Tyrosine-derived nanospheres for enhanced topical skin penetration.

The objective of this study was to investigate the passive skin penetration of lipophilic model agents encapsulated within tyrosine-derived nanospheres. The nanospheres were formed by the self-assembly of a biodegradable, non-cytotoxic ABA triblock copolymer. The A-blocks were poly(ethylene glycol) and the hydrophobic B-blocks were oligomers of suberic acid and desaminotyrosyl-tyrosine alkyl esters. These nanospheres had an average hydrodynamic diameter of about 50nm and formed strong complexes with fluorescent dyes, 5-dodecanoylaminofluorescein (DAF, LogD=7.54) and Nile Red (NR, LogD=3.10). These dyes have been used here as models for lipophilic drugs. The distribution of topically applied nanosphere-dye formulations was studied in human cadaver skin using cryosectioning and fluorescence microscopy. Permeation analysis (quantified fluorescence) over a 24h period revealed that the nanospheres delivered nine times more NR to the lower dermis than a control formulation using propylene glycol. For DAF, the nanosphere formulation was 2.5 times more effective than the propylene glycol based control formulation. We conclude that tyrosine-derived nanospheres facilitate the transport of lipophilic substances to deeper layers of the skin, and hence may be useful in topical delivery applications.

Glass transition temperature prediction of polymers through the mass-per-flexible-bond principle.

A semi-empirical method based on the mass-per-flexible-bond (M/f) principle was used to quantitatively explain the large range of glass transition temperatures (T(g)) observed in a library of 132 L-tyrosine derived homo, co- and terpolymers containing different functional groups. Polymer class specific behavior was observed in T(g) vs. M/f plots, and explained in terms of different densities, steric hindrances and intermolecular interactions of chemically distinct polymers. The method was found to be useful in the prediction of polymer T(g). The predictive accuracy was found to range from 6.4 to 3.7 K, depending on polymer class. This level of accuracy compares favorably with (more complicated) methods used in the literature. The proposed method can also be used for structure prediction of polymers to match a target T(g) value, by keeping the thermal behavior of a terpolymer constant while independently choosing its chemistry. Both applications of the method are likely to have broad applications in polymer and (bio)material science.