Data integration of non-animal tests for the development of a test battery to predict the skin sensitizing potential and potency of chemicals

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

“…In these approaches if a final result is equivocal, expert opinion is needed to complete prediction. Examples of sequential strategies include human cell line activation test (h-CLAT)-direct peptide reactivity assay (DPRA) sequence [35]; DPRA, KeratinoSens or the expression of 10 biomarker genes in a human HaCaT keratinocyte cell line, and h-CLAT sequence [36]. In addition, the results of four in silico approaches (MultiCASE, CAESAR, DEREK, and the OECD QSAR Toolbox) are considered by combining them into a single result using naive Bayesian technique by [36].…”

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

“…"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).…”

“…For example, Kao DA (Nukada et al, 2013;Takenouchi et al, 2015) covers the first and third key event, whereas the EC-JRC DA covers the molecular initiating event (MIE) only (Dimitrov et al, 2005;Asturiol et al, 2016), which is considered to determine the final conclusion on the skin sensitisation potential (Asturiol et al, 2016). Given that only animal tests can provide information on the fourth key event (i.e.…”

“…Hence, ignoring testing costs means that existing DAs do not provide an endogenous stopping rule to testing. Rather, the stopping rule for deterministic approaches such as the "2 out of 3" ITS (Bauch et al 2012;Urbisch et al 2015b), the RIVM STS (van der Veen et al, 2014a;van der Veen et al, 2014b) and the Kao DA suggestions (Nukada et al, 2013;Takenouchi et al, 2015) is often based on exogenous decision rules such as AOP coverage. In probabilistic DAs such as the BN-ITS Jaworska et al, 2013;Jaworska et al, 2015), the stopping rule is exogenously determined setting information targets i.e.…”

“…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.…”