Abstract
Stimulated Raman scattering (SRS) microscopy is a non-invasive, label free chemical imaging tool used to acquire quantitative distribution of chemicals as they penetrate the skin. The submicron spatial resolution images can provide insights on their rate of permeation and mechanistic pathways. This understanding of interaction of chemicals with skin tissue can aid in development of topical formulations for effective dermal delivery. However, the quantification of penetration is hampered by significant superposition of Raman spectra of skin constituents with exogeneous chemicals. The present study demonstrates the methodology for disentangling of exogeneous contributions and measuring their permeation profile through human skin combining SRS measurements with spectral unmixing methods. In this research, we investigated the spectral decomposition capability of Multivariate Curve Resolution - Alternating Least Square (MCR-ALS) using SRS images of human skin dosed with retinol-based formulation. Using MCR-ALS, distribution of retinol is estimated at each pixel in SRS image in an attempt to quantify the amount permeated in different depths and analyse its penetration pathways. In the study, implying spectral equality constraint on retinol in the multivariate analysis ensured the profiling of diffusion kinetics as well as reduction of rotational ambiguity in the analysis. This work demonstrates the potentiality of combining Raman imaging technique with spectral unmixing methods for direct observation and quantification of amount of penetration