The potential for nanoparticles to be engineered to carry therapeutic molecules, accumulate at the target site and enter cells has driven research into their drug delivery applications for years. Research efforts have developed and tested a range of nanosized drug delivery structures, including liposomes, carbon nanotubes and mesoporous silica nanoparticles.
A team, working out of Cornell University, has added 12-sided, highly-symmetrical silica cages to the list of nanostructures with drug delivery applications. Writing in Nature, the researchers outline how the nature of the cages may make them suitable for the transit of sensitive therapeutic payloads.
“Given the versatility of silica surface chemistry, and the ability to distinguish the inside and outside of the cage via micelle-directed synthesis, one can readily conceive of cage derivatives of many kinds, which may exhibit unusual properties and be useful in applications ranging from catalysis to drug delivery,” explained the researchers.
The authors cite research into C-Dots to support their argument about the medical potential of the silica cages. C-Dots, an abbreviation of “Cornell Dots”, are silica nanoparticles that can be conjugated to a drug or radiotherapeutic to serve as a delivery vehicle.
Elucida Oncology, a startup founded to develop and commercialise the C-Dot platform, is yet to take a nanoparticle-based therapy into human testing. However, early clinical trials of C-Dots as diagnostic imaging probes suggest they are safe and congregate at disease sites, potentially making them viable drug delivery vehicles.
The authors of the silica cage paper, some of whom helped to discover and refine C-Dots, see these clinical studies as a positive for their latest nanoparticles – this optimism is underpinned by the similarities between the size and surface properties of the two structures.
The researchers discovered the cage structures by drawing on a method previously used to analyse proteins and other biological materials; earlier efforts used algorithms to predict the 3D structure of a material based on 2D images of the substance in different orientations.
For the silica cage project, Cornell researchers analysed 19,000 single-particle images captured by cryo-electron microscopes. The pictures revealed some information about the nanoparticles but the limitations of imaging technology at such scales meant details of the structures were hidden from the researchers.
To compensate for the shortcomings of individual images, the researchers used an algorithm to sort the images and reconstruct the structure of the nanoparticles. This led to simulated projections of the structure of the nanoparticles and, ultimately, to a 3D model.