Artificial neural network analysis for predicting human percutaneous absorption taking account of vehicle properties

作者：Atobe Tomomi; Mori Masaaki; Yamashita Fumiyoshi; Hashida Mitsuru; Kouzuki Hirokazu^*

来源：Journal of Toxicological Sciences, 2015, 40(2): 277-294.

DOI：10.2131/jts.40.277

• Percutaneous absorption
• Risk assessment
• In silico
• Artificial neural network
• Vehicle

An in silico method for predicting percutaneous absorption of cosmetic ingredients was developed by using artificial neural network (ANN) analysis to predict the human skin permeability coefficient (log K-p), taking account of the physicochemical properties of the vehicle, and the apparent diffusion coefficient (log D). Molecular weight and octanol-water partition coefficient (log P) of chemicals, and log P of the vehicles, were used as molecular descriptors for predicting log K-p and log D of 359 samples, for which literature values of either or both of log K-p and log D were available. Adaptivity of the ANN model was evaluated in comparison with a multiple linear regression model (MLR) by calculating the root-mean-square (RMS) errors. Accuracy and robustness were confirmed by 10-fold cross-validation. The predictive RMS errors of the ANN model were smaller than those of the MLR model (log K-p; 0.675 vs 0.887, log D; 0.553 vs 0.658), indicating superior performance. The predictive RMS errors for log K-p and log D with the ANN model after 10-fold cross-validation analysis were 0.723 and 0.606, respectively. Moreover, we estimated the cumulative amounts of chemicals permeated into the skin during 24 hr (Q24hr) from the values of log K-p and log D by applying Fick's law of diffusion. Our results suggest that this newly established ANN analysis method, taking account of the property of the vehicle, could contribute to non-animal risk assessment of cosmetic ingredients by providing a tool for calculating Q24hr, which is required for evaluating the margin of safety.

• 出版日期2015-4