Evaluating the performance of integrated approaches for hazard identification of skin sensitizing chemicals

Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays

There is a pressing need for non-animal methods to predict skin sensitisation potential and a number of in chemico and in vitro assays have been designed with this in mind. However, some compounds can fall outside the applicability domain of these in chemico/in vitro assays and may not be predicted accurately. Rule-based in silico models such as Derek Nexus are expert-derived from animal and/or human data and the mechanism-based alert domain can take a number of factors into account (e.g. abiotic/biotic activation). Therefore, Derek Nexus may be able to predict for compounds outside the applicability domain of in chemico/in vitro assays. To this end, an integrated testing strategy (ITS) decision tree using Derek Nexus and a maximum of two assays (from DPRA, KeratinoSens, LuSens, h-CLAT and U-SENS) was developed. Generally, the decision tree improved upon other ITS evaluated in this study with positive and negative predictivity calculated as 86% and 81%, respectively. Our results demonstrate that an ITS using an in silico model such as Derek Nexus with a maximum of two in chemico/in vitro assays can predict the sensitising potential of a number of chemicals, including those outside the applicability domain of existing non-animal assays.

Bayesian integrated testing strategy (ITS) for skin sensitization potency assessment: a decision support system for quantitative weight of evidence and adaptive testing strategy

The presented Bayesian network Integrated Testing Strategy (ITS-3) for skin sensitization potency assessment is a decision support system for a risk assessor that provides quantitative weight of evidence, leading to a mechanistically interpretable potency hypothesis, and formulates adaptive testing strategy for a chemical. The system was constructed with an aim to improve precision and accuracy for predicting LLNA potency beyond ITS-2 (Jaworska et al., J Appl Toxicol 33(11):1353-1364, 2013) by improving representation of chemistry and biology. Among novel elements are corrections for bioavailability both in vivo and in vitro as well as consideration of the individual assays' applicability domains in the prediction process. In ITS-3 structure, three validated alternative assays, DPRA, KeratinoSens and h-CLAT, represent first three key events of the adverse outcome pathway for skin sensitization. The skin sensitization potency prediction is provided as a probability distribution over four potency classes. The probability distribution is converted to Bayes factors to: 1) remove prediction bias introduced by the training set potency distribution and 2) express uncertainty in a quantitative manner, allowing transparent and consistent criteria to accept a prediction. The novel ITS-3 database includes 207 chemicals with a full set of in vivo and in vitro data. The accuracy for predicting LLNA outcomes on the external test set (n = 60) was as follows: hazard (two classes)-100 %, GHS potency classification (three classes)-96 %, potency (four classes)-89 %. This work demonstrates that skin sensitization potency prediction based on data from three key events, and often less, is possible, reliable over broad chemical classes and ready for practical applications.

Development of an in vitro test to identify respiratory sensitizers in bronchial epithelial cells using gene expression profiling

Chemicals that induce asthma at the workplace are substances of concern. At present, there are no widely accepted methods to identify respiratory sensitizers, and classification of these substances is based on human occupational data. Several studies have contributed to understanding the mechanisms involved in respiratory sensitization, although uncertainties remain. One point of interest for respiratory sensitization is the reaction of the epithelial lung barrier to respiratory sensitizers. To elucidate potential molecular effects of exposure of the epithelial lung barrier, a gene expression profile was created based on a DNA microarray experiment using the bronchial epithelial cell line 16 HBE14o(-). The cells were exposed to 12 respiratory sensitizers and 10 non-sensitizers. For statistical analysis, we used a class prediction approach that combined three machine learning algorithms, leave-one-compound-out cross validation, and majority voting per tested compound. This approach allowed for a prediction accuracy of 95%. Identified predictive genes were mainly associated with the cytoskeleton and barrier function of the epithelial cell. Several of these genes were reported to be associated with asthma as well. Taken together, this indicates that pulmonary barrier function is an important target for respiratory sensitizers and associated genes can be used to predict the respiratory sensitization potential of chemicals.

Unsaturated compounds induce up-regulation of CD86 on dendritic cells in the in vitro sensitization assay LCSA

Unsaturated compounds are known to cause false-positive reactions in the local lymph node assay (LLNA) but not in the guinea pig maximization test. We have tested a panel of substances (succinic acid, undecylenic acid, 1-octyn-3-ol, fumaric acid, maleic acid, linoleic acid, oleic acid, alpha-linolenic acid, squalene, and arachidonic acid) in the loose-fit coculture-based sensitization assay (LCSA) to evaluate whether unspecific activation of dendritic cells is a confounder for sensitization testing in vitro. Eight out of 10 tested substances caused significant up-regulation of CD86 on dendritic cells cocultured with keratinocytes and would have been classified as sensitizers; only succinic acid was tested negative, and squalene had to be excluded from data analysis due to poor solubility in cell culture medium. Based on human data, only undecylenic acid can be considered a true sensitizer. The true sensitizing potential of 1-octyn-3-ol is uncertain. Fumaric acid and its isomer maleic acid are not known as sensitizers, but their esters are contact allergens. A group of 18- to 20-carbon chain unsaturated fatty acids (linoleic acid, oleic acid, alpha-linolenic acid, and arachidonic acid) elicited the strongest reaction in vitro. This is possibly due to the formation of pro-inflammatory lipid mediators in the cell culture causing nonspecific activation of dendritic cells. In conclusion, both the LLNA and the LCSA seem to provide false-positive results for unsaturated fatty acids. The inclusion of T cells in dendritic cell-based in vitro sensitization assays may help to eliminate false-positive results due to nonspecific dendritic cell activation. This would lead to more accurate prediction of sensitizers, which is paramount for consumer health protection and occupational safety.

Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes

An adverse outcome pathway (AOP) describes the causal linkage between initial molecular events and an adverse outcome at individual or population levels. Whilst there has been considerable momentum in AOP development, far less attention has been paid to how AOPs might be practically applied for different regulatory purposes. This paper proposes a scientific confidence framework (SCF) for evaluating and applying a given AOP for different regulatory purposes ranging from prioritizing chemicals for further evaluation, to hazard prediction, and ultimately, risk assessment. The framework is illustrated using three different AOPs for several typical regulatory applications. The AOPs chosen are ones that have been recently developed and/or published, namely those for estrogenic effects, skin sensitisation, and rodent liver tumor promotion. The examples confirm how critical the data-richness of an AOP is for driving its practical application. In terms of performing risk assessment, human dosimetry methods are necessary to inform meaningful comparisons with human exposures; dosimetry is applied to effect levels based on non-testing approaches and in vitro data. Such a comparison is presented in the form of an exposure:activity ratio (EAR) to interpret biological activity in the context of exposure and to provide a basis for product stewardship and regulatory decision making.