Predictive testing in contact dermatitis. Allergic contact dermatitis

LuSens: a keratinocyte based ARE reporter gene assay for use in integrated testing strategies for skin sensitization hazard identification

Allergic contact dermatitis can develop following repeated exposure to allergenic substances. To date, hazard identification is still based on animal studies as non-animal alternatives have not yet gained global regulatory acceptance. Several non-animal methods addressing key-steps of the adverse outcome pathway (OECD, 2012) will most likely be needed to fully address this effect. Among the initial cellular events is the activation of keratinocytes and currently only one method, the KeratinoSens™, has been formally validated to address this event. In this study, a further method, the LuSens assay, that uses a human keratinocyte cell line harbouring a reporter gene construct composed of the antioxidant response element (ARE) of the rat NADPH:quinone oxidoreductase 1 gene and the luciferase gene. The assay was validated in house using a selection of 74 substances which included the LLNA performance standards. The predictivity of the LuSens assay for skin sensitization hazard identification was comparable to other non-animal methods, in particular to the KeratinoSens™. When used as part of a testing battery based on the OECD adverse outcome pathway for skin sensitization, a combination of the LuSens assay, the DPRA and a dendritic cell line activation test attained predictivities similar to that of the LLNA.

Computer models versus reality: how well do in silico models currently predict the sensitization potential of a substance

National legislations for the assessment of the skin sensitization potential of chemicals are increasingly based on the globally harmonized system (GHS). In this study, experimental data on 55 non-sensitizing and 45 sensitizing chemicals were evaluated according to GHS criteria and used to test the performance of computer (in silico) models for the prediction of skin sensitization. Statistic models (Vega, Case Ultra, TOPKAT), mechanistic models (Toxtree, OECD (Q)SAR toolbox, DEREK) or a hybrid model (TIMES-SS) were evaluated. Between three and nine of the substances evaluated were found in the individual training sets of various models. Mechanism based models performed better than statistical models and gave better predictivities depending on the stringency of the domain definition. Best performance was achieved by TIMES-SS, with a perfect prediction, whereby only 16% of the substances were within its reliability domain. Some models offer modules for potency; however predictions did not correlate well with the GHS sensitization subcategory derived from the experimental data. In conclusion, although mechanistic models can be used to a certain degree under well-defined conditions, at the present, the in silico models are not sufficiently accurate for broad application to predict skin sensitization potentials.

Prediction of skin sensitization with a particle swarm optimized support vector machine

Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.