Theoretical and Computational Generalizations on Hyperthermia using Magnetic Nanoparticles including Optimization, Control, and Aggregation

Abstract

Iron Oxide Nanoparticles (IONPs) are a multifunctional nano-material that allows for MRI imaging, intravenous-controlled drug movement, and hyperthermia. The objective of this study is to optimize and control IONP hyperthermia and cope with aggregation using Finite Element (FE) Modeling and statistical physics.

The FE model is first used to demonstrate the advantages of changing IONP heat dissipation in time, which can increase energy density inside tumors while decreasing the energy delivered in healthy tissue. Here, this is defined as target-specificity. Second, this model is used to demonstrate that time-dependent IONP heat dissipation allows for control of temperature distributions inside the body. Third, the FE model is used to solve the temperature distributions resulting from capillary diffusion of IONPs. This study shows that capillary diffusion combined with direct injection results in improved homogeneity of temperature distributions. Fourth, using a square-difference scheme, non-time domain parameters including the number of IONP injections, the location of injections, IONP distribution width, and heating intensity are optimized to improve target-specificity and temperature homogeneity. Collectively, this study contributes to hyperthermia by optimizing time- and non-time- domain parameters, controlling hyperthermia, and quantifying aggregation with a new theory.