Current and future applications of toxicogenomics: Results summary of a survey from the HESI Genomics State of Science Subcommittee

Toxicogenomics Approaches to Address Toxicity and Carcinogenicity in the Liver

Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, transcriptomic approaches are predominantly used in toxicogenomics. Toxicogenomics is being used to understand the mechanisms of toxicity and carcinogenicity, evaluate the translational relevance of toxicological responses from in vivo and in vitro models, and identify predictive biomarkers of disease and exposure. In this session, a brief overview of various transcriptomic technologies and practical considerations related to experimental design was provided. The advantages of gene network analyses to define mechanisms were also discussed. An assessment of the utility of toxicogenomic technologies in the environmental and pharmaceutical space showed that these technologies are being increasingly used to gain mechanistic insights and determining the translational relevance of adverse findings. Within the environmental toxicology area, there is a broader regulatory consideration of benchmark doses derived from toxicogenomics data. In contrast, these approaches are mainly used for internal decision-making in pharmaceutical development. Finally, the development and application of toxicogenomic signatures for prediction of apical endpoints of regulatory concern continues to be area of intense research.

The evolving role of investigative toxicology in the pharmaceutical industry

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

Comparing the Predictivity of Human Placental Gene, microRNA, and CpG Methylation Signatures in Relation to Perinatal Outcomes

Molecular signatures are being increasingly integrated into predictive biology applications. However, there are limited studies comparing the overall predictivity of transcriptomic vs. epigenomic signatures in relation to perinatal outcomes. This study set out to evaluate mRNA and microRNA (miRNA) expression and cytosine-guanine dinucleotide (CpG) methylation signatures in human placental tissues and relate these to perinatal outcomes known to influence maternal/fetal health; namely, birth weight, placenta weight, placental damage, and placental inflammation. The following hypotheses were tested: (1) different molecular signatures will demonstrate varying levels of predictivity towards perinatal outcomes, and (2) these signatures will show disruptions from an example exposure (i.e., cadmium) known to elicit perinatal toxicity. Multi-omic placental profiles from 390 infants in the Extremely Low Gestational Age Newborns cohort were used to develop molecular signatures that predict each perinatal outcome. Epigenomic signatures (i.e., miRNA and CpG methylation) consistently demonstrated the highest levels of predictivity, with model performance metrics including R^2 (predicted vs. observed) values of 0.36-0.57 for continuous outcomes and balanced accuracy values of 0.49-0.77 for categorical outcomes. Top-ranking predictors included miRNAs involved in injury and inflammation. To demonstrate the utility of these predictive signatures in screening of potentially harmful exogenous insults, top-ranking miRNA predictors were analyzed in a separate pregnancy cohort and related to cadmium. Key predictive miRNAs demonstrated altered expression in association with cadmium exposure, including miR-210, known to impact placental cell growth, blood vessel development, and fetal weight. These findings inform future predictive biology applications, where additional benefit will be gained by including epigenetic markers.

Drivers of and Obstacles to the Adoption of Toxicogenomics for Chemical Risk Assessment: Insights from Social Science Perspectives

BACKGROUND

Some 20 y ago, scientific and regulatory communities identified the potential of omics sciences (genomics, transcriptomics, proteomics, metabolomics) to improve chemical risk assessment through development of toxicogenomics. Recognizing that regulators adopt new scientific methods cautiously given accountability to diverse stakeholders, the scope and pace of adoption of toxicogenomics tools and data have nonetheless not met the ambitious, early expectations of omics proponents.

OBJECTIVE

Our objective was, therefore, to inventory, investigate, and derive insights into drivers of and obstacles to adoption of toxicogenomics in chemical risk assessment. By invoking established social science frameworks conceptualizing innovation adoption, we also aimed to develop recommendations for proponents of toxicogenomics and other new approach methodologies (NAMs).

METHODS

We report findings from an analysis of 56 scientific and regulatory publications from 1998 through 2017 that address the adoption of toxicogenomics for chemical risk assessment. From this purposeful sample of toxicogenomics discourse, we identified major categories of drivers of and obstacles to adoption of toxicogenomics tools and data sets. We then mapped these categories onto social science frameworks for conceptualizing innovation adoption to generate actionable insights for proponents of toxicogenomics.

DISCUSSION

We identify the most salient drivers and obstacles. From 1998 through 2017, adoption of toxicogenomics was understood to be helped by drivers such as those we labeled Superior scientific understanding, New applications, and Reduced cost & increased efficiency but hindered by obstacles such as those we labeled Insufficient validation, Complexity of interpretation, and Lack of standardization. Leveraging social science frameworks, we find that arguments for adoption that draw on the most salient drivers, which emphasize superior and novel functionality of omics as rationales, overlook potential adopters' key concerns: simplicity of use and compatibility with existing practices. We also identify two perspectives-innovation-centric and adopter-centric-on omics adoption and explain how overreliance on the former may be undermining efforts to promote toxicogenomics. https://doi.org/10.1289/EHP6500.

Factors Affecting the Perception of New Approach Methodologies (NAMs) in the Ecotoxicology Community

Given current legislative mandates to assess the safety of thousands of chemicals and the slow pace at which conventional testing proceeds, there is a need to accelerate chemical risk assessment. Governments and businesses are increasingly interested in new approach methodologies (NAMs) that promise to reduce costs and delays. We explore 5 sociological factors within the ecotoxicology community that can influence the perception of NAMs: 1) professional profile (educational cohort, employer), 2) internal science communication within professional forums, 3) concern for "error cost," 4) collaboration across stakeholders, and 5) fundamental beliefs regarding toxicology. We conducted an online survey (n = 171; 2018) asking participants about their experiences and perspectives at events of the Society of Environmental Toxicology and Chemistry (SETAC) to assess 1) how NAMs are discussed compared to conventional testing and 2) how respondents perceive their viability. We developed ordered logistic regression (OLR) models to understand the influence of exploratory variables (cohort, core views on toxicology, frequency of collaboration) on respondents' evaluation of the viability of different NAMs. Our results showed that 1) NAMs were more likely than conventional methods to be challenged in forum discussions, which may be fueled by concerns for error costs in regulatory decision making; 2) perceptions of the viability of NAMs tended to follow a "pattern of familiarity," whereby respondents that were more knowledgeable about a test method tended to find it more viable; 3) respondents who agreed with the Paracelsus maxim had a greater likelihood of finding conventional testing viable; and 4) the more a respondent reported collaborating with industry on alternative testing strategies, the more likely she or he was to report that NAMs were less viable. These results suggest that there are professional and organizational barriers to greater acceptance of NAMs that can be addressed through a social learning process within the professional community. Integr Environ Assess Manag 2020;16:269-281. © 2020 SETAC.

From Classical Toxicology to Tox21: Some Critical Conceptual and Technological Advances in the Molecular Understanding of the Toxic Response Beginning From the Last Quarter of the 20th Century

Toxicology has made steady advances over the last 60+ years in understanding the mechanisms of toxicity at an increasingly finer level of cellular organization. Traditionally, toxicological studies have used animal models. However, the general adoption of the principles of 3R (Replace, Reduce, Refine) provided the impetus for the development of in vitro models in toxicity testing. The present commentary is an attempt to briefly discuss the transformation in toxicology that began around 1980. Many genes important in cellular protection and metabolism of toxicants were cloned and characterized in the 80s, and gene expression studies became feasible, too. The development of transgenic and knockout mice provided valuable animal models to investigate the role of specific genes in producing toxic effects of chemicals or protecting the organism from the toxic effects of chemicals. Further developments in toxicology came from the incorporation of the tools of "omics" (genomics, proteomics, metabolomics, interactomics), epigenetics, systems biology, computational biology, and in vitro biology. Collectively, the advances in toxicology made during the last 30-40 years are expected to provide more innovative and efficient approaches to risk assessment. A goal of experimental toxicology going forward is to reduce animal use and yet be able to conduct appropriate risk assessments and make sound regulatory decisions using alternative methods of toxicity testing. In that respect, Tox21 has provided a big picture framework for the future. Currently, regulatory decisions involving drugs, biologics, food additives, and similar compounds still utilize data from animal testing and human clinical trials. In contrast, the prioritization of environmental chemicals for further study can be made using in vitro screening and computational tools.

Validation of Transcriptomics-Based In Vitro Methods

The field of transcriptomics has expanded rapidly during the last decades. This methodology provides an exceptional framework to study not only molecular changes underlying the adverse effects of a given compound, but also to understand its Mode of Action (MoA). However, the implementation of transcriptomics-based tests within the regulatory arena is not a straightforward process. One of the major obstacles in their regulatory implementation is still the interpretation of this new class of data and the judgment of the level of confidence of these tests. A key element in the regulatory acceptance of transcriptomics-based tests is validation, which still represents a major challenge. Although important advances have been made in the development and standardisation of such tests, to date there is limited experience with their validation. Taking into account the experience acquired so far, this chapter describes those aspects that were identified as important in the validation process of transcriptomics-based tests, including the assessment of standardisation, reliability and relevance. It also critically discusses the challenges posed to validation in relation to the specific characteristics of these approaches and their application in the wider context of testing strategies.

Barriers to the use of toxicogenomics data in human health risk assessment: A survey of Canadian risk assessors

Regulatory agencies worldwide need to modernize human health risk assessment (HHRA) to meet challenges of the 21st century. Toxicogenomics is at the core of this improvement. Today, however, the use of toxicogenomics data in HHRA is very limited. The purpose of this survey was to identify barriers to the application of toxicogenomics data in HHRA by human health risk assessors. An online survey targeting Canadian risk assessors gathered information on their knowledge and perception of toxicogenomics, their current and future inclusion of toxicogenomics data in HHRA, and barriers to the use of such data. Twenty-nine (29) participants completed a questionnaire after 2 months of solicitation. The results show that the application of toxicogenomics data in Canada is marginal, with 85% of respondents reporting that they never or rarely used such data. Knowledge of toxicogenomics by Canadian risk assessors is also limited: about two-thirds of respondents (68%) were not at all or only slightly familiar with the concept. Lack of guidelines for toxicogenomics data interpretation, data quality assessment and on their use in HHRA, were found to be major barriers. In conclusion, there is a need for interventions aimed at facilitating the use of toxicogenomics data in HHRA, when available.

Systems Biology to Support Nanomaterial Grouping

The assessment of potential health risks of engineered nanomaterials (ENMs) is a challenging task due to the high number and great variety of already existing and newly emerging ENMs. Reliable grouping or categorization of ENMs with respect to hazards could help to facilitate prioritization and decision making for regulatory purposes. The development of grouping criteria, however, requires a broad and comprehensive data basis. A promising platform addressing this challenge is the systems biology approach. The different areas of systems biology, most prominently transcriptomics, proteomics and metabolomics, each of which provide a wealth of data that can be used to reveal novel biomarkers and biological pathways involved in the mode-of-action of ENMs. Combining such data with classical toxicological data would enable a more comprehensive understanding and hence might lead to more powerful and reliable prediction models. Physico-chemical data provide crucial information on the ENMs and need to be integrated, too. Overall statistical analysis should reveal robust grouping and categorization criteria and may ultimately help to identify meaningful biomarkers and biological pathways that sufficiently characterize the corresponding ENM subgroups. This chapter aims to give an overview on the different systems biology technologies and their current applications in the field of nanotoxicology, as well as to identify the existing challenges.

A risk assessment-driven quantitative comparison of gene expression profiles in PBMCs and white adipose tissue of humans and rats after isoflavone supplementation

Quantitative insight into species differences in risk assessment is expected to reduce uncertainty and variability related to extrapolation from animals to humans. This paper explores quantification and comparison of gene expression data between tissues and species from intervention studies with isoflavones. Gene expression data from peripheral blood mononuclear cells (PBMCs) and white adipose tissue (WAT) after 8wk isoflavone interventions in postmenopausal women and ovariectomized F344 rats were used. A multivariate model was applied to quantify gene expression effects, which showed 3-5-fold larger effect sizes in rats compared to humans. For estrogen responsive genes, a 5-fold greater effect size was found in rats than in humans. For these genes, intertissue correlations (r = 0.23 in humans, r = 0.22 in rats) and interspecies correlation in WAT (r = 0.31) were statistically significant. Effect sizes, intertissue and interspecies correlations for some groups of genes within energy metabolism, inflammation and cell cycle processes were significant, but weak. Quantification of gene expression data reveals differences between rats and women in effect magnitude after isoflavone supplementation. For risk assessment, quantification of gene expression data and subsequent calculation of intertissue and interspecies correlations within biological pathways will further strengthen knowledge on comparability between tissues and species.

Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: challenges, opportunities, and research needs

A critical challenge for environmental chemical risk assessment is the characterization and reduction of uncertainties introduced when extrapolating inferences from one species to another. The purpose of this article is to explore the challenges, opportunities, and research needs surrounding the issue of how genomics data and computational and systems level approaches can be applied to inform differences in response to environmental chemical exposure across species. We propose that the data, tools, and evolutionary framework of comparative genomics be adapted to inform interspecies differences in chemical mechanisms of action. We compare and contrast existing approaches, from disciplines as varied as evolutionary biology, systems biology, mathematics, and computer science, that can be used, modified, and combined in new ways to discover and characterize interspecies differences in chemical mechanism of action which, in turn, can be explored for application to risk assessment. We consider how genetic, protein, pathway, and network information can be interrogated from an evolutionary biology perspective to effectively characterize variations in biological processes of toxicological relevance among organisms. We conclude that comparative genomics approaches show promise for characterizing interspecies differences in mechanisms of action, and further, for improving our understanding of the uncertainties inherent in extrapolating inferences across species in both ecological and human health risk assessment. To achieve long-term relevance and consistent use in environmental chemical risk assessment, improved bioinformatics tools, computational methods robust to data gaps, and quantitative approaches for conducting extrapolations across species are critically needed. Specific areas ripe for research to address these needs are recommended.

Comparing next-generation sequencing and microarray technologies in a toxicological study of the effects of aristolochic acid on rat kidneys

RNA-Seq has been increasingly used for the quantification and characterization of transcriptomes. The ongoing development of the technology promises the more accurate measurement of gene expression. However, its benefits over widely accepted microarray technologies have not been adequately assessed, especially in toxicogenomics studies. The goal of this study is to enhance the scientific community's understanding of the advantages and challenges of RNA-Seq in the quantification of gene expression by comparing analysis results from RNA-Seq and microarray data on a toxicogenomics study. A typical toxicogenomics study design was used to compare the performance of an RNA-Seq approach (Illumina Genome Analyzer II) to a microarray-based approach (Affymetrix Rat Genome 230 2.0 arrays) for detecting differentially expressed genes (DEGs) in the kidneys of rats treated with aristolochic acid (AA), a carcinogenic and nephrotoxic chemical most notably used for weight loss. We studied the comparability of the RNA-Seq and microarray data in terms of absolute gene expression, gene expression patterns, differentially expressed genes, and biological interpretation. We found that RNA-Seq was more sensitive in detecting genes with low expression levels, while similar gene expression patterns were observed for both platforms. Moreover, although the overlap of the DEGs was only 40-50%, the biological interpretation was largely consistent between the RNA-Seq and microarray data. RNA-Seq maintained a consistent biological interpretation with time-tested microarray platforms while generating more sensitive results. However, there is clearly a need for future investigations to better understand the advantages and limitations of RNA-Seq in toxicogenomics studies and environmental health research.

Developing predictive CSF biomarkers-a challenge critical to success in Alzheimer's disease and neuropsychiatric translational medicine

The need to develop effective treatments for Alzheimer's disease has been confounded by repeated clinical failures where promising new chemical entities that have been extensively characterized in preclinical models of Alzheimer's disease have failed to show efficacy in the human disease state. This has been attributed to: the selection of drug targets that have yet to be shown as causal to the disease as distinct from being the result of the disease process, a lack of congruence in the animal models of Alzheimer's disease, wild-type and transgenic, to the human disease, and the enrollment of patients in proof of concept clinical trials who are at too advanced a stage of the disease to respond to any therapeutic. The development of validated biomarkers that can be used for disease diagnosis and progression is anticipated to improve patient enrollment in clinical trials, to develop new animal models and to identify new disease targets for drug discovery. The present review assesses the status of current efforts in developing CSF biomarkers for Alzheimer's disease and briefly discusses the status of CSF biomarker efforts in schizophrenia, depression, Parkinson's disease and multiple sclerosis.

Utilizing toxicogenomic data to understand chemical mechanism of action in risk assessment

The predominant role of toxicogenomic data in risk assessment, thus far, has been one of augmentation of more traditional in vitro and in vivo toxicology data. This article focuses on the current available examples of instances where toxicogenomic data has been evaluated in human health risk assessment (e.g., acetochlor and arsenicals) which have been limited to the application of toxicogenomic data to inform mechanism of action. This article reviews the regulatory policy backdrop and highlights important efforts to ultimately achieve regulatory acceptance. A number of research efforts on specific chemicals that were designed for risk assessment purposes have employed mechanism or mode of action hypothesis testing and generating strategies. The strides made by large scale efforts to utilize toxicogenomic data in screening, testing, and risk assessment are also discussed. These efforts include both the refinement of methodologies for performing toxicogenomics studies and analysis of the resultant data sets. The current issues limiting the application of toxicogenomics to define mode or mechanism of action in risk assessment are discussed together with interrelated research needs. In summary, as chemical risk assessment moves away from a single mechanism of action approach toward a toxicity pathway-based paradigm, we envision that toxicogenomic data from multiple technologies (e.g., proteomics, metabolomics, transcriptomics, supportive RT-PCR studies) can be used in conjunction with one another to understand the complexities of multiple, and possibly interacting, pathways affected by chemicals which will impact human health risk assessment.