A Decade of Toxicogenomic Research and Its Contribution to Toxicological Science

Abstract

Toxicogenomics enjoyed considerable attention as a ground-breaking addition to conventional toxicology assays at its inception. However, the pace at which toxicogenomics was expected to perform has been tempered in recent years. Next to cost, the lack of advanced knowledge discovery and data mining tools significantly hampered progress in this new field of toxicological sciences. Recently, two of the largest toxicogenomics databases were made freely available to the public. These comprehensive studies are expected to stimulate knowledge discovery and development of novel data mining tools, which are essential to advance this field. In this review, we provide a concise summary of each of these two databases with a brief discussion on the commonalities and differences between them. We place our emphasis on some key questions in toxicogenomics and how these questions can be appropriately addressed with the two databases. Finally, we provide a perspective on the future direction of toxicogenomics and how new technologies such as RNA-Seq may impact this field.

Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity

“…[63][64][65] However, inclusion of genomic studies in formal drug safety assessment remains limited, 66 with relatively few reports describing significant insights from expression profiling that complement traditional nonclinical studies. 67,68 This may be due in part to a focus on gene signatures as chemical classifiers and a poor understanding of how selected genes from case-by-case analyses fit into the larger context of organ injury.…”

The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing

“…B) The gene dao (D-amino-acid oxidase) is associated with a number of the gold compounds according to the Comparative Toxicogenomics Database (CTD), sorted by frequency of chemical-gene associations. [36] C) Protein-protein association network around the Asah1 (N-acylsphingosine amidohydrolase) protein from String 9.0. database (Supporting Information SI Table 7). [38] D) Compounds associated with the asah1 gene in the CTD.…”

“…[36] Recently this situation has been addressed by the release of two such datasets: the TGP (TG-GATEs) dataset [37] and the DrugMatrix. [38] Both have a uniform experimental design, assess a large number of marketed drugs alongside standard toxicological model compounds (such as acetaminophen) and provide in vitro as well as in vivo data for comparative analysis.…”

“…The TG-GATEs dataset includes information on 170 compounds, the DrugMatrix covers over 600 compounds and there are 73 drugs in common between the two datasets. Paired compounds -i.e., compounds that are closely related structurally but nevertheless have different toxicity profiles -comprise 16 pairs [36] and can be used as controls to develop more accurate biomarker signatures for toxicity. SEURAT-1 gold compound selection also includes the use of paired compounds, e.g.…”

Cancer Biology, Toxicology and Alternative Methods Development Go Hand‐in‐Hand

“…Summarized under a concept termed 'Toxicity Testing in the 21st Century' or 'Tox21', biochemical and cell-based in vitro assays coupled with bioinformatics and modelling-driven in silico assays are now considered key to transforming toxicology from a previously animal-based testing practice into a computational science built on systems biology [2,4,6,[9][10][11]13]. The resulting novel research field is variably termed 'systems toxicology', 'toxicogenomics' or 'computational toxicology' [3,4,8,14,15]. Such effort typically relies on informatics-driven and modelling-based analyses of results from several experimental systems and data-rich technologies for measuring pools of biological molecules, such as mRNAs, the overall aim being to interdependently analyse toxicity data and profiling results for understanding and predicting toxicity.…”