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Allergic contact dermatitis (ACD) may develop in workers and consumers exposed to skin-sensitizing chemicals and products, which include chemicals such as formaldehyde, formulations such as pesticides, and metals such as nickel. To prevent such exposure, regulatory agencies require the testing of chemicals and products to determine their potential to cause ACD.
Widely used test methods for detecting ACD hazard potential of chemicals use guinea pigs or mice. However, international restrictions on animal testing for cosmetics and other products and an advanced mechanistic understanding of the adverse outcome pathway for skin sensitization are driving interest in non-animal test methods.
NICEATM and ICCVAM scientists developed a defined approach that uses data from three in vitro tests -- the DPRA, h-CLAT, and KeratinoSens assay -- six physicochemical properties, and an in silico read-across prediction of skin sensitization hazard as inputs to machine learning approaches to predict murine local lymph node assay (LLNA) outcomes and human skin sensitization hazard. Using particular combinations of inputs and machine learning approaches yielded more accurate predictions of LLNA or human skin sensitization hazard than any of the in chemico, in vitro, or in silico methods alone. This effort was described in three journal articles that each focused on different strategies and targets:
The EASA is a chemical assay that measures light absorbance or a fluorescent signal in proportion to a chemical’s tendency to bind to probe chemicals that mimic proteins. Binding of a chemical to skin proteins is the first step in the development of ACD. A validation study of the EASA began in 2017, with four ICCVAM agencies participating in the study. NICEATM is coordinating the study and members of the ICCVAM Skin Sensitization Workgroup are serving on the study management team. Efforts in 2017 focused on testing a small group of blinded chemicals for a preliminary assessment of accuracy and reliability; the study is expected to run through the end of 2018.
Ideally, alternative models developed to identify potential human skin sensitizers are evaluated using human skin sensitization data. However, human skin sensitization tests on the same chemical can produce variable results. This can make assessment of alternative models extremely challenging. NICEATM is using human skin sensitization data in the Integrated Chemical Environment and data obtained via industry consortia to compile a data set to better characterize the variability of human skin sensitization data.
NTP is coordinating testing of more than 200 chemicals nominated by ICCVAM agencies to expand the chemical space coverage for a defined approach for identifying skin sensitizers. The nominated chemicals all have existing LLNA data and include pesticides, formulations, industrial chemicals, and other chemicals of interest to ICCVAM agencies. Chemicals are being tested using three in vitro test methods that map to key events in the skin sensitization adverse outcome pathway. Testing began in 2016 and is scheduled to be completed in 2019. The data from this study will enable an evaluation of the appropriateness of a defined approach using these three in vitro methods for various regulatory applications.
NICEATM collaborated with the Cosmetics Europe Skin Tolerance Task Force to evaluate defined approaches for prediction of skin sensitization hazard submitted to the OECD. NICEATM has evaluated six defined approaches against a set of previously untested chemicals with in vitro and in silico data provided by Cosmetics Europe. Journal articles describing the data sets and the performance of the defined approaches will be published in 2018.
In a related effort, a joint proposal to develop a performance-based test guideline for defined approaches to skin sensitization testing and assessment, developed collaboratively by ICCVAM, NICEATM, and ICATM partners (Health Canada and EURL ECVAM), was submitted to the OECD in 2016 and included on the OECD workplan in 2017.