Speaker
Description
The tumor microenvironment (TME) presents significant physical barriers, such as elevated interstitial fluid pressure (IFP) arising from disorganized and leaky vasculature together with a dense extracellular matrix (ECM). These barriers limit the effectiveness of systemic therapies by restricting drug penetration into the tumor core \cite{N20}. We develop a coupled mathematical model to investigate how ECM remodeling influences drug delivery within solid tumors. The model is formulated as a system of nonlinear partial differential equations describing the spatiotemporal interactions among tumor growth, vasculature, IFP, and drug concentration \cite{Y17}. To capture the structural role of the ECM, the model explicitly incorporates key constituents, including collagen, hyaluronic acid, and elastin, which together regulate interstitial hydraulic resistance and fluid pressure.
We focus on collagen normalization as a therapeutic priming strategy by modeling collagenase-mediated degradation of collagen fibers prior to chemotherapy administration. Simulations demonstrate that partial collagen degradation substantially enhances drug penetration throughout the tumor domain, resulting in improved therapeutic exposure compared to chemotherapy alone. Furthermore, the model enables systematic investigation of treatment schedules, providing a predictive computational framework for optimizing combination strategies involving ECM-targeted therapies and cytotoxic agents.
Bibliography
@article{Y17,
author = {Yonucu, Sirin and Yılmaz, Defne and Phipps, Colin and Unlu, Mehmet Burcin and Kohandel, Mohammad},
title = {Quantifying the effects of antiangiogenic and chemotherapy drug combinations on drug delivery and treatment efficacy},
journal = {PLOS Computational Biology},
volume = {13},
number = {9},
pages = {e1005724},
year = {2017},
doi = {10.1371/journal.pcbi.1005724},
publisher = {Public Library of Science}
}
@article{N20,
author = {Nia, Hadi T. and Munn, Lance L. and Jain, Rakesh K.},
title = {Physical traits of cancer},
journal = {Science},
volume = {370},
number = {6516},
pages = {eaaz0868},
year = {2020},
doi = {10.1126/science.aaz0868},
publisher = {American Association for the Advancement of Science}
}
