Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours

Angela d’Esposito, Paul W. Sweeney, Morium Ali, Magdy Saleh, Rajiv Ramasawmy, Thomas A. Roberts, Giulia Agliardi, Adrien Desjardins, Mark F. Lythgoe, R. Barbara Pedley, Rebecca Shipley and Simon Walker-Samuel - Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, London, UK. Department of Mechanical Engineering, University College London, London, UK. Department of Medical Physics, University College London, London, UK. Cancer Institute, University College London, London, UK. These authors contributed equally: Angela d’Esposito, Paul W. Sweeney.

Understanding the uptake of a drug by diseased tissue, and the drug’s subsequent spatiotemporal distribution, are central factors in the development of effective targeted therapies. However, the interaction between the pathophysiology of diseased tissue and individual therapeutic agents can be complex, and can vary across tissue types and across subjects. Here, we show that the combination of mathematical modelling, high-resolution optical imaging of intact and optically cleared tumour tissue
from animal models, and in vivo imaging of vascular perfusion predicts the heterogeneous uptake, by large tissue samples, of specific therapeutic agents, as well as their spatiotemporal distribution. In particular, by using murine models of colorectal cancer and glioma, we report and validate predictions of steady-state blood flow and intravascular and interstitial fluid pressure in tumours, of the spatially heterogeneous uptake of chelated gadolinium by tumours, and of the effect of a vascular disrupting agent on tumour vasculature.

This complex physiological-pharmacological landscape
requires careful analysis in order to be fully understood. The
numerical modelling component of REANIMATE consists of
two steps. First, a solution is sought from a set of coupled fluid dynamics models that describe steady-state vascular and interstitial fluid transport. Second, the steady-state solution (or set of solutions) is used to parameterize a time-dependent model that describes the vascular and interstitial uptake of exogenously administered material. This can be used, for example, to model the heterogeneous pharmacokinetics of drug or imaging contrast agents, or the delivery of individual particles (for example T cells, antibodies), and terms can be introduced to describe drug targeting and metabolism.

How Amira-Avizo Software is used

Image processing and vessel segmentation. Reconstructed OPT data were used to generate whole-tumour blood vessel networks. First, a 3D Gaussian filter with a width of 50 pixels (corresponding to a physical size of 300 μ m, greater than the largest vessel diameter) was applied. The filtered data were subtracted from the original data to remove background variations in autofluorescence.
A 3D Frangi filter was then applied (Matlab, MathWorks) to enhance vessel-like structures. The response to the filter was thresholded to segment blood vessels from background. Skeletonization of these thresholded data was performed in Amira (Thermo Fisher Scientific), which also converted the data into graph format (that is nodes and segments with associated radii). To ensure that vessel structures were accurately represented, 2D sections from the original image data were swept through reconstructed 3D networks (in Amira), with visual inspection used for accordance between vessel location and thickness, and the location of the fluorescence signal.