In Silico Prediction of Skin Permeability Using a Two-QSAR Approach.

ABSTRACT: Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as re

SUBMITTER: Wu YW 

PROVIDER: S-EPMC9143389 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature