Simulation platform for self-assembly structures in MRI-guided nanorobotic drug delivery systems

Magnetic Resonance Imaging (MRI) guided nanorobotic systems that could perform diagnostic, curative and reconstructive treatments in the human body at the cellular and sub-cellular level in a controllable manner have recently been proposed. The concept of a MRI-guided nanorobotic system is based on the use of a MRI scanner to induce the required external driving forces to guide magnetic nanocapsules to a specific target. However, the maximum magnetic gradient specifications of existing clinical MRI systems are not capable of driving superparamagnetic nanocapsules against the blood flow and therefore these MRIs do not allow for navigation. The present paper proposes a way to overcome this critical drawback through the formation of micron size agglomerations where their size can be regulated by external magnetic stimuli. This approach is investigated through modeling of the physics that govern the self-assembly of the nanoparticles. Additionally a computational tool has been developed that incorporates the derived models and performs simulation, visualization and post-processing analysis. Preliminary simulation results demonstrate that external magnetic field causes aggregation of nanoparticles while they flow in the vessel. This is a promising result -in accordance with similar experimental results- and encourages further investigation on the nanoparticle based self-assembly structures for use in nanorobotic drug delivery.

[1]  Antoine Ferreira,et al.  Nonlinear modeling and robust controller-observer for a magnetic microrobot in a fluidic environment using MRI gradients , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Christoph Alexiou,et al.  Delivery of superparamagnetic nanoparticles for local chemotherapy after intraarterial infusion and magnetic drug targeting. , 2007, Anticancer research.

[3]  J. De Baerdemaeker,et al.  Discrete element modelling for process simulation in agriculture , 2003 .

[4]  C Van Hoof,et al.  Self-assembly from milli- to nanoscales: methods and applications , 2009, Journal of micromechanics and microengineering : structures, devices, and systems.

[5]  B. Behkam,et al.  Bacterial flagella-based propulsion and on/off motion control of microscale objects , 2007 .

[6]  Sylvain Martel,et al.  Magnetic microparticle steering within the constraints of an MRI system: proof of concept of a novel targeting approach , 2007, Biomedical microdevices.

[7]  J. Santamaría,et al.  Magnetic nanoparticles for drug delivery , 2007 .

[8]  H. Brooks,et al.  Medical physiology , 1961 .

[9]  A. Pries,et al.  Biophysical aspects of blood flow in the microvasculature. , 1996, Cardiovascular research.

[10]  Uday B. Kompella,et al.  Nanoparticle technology for drug delivery , 2006 .

[11]  Bradley J. Nelson,et al.  Modeling and Control of Untethered Biomicrorobots in a Fluidic Environment Using Electromagnetic Fields , 2006, Int. J. Robotics Res..

[12]  S. Martel,et al.  Magnetic nanoparticles encapsulated into biodegradable microparticles steered with an upgraded magnetic resonance imaging system for tumor chemoembolization. , 2009, Biomaterials.

[13]  Sylvain Martel,et al.  A computer-assisted protocol for endovascular target interventions using a clinical MRI system for controlling untethered microdevices and future nanorobots , 2008, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[14]  D. A. Dunnett Classical Electrodynamics , 2020, Nature.

[15]  Renzo Di Felice,et al.  Empirical Relationships for the Terminal Settling Velocity of Spheres in Cylindrical Columns , 1999 .

[16]  Peter C. Searson,et al.  Magnetic trapping and self-assembly of multicomponent nanowires , 2002 .

[17]  R. Fox,et al.  Classical Electrodynamics, 3rd ed. , 1999 .