An efficient stochastic steering strategy of magnetic particles in vascular networks

One of the primary challenges of magnetic drug targeting is to achieve efficient and accurate delivery of drug particles to the desired sites in complex physiological conditions. Though a majority of drugs are delivered through intravenous administration, until now, the kinematics and dynamics of drug particles influenced by the magnetic field, vascular topology and blood flows are still less understood. In this work, a multi-physics dynamical model which captures transient particle motions in both artificial and in vivo-like 3D vascular networks manipulated by the time-varying magnetic field is developed. Based on the model, it is found that particles which perform a random walk with correlated speed and persistence (RWSP motion) inspired by the migratory motion of immune and metastasis cells have higher mobility and navigation ability in both 2D and 3D tree-like and web-like networks. Moreover, to steer particles to perform the efficient RWSP motion, a stochastic magnetic steering strategy which uses time-varying gradient magnetic field is proposed. Parameters of the steering strategy is optimized and the capability of controlling particles to achieve fast spreading and transport in the vascular networks is demonstrated. In addition, the influence of heterogeneous flows in the vascular networks on the particle steering efficiency is discussed. Overall, the numerical model and the magnetic steering strategy can be widely used in the drug delivery systems for precise medicine research.