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

The GARD platform for potency assessment of skin sensitizing chemicals

“…Approaches for the prediction of skin sensitizer potency have been published and were recently reviewed by Ezendam et al (2016), such as assays targeting KE2 (epidermal equivalent sensitizer potency assay (Teunis et al, 2014), SENS-IS (Cottrez et al, 2015)) and the U-SENS assay modelling KE3 (Piroird et al, 2015). Furthermore, in silico models, often combining information from several in vitro methods, have been described, for example QSAR (Dearden et al, 2015), artificial neural networks (Tsujita-Inoue et al, 2014), probabilistic models and integrated testing strategy (ITS) approaches including a Bayesian model (Jaworska et al, 2013(Jaworska et al, , 2015Luechtefeld et al, 2015;Natsch et al, 2015).…”

Integrated Testing Strategies for Skin Sensitization Hazard and Potency Assessment—State of the Art and Challenges

“…ITS potency assessment approaches developed to date include 3-way and 4-way LLNA EC3 deterministic classification [19][20][21], pEC3 (molar equivalent of EC3) prediction [22][23][24], 4-way probabilistic EC3 classification [25][26][27] and 4-way probabilistic pEC3 classification with a possibility to estimate any percentile of pEC3 distribution [28]. All other ITS approaches mentioned in this mini-review are for hazard estimation only.…”

An economic approach to non-animal toxicity testing for skin sensitisation

“…Applications of machine learning methods to the construction of DAs are Bayesian networks (BN) (Jaworska et al, 2011;Jaworska et al, 2013), Artificial Neural Networks (ANN) (Hirota et al, 2013;Tsujita-Inoue et al, 2014;Hirota et al, 2015;Tsujita-Inoue et al, 2015), Naïve…”

“…"2 out of 3" ITS approach: (Bauch et al, 2012;Natsch et al, 2013;Urbisch et al, 2015a); Kao ITS and Kao STS: (Nukada et al, 2013;Takenouchi et al, 2015); RIVM STS:(van der Veen et al, 2014a;van der Veen et al, 2014b): Stacking meta model: (Gomes, 2012); IDS: (Matheson, 2015;Strickland et al, 2016); BN ITS: Jaworska et al, 2013;Jaworska et al, 2015); ANN ITS: (Hirota et al, 2013;Tsujita-Inoue et al, 2014;Hirota et al, 2015); EC-JRC: (Dimitrov et al, 2005;Asturiol et al, 2016); Global and local regression models: ; IATA: (Patlewicz et al, 2014, Patlewicz et al, 2015 was checked using the leave-one-out validation (Strickland et al, 2016). For the ANN-ITS, the ability of the model to predict the final decision outcome on the skin sensitisation potential was validated using the 10-fold cross validation approach (Hirota et al, 2013).…”

“…Hence, they are presented in different ways, for example in the form of qualitative flowcharts (Grindon et al, 2008e;Mekenyan et al, 2010;ECHA, 2014a;Patlewicz et al, 2015), probabilistic approaches (machine learning) applying Artificial Neural Networks (Hirota et al, 2013;Tsujita-Inoue et al, 2014;Hirota et al, 2015) or Bayesian Networks (Jaworska and Hoffmann, 2010;Jaworska et al, 2013;Jaworska et al, 2015), and as deterministic approaches based on a "majority vote" decision rule for batteries of testing methods (Bauch et al, 2012;van der Veen et al, 2014a;van der Veen et al, 2014b;Urbisch et al, 2015a) or scorebased batteries of testing methods (Ellison et al, 2010;Nukada et al, 2013;Takenouchi et al, 2015). In addition, a regression analysis model ) and a quantitative model using the toxico-kinetics and toxico-dynamics modelling (MacKay et al, 2013) are used.…”