Li Heng-Hong, Yauk Carole L, Chen Renxiang, Hyduke Daniel R, Williams Andrew, Frötschl Roland, Ellinger-Ziegelbauer Heidrun, Pettit Syril, Aubrecht Jiri, Fornace Albert J
Department of Oncology, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, United States.
Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada.
Front Big Data. 2019 Oct 8;2:36. doi: 10.3389/fdata.2019.00036. eCollection 2019.
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 chromosomal damage (CD) assays. The risk management of compounds with positive findings is a major challenge and requires complex, 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. We proposed to use this biomarker in conjunction with current genotoxicity testing batteries to differentiate compounds with irrelevant "false" positive findings in the CD assays from true DNA damaging agents (i.e., for de-risking agents that are clastogenic but not ). We validated the performance of the TGx-DDI biomarker to identify true DNA damaging agents, assessed intra- and inter- laboratory reproducibility, and cross-platform performance. Recently, to augment the application of this biomarker, we developed a high-throughput cell-based genotoxicity testing system using the NanoString nCounter® technology. Here, we review the status of TGx-DDI development, its integration in the genotoxicity testing paradigm, and progress to date in its qualification at the US Food and Drug Administration (FDA) as a drug development tool. If successfully validated and implemented, the TGx-DDI biomarker assay is expected to significantly augment the current strategy for the assessment of genotoxic hazards for drugs and chemicals.
遗传毒性测试是全球监管机构对候选药物、环境和工业化学品进行安全性评估范式的重要组成部分。当前的遗传毒性测试组合存在大量无关的阳性结果,尤其是在染色体损伤(CD)检测中。对有阳性结果的化合物进行风险管理是一项重大挑战,需要复杂、耗时且成本高昂的后续策略,包括动物试验。因此,监管机构迫切需要新的测试方法来满足立法要求。我们利用机器学习识别出一组在人类细胞中对DNA损伤有可预测反应的转录本,我们将其称为TGx-DDI生物标志物,最初称为TGx-28.65。我们建议将该生物标志物与当前的遗传毒性测试组合结合使用,以区分在CD检测中具有无关“假”阳性结果的化合物与真正的DNA损伤剂(即用于对具有致断裂性但并非……的药物进行风险排除)。我们验证了TGx-DDI生物标志物识别真正DNA损伤剂的性能,评估了实验室内和实验室间的可重复性以及跨平台性能。最近,为了扩大该生物标志物的应用,我们利用NanoString nCounter®技术开发了一种基于细胞的高通量遗传毒性测试系统。在此,我们回顾TGx-DDI的发展现状、其在遗传毒性测试范式中的整合以及迄今为止在美国食品药品监督管理局(FDA)作为药物开发工具进行鉴定的进展。如果成功验证并实施,TGx-DDI生物标志物检测有望显著增强当前评估药物和化学品遗传毒性危害的策略。