Open Collections

Mollaheydar, Elahe

University of British Columbia

In recent years radiopharmaceutical therapy (RPT) has emerged as a promising method in the field of theranostics. This approach combines diagnostic and therapy to target cancer cells in the body in an effective way. Despite the potential and effectiveness of RPT, there are still more insights to get about its complex dynamics and interaction with our body in different scales that are not fully understood. This understanding is a crucial step towards improving therapies and have more accurate models for optimizing and personalizing treatment plans. In this work, we develop a framework to model tumor growth and the radiobiological effects of Lu-177 radiopharmaceutical therapy (RPT) on tumors using a computationally efficient implementation. The role of this work in the field of theranostics and RPT is to advance our understanding of the complex dynamics involved in the interaction between tumor tissue and radiopharmaceuticals. Using the Hybrid Automata Library (HAL), we constructed a multiscale model that integrates various scales involved in this problem to simulate the interaction of therapy and tumor cells and the radiobiological consequences. The model captures oxygen diffusion from the vasculature and a compartmental model for radiopharmaceutical distribution within the tumor compartment that connects the compartmental model to the radiobiological survival model. These two environmental substrates drive the dynamics of cellular updates, including cell type transitions, division, and death. Our computational framework is extendable and modular, allowing for the incorporation of additional dynamics related to radiopharmaceutical therapy. We investigated two important aspects of RPT dynamics using the developed model. First, we explored the impact of different vascular morphologies and densities on tumor evolution and cell population dynamics. Second, we examined the therapeutic effects on tumors under various conditions using multiple treatment schedules. The model reveals important insights about the radiobiological results of RPT on tumors that highlighting for further research and clinical data to optimize therapy for individual patients. Additionally, the computational framework offers the potential to be expanded for more detailed investigations into tumor and therapy dynamics.

Text

2024-09-16

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/89255

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/