The study of transdermal delivery mechanism of liposomes using stratum corneum lipid membrane model

초록

A reliable assessment of skin permeation is essential for the rational design of transdermal formulations which effectively deliver active ingredients. In particular, a skin screening platform capable of evaluating skin permeability and elucidating the penetration mechanisms of skin penetration enhancers (SPEs), such as liposomes, is critical for advancing transdermal delivery systems. Here, we developed stratum corneum (SC) lipid membrane models which reproduce the barrier properties of human SC and enable mechanistic study of liposomal transdermal delivery. The models were prepared by annealing ceramide, free fatty acids, cholesterol, and cholesterol sulfate on polycarbonate membranes, forming orthorhombic (OR) and hexagonal (HEX) lipid packing structures representing healthy and impaired barriers, respectively. ATR-FTIR and wide-angle X-ray scattering (WAXS) analyses showed that fatty acid chain length governed lipid packing, with long chains promoting OR and short chains favoring HEX packing structure. Franz diffusion experiments demonstrated permeability trends comparable to those reported for excised human SC, with strong correlations between two systems (Pearson correlation coefficients of 0.869 for kp and 0.943 for tlag). When liposomes permeated through this model, liposomal niacinamide showed markedly enhanced permeation compared to both aqueous and dipropylene glycol (DPG) formulations. Post-permeation FTIR analysis revealed increased orthorhombicity of SC lipids, while complementary elemental analyses suggested that this effect was driven by cholesterol integration into the SC lipids, supporting a vesicle-lipid fusion mechanism. Collectively, this SC lipid membrane model enables quantitative permeability analysis and mechanistic studies of SPEs, supporting the rational optimization of transdermal delivery systems in dermocosmetic and pharmaceutical applications. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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