Progress in data interoperability to support computational toxicology and chemical safety evaluation

Current state of data stewardship tools in life science

In today's data-centric landscape, effective data stewardship is critical for facilitating scientific research and innovation. This article provides an overview of essential tools and frameworks for modern data stewardship practices. Over 300 tools were analyzed in this study, assessing their utility, relevance to data stewardship, and applicability within the life sciences domain.

Toward Property-Based Regulation

An expanding web of adverse impacts on people and the environment has been steadily linked to anthropogenic chemicals and their proliferation. Central to this web are the regulatory structures intended to protect human and environmental health through the control of new molecules. Through chronically insufficient and inefficient action, the current chemical-by-chemical regulatory approach, which considers regulation at the level of chemical identity, has enabled many adverse impacts to develop and persist. Recognizing the link between fundamental physicochemical properties and hazards, we describe a new paradigm─property-based regulation. By regulating physicochemical properties, we show how governments can delineate and enforce safe chemical spaces, increasing the scalability of chemical assessments, reducing the time and resources to regulate a substance, and providing transparency for chemical designers. We highlight sparse existing property-based approaches and demonstrate their applicability using bioaccumulation as an example. Finally, we present a path to implementation in the United States, prescribing roles and steps for government, nongovernmental organizations, and industry to accelerate this transition, to the benefit of all.

Weight of evidence for cross-species conservation of androgen receptor-based biological activity

The U.S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP) is tasked with assessing chemicals for their potential to perturb endocrine pathways, including those controlled by androgen receptor (AR). To address challenges associated with traditional testing strategies, EDSP is considering in vitro high-throughput screening assays to screen and prioritize chemicals more efficiently. The ability of these assays to accurately reflect chemical interactions in nonmammalian species remains uncertain. Therefore, a goal of the EDSP is to evaluate how broadly results can be extrapolated across taxa. To assess the cross-species conservation of AR-modulated pathways, computational analyses and systematic literature review approaches were used to conduct a comprehensive analysis of existing in silico, in vitro, and in vivo data. First, molecular target conservation was assessed across 585 diverse species based on the structural similarity of ARs. These results indicate that ARs are conserved across vertebrates and are predicted to share similarly susceptibility to chemicals that interact with the human AR. Systematic analysis of over 5000 published manuscripts was used to compile in vitro and in vivo cross-species toxicity data. Assessment of in vitro data indicates conservation of responses occurs across vertebrate ARs, with potential differences in sensitivity. Similarly, in vivo data indicate strong conservation of the AR signaling pathways across vertebrate species, although sensitivity may vary. Overall, this study demonstrates a framework for utilizing bioinformatics and existing data to build weight of evidence for cross-species extrapolation and provides a technical basis for extrapolating hAR-based data to prioritize hazard in nonmammalian vertebrate species.

A non-clinical and clinical IUCLID database for 530 pharmaceuticals (part I): Methodological aspects of its development

A new IUCLID database is provided containing results from non-clinical animal studies and human information for 530 approved drugs. The database was developed by extracting data from pharmacological reviews of repeat-dose, carcinogenicity, developmental, and reproductive toxicity studies. In the database, observed and no-observed effects are linked to the respective effect levels, including information on severity/incidence and transiency/reversibility. It also includes some information on effects in humans, that were extracted from relevant sections of standard product labels of the approved drugs. The database is complemented with a specific ontology for reporting effects that was developed as an improved version of the Ontology Lookup Service's mammalian and human phenotype ontologies and includes different hierarchical levels. The developed ontology contains novel and unique standardized terms, including ontological terms for reproductive and endocrine effects. The new IUCLID database aims to facilitate correlation and concordance analyses based on the link between observed and no-observed effects and their respective effect levels. In addition, it offers a robust dataset on drug information for the pharmaceutical industry and research. The reported ontology supports the analyses of toxicological information, especially for reproductive and endocrine endpoints and can be used to encode legacy data or develop additional ontologies. The new database and ontology can be used to support the development of alternative non-animal approaches, to elucidate mechanisms of toxicity, and to analyse human relevance. The IUCLID database is provided free of charge at https://iuclid6.echa.europa.eu/us-fda-toxicity-data.

Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study

The need to test chemicals in a timely and cost-effective manner has driven the development of new alternative methods (NAMs) that utilize in silico and in vitro approaches for toxicity prediction. There is a wealth of existing data from human studies that can aid in understanding the ability of NAMs to support chemical safety assessment. This study aims to streamline the integration of data from existing human cohorts by programmatically identifying related variables within each study. Study variables from the Atherosclerosis Risk in Communities (ARIC) study were clustered based on their correlation within the study. The quality of the clusters was evaluated via a combination of manual review and natural language processing (NLP). We identified 391 clusters including 3,285 variables. Manual review of the clusters containing more than one variable determined that human reviewers considered 95% of the clusters related to some degree. To evaluate potential bias in the human reviewers, clusters were also scored via NLP, which showed a high concordance with the human classification. Clusters were further consolidated into cluster groups using the Louvain community finding algorithm. Manual review of the cluster groups confirmed that clusters within a group were more related than clusters from different groups. Our data-driven approach can facilitate data harmonization and curation efforts by providing human annotators with groups of related variables reflecting the themes present in the data. Reviewing groups of related variables should increase efficiency of the human review, and the number of variables reviewed can be reduced by focusing curator attention on variable groups whose theme is relevant for the topic being studied.

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

Environmental health sciences (EHS) span many diverse disciplines. Within the EHS community, the National Institute of Environmental Health Sciences Superfund Research Program (SRP) funds multidisciplinary research aimed to address pressing and complex issues on how people are exposed to hazardous substances and their related health consequences with the goal of identifying strategies to reduce exposures and protect human health. While disentangling the interrelationships that contribute to environmental exposures and their effects on human health over the course of life remains difficult, advances in data science and data sharing offer a path forward to explore data across disciplines to reveal new insights. Multidisciplinary SRP-funded teams are well-positioned to examine how to best integrate EHS data across diverse research domains to address multifaceted environmental health problems. As such, SRP supported collaborative research projects designed to foster and enhance the interoperability and reuse of diverse and complex data streams. This perspective synthesizes those experiences as a landscape view of the challenges identified while working to increase the FAIR-ness (Findable, Accessible, Interoperable, and Reusable) of EHS data and opportunities to address them.

The ECOTOXicology Knowledgebase: A Curated Database of Ecologically Relevant Toxicity Tests to Support Environmental Research and Risk Assessment

The need for assembled existing and new toxicity data has accelerated as the amount of chemicals introduced into commerce continues to grow and regulatory mandates require safety assessments for a greater number of chemicals. To address this evolving need, the ECOTOXicology Knowledgebase (ECOTOX) was developed starting in the 1980s and is currently the world's largest compilation of curated ecotoxicity data, providing support for assessments of chemical safety and ecological research through systematic and transparent literature review procedures. The recently released version of ECOTOX (Ver 5, www.epa.gov/ecotox) provides single-chemical ecotoxicity data for over 12,000 chemicals and ecological species with over one million test results from over 50,000 references. Presented is an overview of ECOTOX, detailing the literature review and data curation processes within the context of current systematic review practices and discussing how recent updates improve the accessibility and reusability of data to support the assessment, management, and research of environmental chemicals. Relevant and acceptable toxicity results are identified from studies in the scientific literature, with pertinent methodological details and results extracted following well-established controlled vocabularies and newly extracted toxicity data added quarterly to the public website. Release of ECOTOX, Ver 5, included an entirely redesigned user interface with enhanced data queries and retrieval options, visualizations to aid in data exploration, customizable outputs for export and use in external applications, and interoperability with chemical and toxicity databases and tools. This is a reliable source of curated ecological toxicity data for chemical assessments and research and continues to evolve with accessible and transparent state-of-the-art practices in literature data curation and increased interoperability to other relevant resources. Environ Toxicol Chem 2022;41:1520-1539. © 2022 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements

Regulatory agencies around the world have committed to reducing or eliminating animal testing for establishing chemical safety. Adverse outcome pathways can facilitate replacement by providing a mechanistic framework for identifying the appropriate non-animal methods and connecting them to apical adverse outcomes. This study separated 11,992 chemicals with curated rat oral acute toxicity information into clusters of structurally similar compounds. Each cluster was then assigned one or more ToxCast/Tox21 assays by looking for the minimum number of assays required to record at least one positive hit call below cytotoxicity for all acutely toxic chemicals in the cluster. When structural information is used to select assays for testing, none of the chemicals required more than four assays and 98% required two assays or less. Both the structure-based clusters and activity from the associated assays were significantly associated with the GHS toxicity classification of the chemicals, which suggests that a combination of bioactivity and structural information could be as reproducible as traditional in vivo studies. Predictivity is improved when the in vitro assay directly corresponds to the mechanism of toxicity, but many indirect assays showed promise as well. Given the lower cost of in vitro testing, a small assay battery including both general cytotoxicity assays and two or more orthogonal assays targeting the toxicological mechanism could be used to improve performance further. This approach illustrates the promise of combining existing in silico approaches, such as the Collaborative Acute Toxicity Modeling Suite (CATMoS), with structure-based bioactivity information as part of an efficient tiered testing strategy that can reduce or eliminate animal testing for acute oral toxicity.

Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome

Background: The chemical part of the exposome, including drugs, may explain the increase of health effects with outcomes such as infertility, allergies, metabolic disorders, which cannot be only explained by the genetic changes. To better understand how drug exposure can impact human health, the concepts of adverse outcome pathways (AOPs) and AOP networks (AONs), which are representations of causally linked events at different biological levels leading to adverse health, could be used for drug safety assessment.

Methods: To explore the action of drugs across multiple scales of the biological organization, we investigated the use of a network-based approach in the known AOP space. Considering the drugs and their associations to biological events, such as molecular initiating event and key event, a bipartite network was developed. This bipartite network was projected into a monopartite network capturing the event–event linkages. Nevertheless, such transformation of a bipartite network to a monopartite network had a huge risk of information loss. A way to solve this problem is to quantify the network reduction. We calculated two scoring systems, one measuring the uncertainty and a second one describing the loss of coverage on the developed event–event network to better investigate events from AOPs linked to drugs.

Results: This AON analysis allowed us to identify biological events that are highly connected to drugs, such as events involving nuclear receptors (ER, AR, and PXR/SXR). Furthermore, we observed that the number of events involved in a linkage pattern with drugs is a key factor that influences information loss during monopartite network projection. Such scores have the potential to quantify the uncertainty of an event involved in an AON, and could be valuable for the weight of evidence assessment of AOPs. A case study related to infertility, more specifically to “decrease, male agenital distance” is presented.

Conclusion: This study highlights that computational approaches based on network science may help to understand the complexity of drug health effects, with the aim to support drug safety assessment.

High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures

The ToxCast in vitro screening program has provided concentration-response bioactivity data across more than a thousand assay endpoints for thousands of chemicals found in our environment and commerce. However, most ToxCast screening assays have evaluated individual biological targets in cancer cell lines lacking integrated physiological functionality (such as receptor signaling, metabolism). We evaluated differentiated HepaRGTM cells, a human liver-derived cell model understood to effectively model physiologically relevant hepatic signaling. Expression of 93 gene transcripts was measured by quantitative polymerase chain reaction using Fluidigm 96.96 dynamic arrays in response to 1060 chemicals tested in eight-point concentration-response. A Bayesian framework quantitatively modeled chemical-induced changes in gene expression via six transcription factors including: aryl hydrocarbon receptor, constitutive androstane receptor, pregnane X receptor, farnesoid X receptor, androgen receptor, and peroxisome proliferator-activated receptor alpha. For these chemicals the network model translates transcriptomic data into Bayesian inferences about molecular targets known to activate toxicological adverse outcome pathways. These data also provide new insights into the molecular signaling network of HepaRGTM cell cultures.

Knowledge Organization Systems for Systematic Chemical Assessments

BACKGROUND

Although the implementation of systematic review and evidence mapping methods stands to improve the transparency and accuracy of chemical assessments, they also accentuate the challenges that assessors face in ensuring they have located and included all the evidence that is relevant to evaluating the potential health effects an exposure might be causing. This challenge of information retrieval can be characterized in terms of "semantic" and "conceptual" factors that render chemical assessments vulnerable to the streetlight effect.

OBJECTIVES

This commentary presents how controlled vocabularies, thesauruses, and ontologies contribute to overcoming the streetlight effect in information retrieval, making up the key components of Knowledge Organization Systems (KOSs) that enable more systematic access to assessment-relevant information than is currently achievable. The concept of Adverse Outcome Pathways is used to illustrate what a general KOS for use in chemical assessment could look like.

DISCUSSION

Ontologies are an underexploited element of effective knowledge organization in the environmental health sciences. Agreeing on and implementing ontologies in chemical assessment is a complex but tractable process with four fundamental steps. Successful implementation of ontologies would not only make currently fragmented information about health risks from chemical exposures vastly more accessible, it could ultimately enable computational methods for chemical assessment that can take advantage of the full richness of data described in natural language in primary studies. https://doi.org/10.1289/EHP6994.

Community Approaches for Integrating Environmental Exposures into Human Models of Disease

BACKGROUND

A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently, the available data links a majority of known coding human genes to phenotypes, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist.

OBJECTIVES

Our objective is to foster development of community-driven data-reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. To this end, we present a preliminary semantic data model and use cases and competency questions for further community-driven model development and refinement.

DISCUSSION

There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Critical to success is the development of a community-driven data model for describing environmental exposures and linking them to existing models of human disease. https://doi.org/10.1289/EHP7215.

Benchmark dose analyses of toxic endpoints in lung cells provide sensitivity and toxicity ranking across metal oxide nanoparticles and give insights into the mode of action

INTRODUCTION

The benchmark dose (BMD) is a dose that produces a predetermined change in the response rate of an adverse effect. This approach is increasingly utilized to analyze quantitative dose-response relationships. To proof this concept, statistical analysis was compared with the BMD approach in order to rank the sensitivity as well as the toxicity and to describe the mode of action.

METHODS

Bronchial (BEAS-2B) and alveolar epithelial cells (A549) were exposed to a wide concentration range (0.4-100 μg/mL) of five metal oxide nanoparticles (CeO₂, CuO, TiO₂, ZnO, ZrO₂). Eight toxicity endpoints were determined representing integrity of lysosomal and cell membrane, oxidative stress level, glutathione based detoxification (glutathione S-transferase), oxidative metabolism (cytochrome P450), alteration of the mitochondrial membrane potential, alteration of phase II antioxidative enzyme (NAD(P)H:quinone oxidoreductase), and de novo DNA synthesis.

RESULTS

Based on the BMD calculated for the most sensitive test, the toxicity decreased in the following order: ZnO > CuO > TiO₂>ZrO₂>CeO₂ in BEAS-2B. Both statistical evaluation methods revealed a higher sensitivity of BEAS-2B cells. The BMD-derived mode of action for CuO confirmed the existing hypotheses and provided insights into less known mechanisms.

CONCLUSION

The findings proofed that BMD analysis is an effective tool to evaluate different aspects of risk assessment.

New ideas for non-animal approaches to predict repeated-dose systemic toxicity: Report from an EPAA Blue Sky Workshop

The European Partnership for Alternative Approaches to Animal Testing (EPAA) convened a 'Blue Sky Workshop' on new ideas for non-animal approaches to predict repeated-dose systemic toxicity. The aim of the Workshop was to formulate strategic ideas to improve and increase the applicability, implementation and acceptance of modern non-animal methods to determine systemic toxicity. The Workshop concluded that good progress is being made to assess repeated dose toxicity without animals taking advantage of existing knowledge in toxicology, thresholds of toxicological concern, adverse outcome pathways and read-across workflows. These approaches can be supported by New Approach Methodologies (NAMs) utilising modern molecular technologies and computational methods. Recommendations from the Workshop were based around the needs for better chemical safety assessment: how to strengthen the evidence base for decision making; to develop, standardise and harmonise NAMs for human toxicity; and the improvement in the applicability and acceptance of novel techniques. "Disruptive thinking" is required to reconsider chemical legislation, validation of NAMs and the opportunities to move away from reliance on animal tests. Case study practices and data sharing, ensuring reproducibility of NAMs, were viewed as crucial to the improvement of non-animal test approaches for systemic toxicity.

Going All In: A Strategic Investment in In Silico Toxicology

As vast numbers of new chemicals are introduced to market annually, we are faced with the grand challenge of protecting humans and the environment while minimizing economically and ethically costly animal testing. In silico models promise to be the solution we seek, but we find ourselves at crossroads of future development efforts that would ensure standalone applicability and reliability of these tools. A conscientious effort that prioritizes experimental testing to support the needs of in silico models (versus regulatory needs) is called for to achieve this goal. Using economic analogy in the title of this work, we argue that a prudent investment is to go all-in to support in silico model development, rather than gamble our future by keeping the status quo of a "balanced portfolio" of testing approaches. We discuss two paths to future in silico toxicology-one based on big-data statistics ("broadsword"), and the other based on direct modeling of molecular interactions ("scalpel")-and offer rationale that the latter approach is more transparent, is better aligned with our quest for fundamental knowledge, and has a greater potential to succeed if we are willing to transform our toxicity-testing paradigm.