Data warehousing

DC is supporting efforts in the direction of FAIR data management (findability, accessibility, interoperability and reusability) currently in the areas of chemical and nanomaterial risk assessment and personalized medicine.

On one hand, reference data warehouses like ToxBank are developed following best-practice approaches regarding standard formats and ontology-based data annotation.

On the other hand, we are working on improved data management concepts and guidelines for the use of ontologies including harmonizations of these in the OpenTox working groups and the OpenRiskNet project.

Database APIs

Ease of access to data is a priority for us - we are working on making many important data sources accessible in real time by providing harmonized Application Programming Interfaces (APIs).

When using a tool like R, KNIME, Python or even just a web browser, this allows you to get data in a matter of seconds compared to the large effort needed when you have to download zip files and manually parse them.

Furthermore it becomes trivial to rerun workflows when new data is released or to create workflows that need to work on several data sources at once. On top of these APIs, we are working on web based user interfaces that provide rich query and filter capabilities for all data sets. Sharing data then becomes as easy as communicating a web resource address (URL).

ToxBank Wiki

Well characterized reference compounds for specific adverse effects are of high importance as positive controls in safety assessment as well as for the elucidation of mechanisms of toxicology and the development of adverse outcome pathways.

The ToxBank Gold Compounds Wiki provides a best-practice resource for such reference compounds in the area of hepatotoxins, cardiotoxins and renal toxins.

For each compound, information was collected on in vivo and PK-ADME data, physicochemical properties and suppliers. Additionally, links to experimental hazard and exposure data are given resulting from the SEURAT-1 project or from other public resources.

Integration of in vitro omics data into risk assessment

We propose an integration of human in vitro transcriptomics data and modelling tools with the adverse outcome pathway (AOP) concept, in order to identify areas of concern and support an evidence-driven risk assessment. Identification of eventual data and methodology gaps is also an objective of the present framework.

Starting from omics in vitro human data, the approach includes the identification of relevant pathways, identification of analogue compounds and the verification versus specific AOPs.

To quickly identify areas of concern, omics data represents a good starting point, since they show general adaptations of the cell to the exposure. The most relevant molecular pathways are selected and further analyzed for genes, diseases and chemicals associations followed by a connectivity mapping to genomic profiles of other compounds with similar modes-of-action.

Further the cross checking of adverse effects versus specific AOP key events, using the information from the AOP-Knowledge Base is performed. This approach could support the risk assessment of individual or group of compounds starting from the transcriptomics profiles, identify data gaps and eventually propose additional testing.