Predicting red blood cell traffcking and capillary hemodynamics in angiogenic and tumor microcirculation in silico

Objective: Angiogenic and tumor microvasculatures are known to have abnormal topology due to the presence of frequent vessel junctions, irregular and deflated blood vessels, multi-furcations, and tessellated vessel organization. Although recent advances in imaging techniques in vivo have enabled mapping such vasculatures at high spatial resolution, simultaneous measurements of hemodynamic parameters, such as the wall shear stress (WSS) with full 3D details, remain a challenge. Theoretical network flow models, often used for hemodynamic predictions in such experimentally acquired images, cannot provide the full 3D hemodynamic details either, as these models treat each blood vessel as 1D segment and do not explicitly model red blood cells (RBCs). To overcome this limitation, we have developed a high-fidelity, 3D Computational Fluid Dynamics modeling to predict the flow of a large number of deformable RBCs through physiologically realistic tumor/angiogenic microvascular networks in silico. Methods: We use in vivo images to create such vascular networks in silico and then predict RBC traffcking and capillary hemodynamics. Deformation of every flowing RBC is considered with high accuracy, and 3D geometry of each vessel is accurately modeled. Flow is driven by specifying physiological pressure boundary conditions. Model predictions have been validated against in vivo data. This in-house predictive tool is versatile, can be applied to any microvascular network image obtained in vivo in any organ, and can predict trajectories of diverse cell types including leukocytes, platelets and circulating tumor cells, drug and molecular transport in capillary blood, and cell-vessel adhesion. Results: We provide quantitative differences between healthy microvascular networks and tumor/angiogenic networks in terms of RBC distribution, perfusion, and wall shear stress. Our model shows increased heterogeneity in RBC and flow distribution in both tumor and angiogenic vasculatures than the healthy one. Also, we predict reduced flow and hematocrit in several vessels in both tumor and angiogenic vasculatures. Interestingly, several vessels in the angiogenic vasculature are predicted to have higher flow than the healthy one, while most vessels in the tumor vasculature show flow reduction. This in silico prediction is consistent with a recent in vivo study which showed higher flow in peri-tumor region and reduced flow in tumor. We further predict a significant heterogeneity in WSS and WSS gradient, blood velocity profiles, and near-wall RBC-depleted region. Conclusion: In conclusion, we have developed a versatile, in silico model that allows high-fidelity prediction of capillary hemodynamics in tumor microcirculation and provide information on hemodynamic variables that are not readily measurable in vivo but have physiological significance in tumor progression and treatment. NIH (R01EY033003) and NSF (CBET1804591). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.