Poster: ToxBank Integrated Data Analysis

A ToxBank Integrated Data Analysis of SEURAT-1 Reference Compounds

Barry Hardy / Douglas Connect, Glenn Myatt / Leadscope, Pekka Kohonen and Roland Grafström / Karolinska Institutet


Barry Hardy, Douglas Connect GmbH, Switzerland

OpenTox Euro 2014 Poster: ToxBank Integrated Data Analysis

The SEURAT-1 (Safety Evaluation Ultimately Replacing Animal Testing-1) research cluster is comprised of seven EU FP7 Health projects and is co-financed by Cosmetics Europe. The SEURAT-1 strategy is to adopt a mode-of-action framework to describe repeated dose toxicity to derive predictions of in vivo toxicity responses.

ToxBank is the cross-cluster infrastructure project which provides a web-accessible shared repository of research data and protocols. Experiments generate dose response data over multiple timepoints using different omics platforms including transcriptomics, proteomics, metabolomics, and epigenetics over different cell lines and a common set of reference compounds (details available at Data is also generated from functional assays and bioreactors and supplemented with in silico approaches.

This complex and heterogeneous data is consolidated and harmonized through the ToxBank data warehouse in order to perform an integrated data analysis. We describe for 14 reference compounds the meta-analysis of currently public data including Open TG-GATEs human in vitro liver data of the reference compounds including reactive compounds (e.g., acetaminophen, CCl4), mitochondrial disruptors (e.g., Rotenone), promiscuous binders (e.g., valproic acid, amiadarone), nuclear hormone receptor ligands (e.g., tamoxifen, WY14643), selective binders (e.g. fluoxetine) and cardiotoxins (e.g., Doxorubicin, Nifedipine). Adverse events of interest that are represented include cytotoxicity, fibrosis, steatosis, cholestasis and phospholipidosis.


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4) Kohonen, P., et al, “The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing”, Molecular Informatics, Special Issue: Advances in Computational Toxicology, (2013), Volume 32, Issue 1, 47–63.