Delivery of therapy-loaded nanoparticles (NP) via inhalation is an innovative medical technology shown to successfully reduce heart failure in mice, improving the efficiency of drug delivery to increase efficacy and reduce adverse side effects. However, our understanding of how exactly therapeutic NP are distributed and absorbed by heart tissue remains limited, and how to accelerate this technology to humans is a major challenge. To overcome this problem we developed an open-source finite element model of myocardial perfusion based on poroelasticity, where perfused tissue is treated as a sponge-like continuum material, coupled with a 1D model of the coronary blood flow to simulate perfusion patterns. A novel particle-tracking based method that is stable under high Peclet number flow is employed to study NP distribution. The model was verified using a number of benchmark tests on a unit square model and is shown to be convergent, consistent and stable. Due to the coupling of the fluid component with active heart mechanics, the model requires the use of smaller time steps and finer meshes, increasing computational costs significantly. We demonstrate how averaging methods help overcome this problem, allowing for more efficient multiphysics simulations. Furthermore, by demonstrating NP deposition in a human left ventricle, we show how this model can help optimise targeted drug delivery by and reveal the mechanisms of adverse myocardial remodelling. By improving the efficiency of current state-of-the-art perfusion model we provide the first step towards optimising the targeted delivery of NP and reveal the patterns and mechanisms behind adverse myocardial remodelling leading to heart failure.