Nano-engineered intelligent delivery systems
Data points plotted using data generated from Malvern Nanosizer ZS. (a) Hydrodynamic size distribution of PNIPAM and AuNP/PNIPAM as a result of Temperature Elevation and (b) Heat induced swelling of 12nm AuNP conjugated with PNIPAM.
One method proposed to fight multi-drug resistant bacteria is that of an intelligent delivery system which targets drugs at specific cells, minimising unwanted side effects from drug treatments. Researchers from Monash University, including MCN Technology Fellows Associate Professor Wenlong Cheng and Dr. Christina Cortez-Jugo are working on a project to develop a rational design of multi-materials composite particles for targeted, light-controllable drug delivery.
The team are looking to simulate an intelligent delivery system, whereby gold nanoparticles are incorporated with poly N-isopropylacrylamide (PNIPAM) to formulate an intelligent delivery vehicle to combat multidrug resistance.
Preliminary work suggests that gold nanoparticles could be successfully incorporated into PNIPAM particles using a rotating mixer without destroying the stimuli-responsive properties of PNIPAM. To observe this, the hydrodynamic diameters and zeta potentials of Au-PNIPAM nanoparticles were characterised under various temperatures using the Malven Nanosizer ZS at MCN. The results suggest that at a lower critical solution temperature, PNIPAM was not altered with the incorporation of gold nanoparticles which range in size from 3.5nm to 45nm. The size of the nanoparticles was confirmed via the use of the Field Emission Gun Scanning Electron Microscope.
All instruments and equipment used to formulate and characterise the Au-PNIPAM particles were located at MCN. The facilities provided by MCN in the Biochemistry and PC2 Laboratories allowed characterisations to be carried out to a greater depth and verification of the results achieved via imaging of the formulated particles. Further studies using the intelligent delivery vehicle for delivery of vaccine and drugs are forecast using MCN’s microarray system.