliferation, viability, survival, cell-to-cell communication, and organization by dedicated techniques, further detailed in Task 1. On the path to give insights on clinical dosimetry, we will move towards hundreds of μm, and model the bio-effect, this time, at a metastatic niche scale. The input for this challenging task will be the data from cellular models. It is recognized that 3D cell models closely mimic the in vivo architecture, however, large uncertainties are observed when we try to quantify the biological outcome, mainly to the heterogeneous distribution of IR, across it. We already experienced that [6, 7, 11, 12]. How can we overcome this? By developing sophisticated analysis, taking advantage of the complementarities of the different tasks. Imaging techniques (Task 2), such as immunofluorescence, confocal, elemental distribution by PIXE, autoradiography, allow us not only to localize but also to quantify damage. This data, together with survival cell curves and radionuclide internalization (Task 1) and MC nano and micro dosimetry results (Task 3), enable to study a possible correlation among nano/micro energy deposition pattern distribution and radiation effect at metastatic level. Thus, Cell.DOT will reveal the importance of biologically-weighted dosimetry, towards clinical dosimetry, impacting in the metrology of IR.