CFD MODELING IN DESIGN AND EVALUATION OF AN ENDOVASCULAR CHEMOFILTER DEVICE
Intra-Arterial Chemotherapy (IAC) is a preferred treatment for the primary liver cancer, despite its adverse side-effects. During IAC, a mixture of chemotherapeutic drugs, e.g. Doxorubicin, is injected into an artery supplying the tumor. A fraction of Doxorubicin is absorbed by the tumor, but the remaining drug passes into systemic circulation, causing irreversible heart failure. The efficiency and safety of the IAC can be improved by chemical filtration of the excessive drugs with a catheter-based Chemofilter device, as proposed by a team of neuroradilogists.
The objective of my work was to optimize the hemodynamic and drug binding performance of the Chemofilter device, using Computational Fluid Dynamics (CFD) modeling. For this, I investigated the performance of two distinct Chemofilter configurations: 1) a porous “Chemofilter basket” formed by a lattice of micro-cells and 2) a non-porous “honeycomb Chemofilter” consisting of parallel hexagonal channels. A multiscale modeling approach was developed to resolve the flow through a representative section of the porous membrane and subsequently characterize the overall performance of the device. A heat and mass transfer analogy was utilized to facilitate the comparison of alternative honeycomb configurations.A multiphysics approach was developed for modeling the electrochemical binding of Doxorubicin to the anionic surface of the Chemofilter. An effective diffusion coefficient was derived based on dilute and concentrated solution theory, to account for the induced migration of ions. Computational predictions were supported by results of in-vivo studies performed by collaborators. CFD models showed that the honeycomb Chemofilter is the most advantageous configuration with 66.8% drug elimination and 2.9 mm-Hg pressure drop across the device. Another facet of the Chemofilter project was its surface design with shark-skin inspired texturing, which improves the binding performance by up to 3.5%. Computational modeling enables optimization of the chemofiltration device, thus allowing the increase of drug dose while reducing systemic toxicity of IAC.