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Review
. 2019 Feb;40(2):92-103.
doi: 10.1016/j.tips.2018.12.001. Epub 2018 Dec 26.

Toxicogenomics: A 2020 Vision

Zhichao Liu  1 , Ruili Huang  2 , Ruth Roberts  3 , Weida Tong  4
Affiliations
Review

Toxicogenomics: A 2020 Vision

Zhichao Liu et al. Trends Pharmacol Sci. 2019 Feb.

Abstract

Toxicogenomics (TGx) has contributed significantly to toxicology and now has great potential to support moves towards animal-free approaches in regulatory decision making. Here, we discuss in vitro TGx systems and their potential impact on risk assessment. We raise awareness of the rapid advancement of genomics technologies, which generates novel genomics features essential for enhanced risk assessment. We specifically emphasize the importance of reproducibility in utilizing TGx in the regulatory setting. We also highlight the role of machine learning (particularly deep learning) in developing TGx-based predictive models. Lastly, we touch on the topics of how TGx approaches could facilitate adverse outcome pathways (AOP) development and enhance read-across strategies to further regulatory application. Finally, we summarize current efforts to develop TGx for risk assessment and set out remaining challenges.

Keywords: adverse outcome pathways; deep learning; regulatory sciences; reproducibility; toxicogenomics.

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Figures

Figure 1
Figure 1. The pyramid of toxicogenomics (TGx) towards regulatory decision making.
Some outstanding questions and potential solutions for promoting TGx in respect of decision making are shown.
Figure 2
Figure 2. Application of novel genomics technologies in TGx.
Some novel genomics technologies such as RNA-Seq and TempO-Seq™ have been well established in the TGx field. The novel genomics features beyond mRNA including miRNA, ncRNA, and circular RNAs provided extra resolution for identifying and understanding the underlying mechanism of toxicities. Bioinformatics approaches including data integration, systems biology, and the network could serve as a bridge to facilitate the utilization of novel genomics technologies in TGx.
Figure 3
Figure 3. Key challenges of reproducibility in toxicogenomics.
The reproducibility of toxicogenomics include both biological and technical sides. The data are generated from different cell lines in various labs with different technologies. The repeatability and reproducibility necessities in different influential factors including cell types, genomics platforms and data analysis.
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