A fluid mechanics approach to understanding and optimizing magnetic drug targeting
Abstract/Contents
- Abstract
- Magnetic drug targeting (MDT) is a noninvasive medical technique that has been proposed for treating diseases that are localized in the body. Currently, drugs meant to treat such conditions are inefficient and often damage healthy tissue because they spread throughout the blood stream. MDT combats this problem by steering the majority of the medicine to the right location in the body. An ideal MDT treatment would involve chemically binding the drug to magnetic particles, injecting the particles into the bloodstream, magnetically steering them through the arterial network, and trapping them near the diseased area so the drug they carry has time to diffuse into the surrounding tissue. There is still much to learn about how to manufacture drug-coated magnetic particles, image these particles in vivo, and control them using magnetic fields. This thesis focuses on understanding the dynamics of magnetic particles moving through the blood stream. A preliminary simulation and experiment were performed to determine realistic ranges of particle, flow, and magnetic field parameters within which MDT could work. Based on the results, an expanded simulation was performed and used to predict optimal conditions for successful magnetic drug targeting. The preliminary simulation and experiment evaluated the feasibility of stopping magnetic particles in a straight tube flow with conditions similar to those in a large artery. It was found that unrealistically high magnetic field gradients were needed to control particles small enough to safely inject into the circulatory system because the fluid drag force on the particles was too large to overcome with magnetic force. Composite spheres, made of micron-sized magnetic particles embedded in agarose (which could potentially be broken up in vivo), were much easier to control magnetically in the same setup. In order to develop an understanding of the dynamics of a cluster of small magnetic particles moving through the circulatory system, an expanded simulation was developed to track the motion of such a cluster in an artery-like flow. The hope was that the presence of an extended cloud of particles would reduce the average drag force per particle and thus small particles would be easier to control magnetically. Three unclosed forces needed to be modeled for this simulation. First, the viscous force on the particles was computed by formulating a non-Newtonian model for blood. Extensive simulations showed that non-Newtonian arterial flows differed significantly from Newtonian ones even in large arteries. Second, the interparticle magnetic force was calculated by developing a numerical method that summed magnetic interactions between grid cells instead of individual particle pairs. This approach is much more efficient than summing the forces for all particle pairs and is accurate as long as the grid is well-resolved and the local gradients of magnetic particle concentration are nonzero. Finally, the dispersion coefficient of the particles caused by their interactions with blood cells was computed by performing a separate Monte Carlo simulation of particles moving through a field of red blood cells with variable shear rate, hematocrit, and particle terminal velocity. The results of the expanded simulation showed that it was possible, but not easy, to slow down a particle cluster moving through a straight artery and somewhat easier to steer a particle cluster down one branch of an arterial bifurcation. In both cases, diffusion prevented successful control of the particle cluster long-term.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Cherry, Erica M | |
|---|---|---|
| Associated with | Stanford University, Department of Mechanical Engineering. | |
| Primary advisor | Eaton, John K | |
| Thesis advisor | Eaton, John K | |
| Thesis advisor | Iaccarino, Gianluca | |
| Thesis advisor | Mani, Ali, (Professor of mechanical engineering) | |
| Advisor | Iaccarino, Gianluca | |
| Advisor | Mani, Ali, (Professor of mechanical engineering) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Erica M. Cherry. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Erica Michelle Cherry
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...