Enhancing Simulations of Superparamagnetic Magnetic Drug Delivery to Predict Efficacy of Treatment

Abstract

Globally, leukemia and skin cancers are the most common types of cancers. However, current measures (radiation, chemotherapy, etc.) cannot exercise high accuracy when targeting such tumors and cancers, causing extensive damage to surrounding tissue and leading to the adverse effects commonly associated with treatments such as chemotherapy. Magnetic drug delivery applies anticancer nanoparticles as a component vehicle for charged, targeted treatment. Building upon prior research, a stochastic numerical model was enhanced to simulate the motion of a superparamagnetic cluster suspended in different types of flow while being guided by an external magnet to travel to a target. The model supplanted the assumed Carreau blood flow to a realistic Hagen-Poiseuille flow under the influence of a magnetic field. The specific application of such clusters is magnetic drug targeting, with clusters in the range of 10–200 nm radii. Using a magnetic dipole model for the external magnet close to the surface of the skin, the time of arrival at the target was calculated through a physics-informed neural network by defining the pathway for the clusters. Variations in the release position, background flow, magnetic field strength, number of clusters, and stochastic effects are assessed to see how they affect the capture rate. The capture rate is found to depend weakly on variations in the velocity profile, and strongly on the cluster size, the magnetic moment, and the distance between the magnet and the blood vessel wall. The model is validated with existing data and good agreement with experimental results is shown