Poster: Towards distinguishing adaptive and adverse response to chemicals using gene expression data

Gene expression data is increasingly used for risk assessment due to both its ease of generation as well as its ability to provide mechanistic insight into compound action. Current methods rely on modelling the dose-response characteristics present in gene expression data to identify points of departure in order to define the lowest dose of a compound that might trigger a transcriptional response (the No Observed Transcriptional Effect Level/NOTEL). However, this approach does not discriminate appropriately between adaptive and toxic responses as it includes all transcriptional activity. In risk assessment as well as in drug development we may end up stopping the testing of a given compound/drug because of its biological activity even if that activity is not of concern. In this context, developing a framework with the ability to discriminate between compound adaptive and adverse effects is of paramount importance for improving current risk assessment strategies for chemicals and drugs.

This poster was presented by Danilo Basili (University of Cambridge).

View the poster here.


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