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Hazard characterisation of acrylates by in vitro and ex vivo models - an update of the ExITox project

: Schramm, Sinja-Fee; Wehr, Matthias; Danov, Olga; Obernolte, Helena; Knebel, Jan; Koschmann, J.; Meckbach, C.; Niehof, Monika; Braubach, Peter; Jonigk, Danny; Hansen, Tanja; Braun, Armin; Sewald, Katherina; Escher, Sylvia E.

The Toxicologist 168 (2019), Nr.1, Abstract PS 1856
ISSN: 0731-9193
Society of Toxicology (Annual Meeting) <58, 2019, Baltimore/Md.>
Fraunhofer ITEM ()

ExITox (Explain Inhalation Toxicology) aims to develop an integrated approach for the hazard characterization of inhalable compounds without in vivo animal testing. Different in vitro and in silico models are developed by assessing groups of compounds that share structural properties and induced a shared toxicological effect pattern in existing in vivo studies with repeated exposure via inhalation. The shared toxicological effects in these read-across groups might indicate a shared mode of action. We hypothesize that in vitro data can be used to strengthen the read-across hypothesis e.g. by showing shared adverse outcome pathways. We selected a group of four aliphatic acrylates, which induced inflammation and hyperplasia in the respiratory ract of the test animals at dose levels ranging from 15-25 ppm. These acrylates were tested submerse in A549 cells and human precision-cut lung slices (PCLS) dose-dependently. Exposure occured over three days for one hour daily. In A549 cells the cytotoxicity of acrylates was assessed with EC50 values of 2.15, 3.7, 1.12 and 1.57 mM for methyl acylate, ethyl acrylate, propyl acrylate and butyl acrylate, respectively. Comparable to cells, PCLS showed similar sensitivity with the EC50 values of 1.61, 3.02, 2.99 and 4.48 mM for methyl acylate, ethyl acrylate, propyl acrylate and butyl acrylate, respectively. The bioinformatic process chain includes analysis of whole transcriptome with RNA-seq technology of all compounds to detect the expression changes, focus on regulation via enhancers and underlying miRNA levels, involves network analysis to identify potential biomarker up to a postulated diagnostic profile for each specific read-across group. The results for DEG and miRNA will be presented. This work was supported by BMBF grant FKZ 031A269A-D.