Prediction of human skin permeability using artificial neural network (ANN) modeling.
OBJECTIVE
To develop an artificial neural network (ANN) model for predicting skin permeability (log K(p)) of new chemical entities.


METHODS
A large dataset of 215 experimental data points was compiled from the literature. The dataset was subdivided into 5 subsets and 4 of them were used to train and validate an ANN model. The same 4 datasets were also used to build a multiple linear regression (MLR) model. The remaining dataset was then used to test the 2 models. Abraham descriptors were employed as inputs into the 2 models. Model predictions were compared with the experimental results. In addition, the relationship between log K(p) and Abraham descriptors were investigated.


RESULTS
The regression results of the MLR model were n=215, determination coefficient (R(2))=0.699, mean square error (MSE)=0.243, and F=493.556. The ANN model gave improved results with n=215, R(2)=0.832, MSE=0.136, and F=1050.653. The ANN model suggests that the relationship between log K(p) and Abraham descriptors is non-linear.


CONCLUSIONS
The study suggests that Abraham descriptors may be used to predict skin permeability, and the ANN model gives improved prediction of skin permeability.
Chen
Long-jian
LJ
College of Engineering, China Agricultural University, Beijing 100083, China.
Lian
Guo-ping
GP
Han
Lu-jia
LJ
D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010539:Permeability / Q000187:drug effects; D011237:Predictive Value of Tests; D012869:Skin Absorption / Q000502:physiology*; D013358:Subject Headings