Prediction of human skin permeability using artificial neural network (ANN) modeling

Authors: CHEN, Long-jian¹; LIAN, Guo-ping²; HAN, Lu-jia

Source: Acta Pharmacologica Sinica, Volume 28, Number 4, April 2007 , pp. 591-600(10)

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Abstract:

Aim: 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 (R²)=0.699, mean square error (MSE)=0.243, and F=493.556. The ANN model gave improved results with n=215, R²=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. Conclusion: The study suggests that Abraham descriptors may be used to predict skin permeability, and the ANN model gives improved prediction of skin permeability.

Keywords: ANN model; diffusion; permeability; quantitative structure-activity relationship; skin

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1745-7254.2007.00528.x

Affiliations: 1: College of Engineering, China Agricultural University, Beijing 100083, China 2: Unilever Corporate Research, Bedford MK44 1LQ, UK

Publication date: 2007-04-01