Biology of human hair: Know your hair to control it

Hair can be engineered at different levels—its structure and surface—through modification of its constituent molecules, in particular proteins, but also the hair follicle (HF) can be genetically altered, in particular with the advent of siRNA-based applications. General aspects of hair biology are reviewed, as well as the most recent contributions to understanding hair pigmentation and the regulation of hair development. Focus will also be placed on the techniques developed specifically for delivering compounds of varying chemical nature to the HF, indicating methods for genetic/biochemical modulation of HF components for the treatment of hair diseases. Finally, hair fiber structure and chemical characteristics will be discussed as targets for keratin surface functionalization

Applying response surface methodology to optimize nimesulide permeation from topical formulation

Nimesulide is a non-steroidal anti-inflammatory drug that acts through selective inhibition of COX-2 enzyme. Poor bioavailability of this drug may leads to local toxicity at the site of aggregation and hinders reaching desired therapeutic effects. This study aimed at formulating and optimizing topically applied lotions of nimesulide using an experimental design approach, namely response surface methodology. The formulated lotions were evaluated for pH, viscosity, spreadability, homogeneity and in vitro permeation studies through rabbit skin using Franz diffusion cells. Data were fitted to linear, quadratic and cubic models and best fit model was selected to investigate the influence of permeation enhancers, namely propylene glycol and polyethylene glycol on percutaneous absorption of nimesulide from lotion formulations. The best fit quadratic model explained that the enhancer combination at equal levels significantly increased the flux and permeability coefficient. The model was validated by comparing the permeation profile of optimized formulations’ predicted and experimental response values, thus, endorsing the prognostic ability of response surface methodology