Hypotonic, gel-forming delivery system for vaginal drug administration.

Vaginal drug delivery is often preferred over systemic delivery to reduce side effects and increase efficacy in treating diseases and conditions of the female reproductive tract (FRT). Current vaginal products have drawbacks, including spontaneous ejection of drug-eluting rings and unpleasant discharge from vaginal creams. Here, we describe the development and characterization of a hypotonic, gel-forming, Pluronic-based delivery system for vaginal drug administration. The rheological properties were characterized with and without common hydrogel polymers to demonstrate the versatility. Both qualitative and quantitative approaches were used to determine the Pluronic F127 concentration below the critical gel concentration (CGC) that was sufficient to achieve gelation when formulated to be hypotonic to the mouse vagina. The hypotonic, gel-forming formulation was found to form a thin, uniform gel layer along the vaginal epithelium in mice, in contrast to the rapidly forming conventional gelling formulation containing polymer above the CGC. When the hypotonic, gel-forming vehicle was formulated in combination with a progesterone nanosuspension (ProGel), equivalent efficacy was observed in the prevention of chemically-induced preterm birth (PTB) compared to commercial Crinone® vaginal cream. Further, ProGel showed marked benefits in reducing unpleasant discharge, reducing product-related toxicity, and improving compatibility with vaginal bacteria in vitro. A hypotonic, gel-forming delivery system may be a viable option for therapeutic delivery to the FRT.

On the effect of neuronal spatial subsampling in small-world networks.

The analysis of real-world networks of neurons is biased by the current ability to measure just a subsample of the entire network. It is thus relevant to understand if the information gained in the subsamples can be extended to the global network to improve functional interpretations. Here we showed how average clustering coefficient (CC), average path length (PL), and small-world propensity (SWP) scale when spatial sampling is applied to small-world networks. This extraction mimics the measurement of physical neighbors by means of electrical and optical techniques, both used to study neuronal networks. We applied this method to in silico and in vivo data and we found that the analyzed properties scale with the size of the sampled network and the global network topology. By means of mathematical manipulations, the topology dependence was reduced during scaling. We highlighted the behaviors of the descriptors that, qualitatively, are shared by all the analyzed networks and that allowed an approximated prediction of those descriptors in the global graph using the subgraph information. In contrast, below a spatial threshold, any extrapolation failed; the subgraphs no longer contain enough information to make predictions. In conclusion, the size of the chosen subgraphs is critical to extend the findings to the global network.