Creation of putative AOPs for DNT by taking existing data on basic molecular developmental neuroscience as well as DNT into account that will foster: |
Targeted generation of missing molecular-, cellular-, tissue- and organism-level data using in vitro and in vivo methods to develop validated AOPs |
Identification of MIEs and/or KEs in priority AOPs for which cell models/alternative organisms must be generated |
Generation of chemical training and testing sets for the use in assay development and validation |
Generation of data sets for large numbers of chemical that allows qualification/validation of assay use that is “fit for purpose”, including: |
Comparison of results across assays with similar endpoints |
Comparison of results of different assays across chemicals |
Development of in silico models (e.g. QSAR, docking models) |
Development of a DNT alternative methods testing battery for the use in routine screening of new and existing chemicals |
Development of predictive computational models based on AOPs that assess reliability of both individual test methods and the DNT testing battery, including: |
Definition of model- and endpoint-specific quantitative cut-off values for delineating adversity |
Development and incorporation of qualitative and quantitative species-specific differences in signalling pathway-driven guidance of developmental processes |
Generation of case studies for use of AOP-based DNT screening data in regulatory decisions, including: |
Use in multiple types of regulatory decision such as read across, prioritization for further testing and replacement of in vivo testing requirements |