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Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose–response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment. Toxicogenomics leverages molecular data to predict toxicological effects. In this Review, the authors summarize innovations in transcriptomics and emerging methods, such as single-cell technologies and multi-omics, that offer detailed insights into toxicological mechanisms to enhance hazard prediction and risk assessment.

How can we best reduce the risk of severe adverse reactions to marketed drugs? An international group of scientists argues that a global research network is needed to identify genetically at-risk populations. Going global Nearly 2 million Americans a year experience a serious adverse drug reaction (SADR) when using marketed drugs, and 100,000 die. The figures are similar in other developed countries. The search for predictive genetic tests for SADRs is therefore of vital importance. In a Commentary, Giacomini et al . argue that an important step towards this goal is the creation of a global pharmacogenomics network for the study of SADRs. Some large-scale projects exist — EUDRAGENE in Europe, GATC in Canada — but only a global network will bring the necessary patient numbers.

Background The increasing use of engineered nanomaterials (ENMs) of varying physical and chemical characteristics poses a great challenge for screening and assessing the potential pathology induced by these materials, necessitating novel toxicological approaches. Toxicogenomics measures changes in mRNA levels in cells and tissues following exposure to toxic substances. The resulting information on altered gene expression profiles, associated pathways, and the doses at which these changes occur, are used to identify the underlying mechanisms of toxicity and to predict disease outcomes. We evaluated the applicability of toxicogenomics data in identifying potential lung-specific (genomic datasets are currently available from experiments where mice have been exposed to various ENMs through this common route of exposure) disease outcomes following exposure to ENMs. Methods Seven toxicogenomics studies describing mouse pulmonary responses over time following intra-tracheal exposure to increasing doses of carbon nanotubes (CNTs), carbon black, and titanium dioxide (TiO 2 ) nanoparticles of varying properties were examined to understand underlying mechanisms of toxicity. mRNA profiles from these studies were compared to the publicly available datasets of 15 other mouse models of lung injury/diseases induced by various agents including bleomycin, ovalbumin, TNFα, lipopolysaccharide, bacterial infection, and welding fumes to delineate the implications of ENM-perturbed biological processes to disease pathogenesis in lungs. Results The meta-analysis revealed two distinct clusters—one driven by TiO 2 and the other by CNTs. Unsupervised clustering of the genes showing significant expression changes revealed that CNT response clustered with bleomycin injury and bacterial infection models, both of which are known to induce lung fibrosis, in a post-exposure-time dependent manner, irrespective of the CNT’s physical-chemical properties. TiO 2 samples clustered separately from CNTs and disease models. Conclusions These results indicate that in the absence of apical toxicity data, a tiered strategy beginning with short term, in vivo tissue transcriptomics profiling can effectively and efficiently screen new ENMs that have a higher probability of inducing pulmonary pathogenesis.

A number of environmental toxicants affect our health through physical, biological or chemical mechanisms. There is growing evidence indicating that microRNA (miRNA) plays an important role in toxicogenomics, disease aetiology and the effect of toxicants. This article summarises recent findings on miRNAs associated with various toxicants and those targeted in the development of therapeutics. Environmental epigenetic studies have revealed the role of miRNAs in the regulation of gene activities induced by environmental changes after exposure to toxic substances. Toxicant-induced changes in miRNA expression have a potential to be informative markers in the evaluation of toxicant risks. miRNAs are now considered to be predictive biomarkers or indicators of tissue injury due to toxicant exposure; thus, miRNAs can also be utilised as therapeutic targets.

Drug-induced liver injury (DILI) is a frequent cause for the termination of drug development programs and a leading reason of drug withdrawal from the marketplace. Unfortunately, the current preclinical testing strategies, including the regulatory-required animal toxicity studies or simple tests, are insufficiently powered to predict DILI in patients reliably. Notably, the limited predictive power of such testing strategies is mostly attributed to the complex nature of DILI, a poor understanding of its mechanism, a scarcity of human hepatotoxicity data and inadequate bioinformatics capabilities. With the advent of high-content screening assays, toxicogenomics and bioinformatics, multiple end points can be studied simultaneously to improve prediction of clinically relevant DILIs. This review focuses on the current state of efforts in developing predictive models from diverse data sources for potential use in detecting human hepatotoxicity, and also aims to provide perspectives on how to further improve DILI prediction.

Genomic information and tools are beginning to be used to increase our understanding of how organisms of all types interact with their environment. The study of the expression of all genes, at the genome, transcriptome, proteome and metabolome level, in response to exposure to a toxicant, is known as toxicogenomics. Here, we show how this new field of environmental genomics has enhanced the development of fundamental knowledge on the mechanisms behind the toxicity of and resistance to the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D). Although 2,4-D is one of the most successfully and widely used herbicides, its intensive use has led to the emergence of resistant weeds and might give rise to several toxicological problems when present in concentrations above those recommended. This review summarizes recent mechanistic insights into 2,4-D toxicity and the corresponding adaptive responses based on studies carried out using Saccharomyces cerevisiae and Arabidopsis thaliana as model organisms.

Background: In spite of the application of toxicogenomic (TGx) data to the field of toxicology for the past 10 years, the broad implementation and full impact of TGx for chemical and drug evaluation to improve decision making within organizations and by policy makers has not been achieved. Objectives: The goal of the Health and Environmental Sciences Institute (HESI) Committee on the Application of Genomics to Mechanism-based Risk Assessment was to construct and summarize a multisector survey, addressing key issues and perspectives on the current and future practical uses and challenges of implementing TGx data to facilitate discussions for decision making within organizations and by policy makers. Methods: An online survey to probe the current status and future challenges facing the field of TGx for drug and chemical evaluation in experimental and nonclinical models was taken by scientists and scientific decision/policy makers actively engaged in the field of TGx within industrial, academic, and regulatory sectors of the United States, Europe, and Japan. For this survey, TGx refers specifically to the analysis of gene expression responses to evaluate xenobiotic exposure in experimental and preclinical models. Results: The survey results are summarized from questions covering broad areas including technology used, organizational capacity and resource allocation, experimental approaches, data storage and exchange, perceptions of benefits and hurdles, and future expectations. Conclusions: The survey findings provide valuable information on the current state of the science of TGx applications and identify key areas in which TGx will have an impact as well as the key hurdles in applying TGx data to address issues. The findings serve as a public resource to facilitate discussions on the focus of future TGx efforts to ensure that a maximal benefit can be obtained from toxicogenomic studies.

The value of genomic approaches in hypothesis generation is being realized as a tool for understanding toxicity and consequently contributing to an assessment of drug and chemical safety. In 1999 the membership of the International Life Sciences Institute Health and Environmental Sciences Institute formed a committee to develop a collaborative scientific program to address issues, challenges, and opportunities afforded by the emerging field of toxicogenomics. Experts and advisors from academia and government laboratories participate on the committee, along with approximately 30 corporate member organizations from the pharmaceutical, agrochemical, chemical, and consumer products industries. The committee has designed, conducted, and analyzed numerous toxicogenomic experiments within the broad fields of hepatotoxicity, nephrotoxicity, and genotoxicity. The considerable body of data generated by these programs has been instrumental in increasing understanding of sources of biological and technical variability in the alignment of toxicant-induced transcription changes with the accepted mechanism of action of these agents and the challenges in the consistent analysis and sharing of the voluminous data sets generated by these approaches. Recognizing the importance of standardized microarray data formats and public repository databases as the mechanism by which microarray data can be compared and interpreted by the scientific community, the committee has partnered with the European Bioinformatics Institute to develop a database to house the data generated by its collaborative research.

The mapping of the human genome and the determination of corresponding gene functions, pathways, and biological mechanisms are driving the emergence of the new research fields of toxicogenomics and systems toxicology. Many technological advances such as microarrays are enabling this paradigm shift that indicates an unprecedented advancement in the methods of understanding the expression of toxicity at the molecular level. At the National Center for Toxicological Research (NCTR) of the U.S. Food and Drug Administration, core facilities for genomic, proteomic, and metabonomic technologies have been established that use standardized experimental procedures to support centerwide toxicogenomic research. Collectively, these facilities are continuously producing an unprecedented volume of data. NCTR plans to develop a toxicoinformatics integrated system (TIS) for the purpose of fully integrating genomic, proteomic, and metabonomic data with the data in public repositories as well as conventional in vitro and in vivo toxicology data. The TIS will enable data curation in accordance with standard ontology and provide or interface a rich collection of tools for data analysis and knowledge mining. In this article the design, practical issues, and functions of the TIS are discussed through presenting its prototype version, Array Track, for the management and analysis of DNA microarray data. ArrayTrack is logically constructed of three linked components: a) a library (LIB) that mirrors critical data in public databases; b) a database (MicroarrayDB) that stores microarray experiment information that is Minimal Information About a Microarray Experiment (MIAME) compliant; and c) tools (TOOL) that operate on experimental and public data for knowledge discovery. Using ArrayTrack, we can select an analysis method from the TOOL and apply the method to selected microarray data stored in the MicroarrayDB; the analysis results can be linked directly to gene information in the LIB.

Microarrays provide an unprecedented opportunity for comprehensive concurrent analysis of thousands of genes. The global analysis of the response of genes to a toxic insult (toxicogenomics), as opposed to the historical method of examining a few select genes, provides a more complete picture of toxicologically significant events. Here we examine the utility of microarrays for providing mechanistic insights into the response of cells to DNA damage. Our data indicate that the value of the technology is in its potential to provide mechanistic insight into the mode of action of a genotoxic compound. Array-based expression profiling may be useful for differentiating compounds that interact directly with DNA from those compounds that are genotoxic via a secondary mechanism. As such, genomic microarrays may serve as a valuable alternative methodology that helps discriminate between these two classes of compounds.