ExITox

Acronym: ExITox (Explain Inhalation Toxicity)

Full title of the project: “Development of an integrated testing strategy for the prediction of toxicity after repeated dose inhalation exposure: a proof of concept”

This project aims at developing an integrated testing strategy (ITS) for the human health risk assessment of repeated dose toxicity after inhalation exposure for the replacement of de novo animal testing. In the pilot phase chemicals with different mode of action will be selected and tested with human precision cut lung slices (PCLS) and human pulmonary cell cultures in order to identify route specific biomarkers. Genome wide transcriptome analyses will be conducted in these models and evaluated using bioinformatics methods. These results will be complemented with data mining results and QSAR predictions. Further, structurally related chemicals will be tested in addition to investigate the possibility of the test system to support read across. The outcome of this pilot project will be a proposal for an integrated testing strategy for respiratory toxicity. Further validation e.g. testing of a broader spectrum of chemicals is foreseen in a follow up project. The proposed ITS, the developed methodologies on data sharing and data integration are not limited to the evaluation of transcriptome data but allow to integrate proteome and metabolome data in a follow up project.

The project is funded by the German Federal Ministry of Education and Research (BMBF) in the framework of the call e:ToP.

Funding period: 01.11.2013 – 31.10.2015

Partners:

Dr. S. Escher, Fraunhofer ITEM, Hannover (coordinator)
Dr. K. Sewald, Airway Immunology, Fraunhofer ITEM, Hannover
Dr. M. Niehof, In Vitro and Mechanistic Toxicology, Fraunhofer ITEM, Hannover
Dr. C. Helma, Inst. f. Physics/In Silico Toxicol. Group, Albert Ludwigs University of Freiburg
Dr. A. Kel, geneXplain GmbH, Wolfenbüttel

Publications:

Bhar, A., Haubrock, M., Mukhopadhyay, A., Wingender, E:
Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes

BMC Bioinformatics 16, 200 (2015).
doi:10.1186/s12859-015-0635-8  link

Koschmann, J., Bhar, A., Stegmaier,P., Kel, A.E. and Wingender, E.:
“Upstream Analysis”: An integrated promoter-pathway analysis approach to causal interpretation of microarray data
Microarrays 4, 270-286 (2015).
doi:10.3390/microarrays4020270  link