No abstract

-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.

In silico risk assessment for skin sensitization using arti ia neura net ork ana sis oko su ita noue omomi to e ori iko irota akao s ikaga an irokazu ouzuki

Although photoallergens require UV energy for antigen formation, the subsequent immune response is considered to be the same as in ordinary skin sensitization. Therefore, in vitro tests for skin sensitization should also be applicable for photoallergy testing. In this study, we examined whether activation of the Keap1 (Kelch-like ECH-associated protein 1)-Nrf2 (nuclear factor-erythroid 2-related factor 2)-ARE (antioxidant response element) pathway could be used to assess the photoallergenic potential of chemicals, using the reporter cell line AREc32 or KeratinoSens(TM) . First, we identified an appropriate UVA irradiation dose [5 J cm(-2) irradiation in phosphate-buffered saline (PBS)] by investigating the effect of UV irradiation on ARE-dependent gene induction using untreated or 6-methylcoumarin (6-MC)-treated cells. Irradiation of well-known photoallergens under this condition increased ARE-dependent gene expression by more than 50% compared with both vehicle and non-irradiated controls. When the cut-off value for detecting photoallergens was set at 50% induction, the accuracy of predicting photoallergenic/phototoxic chemicals was 70% in AREc32 cells and 67% in KeratinoSens(TM) cells, and the specificity was 100% in each case. We designate these assays as a photo-ARE assay and photo-KeratinoSens(TM) , respectively. Our results suggest that activation of the Keap1-Nrf2-ARE pathway is an effective biomarker for evaluating both photoallergenic and phototoxic potentials. Either of the above tests might be a useful component of a battery of in vitro tests/in silico methods for predicting the photoallergenicity and phototoxicity of chemicals. Copyright © 2015 John Wiley & Sons, Ltd.