Genotoxicity testing is an essential component of the safety assessment paradigm required by regulatory agencies world-wide for analysis of drug candidates, and environmental and industrial chemicals. Current genotoxicity testing batteries feature a high incidence of irrelevant positive findings – particularly for in vitro chromosomal damage (CD) assays. The risk management of compounds with positive in vitro findings is a major challenge and requires complicated, time consuming and costly follow-up strategies including animal testing. Thus, regulators are urgently in need of new testing approaches to meet legislated mandates. Using machine learning, we identified a set of transcripts that responds predictably to DNA-damage in human cells that we refer to as the TGx-DDI biomarker, which was originally referred to as TGx-28.65.
Abstract text online: https://www.frontiersin.org/articles/10.3389/fdata.2019.00036/abstract