Development of novel in vitro photosafety assays focused on the Keap1-Nrf2-ARE pathway.

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.

In silico risk assessment for skin sensitization using artificial neural network analysis.

The sensitizing potential of chemicals is usually identified and characterized using in vivo methods such as the murine local lymph node assay (LLNA). Due to regulatory constraints and ethical concerns, alternatives to animal testing are needed to predict the skin sensitization potential of chemicals. For this purpose, an integrated evaluation system employing multiple in vitro and in silico parameters that reflect different aspects of the sensitization process seems promising. We previously reported that LLNA thresholds could be well predicted by using an artificial neural network (ANN) model, designated iSENS ver. 2 (integrating in vitro sensitization tests version 2), to analyze data obtained from in vitro tests focused on different aspects of skin sensitization. Here, we examined whether LLNA thresholds could be predicted by ANN using in silico-calculated descriptors of the three-dimensional structures of chemicals. We obtained a good correlation between predicted LLNA thresholds and reported values. Furthermore, combining the results of the in vitro (iSENS ver. 2) and in silico models reduced the number of chemicals for which the potency category was under-estimated. In conclusion, the ANN model using in silico parameters was shown to be have useful predictive performance. Further, our results indicate that the combination of this model with a predictive model using in vitro data represents a promising approach for integrated risk assessment of skin sensitization potential of chemicals.

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

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 Kp), 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 Kp and log D of 359 samples, for which literature values of either or both of log Kp 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 Kp; 0.675 vs 0.887, log D; 0.553 vs 0.658), indicating superior performance. The predictive RMS errors for log Kp 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 Kp 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.

Skin sensitization risk assessment model using artificial neural network analysis of data from multiple in vitro assays.

The sensitizing potential of chemicals is usually identified and characterized using in vivo methods such as the murine local lymph node assay (LLNA). Due to regulatory constraints and ethical concerns, alternatives to animal testing are needed to predict skin sensitization potential of chemicals. For this purpose, combined evaluation using multiple in vitro and in silico parameters that reflect different aspects of the sensitization process seems promising. We previously reported that LLNA thresholds could be well predicted by using an artificial neural network (ANN) model, designated iSENS ver.1 (integrating in vitro sensitization tests version 1), to analyze data obtained from two in vitro tests: the human Cell Line Activation Test (h-CLAT) and the SH test. Here, we present a more advanced ANN model, iSENS ver.2, which additionally utilizes the results of antioxidant response element (ARE) assay and the octanol-water partition coefficient (LogP, reflecting lipid solubility and skin absorption). We found a good correlation between predicted LLNA thresholds calculated by iSENS ver.2 and reported values. The predictive performance of iSENS ver.2 was superior to that of iSENS ver.1. We conclude that ANN analysis of data from multiple in vitro assays is a useful approach for risk assessment of chemicals for skin sensitization.