Quantitative prediction of ionization effect on human skin permeability

[Display omitted] Although skin permeability of an active ingredient can be severely affected by its ionization in a dose solution, most of the existing prediction models cannot predict such impacts. To provide reliable predictors, we curated a novel large dataset of in vitro human skin permeability...

Full description

Saved in:
Bibliographic Details
Published in:International journal of pharmaceutics 2017-04, Vol.522 (1-2), p.222-233
Main Authors: Baba, Hiromi, Ueno, Yusuke, Hashida, Mitsuru, Yamashita, Fumiyoshi
Format: Article
Language:English
Subjects:
Databases, Factual
Gaussian process
Humans
Hydrogen-Ion Concentration
Ionization effect
Ions - chemistry
Least-Squares Analysis
Linear Models
Normal Distribution
Permeability
Predictive Value of Tests
QSPR
Quantitative Structure-Activity Relationship
Skin Absorption
Skin permeability
Solubility
Support Vector Machine
Support vector regression
Transdermal absorption
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:[Display omitted] Although skin permeability of an active ingredient can be severely affected by its ionization in a dose solution, most of the existing prediction models cannot predict such impacts. To provide reliable predictors, we curated a novel large dataset of in vitro human skin permeability coefficients for 322 entries comprising chemically diverse permeants whose ionization fractions can be calculated. Subsequently, we generated thousands of computational descriptors, including LogD (octanol–water distribution coefficient at a specific pH), and analyzed the dataset using nonlinear support vector regression (SVR) and Gaussian process regression (GPR) combined with greedy descriptor selection. The SVR model was slightly superior to the GPR model, with externally validated squared correlation coefficient, root mean square error, and mean absolute error values of 0.94, 0.29, and 0.21, respectively. These models indicate that Log D is effective for a comprehensive prediction of ionization effects on skin permeability. In addition, the proposed models satisfied the statistical criteria endorsed in recent model validation studies. These models can evaluate virtually generated compounds at any pH; therefore, they can be used for high-throughput evaluations of numerous active ingredients and optimization of their skin permeability with respect to permeant ionization.
ISSN:0378-5173
1873-3476
DOI:10.1016/j.ijpharm.2017.03.009