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. This study reports a method for disentangling exogeneous contributions and measuring their permeation profile through human skin combining SRS measurements with chemometrics. We investigated the spectral decomposition capability of multivariate curve resolution - alternating least squares (MCR-ALS) using hyperspectral SRS images of skin dosed with 4-cyanophenol. By performing MCR-ALS on the fingerprint region spectral data, the distribution of 4-cyanophenol in skin was estimated in an attempt to quantify the amount permeated at different depths. The reconstructed distribution was compared with the experimental mapping of CN, a strong vibrational peak in 4-cyanophenol where the skin is spectroscopically silent. The similarity between MCR-ALS resolved and experimental distribution in skin dosed for 4 h was 0.79 which improved to 0.91 for skin dosed for 1 h. The correlation was observed to be lower for deeper layers of skin where SRS signal intensity is low which is an indication of low sensitivity of SRS. This work is the first demonstration, to the best of our knowledge, of combining SRS imaging technique with spectral unmixing methods for direct observation and mapping of the chemical penetration and distribution in biological tissues.

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. Further, 1924 molecules from these screened molecules were classified as sweet molecules. The shortlisted 1354 molecules were subjected to ADMET analysis. Finally, 60 molecules were arrived at with no toxicity and acceptable oral bioavailability as potential sweetener candidates. Further, molecular docking of these molecules on sweet taste receptor performed to obtain their binding energy, binding sites and correlation with sweetness index. The developed framework offers a convenient route for fast screening of molecules prior to synthesis and testing.

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). The whole dataset was randomly split into training and test sets in a 70:30 ratio. The models were developed using Genetic Function Approximation (Rtest2 = 0.832) and Artificial Neural Network (Rtest2 = 0.831). Our models thus offer a convenient route for fast screening of molecules prior to synthesis and testing. Additionally, this study can supplement a molecular modelling approach to improve binding of molecules with sweet taste receptors, leading to design of novel sweeteners.