In Silico Prediction of Stratum Corneum Partition Coefficients via COSMOmic and Molecular Dynamics Simulations

利用 COSMOmic 和分子动力学模拟对角质层分配系数进行计算机预测

阅读:1

Abstract

Stratum corneum (SC) is the main barrier of human skin where the inter-corneocytes lipids provide the main pathway for transdermal permeation of functional actives of skin care and health. Molecular dynamics (MD) has been increasingly used to simulate the SC lipid bilayer structure so that the barrier property and its affecting factors can be elucidated. Among reported MD simulation studies, solute partition in the SC lipids, an important parameter affecting SC permeability, has received limited attention. In this work, we combine MD simulation with COSMOmic to predict the partition coefficients of dermatologically relevant solutes in SC lipid bilayer. Firstly, we run MD simulations to obtain equilibrated SC lipid bilayers with different lipid types, compositions, and structures. Then, the simulated SC lipid bilayer structures are fed to COSMOmic to calculate the partition coefficients of the solutes. The results show that lipid types and bilayer geometries play a minor role in the predicted partition coefficients. For the more lipophilic solutes, the predicted results of solute partition in SC lipid bilayers agree well with reported experimental values of solute partition in extracted SC lipids. For the more hydrophilic molecules, there is a systematical underprediction. Nevertheless, the MD/COSMOmic approach correctly reproduces the phenomenological correlation between the SC lipid/water partition coefficients and the octanol/water partition coefficients. Overall, the results show that the MD/COSMOmic approach is a fast and valid method for predicting solute partitioning into SC lipids and hence supporting the assessment of percutaneous absorption of skin care ingredients, dermatological drugs as well as environmental pollutants.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。