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.
Original language | English |
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Pages (from-to) | 92-103 |
Number of pages | 12 |
Journal | Trends in Pharmacological Sciences |
Volume | 40 |
Issue number | 2 |
Early online date | 26 Dec 2018 |
DOIs | |
Publication status | Published - 1 Feb 2019 |
Keywords
- Toxicogenomics
- reproducibility
- adverse outcome pathways
- regulatory sciences
- deep learning
- toxicogenomics
ASJC Scopus subject areas
- Toxicology
- Pharmacology