Toxicogenomics: A 2020 Vision

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 readacross strategies to further regulatory application. Finally, we summarize current efforts to develop TGx for risk assessment and set out remaining challenges. Toxicogenomics in Regulatory Application: Challenges and OpportunitiesAnimal models are used to assess and avoid risk to humans from exposure to potential hazards, but their use is under constant review, especially in the light of some reports of poor extrapolation for complex endpoints, such as hepatotoxicity and carcinogenicity. Consequently, 21st century toxicology emphasizes alternative means of risk assessment and the promotion of the 3Rs (replacement, reduction, and refinement of animals in toxicology testing) [1]. In Europe, great efforts have been made to advance the 3Rs with the aim of developing animal-free risk assessment methodologies. To this end, several high-profile programs are underway, such as the Framework Programme 7 (FP7), Horizon 2020, and some publicprivate partnerships, including Safety Evaluation Ultimately Replacing Animal Testing (SEURAT-1) and the Innovative Medicines Initiative (IMI). Furthermore, a series of EU Legislative directives have been developed and implemented over the past three decades, with an emphasis on moving away from animal testing; since 2013, animal models have been prohibited for testing cosmetics or household products in the EU, as well as in Israel and India [2]. In the US, government-initiated efforts comprise advanced regulatory sciences proposed by the US FDA [3] and Tox21 [4] [which involves four government agencies, including the Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences, National Institute of Environmental Health Sciences, and the FDA] and ToxCast [5] (by the EPA). These ongoing efforts actively advocate and promote in silico and in vitro approaches, including toxicogenomics (TGx) (see Glossary), for prioritization and also for a potential application in risk assessment.TGx, as a subdiscipline of toxicology, has been successfully implemented to address critical issues and questions in a broad spectrum of toxicology. The rapid advancement of nextgeneration sequencing (NGS) technologies has gained traction in clinical application, particularly in personalized cancer diagnosis and prognosis, offering great op...

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects

“…Furthermore, the field is evolving and currently working to reduce animal tests by replacing them with in vitro experiments. Developing TGx towards regulatory decision making depends on multiple technical and conceptual solutions that are currently coming together [6,147]. It is difficult to say when these methods will replace animal tests, but they can already be used as convincing supporting evidence.…”

“…Thus, new strategies are needed to keep up the pace of the new substances and materials entering the market as well as to support the reduction of animal experiments. To overcome this bottleneck in the testing of the adverse effects, TGx approaches have been suggested as a complementing method for traditional safety assessment [6,7]. This has led to the establishment of several prominent programs in the EU and worldwide aiming to generate omics-based big data.…”

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

“…Classic techniques such as linear and logistic models have been the first to be applied in such modelling tasks and can still be considered the methods of choice, especially when analyzing small datasets. More recently, novel methodologies based on artificial intelligence (AI) and deep learning (DL) have been used with great success in a wide range of applications, including image analysis, and also for the development of TGx-based predictive models [27]. These new approaches are envisaged to produce more accurate predictions and open new horizons to the identification of biomarkers with discrimination performance and predictive ability [5,28].…”

Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

“…Moreover, in vitro data predicted carcinogenesis in rats based on short-term TGx data (Watanabe et al, 2007;Ellinger-Ziegelbauer et al, 2008). To summarize, in vitro assays as alternatives for TGx play a vital role in the next-generation risk assessment paradigm and have tremendous potential to promote non-animal testing in TGx systems (Liu et al, 2018c).…”