Mathematical modeling in nanotechnology to optimize nanoparticle design for nanomedicine

Nanotech drug delivery vehicles may soon improve because it turns out that even those that appear to be promising are not well-designed. In a Nanowerk Spotlight Michael Berger reports work from nanomedicine pioneer Mauro Ferrari on how to improve the design of nanoparticles for drug delivery. From “Mathematical engines of nanomedicine“:

…Although nanotechnology offers great visions of improved, personalized treatment of disease, at the same time it renders the problem of selecting the candidates for biological testing astronomically more complex. The new notion of ‘design maps’ for nanovectors — similar to the concept of the periodic table for chemical elements — could provide guidance for the development of optimized injectable nanocarriers through mathematical modeling.

“The number of potential combinatorial variations that can be developed by choosing different nanoparticle core materials, targeting moieties, and payload molecules is very large” Prof. Mauro Ferrari tells Nanowerk. “It is easy to compile a catalogue of 100 realistic choices in each of these entry categories. Their combination results in 1 million possible candidates. Choosing just 10 different particle diameters in the range of say 10-500 nm, the total number rises to 10 million. To give the fullest power to the method, each of the 1 million molecules in an a priori, ‘conventional’ combinatorial library may be used as payload: molecular docking for specificity becomes less stringent a criterion if the target selectivity is accomplished by the nanovector, and not solely the drug. The total number of candidates is now 100 billions. Even more awe-inspiring numbers are obtained by considering variations in shape, multiple targeting moieties, biological barriers, avoidance mechanisms, and multiple payloads.”

Ferrari, the Director of the Research Center for NanoMedicine in The Brown Foundation Institute of Molecular Medicine for the Prevention of Human Diseases at The University of Texas Health Science Center at Houston, points out that the problem — and, with it, the great opportunity for the mathematically inclined — is that there exist no consensus on nanovector selection criteria, as there are for drug libraries.

…Ferrari’s group is the first to use mathematics to guide nanotechnology development in drug delivery and nanomedical formulations, in effect bringing nanomedicine closer to the pharma world, where computer modeling has been used for a long time.

…The bad news here is that these results indicate that almost all the nanocarriers that are in the clinic or in the preclinical pipeline are basically the worst possible size and shape for their intended purpose. The good news is that, nevertheless, these nanovectors already have proven quite successful in practice. “The pleasant nature of our unpleasant discoveries is that even small nanoparticle design improvements, fueled by mathematical analysis, will lead to greater success in the fight against human disease” says Ferrari.


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