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Integrated Decision Strategies for Skin Sensitization Hazard
Citation:
Strickland, J., Q. Zang, N. Kleinstreuer, M. Paris, D. Lehmann, N. Choksi, J. Matheson, A. Jacobs, A. Lowit, D. Allen, AND W. Casey. Integrated Decision Strategies for Skin Sensitization Hazard. JOURNAL OF APPLIED TOXICOLOGY. John Wiley & Sons, Ltd., Indianapolis, IN, 36(9):1150-62, (2016).
Impact/Purpose:
The Interagency Coordinating Committee on the Development of Alternative Methods (ICCVAM) is composed of representatives from 15 U.S. Federal regulatory and research agencies that use toxicological safety testing information. One of the top priorities of ICCVAM is the development and evaluation of non-animal approaches to identify potential skin sensitizers. This abstract describes an ongoing effort to an integrated decision strategy for predicting human skin sensitization hazard.
Description:
One of the top priorities of the Interagency Coordinating Committee for the Validation of Alternative Methods (ICCVAM) is the identification and evaluation of non-animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events of the process have been well characterized in an adverse outcome pathway (AOP) proposed by the Organisation for Economic Co-operation and Development (OECD). Accordingly, ICCVAM is working to develop integrated decision strategies based on the AOP using in vitro, in chemical and in silica information. Data were compiled for 120 substances tested in the murine local lymph node assay (LLNA), direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens assay. Data for six physicochemical properties, which may affect skin penetration, were also collected, and skin sensitization read-across predictions were performed using OECD QSAR Toolbox. All data were combined into a variety of potential integrated decision strategies to predict LLNA outcomes using a training set of 94 substances and an external test set of 26 substances. Fifty-four models were built using multiple combinations of machine learning approaches and predictor variables. The seven models with the highest accuracy (89-96% for the test set and 96-99% for the training set) for predicting LLNA outcomes used a support vector machine (SVM) approach with different combinations of predictor variables. The performance statistics of the SVM models were higher than any of the non-animal tests alone and higher than simple test battery approaches using these methods. These data suggest that computational approaches are promising tools to effectively integrate data sources to identify potential skin sensitizers without animal testing.
