OpenRiskNet is a 3-year project funded under the Horizon 2020 EINFRA-22-2016 Programme. The main objective is to provide an open e-Infrastructure providing resources and services to a variety of communities requiring risk-assessment, including chemicals, cosmetic ingredients, therapeutic agents and nanomaterials. OpenRiskNet will work with a network of partners, organized within an Associated Partners Programme.
Toxicology and risk assessment are undergoing a paradigm shift, from a phenomenological to a mechanistic discipline based on in vitro and in silico approaches that represent an important alternative to classical animal testing applied to the evaluation of chronic and systemic toxicity risks. Large databases and highly sophisticated methods, algorithms and tools are available for different tasks such as hazard prediction, toxicokinetics, and in vitro – in vivo extrapolations to support this transition. However, since these services are developed independently and provided by different groups worldwide, there is no standardized way to access the data or run modeling workflows. To overcome the fragmentation of data and tools, OpenRiskNet will provide open e-Infrastructure resources and services supporting different scientific communities.
OpenRiskNet combines the achievements from earlier projects for generating modeling and validation workflows, knowledge integration and data management as well as including ongoing projects and important stakeholders through an associated partner program.
The main components of the infrastructure will be an interoperability layer added to every service to describe the functionality and guaranteeing technical and semantic interoperability, a discovery service, deployment options based on container technology, and packaging of the infrastructure into virtual instances. This will be complemented by training and support on the integration of specific services based on a prototype implementation, usage of standard file formats for data sharing including the generation of templates for data and metadata, as well as the harmonized usage of ontologies.
Case studies will demonstrate the applicability of the infrastructure in productive settings supporting research and innovation in safer product design and risk assessment.