Author: Alejandro Bertolet, Michael D. Farwell, Sarah Gitto, Victor V. Onecha, Daniel Pryma, Aladdin Riad 👨🔬
Affiliation: Department of Pathology and Laboratory Medicine, Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Department of Radiology, Perelman School of Medicine, Perelman School of Medicine at the University of Pennsylvania, Massachusetts General Hospital and Harvard Medical School 🌍
Purpose: Responses to ionizing radiation (IR) strongly correlate with the absorbed dose. At the cellular level, this is typically described by a linear-quadratic relationship provided the cell population receives a homogeneous dose and an instantaneous irradiation. However, when cell cultures are exposed to radiopharmaceutical therapy (RPT) agents, none of these conditions are met, which modifies the cellular response to a given dose. In this work, we present a mathematical model for cell response to RPT agents based on spatiotemporally heterogeneous dosimetry calculated with the TOPAS Monte Carlo toolkit.
Methods: We consider four states for a given cell: healthy, quiescent, senescent, and apoptotic. Transitions across these states were defined with rules dependent on the instantaneous and accumulated dose per cell. Time was discretized, and for each cell, the probability of being at a given state evolved according to these rules. To test the model, we used data from viability assays with the JHOS4 and JHOS4PR(PARP-resistant) cell lines treated with [211At]PTT, a targeted alpha-therapy agent that binds to PARP1. At each time, [211At]PTT concentrations moved across compartments: medium, cytoplasm, and nucleus, and dose over time was recorded for each individual cell. We optimized the parameters using SciPy python package to minimize the Root Mean Square Error (RMSE) between experimental data and model estimations.
Results: The resulting RMSE values were 5.2% for JHOS4 cells and 2.1% for JHOS4PR cells,which is within the experimental uncertainties of the viability assays.
Conclusion: This work presents the first version of a computational model capable of investigating spatiotemporal heterogeneities in cell response. Future efforts will focus on refining the model by incorporating additional biological factors and validating it across diverse experimental conditions.