Characterisation of skin penetration pathways using stimulated Raman scattering microscopy.

Understanding the mechanisms governing the penetration of substances into the skin is crucial for the development of safe and effective topical drug delivery systems and skincare products. This study examined the partitioning of model permeants into human skin, by assessing six substances with diverse logP values. We employed stimulated Raman scattering (SRS) microscopy, an ambient, label-free optical imaging technique known for its ability to provide chemical distribution with subcellular resolution.

Measurement of chemical penetration in skin using Stimulated Raman scattering microscopy and multivariate curve resolution - alternating least squares.

The mechanistic understanding of skin penetration underpins the design, efficacy and risk assessment of many high-value products including functional personal care products, topical and transdermal drugs. Stimulated Raman scattering (SRS) microscopy, a label free chemical imaging tool, combines molecular spectroscopy with submicron spatial information to map the distribution of chemicals as they penetrate the skin. However, the quantification of penetration is hampered by significant interference from Raman signals of skin constituents.

In-silico screening of database for finding potential sweet molecules: A combined data and structure based modeling approach.

In this study, we present a framework comprises of several independent modules which are built upon data based (structure activity relationship and classification model) and structure (molecular docking) based for identifying possible sweeteners from a vast database of natural molecules. A large database, Universal Natural Products Database (UNPD) consisting of 213,210 compounds was screened using the developed framework. At first, 10,184 molecules structurally similar to the known sweeteners were identified in the database.

In-silico prediction of sweetness using structure-activity relationship models.

Quantitative structure activity relationship (QSAR) models appear to be an ideal tool for quick screening of promising candidates from a vast library of molecules, which can then be further designed, synthesized and tested using a combination of rigorous first principle simulations, such as molecular docking, molecular dynamics simulation and experiments. In this study, QSAR models have been built with an extensive dataset of 487 compounds to predict the sweetness potency relative to sucrose (ranging 0.2-220,000).