Ultrasound and magnetism let drug-delivery nanobeads travel upstream
Although various groups are already working on nanoparticles that could be used for directed drug delivery via the bloodstream, most of those particles are designed to "go with the flow." Now, however, Swiss researchers have created ones that can actually travel upstream.
Developed at the ETH Zurich research institute, the new nanoparticles are in fact tiny magnetic beads made of iron oxide combined with a polymer. They could conceivably also be loaded up with pharmaceuticals that had to be delivered to a specific location in the body. Each bead is just 3 micrometers wide.
In lab tests, the beads were placed in liquid which was flowing through a glass tube that had an inside diameter of 150 to 300 micrometers – about the width a blood vessel. When subjected to an externally applied magnetic field, the beads clustered into a "swarm" about 15 to 40 micrometers in width.
Next, directed pulses of ultrasound were used to move that swarm up against one wall of the tube. By subsequently switching to a rotating magnetic field, the scientists were able to push the swarm along the length of the tube, in the direction opposite to that of the liquid flow.
Putting the swarm up against the wall of the tube helped, as the friction between the liquid and the glass made the current weaker there. Canoeists paddling upstream on rivers use a similar trick, in that they stay close to the shoreline where the water is moving slower.
The scientists now plan on seeing how the technology works within the actual blood vessels of animals. It is hoped that the beads could ultimately be used not only for targeted drug delivery, but also in microsurgery procedures such as the unclogging of blocked blood vessels.
"As both ultrasound waves and magnetic fields penetrate body tissue, our method is ideal for controlling microvehicles inside the body," says Prof. Daniel Ahmed, who is leading the project along with Prof. Bradley Nelson.
The magnetic beads can be seen in upstream action, in the video below.
A paper on the research was recently published in the journal Nature Machine Intelligence.
Source: ETH Zurich