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

The Journal of Toxicological Sciences
Kyoko Tsujita-InoueHirokazu Kouzuki

Abstract

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 ...Continue Reading

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Citations

Jan 23, 2016·Regulatory Toxicology and Pharmacology : RTP·Donna S MacmillanChristopher G Barber
Jun 22, 2017·Toxicological Sciences : an Official Journal of the Society of Toxicology·Henrik Johansson, Robin Gradin
Oct 30, 2015·Journal of Applied Toxicology : JAT·Kyoko Tsujita-InoueHirokazu Kouzuki
Nov 3, 2016·Alternatives to Laboratory Animals : ATLA·Terry W SchultzOvanes G Mekenyan
Jan 29, 2018·Toxicology in Vitro : an International Journal Published in Association with BIBRA·Andy ForrerydMalin Lindstedt

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