Novel topical skin hydration agent containing Anadenanthera colubrina polysaccharide-standardized herbal preparation.

BACKGROUND: Hydration is an important factor to promote skin barrier function, metabolism, and appearance. In this process, the presence of aquaglyceroporins, envelope and lipid synthesis, and metabolism proteins are essential to provide greater corneocyte cohesion and to form a barrier avoiding transepidermal water loss. OBJECTIVE: We evaluated the effects of a new topical pigment-free agent containing an Anadenanthera colubrina polysaccharide-rich dermocosmetic preparation (ACP) on the aquaporin-3 (AQP-3), filaggrin (FLG), involucrin (INV), glucocerebrosidase (GBA), and elongation of very-long-chain fatty acid (ELOVL) proteins production in skin human fragments, as well as on the transepidermal water loss in a double-blind placebo-controlled clinical trial. METHODS: AQP3, FLG, INV, GBA, and ELOVL3 levels were measured by immunofluorescence analysis in human skin explants. Clinical trial was conducted to evaluate the effects of ACP 1% and ACP 3% on the transepidermal water loss (TEWL). RESULTS: Image and statistical analysis showed that ACP 3% significantly increased at 90% the expression of AQP3. Similarly, ACP 3% was able to promote a significant increase of 68% and 51% in FLG and INV, respectively. ACP 3% produced no effects on the GBA and ELOVL3 proteins. Transepidermal water loss was significantly reduced in human volunteers under treatment with ACP 1% and ACP 3%. CONCLUSION: ACP reduced transepidermal water loss in a clinical trial, promoting human skin hydration. These effects were related to modulation of the AQP3, FLG, and INV as evidenced by immunofluorescence assay. This way, A colubrina polysaccharide-rich phytopharmaceutical preparation is an effective additive product to skin hydration.

On the virtues of automated quantitative structure-activity relationship: the new kid on the block.

Quantitative structure-activity relationship (QSAR) has proved to be an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models. Results demonstrate that AutoQSAR produces models of improved or similar quality to those generated by practitioners in the field but in just a fraction of the time. Despite the potential of the concept to the benefit of the community, the AutoQSAR opportunity has been largely undervalued.