DASS App for skin sensitization prediction using defined approaches
Regulatory agencies require that chemical products be evaluated for their potential to cause skin sensitization. Traditionally, such evaluations involved animal models, but more recently, nonanimal methods have been developed. Defined approaches for skin sensitization (DASS) use standardized interpretation procedures to combine data from multiple nonanimal tests, such as human cell-based experiments, molecular studies, and computational models, to determine if a chemical has potential to cause allergic dermatitis.
While regulatory agencies accept several DASS methods, applying these approaches manually can be complex, slow, and prone to errors. To address this, NICEATM researchers created the DASS App (To et al. 2024), a free, open-source web application that simplifies the use of three regulatory testing strategies: Two-out-of-Three, Integrated Testing Strategy, and Key Event 3/1 Sequential Testing Strategy. This web tool, first launched in 2023, allows users to upload their own data, identify inconsistencies, and generate predictions without needing advanced computational skills or special software installation. DASS App provides a data template and the option to select the assays from which the data are taken. Reference data can be imported from NICE. Outputs are provided in a user-friendly, downloadable format as tables and interactive graphics. By making the application freely accessible online, the developers ensure the tool is available for wide adoption and future improvements. This innovation supports faster, more reliable hazard assessments while advancing the goal of replacing the use of animals with humane and efficient alternatives.
Updates in 2024 and 2025 made the app more user-friendly, including simplified handling of borderline calls, as well as adding new test method inputs to cover similar updates to OECD Test Guideline 497, which contains the Two-out-of-Three and Integrated Testing Strategy defined approaches. DASS App now allows the user to evaluate performance using their own reference data or data from NICE chemical reference lists.