OpenRiskNet e-infrastructure supports the harmonisation and improved interoperability of data and software tools in the area of predictive toxicology and risk assessment (e.g. for chemicals, cosmetic ingredients, therapeutic agents or nanomaterials) The infrastructure is built on virtual research environments (VRE), which can be deployed to workstations as well as public and in-house cloud infrastructures. Services providing data, analysis, modelling and simulation services for risk assessment are integrated into the infrastructure and can be combined to workflows using harmonized and interoperable application programming interfaces. The reference environment is meant to demonstrate the features of the infrastructure and for test calculations only. Development of personalised workflows and production runs can then be performed on VRE deployed on user provided hardware.OpenRiskNet provides resources to enable users to instantiate their own virtual infrastructures populated with the applications and middleware making up the virtual research environment on public or private cloud resources, as well as in-house server/workstations. The reference environment is provided as a quick entry point to test the OpenRiskNet features.The OpenRiskNet e-infrastructure supports many aspects of risk assessment by allowing the integration of toxicology-related data sources, for the implementation and execution of processing and analysis pipelines and for the execution of modelling workflows. The benefits for the users are: a) Improvement of industrial risk assessments, b) Prototyping of new services and apps, c) Access to integrated resources, d) Complete and qualified system, e) Support for innovative product development.Case studies have been defined to test and evaluate the solutions provided by OpenRiskNet to the predictive toxicology and risk assessment community especially regarding the usability of the developed APIs and the interoperability layer. These demonstrate the capabilities to satisfy the requirements of the different stakeholder groups including researchers, risk assessors and regulators and present real-world applications like systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identifying areas of concern based on in vitro and in silico approaches for compounds lacking any previous knowledge from animal experiments: 1) Data curation and creation of pre-reasoned datasets and searching, 2) Modelling for Prediction or Read Across, 3) A systems biology approach for grouping compounds, 4) Metabolism Prediction, 5) Identification and Linking of Data related to AOPWiki, 6) Toxicogenomics-based prediction and mechanism identification, 6) Reverse dosimetry and PBPK prediction.
- Chemical Sciences
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