Over the past decade, a wealth of toxicity data has been generated on a variety of engineered nanomaterials. We believe nanoinformatics approaches can significantly aid in the analysis of published data to guide nanotoxicology research in the direction of unexplored areas. Yet limited databases and annotated datasets are available to support the development of predictive models and automated data curation tools. This work describes a process of building a relational database on the toxicity of copper and copper oxide nanoparticles as well as its use to inform nanotoxicology experiments.
The Adverse Outcome Pathway (AOP) framework is increasingly adopted to integrate data from traditional and emerging toxicity screenings, as well as those from disease research. As the number of AOPs has increased, so has the need to define an AOP in terms that can be interpreted computationally. We will present a comprehensive collection of 172 AOPs annotated by terms from existing biological ontologies and housed in the AOP-Wiki as of December 4, 2016.
New testing approaches that leverage advances in science and technology promise to more efficiently evaluate substances and better protect human health and the environment than traditional laboratory animal methods. A key component to the development and implementation of such non-animal approaches is the availability of curated data, as well as the tools and infrastructure to allow users to utilize the data.
OpenRiskNet is a 3-year project funded by the European Union’s Horizon 2020 program. OpenRiskNet is developing an open e-Infrastructure to provide resources and services to a variety of communities requiring risk assessment (chemicals, cosmetic ingredients, therapeutic agents, nanomaterials, et al.).