AuthorsA. K. Diem and K. Valen-Sendstad
TitleModelling drug delivery via nanoparticle deposition in the myocardium of the left ventricle
AfilliationScientific Computing
Project(s)CUPIDO: Cardio Ultraefficient nanoParticles for Inhalation of Drug prOducts
StatusPublished
Publication TypePoster
Year of Publication2018
PublisherThe Heart by Numbers: Integrating Theory, Computation, and Experiment to Advance Cardiology
Place PublishedBerlin
Keywordsblood flow, cardiac perfusion, darcy flow, nanoparticles
Abstract

The use of nanoparticles (NP) target drug delivery directly to the heart for treatment of diseases via nanoparticles (NP) has been a major goal of cardiovascular research since the early 2000s. The benefits of such a NP drug delivery system would include the reduction of side effects by the administration of smaller dosages, cost reduction and reducing the need for invasive treatments. However, the development of a NP-based drug delivery system also poses a number of challenges, such as the optimisation of physico-chemical parameters of the NP, in order to achieve efficient distribution throughout the tissue. We address this challenge by presenting a finite-element model of NP delivery via perfusion through the myocardium in the left ventricle (LV). Perfusion is represented by the three compartment porous media equations based on Darcy's law. A 0D lumped parameter model is used to represent the inflow boundary condition to the perfusing blood vessels. NP transport is modelled via the scalar transport equations based on reaction-advection-diffusion kinetics, where deposition via endocytosis follows zero order reaction kinetics. The model is solved on a human LV geometry with randomly set arterial entry points throughout the outer surface of the myocardium. Efficiency of NP endocytosis is tested based on varied kinetic rates, initial NP concentration, NP inflow rate, chemical properties of NP, and perfusion pressure. These simulations provide a framework to virtually prototype physico-chemical properties of the NP and predict their distribution within the tissue.

Citation Key26066