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Proposing alerts for pre and pro-haptens (QSAR2016)
Citation:
Patlewicz, G. AND D. Roberts. Proposing alerts for pre and pro-haptens (QSAR2016). Presented at QSAR 2016, Miami, FL, June 13 - 17, 2016. https://doi.org/10.23645/epacomptox.5189275
Impact/Purpose:
Poster presented at the QSAR2016 meeting. This is Insights from an Expert JRC Meeting on pre- and pro-haptens:. This was a small analysis performed as an output of a Expert JRC Meeting on pre and pro-haptens which was intended to provide some guidance of the ability of non-animal test methods to correctly identify sensitisers that require some activation. This is one of 2 analyses. The exercise was useful in a more general sense to start to uncover what the scale of the metabolism problem really is for an endpoint. In actual fact, based on the available data that JRC has compiled, the existing test methods do a decent job of correctly identifying sensitisers that require activation and the scope of those substances that actually require metabolic activation forms a very small subset.
Description:
Predictive testing to identify and characterise substances for their skin sensitisation potential has historically been based on animal tests such as the Local Lymph Node Assay (LLNA). In recent years, regulations in the cosmetics and chemicals sectors has provided a strong impetus to develop and evaluate non-animal alternative methods. The 3 test methods that have undergone extensive development and validation are the direct peptide reactivity assay (DPRA), the KeratinoSensTM and the human Cell Line Activation Test (h-CLAT). Whilst these methods have been shown to perform relatively well in predicting LLNA results (accuracy ~ 80%), a particular concern that has been raised is their ability to predict chemicals that need to be activated to act as sensitisers (either abiotically on the skin (pre-hapten) or metabolically in the skin (pro-hapten)). This study reviewed an EURL ECVAM dataset containing 271 substances for which information was available in the LLNA and for one or more of the three non-animal test methods. The chemical structures of the substances were inspected and each assigned to a reaction mechanistic domain. Fifty-three substances were expected to require activation. Plausible reaction pathways were considered for each of the substances from which three structural alerts were hypothesised: autoxidation to hydroperoxides, aromatic ortho and para-diamino or di phenol derivatives, and aromatic meta-diamino/hydroxy derivatives. For each alert, the available non-animal test data was compared with the LLNA results to understand whether one or other test method was more predictive for these specific substances. Eleven substances were identified as likely to undergo autoxidation resulting in the formation of hydroperoxides. The performance of the 3 methods for these substances was very mixed with no clear pattern. This was anticipated since the test results are very dependent on the actual test sample and similar mixed findings have been found with LLNA data. Twelve substances that fell within the scope of being an aromatic ortho and para-diamino or diphenol derivative were identified. They all were categorised as pre and/or pro-Michael acceptors. All were correctly identified as sensitisers by any of the test methods. There were 12 substances within the Aromatic meta: diamines, aminophenols, di-phenols, and aromatic monoamines alert. This alert comprised 4 aromatic meta amino/hydroxy derivatives and 8 aromatic monoamines. The h-CLAT was found to perform better than either of the other test methods. The ability to extract structural alerts information based on reaction domain and type of activation can be helpful in directing which key event and its associated non-animal test method might be most effective in predicting skin sensitisation potential.This abstract may not reflect U.S. EPA policy.
URLs/Downloads:
DOI: Proposing alerts for pre and pro-haptens (QSAR2016)
PATLEWICZ_ROBERTS_QSAR_2016_PREPROHAPTENS_ALERTS_100616.PDF (PDF, NA pp, 253.146 KB, about PDF)
PROPOSING ALERTS FOR PRE AND PRO_QSAR2016 ABSTRACT.PDF (PDF, NA pp, 48.392 KB, about PDF)