Author:
Kiani Shahvandi Mohammad,Soltani M.,Moradi Kashkooli Farshad,Saboury Babak,Rahmim Arman
Abstract
AbstractWe present comprehensive mathematical modeling of radiopharmaceutical spatiotemporal distributions within vascularized solid tumors. The novelty of the presented model is at mathematical level. From the mathematical viewpoint, we provide a general modeling framework for the process of radiopharmaceutical distribution in the tumor microenvironment to enable an analysis of the effect of various tumor-related parameters on the distribution of different radiopharmaceuticals. We argue that partial differential equations (PDEs), beyond conventional methods, including ODE-based kinetic compartment modeling, can be used to evaluate radiopharmaceutical distribution in both time and space. In addition, we consider the spatially-variable dynamic structure of tumor microvascular networks to simulate blood flow distribution. To examine the robustness of the model, the effects of microvessel density (MVD) and tumor size, as two important factors in tumor prognosis, on the radiopharmaceutical distribution within the tumor are investigated over time (in the present work, we focus on the radiopharmaceutical [18F]FDG, yet the framework is broadly applicable to radiopharmaceuticals). Results demonstrate that the maximum total uptake of [18F]FDG at all time frames occurs in the tumor area due to the high capillary permeability and lack of a functional lymphatic system. As the MVD of networks increases, the mean total uptake in the tumor is also enhanced, where the rate of diffusion from vessel to tissue has the highest contribution and the rate of convection transport has the lowest contribution. The results of this study can be used to better investigate various phenomena and bridge a gap among cancer biology, mathematical oncology, medical physics, and radiology.
Publisher
Springer Science and Business Media LLC
Reference87 articles.
1. Marusyk, A. & Polyak, K. Tumor heterogeneity: Causes and consequences. Biochim. Biophys. Acta Rev. Cancer 1805, 105–117 (2010).
2. Polyak, K. Heterogeneity in breast cancer. J. Clin. Investig. 121, 3786–3788 (2011).
3. Lim, Z.-F. & Ma, P. C. Emerging insights of tumor heterogeneity and drug resistance mechanisms in lung cancer targeted therapy. J. Hematol. Oncol. 12, 1–18 (2019).
4. Souri, M., Soltani, M., Kashkooli, F. M. & Shahvandi, M. K. Engineered strategies to enhance tumor penetration of drug-loaded nanoparticles. J. Control. Release 341, 227–246 (2022).
5. Souri, M. et al. Towards principled design of cancer nanomedicine to accelerate clinical translation. Mater. Today Biol. 1, 1208 (2022).
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献