Adverse Outcome Pathways for Regulatory Applications: Examination of Four Case Studies With Different Degrees of Completeness and Scientific Confidence

Adverse Outcome Pathways Mechanistically Describing Hepatotoxicity

Adverse outcome pathways (AOPs) describe toxicological processes from a dynamic perspective by linking a molecular initiating event to a specific adverse outcome via a series of key events and key event relationships. In the field of computational toxicology, AOPs can potentially facilitate the design and development of in silico prediction models for hazard identification. Various AOPs have been introduced for several types of hepatotoxicity, such as steatosis, cholestasis, fibrosis, and liver cancer. This chapter provides an overview of AOPs on hepatotoxicity, including their development, assessment, and applications in toxicology.

Construction of an Adverse Outcome Pathway for the Cardiac Toxicity of Bisphenol A by Using Bioinformatics Analysis

Bisphenol A (BPA), a common endocrine disruptor, has shown cardiovascular toxicity in several epidemiological studies, as well as in vivo and in vitro experimental studies. However, the related adverse outcome pathway (AOP) of BPA toxicity remains unraveled. This study aimed to develop an AOP for the cardiac toxicity of BPA through bioinformatics analysis. The interactions among BPA, genes, phenotypes, and cardiac toxicity were retrieved from several databases, including the Comparative Toxicogenomics Database, Computational Toxicology, DisGeNet, and MalaCards. The target genes and part of target phenotypes were obtained by Venn analysis and literature screening. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed for target genes by using the DAVID online analysis tool to obtain other target phenotypes. AOP hypotheses from BPA exposure to heart disease were established and evaluated comprehensively by a quantitative weight of evidence (QWOE) method. The target genes included ESR2, MAPK1, TGFB1, and ESR1, and the target phenotypes included heart contraction, cardiac muscle contraction, cellular Ca2+ homeostasis, cellular metabolic process, heart development, etc. Overall, the AOP of BPA cardiac toxicity was deduced to be as follows. Initially, BPA bound mwith ERα/β and then activated the MAPK, AKT, and IL-17 signaling pathways, leading to Ca2+ homeostasis disorder and increased inflammatory response. Subsequently, cardiac function was impaired, causing coronary heart disease, arrhythmia, cardiac dysplasia, and other heart diseases. According to the Bradford-Hill causal considerations, the score of AOP by QWOE was 69, demonstrating a moderate confidence and providing clues on cardiotoxicity-assessment procedure and further studies on BPA.

Identification of molecular initiating events (MIE) using chemical database analysis and nuclear receptor activity assays for screening potential inhalation toxicants

An adverse outcome pathway (AOP) framework can facilitate the use of alternative assays in chemical regulations by providing scientific evidence. Previously, an AOP, peroxisome proliferative-activating receptor gamma (PPARγ) antagonism that leads to pulmonary fibrosis, was developed. Based on a literature search, PPARγ inactivation has been proposed as a molecular initiating event (MIE). In addition, a list of candidate chemicals that could be used in the experimental validation was proposed using toxicity database and deep learning models. In this study, the screening of environmental chemicals for MIE was conducted using in silico and in vitro tests to maximize the applicability of this AOP for screening inhalation toxicants. Initially, potential inhalation exposure chemicals that are active in three or more key events were selected, and in silico molecular docking was performed. Among the chemicals with low binding energy to PPARγ, nine chemicals were selected for validation of the AOP using in vitro PPARγ activity assay. As a result, rotenone, triorthocresyl phosphate, and castor oil were proposed as PPARγ antagonists and stressor chemicals of the AOP. Overall, the proposed tiered approach of the database-in silico-in vitro can help identify the regulatory applicability and assist in the development and experimental validation of AOP.

Adverse Outcome Pathway 'Footprinting': A Novel Approach to the Integration of 21st Century Toxicology Information into Chemical Mixtures Risk Assessment

For over a decade, New Approach Methodologies (NAMs) such as structure-activity/read-across, -omics technologies, and Adverse Outcome Pathway (AOP), have been considered within regulatory communities as alternative sources of chemical and biological information potentially relevant to human health risk assessment. Integration of NAMs into applications such as chemical mixtures risk assessment has been limited due to the lack of validation of qualitative and quantitative application to adverse health outcomes in vivo, and acceptance by risk assessors. However, leveraging existent hazard and dose-response information, including NAM-based data, for mixture component chemicals across one or more levels of biological organization using novel approaches such as AOP 'footprinting' proposed herein, may significantly advance mixtures risk assessment. AOP footprinting entails the systematic stepwise profiling and comparison of all known or suspected AOPs involved in a toxicological effect at the level of key event (KE). The goal is to identify key event(s) most proximal to an adverse outcome within each AOP suspected of contributing to a given health outcome at which similarity between mixture chemicals can be confidently determined. These key events are identified as the 'footprint' for a given AOP. This work presents the general concept, and a hypothetical example application, of AOP footprinting as a key methodology for the integration of NAM data into mixtures risk assessment.

Potential adverse outcome pathways with hazard identification of organophosphate esters

Data-driven analysis and pathway-based approaches contribute to reasonable arrangements of limited resources and laboratory tests for continuously emerging commercial chemicals, which provides opportunities to save time and effort for toxicity research. With the widespread usage of organophosphate esters (OPEs) on a global scale, the concentrations generally reached up to micromolar range in environmental media and even in organisms. However, potential adverse effects and toxicity pathways of OPEs have not been systematically assessed. Therefore, it is necessary to review the current situation, formulate the future research priorities, and characterize toxicity mechanisms via data-driven analysis. Results showed that the early toxicity studies focused on neurotoxicity, cytotoxicity, and metabolic disorders. Then the main focus shifted to the mechanisms of cardiotoxicity, endocrine disruption, hepatocytes, reproductive and developmental toxicity to vulnerable sub-populations, such as infants and embryos, affected by OPEs. In addition, several novel OPEs have been emerging, such as bis(2-ethylhexyl)-phenyl phosphate (HDEHP) and oxidation derivatives (OPAsO) generated from organophosphite antioxidants (OPAs), leading to multiple potential ecological and human health risks (neurotoxicity, hepatotoxicity, developmental toxicity, etc.). Notably, in-depth statistical analysis was promising in encapsulating toxicological information to develop adverse outcome pathways (AOPs) frameworks. Subsequently, network-centric analysis and quantitative weight-of-evidence (QWOE) approaches were utilized to construct and evaluate the putative AOPs frameworks of OPEs, showing the moderate confidences of the developed AOPs. In addition, frameworks demonstrated that several events, such as nuclear receptor activation, reactive oxygen species (ROS) production, oxidative stress, and DNA damage, were involved in multiple different adverse outcome (AO), and these AOs had certain degree of connectivity. This study brought new insights into facilitating the complement of AOP efficiently, as well as establishing toxicity pathways framework to inform risk assessment of emerging OPEs.

Data-driven learning of narcosis mode of action identifies a CNS transcriptional signature shared between whole organism Caenorhabditis elegans and a fish gill cell line

With the large numbers of man-made chemicals produced and released in the environment, there is a need to provide assessments on their potential effects on environmental safety and human health. Current regulatory frameworks rely on a mix of both hazard and risk-based approaches to make safety decisions, but the large number of chemicals in commerce combined with an increased need to conduct assessments in the absence of animal testing makes this increasingly challenging. This challenge is catalysing the use of more mechanistic knowledge in safety assessment from both in silico and in vitro approaches in the hope that this will increase confidence in being able to identify modes of action (MoA) for the chemicals in question. Here we approach this challenge by testing whether a functional genomics approach in C. elegans and in a fish cell line can identify molecular mechanisms underlying the effects of narcotics, and the effects of more specific acting toxicants. We show that narcosis affects the expression of neuronal genes associated with CNS function in C. elegans and in a fish cell line. Overall, we believe that our study provides an important step in developing mechanistically relevant biomarkers which can be used to screen for hazards, and which prevent the need for repeated animal or cross-species comparisons for each new chemical.

Application of ToxCast/Tox21 data for toxicity mechanism-based evaluation and prioritization of environmental chemicals: Perspective and limitations

In response to the need to minimize the use of experimental animals, new approach methodologies (NAMs) using advanced technology have emerged in the 21st century. ToxCast/Tox21 aims to evaluate the adverse effects of chemicals quickly and efficiently using a high-throughput screening and to transform the paradigm of toxicity assessment into mechanism-based toxicity prediction. The ToxCast/Tox21 database, which contains extensive data from over 1400 assays with numerous biological targets and activity data for over 9000 chemicals, can be used for various purposes in the field of chemical prioritization and toxicity prediction. In this study, an overview of the database was explored to aid mechanism-based chemical prioritization and toxicity prediction. Implications for the utilization of the ToxCast/Tox21 database in chemical prioritization and toxicity prediction were derived. The research trends in ToxCast/Tox21 assay data were reviewed in the context of toxicity mechanism identification, chemical priority, environmental monitoring, assay development, and toxicity prediction. Finally, the potential applications and limitations of using ToxCast/Tox21 assay data in chemical risk assessment were discussed. The analysis of the toxicity mechanism-based assays of ToxCast/Tox21 will help in chemical prioritization and regulatory applications without the use of laboratory animals.

Quantitative adverse outcome pathway (qAOP) using bayesian network model on comparative toxicity of multi-walled carbon nanotubes (MWCNTs): safe-by-design approach

While the various physicochemical properties of engineered nanomaterials influence their toxicities, their understanding is still incomplete. A predictive framework is required to develop safe nanomaterials, and a Bayesian network (BN) model based on adverse outcome pathway (AOP) can be utilized for this purpose. In this study, to explore the applicability of the AOP-based BN model in the development of safe nanomaterials, a comparative study was conducted on the change in the probability of toxicity pathways in response to changes in the dimensions and surface functionalization of multi-walled carbon nanotubes (MWCNTs). Based on the results of our previous study, we developed an AOP leading to cell death, and the experimental results were collected in human liver cells (HepG2) and bronchial epithelium cells (Beas-2B). The BN model was trained on these data to identify probabilistic causal relationships between key events. The results indicated that dimensions were the main influencing factor for lung cells, whereas -OH or -COOH surface functionalization and aspect ratio were the main influencing factors for liver cells. Endoplasmic reticulum stress was found to be a more sensitive pathway for dimensional changes, and oxidative stress was a more sensitive pathway for surface functionalization. Overall, our results suggest that the AOP-based BN model can be used to provide a scientific basis for the development of safe nanomaterials.

Adverse outcome pathway (AOP): α2u-globulin nephropathy and kidney tumors in male rats

The National Research Council's vision of using adverse outcome pathways (AOPs) as a framework to assist with toxicity assessment for regulatory requirements of chemical assessment has continued to gain traction since its release in 2007. The need to expand the AOP knowledge base has gained urgency, with the U.S. Environmental Protection Agency's directive to eliminate reliance on animal toxicity testing by 2035. To meet these needs, our goal was to elucidate the AOP for male-rat-specific kidney cancer. Male-rat-specific kidney tumors occur through the ability of structurally diverse substances to induce α2u-globulin nephropathy (α2u-N), a well-studied mode of action (MoA) not relevant in humans that results in kidney tumor formation in male rats. An accepted AOP may help facilitate the differentiation from other kidney tumors MoAs. Following identification and review of relevant in vitro and in vivo literature, both the MIE and subsequent KEs were identified. Based on the weight of evidence from the various resources, the confidence in this AOP is high. Uses of this AOP include hazard identification, development of in vitro assays to determine if the MoA is through α2u-N and not relevant to humans resulting in decreased use of animals, and regulatory applications.

Assessment of RNA extraction protocols from cladocerans

The usage of cladocerans as non-model organisms in ecotoxicological and risk assessment studies has intensified in recent years due to their ecological importance in aquatic ecosystems. The molecular assessment such as gene expression analysis has been introduced in ecotoxicological and risk assessment to link the expression of specific genes to a biological process in the cladocerans. The validity and accuracy of gene expression analysis depends on the quantity, quality and integrity of extracted ribonucleic acid (RNA) of the sample. However, the standard methods of RNA extraction from the cladocerans are still lacking. This study evaluates the extraction of RNA from tropical freshwater cladocerans Moina micrura using two methods: the phenol-chloroform extraction method (QIAzol) and a column-based kit (Qiagen Micro Kit). Glycogen was introduced in both approaches to enhance the recovery of extracted RNA and the extracted RNA was characterised using spectrophotometric analysis (NanoDrop), capillary electrophoresis (Bioanalyzer). Then, the extracted RNA was analysed with reverse transcription polymerase chain reaction (RT-PCR) to validate the RNA extraction method towards downstream gene expression analysis. The results indicate that the column-based kit is most suitable for the extraction of RNA from M. micrura, with the quantity (RNA concentration = 26.90 ± 6.89 ng/μl), quality (A260:230 = 1.95 ± 0.15, A280:230 = 1.85 ± 0.09) and integrity (RNA integrity number, RIN = 7.20 ± 0.16). The RT-PCR analysis shows that the method successfully amplified both alpha tubulin and actin gene at 33-35 cycles (i.e. Ct = 32.64 to 33.48). The results demonstrate that the addition of glycogen is only suitable for the phenol-chloroform extraction method. RNA extraction with high and comprehensive quality control assessment will increase the accuracy and reliability of downstream gene expression, thus providing more ecotoxicological data at the molecular biological level on other freshwater zooplankton species.

From Causal Networks to Adverse Outcome Pathways: A Developmental Neurotoxicity Case Study

The last decade has seen the adverse outcome pathways (AOP) framework become one of the most powerful tools in chemical risk assessment, but the development of new AOPs remains a slow and manually intensive process. Here, we present a faster approach for AOP generation, based on manually curated causal toxicological networks. As a case study, we took a recently published zebrafish developmental neurotoxicity network, which contains causally connected molecular events leading to neuropathologies, and developed two new adverse outcome pathways: Inhibition of Fyna (Src family tyrosine kinase A) leading to increased mortality via decreased eye size (AOP 399 on AOP-Wiki) and GSK3beta (Glycogen synthase kinase 3 beta) inactivation leading to increased mortality via defects in developing inner ear (AOP 410). The approach consists of an automatic separation of the toxicological network into candidate AOPs, filtering the AOPs according to available evidence and length as well as manual development of new AOPs and weight-of-evidence evaluation. The semiautomatic approach described here provides a new opportunity for fast and straightforward AOP development based on large network resources.

Integrated Genomic and Bioinformatics Approaches to Identify Molecular Links between Endocrine Disruptors and Adverse Outcomes

Exposure to Endocrine Disrupting Chemicals (EDC) has been linked with several adverse outcomes. In this review, we examine EDCs that are pervasive in the environment and are of concern in the context of human, animal, and environmental health. We explore the consequences of EDC exposure on aquatic life, terrestrial animals, and humans. We focus on the exploitation of genomics technologies and in particular whole transcriptome sequencing. Genome-wide analyses using RNAseq provides snap shots of cellular, tissue and whole organism transcriptomes under normal physiological and EDC perturbed conditions. A global view of gene expression provides highly valuable information as it uncovers gene families or more specifically, pathways that are affected by EDC exposures, but also reveals those that are unaffected. Hypotheses about genes with unknown functions can also be formed by comparison of their expression levels with genes of known function. Risk assessment strategies leveraging genomic technologies and the development of toxicology databases are explored. Finally, we review how the Adverse Outcome Pathway (AOP) has exploited this high throughput data to provide a framework for toxicology studies.

Adverse Outcome Pathways as Versatile Tools in Liver Toxicity Testing

Adverse outcome pathways (AOPs) are tools to capture and visualize mechanisms driving toxicological effects. They share a common structure consisting of a molecular initiating event, a series of key events connected by key event relationships and an adverse outcome. Development and evaluation of AOPs ideally comply with guidelines issued by the Organization for Economic Cooperation and Development. AOPs have been introduced for major types of hepatotoxicity, which is not a surprise, as the liver is a frequent target for systemic adversity. Various applications for AOPs have been proposed in the areas of toxicology and chemical risk assessment, in particular in relation to the establishment of quantitative structure-activity relationships, the elaboration of prioritization strategies, and the development of novel in vitro toxicity screening tests and testing strategies.

Sublethal Effect Concentrations for Nonpolar Narcosis in the Zebrafish Embryo

Nonpolar narcosis, also known as baseline toxicity, has been described as the minimal toxicity that an organic chemical may elicit based on its lipophilicity. Although lethal effects of narcosis-inducing chemicals (NICs) have been thoroughly investigated, knowledge of sublethal effects is still very limited. We investigated the effects of 3 well-known NICs (phenanthrene, 1,3,5-trichlorobenzene, and pentachlorobenzene) on a variety of organismal endpoints (malformations, swim bladder inflation, respiration, heart rate, swimming activity, and turning angles), which can be plausibly linked to narcosis in zebrafish embryos. Baseline toxicity recorded as mortality is typically observed in similar exposure ranges in a wide variety of species including fish, corresponding to a chemical activity range between 0.01 and 0.1. In the present study, we found that sublethal effects occurred at concentrations approximately 5 times below lethal concentrations. Altered swimming activity and impaired swim bladder inflation were the most sensitive endpoints occurring at exposure levels below the generally accepted threshold for baseline toxicity for 2 out of 3 compounds. Overall, most effective exposure levels across the sublethal endpoints and compounds did fall within the range typically associated with baseline toxicity, and deviations were generally limited to a factor 10. Although there could be benefit in adding sublethal endpoints to toxicity tests, such as the fish embryo acute toxicity (FET) test, based on the present sublethal endpoints and available evidence from our and other studies, the underestimation of toxicity as a result of the sole assessment of mortality as an endpoint in an FET test may be limited for narcosis. Environ Toxicol Chem 2021;40:2802-2812. © 2021 SETAC.

Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.g., narcosis), reactive mechanisms (e.g., electrophilicity and free radical action), and specific mechanisms (e.g., associated with enzyme inhibition). The scheme is organized at three further levels of detail beyond broad domains to separate out the mechanistic group, specific mechanism, and the MIEs responsible. The novelty of this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs, and that it can be updated readily.

Effect-Based Trigger Values for Mixtures of Chemicals in Surface Water Detected with In Vitro Bioassays

Effect-based trigger (EBT) values for in vitro bioassays are important for surface water quality monitoring because they define the threshold between acceptable and poor water quality. They have been derived for highly specific bioassays, such as hormone-receptor activation in reporter gene bioassays, by reading across from existing chemical guideline values. This read-across method is not easily applicable to bioassays indicative of adaptive stress responses, which are triggered by many different chemicals, and activation of nuclear receptors for xenobiotic metabolism, to which many chemicals bind with rather low specificity. We propose an alternative approach to define the EBT from the distribution of specificity ratios of all active chemicals. The specificity ratio is the ratio between the predicted baseline toxicity of a chemical in a given bioassay and its measured specific endpoint. Unlike many previous read-across methods to derive EBTs, the proposed method accounts for mixture effects and includes all chemicals, not only high-potency chemicals. The EBTs were derived from a cytotoxicity EBT that was defined as equivalent to 1% of cytotoxicity in a native surface water sample. The cytotoxicity EBT was scaled by the median of the log-normal distribution of specificity ratios to derive the EBT for effects specific for each bioassay. We illustrate the new approach using the example of the AREc32 assay, indicative of the oxidative stress response, and 2 nuclear receptor assays targeting the peroxisome proliferator-activated receptor gamma and the arylhydrocarbon receptor. The EBTs were less conservative than previously proposed but were able to differentiate untreated and insufficiently treated wastewater from wastewater treatment plant effluent with secondary or tertiary treatment and surface water. Environ Toxicol Chem 2021;40:487-499. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Design, development and assessment of an essential oil based slow release vaporizer against mosquitoes

Mosquitoes (Diptera; Culicidae) are a biting nuisance and are of economic and health importance, especially for people living in tropical countries like India. Given the environmental concerns and health hazards of synthetic insecticides, development of natural products for the control of mosquito and mosquito-borne diseases are needed. In view of this, an essential oil based novel liquid vaporizer formulation with citronella and eucalyptus oils has been developed using a computer aided Artificial Neural Network and Particle Swarm Optimization (ANN-PSO) algorithm approach, aiming to predict the best optimized formulation (OF). Following the development, OF was characterized by Fourier Transform-Infra Red (FT-IR) spectroscopy and gas chromatography-mass spectroscopy (GC-MS). The efficacy of the OF was assessed against two major mosquito vectors viz. Anopheles stephensi and Aedes albopictus using a Peet-Grady chamber. Finally, toxicological impacts of the OF following its inhalation were investigated as per the Organization for Economic Co-operation and Development (OECD) guidelines. The results revealed all the ideal characteristics of the OF which were found to provide a slow release of up to 450 h at room temperature. Most importantly, the OF, exhibited 50% mosquito knock down (KT₅₀) within 11.49±1.34 and 14.15±2.15 min against An. stephensi and Ae. albopictus respectively. Toxicity assessment showed a non toxic nature of the OF following inhalation. Thus the present development would be beneficial for controlling both An. stephensi and Ae. albopictus without any associated health hazards.

Toward an AOP Network-Based Tiered Testing Strategy for the Assessment of Thyroid Hormone Disruption

A growing number of environmental pollutants are known to adversely affect the thyroid hormone system, and major gaps have been identified in the tools available for the identification, and the hazard and risk assessment of these thyroid hormone disrupting chemicals. We provide an example of how the adverse outcome pathway (AOP) framework and associated data generation can address current testing challenges in the context of fish early life stage tests, and fish tests in general. We demonstrate how a suite of assays covering biological processes involved in the underlying toxicological pathways can be implemented in a tiered screening and testing approach for thyroid hormone disruption, using the levels of assessment of the OECD's Conceptual Framework for the Testing and Assessment of Endocrine Disrupting Chemicals as a guide.

Quantitative adverse outcome pathway (qAOP) models for toxicity prediction

The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP models derived from the published definitions, and (iii) identify and assess the existing published qAOP models and associated software tools. As a result, five probabilistic qAOPs and ten mechanistic qAOPs were evaluated against the common features. The review offers an overview of how the qAOP concept has advanced and how it can aid toxicity assessment in the future. Further efforts are required to achieve validation, harmonisation and regulatory acceptance of qAOP models.

Computational approach for collection and prediction of molecular initiating events in developmental toxicity

Developmental toxicity is defined as the occurrence of adverse effects on the developing organism as a result from exposure to a toxic agent. These alterations can have long-term acute effects. Current in vitro models present important limitations and the evaluation of toxicity is not entirely objective. In silico methods have also shown limited success, in part due to complex and varied mechanisms of action that mediate developmental toxicity, which are sometimes poorly understood. In this article, we compiled a dataset of compounds with developmental toxicity categories and annotated mechanisms of action for both toxic and non-toxic compounds (DVTOX). With it, we selected a panel of protein targets that might be part of putative Molecular Initiating Events (MIEs) of Adverse Outcome Pathways of developmental toxicity. The validity of this list of candidate MIEs was studied through the evaluation of new drug-target relationships that include such proteins, but were not part of the original database. Finally, an orthology analysis of this protein panel was conducted to select an appropriate animal model to assess developmental toxicity. We tested our approach using the zebrafish embryo toxicity test, finding positive results.

Acting on uncertainty: real-life mixtures of endocrine disrupting chemicals

Exposure to multiple synthetic chemicals is a permanent feature of modern life. Many of these chemicals are suspected to disrupt endocrine systems of humans and animals. Endocrine disrupting chemicals (EDCs) act at very low concentrations and non-linearly, defying mainstream single-substance chemical regulation. Here we provide an analysis of findings from the first phase of the European Horizon2020-funded “EDC-MixRisk” project as a case of contemporary life-science enterprise, which addresses health-risks related to real-life exposure to mixtures of EDCs. Real-life EDC mixtures were inferred in the project from biological samples taken from pregnant women in a large epidemiological study that followed up their children over several years across major health domains; responses to these mixtures were then experimentally identified, and based on these findings, mixture risk assessment models were developed. The project consequently advocated for European chemical regulation more attentive to real-life exposure. Locating it within historical and sociological analyses of chemical exposure and within the European chemical political context, we argue that scientific uncertainty related to real-life EDC mixture exposure enables a form of epistemological approach and scientific activism, simultaneously in continuity with, and in break from, mainstream toxicology. In a chemically polluted world, this kind of science still occupies a place in the tension between public health and market-oriented regulation.

Mummichog (Fundulus heteroclitus) are less sensitive to 17α-ethinylestradiol (EE2) than other common model teleosts: A comparative review of reproductive effects

The environmental estrogen 17α-ethinylestradiol (EE₂) will depress or completely inhibit egg production in many common model teleosts at low concentrations (≤0.5 ng/L; Runnalls et al., 2015). This inhibition is not seen in the estuarine killifish, or mummichog (Fundulus heteroclitus), even when exposed to 100 ng/L EE₂. This relative insensitivity to EE₂ exposure indicates species-specific mechanisms for compensating for exogenous estrogenic exposure. This review compares various reproductive responses elicited by EE₂ in mummichog to other common model teleosts, such as zebrafish (Danio rerio) and fathead minnow (Pimephales promelas), identifying key endpoints where mummichog differ from other studied fish. For example, EE₂ accumulates primarily in the liver/gall bladder of mummichog, which is different than zebrafish and fathead minnow in which accumulation is predominantly in the carcass. Despite causing species-specific differences in fecundity, EE₂ has been shown to consistently induce hepatic vitellogenin in males and cause feminization/sex reversal during gonadal differentiation in larval mummichog, similar to other species. In addition, while gonadal steroidogenesis and plasma steroid levels respond to exogenous EE_2, it is generally at higher concentrations than observed in other species. In mummichog, production of 17β-estradiol (E₂) by full grown ovarian follicles remains high; unlike other teleost models where E₂ synthesis decreases as 17α,20β-dihydroxy-4-prenen-3-on levels increase to induce oocyte maturation. New evidence in mummichog indicates some dissimilarity in gonadal steroidogenic gene expression responses compared to gene expression responses in zebrafish and fathead minnow exposed to EE₂. The role of ovarian physiology continues to warrant investigation regarding the tolerance of mummichog to exogenous EE₂ exposure. Here we present a comprehensive review, highlighting key biological differences in response to EE₂ exposure between mummichog and other commonly used model teleosts.

Refinement of an OECD test guideline for evaluating the effects of endocrine disrupting chemicals on aromatase gene expression and reproduction using novel transgenic cyp19a1a-eGFP zebrafish

Transgenic fish are powerful models that can provide mechanistic information regarding the endocrine activity of test chemicals. In this study, our objective was to use a newly developed transgenic zebrafish line expressing eGFP under the control of the cyp19a1a promoter in the OECD Fish Short Term Reproduction Assay (TG 229) to provide additional mechanistic information on tested substances. For this purpose, we exposed adult transgenic zebrafish to a reference substance of the TG 229, i.e. prochloraz (PCZ; 1.7, 17.2 and 172.6 μg/L). In addition to "classical" endpoints used in the TG 229 (reproductive outputs, vitellogenin), the fluorescence intensity of the ovaries was monitored at 4 different times of exposure using in vivo imaging. Our data revealed that 172.6 μg/L PCZ significantly decreased the number of eggs laid per female per day and the concentrations of vitellogenin in females, reflecting the decreasing E2 synthesis due to the inhibition of the ovarian aromatase activities. At 7 and 14 days, GFP intensities in ovaries were similar over the treatment groups but significantly increased after 21 days at 17.2 and 172.6 μg/L. A similar profile was observed for the endogenous cyp19a1a expression measured by qPCR thereby confirming the reliability of the GFP measurement for assessing aromatase gene expression. The overexpression of the cyp19a1a gene likely reflects a compensatory response to the inhibitory action of PCZ on aromatase enzymatic activities. Overall, this study illustrates the feasibility of using the cyp19a1a-eGFP transgenic line for assessing the effect of PCZ in an OECD test guideline while providing complementary information on the time- and concentration-dependent effects of the compound, without disturbing reproduction of fish. The acquisition of this additional mechanistic information on a key target gene through in vivo fluorescence imaging of the ovaries was realized without increasing the number of individuals.

Role of Damage-Associated Molecular Patterns in Light of Modern Environmental Research: A Tautological Approach

Two prominent models emerged as a result of intense interdisciplinary discussions on the environmental health paradigm, called the "exposome" concept and the "adverse outcome pathway" (AOP) concept that links a molecular initiating event to the adverse outcome via key events. Here, evidence is discussed, suggesting that environmental stress/injury-induced damage-associated molecular patterns (DAMPs) may operate as an essential integrating element of both environmental health research paradigms. DAMP-promoted controlled/uncontrolled innate/adaptive immune responses reflect the key events of the AOP concept. The whole process starting from exposure to a distinct environmental stress/injury-associated with the presence/emission of DAMPs-up to the manifestation of a disease may be regarded as an exposome. Clinical examples of such a scenario are briefly sketched, in particular, a model in relation to the emerging COVID-19 pandemic, where the interaction of noninfectious environmental factors (e.g., particulate matter) and infectious factors (SARS CoV-2) may promote SARS case fatality via superimposition of both exogenous and endogenous DAMPs.

Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library

The constitutive androstane receptor (CAR) plays pivotal roles in drug-induced liver injury through the transcriptional regulation of drug-metabolizing enzymes and transporters. Thus, identifying regulatory factors for CAR activation is important for understanding its mechanisms. Numerous studies conducted previously on CAR activation and its toxicity focused on in vivo or in vitro analyses, which are expensive, time consuming, and require many animals. We developed a computational model that predicts agonists for the CAR using the Toxicology in the 21st Century 10k library. Additionally, we evaluate the prediction performance of novel deep learning (DL)-based quantitative structure-activity relationship analysis called the DeepSnap-DL approach, which is a procedure of generating an omnidirectional snapshot portraying three-dimensional (3D) structures of chemical compounds. The CAR prediction model, which applies a 3D structure generator tool, called CORINA-generated and -optimized chemical structures, in the DeepSnap-DL demonstrated better performance than the existing methods using molecular descriptors. These results indicate that high performance in the prediction model using the DeepSnap-DL approach may be important to prepare suitable 3D chemical structures as input data and to enable the identification of modulators of the CAR.

Development of a Novel AOP for Cyp2F2-Mediated Lung Cancer in Mice

Traditional methods for carcinogenicity testing rely heavily on the rodent bioassay as the standard for identification of tumorigenic risk. As such, identification of species-specific outcomes and/or metabolism are a frequent argument for regulatory exemption. One example is the association of tumor formation in the mouse lung after exposure to Cyp2F2 ligands. The adverse outcome pathway (AOP) framework offers a theoretical platform to address issues of species specificity that is consistent, transparent, and capable of integrating data from new approach methodologies as well as traditional data streams. A central premise of the AOP concept is that pathway progression from the molecular initiating event (MIE) implies a definable “response-response” (R-R) relationship between each key event (KE) that drives the pathway towards a specific adverse outcome (AO). This article describes an AOP for lung cancer in the mouse from an MIE of Cyp2F2-specific reactive metabolite formation, advancing through KE that include protein and/or nucleic acid adducts, diminished Club Cell 10 kDa (CC10) protein expression, hyperplasia of CC10 deficient Club cells, and culminating in the AO of mixed-cell tumor formation in the distal airways. This tumor formation is independent of route of exposure and our AOP construct is based on overlapping mechanistic events for naphthalene, styrene, ethyl benzene, isoniazid, and fluensulfone in the mouse. This AOP is intended to accelerate the explication of an apparent mouse-specific outcome and serve as a starting point for a quantitative analysis of mouse-human differences in susceptibility to the tumorigenic effects of Cyp2F2 ligands.

When Are Adverse Outcome Pathways and Associated Assays "Fit for Purpose" for Regulatory Decision-Making and Management of Chemicals?

There have been increasing demands for chemical hazard and risk assessments in recent years. Chemical companies have expanded internal product stewardship initiatives, and jurisdictions have increased the regulatory requirements for the manufacture and sale of chemicals. There has also been a shift in chemical toxicity evaluations within the same time frame, with new methodologies being developed to improve chemical safety assessments for both human health and the environment. With increased needs for chemical assessments coupled with more diverse data streams from new technologies, regulators and others tasked with chemical management activities are faced with increasing workloads and more diverse types of data to consider. The Adverse Outcome Pathway (AOP) framework can be applied in different scenarios to integrate data and guide chemical assessment and management activities. In this paper, scenarios of how AOPs can be used to guide chemical management decisions during research and development, chemical registration, and subsequent regulatory activities such as prioritization and risk assessment are considered. Furthermore, specific criteria (e.g., the type and level of AOP complexity, confidence in the AOP, as well as external review and assay validation) are proposed to examine whether AOPs and associated tools are fit for purpose when applied in different contexts. Certain toxicity pathways are recommended as priority areas for AOP research and development, and the continued use of AOPs and defined approaches in regulatory activities are recommended. Furthermore, a call for increased outreach, education, and enhanced use of AOP databases is proposed to increase their utility in chemicals management. Integr Environ Assess Manag 2019;15:633-647. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Computational in Vitro Toxicology Uncovers Chemical Structures Impairing Mitochondrial Membrane Potential

Technological advances in molecular biology have enabled high-throughput screening (HTS) of large chemical libraries. These approaches have provided valuable toxicity data for many physiological responses, including mitochondrial dysfunction. While several quantitative structure-activity relationship (QSAR) models have been developed for mitochondrial dysfunction, there remains a need to identify specific chemical features associated with this response. Thus, the objective of this study was to identify chemical structures associated with altered mitochondrial membrane potential (MMP). To achieve this, we developed computational models to examine the relationship between specific chemotypes (e.g., ToxPrints) and bioactivity in ToxCast/Tox21 HTS assays for altered MMP. The analysis revealed that the "bond:COH_alcohol_aromatic", "bond:COH_alcohol_aromatic_phenol", and "ring:aromatic_benzene" ToxPrints had the highest average correlations (phi coefficient) with ToxCast/Tox21 assay component endpoints for decreased MMP. These structures also constituted a "core" group of ToxPrints for decreased MMP in a force-directed network model and were the most important chemotypes in a random forest (RF) classification model for the "TOX21_MMP_ratio_down" assay component endpoint. Based on multiple lines of evidence, these structures, which are present in numerous chemicals (e.g., aromatic hydrocarbons, pesticides, and industrial chemicals) are likely involved in mitochondrial dysfunction. Because of the hierarchical structure of ToxPrints, these chemotypes were highly convergent and, when excluded from training data, had limited effects on the classification performance as related structures compensated for predictor loss. These results highlight the flexibility of the RF algorithm and ToxPrints for QSAR modeling, which is useful to identify chemicals affecting mitochondrial function.

Transcriptomic analysis of short-term 17α-ethynylestradiol exposure in two Californian sentinel fish species sardine (Sardinops sagax) and mackerel (Scomber japonicus)

Endocrine disrupting chemicals (EDCs) are substances which disrupt normal functioning of the endocrine system by interfering with hormone regulated physiological pathways. Aquatic environments provide the ultimate reservoir for many EDCs as they enter rivers and the ocean via effluent discharges and accumulate in sediments. One EDC widely dispersed in municipal wastewater effluent discharges is 17α-ethynylestradiol (EE2), which is one of the most widely prescribed medicines. EE2 is a bio-active estrogen employed in the majority of oral contraceptive pill formulations. As evidence of the health risks posed by EDCs mount, there is an urgent need to improve diagnostic tools for monitoring the effects of pollutants. As the cost of high throughput sequencing (HTS) diminishes, transcriptional profiling of an organism in response to EDC perturbation presents a cost-effective way of screening a wide range of endocrine responses. Coastal pelagic filter feeding fish species analyzed using HTS provide an excellent tool for EDC risk assessment in the marine environment. Unfortunately, there are limited genome sequence data and annotation for many of these species including Pacific sardine (Sardinops sagax) and chub mackerel (Scomber japonicus), which limits the utility of molecular tools such as HTS to interrogate the effects of endocrine disruption. In this study, we carried out RNA sequencing (RNAseq) of liver RNA harvested from wild sardine and mackerel exposed for 5 h under laboratory conditions to a concentration of 12.5 pM EE2 in the tank water. We developed an analytical framework for transcriptomic analyses of species with limited genomic information. EE2 exposure altered expression patterns of key genes involved in important metabolic and physiological processes. The systems approach presented here provides a powerful tool for obtaining a comprehensive picture of endocrine disruption in aquatic organisms.

Is there a role for the adverse outcome pathway framework to support radiation protection?

PURPOSE

In 2012, the Organization for Economic Cooperation and Development (OECD) formally launched the Adverse Outcome Pathway (AOP) Programme. The AOP framework has the potential for predictive utility in identifying early biological endpoints linked to adverse effects. It uses the weight of correlative evidence to identify a minimal set of measurable key events that link molecular initiating events to an adverse outcome. AOPs have the capability to identify knowledge gaps and priority areas for future research based on relevance to an adverse outcome. In addition, AOPs can identify pathways that are common among multiple stressors, thereby allowing for the possibility of refined risk assessments based on co-exposure considerations. The AOP framework is increasingly being used in chemical and ecological risk assessment; however, its use in the development of radiation-specific pathways has yet to be fully explored. To bring awareness of the AOP framework to the Canadian radiation community, a workshop was held in Canada in June 2018 that brought together radiation experts from Health Canada, the Canadian Nuclear Laboratories, and the Canadian Nuclear Safety Commission.

METHODS

The purpose of the workshop was to share knowledge on the AOP framework, specifically (1) to introduce the concept of the AOP framework and its possible utility to Canadian radiation experts; (2) to provide examples on how it has advanced risk assessment; (3) to discuss an illustrative example specific to ionizing radiation; and lastly (4) to identify the broad benefits and challenges of the AOP framework to the radiation community.

RESULTS

The participants showed interest in the framework, case examples were described and areas of challenge were identified. Herein, we summarize the outcomes of the workshop.

CONCLUSIONS

Overall, participants agreed that by building AOPs in the radiation field, a network of data-sharing initiatives will enhance our interpretation of existing knowledge where current scientific evidence is minimal. They would provide new avenues to understand effects at low-dose and dose-rates and help to quantify the combined effect of multiple stressors on shared mechanistic pathways.

Pathway-based predictive approaches for non-animal assessment of acute inhalation toxicity

New approaches are needed to assess the effects of inhaled substances on human health. These approaches will be based on mechanisms of toxicity, an understanding of dosimetry, and the use of in silico modeling and in vitro test methods. In order to accelerate wider implementation of such approaches, development of adverse outcome pathways (AOPs) can help identify and address gaps in our understanding of relevant parameters for model input and mechanisms, and optimize non-animal approaches that can be used to investigate key events of toxicity. This paper describes the AOPs and the toolbox of in vitro and in silico models that can be used to assess the key events leading to toxicity following inhalation exposure. Because the optimal testing strategy will vary depending on the substance of interest, here we present a decision tree approach to identify an appropriate non-animal integrated testing strategy that incorporates consideration of a substance's physicochemical properties, relevant mechanisms of toxicity, and available in silico models and in vitro test methods. This decision tree can facilitate standardization of the testing approaches. Case study examples are presented to provide a basis for proof-of-concept testing to illustrate the utility of non-animal approaches to inform hazard identification and risk assessment of humans exposed to inhaled substances.

Identification of aldo-keto reductase (AKR7A1) and glutathione S-transferase pi (GSTP1) as novel renal damage biomarkers following exposure to mercury

The kidney is one of the main targets for toxicity induced by xenobiotics. Sensitive detection of early impairment is critical to assess chemical-associated renal toxicity. The aim of this study was to identify potential nephrotoxic biomarkers in rat kidney tissues after exposure to mercury (Hg), a representative nephrotoxicant, and to evaluate these new biomarkers employing in vivo and in vitro systems. Mercuric chloride was administered orally to Sprague-Dawley rats for 2 weeks. Proteomic analysis revealed that aldo-keto reductase (AKR7A1) and glutathione S-transferase pi (GSTP1) were significantly elevated in kidney after Hg exposure. While the levels of conventional nephrotoxic clinical markers including blood urea nitrogen and serum creatinine were not elevated, the mRNA and protein levels of AKR7A1 and GSTP1 were increased upon Hg exposure in a dose-dependent manner. The increases in AKR7A1 and GSTP1 were also observed in rat kidneys after an extended exposure for 6 weeks to low-dose Hg. In in vitro rat kidney proximal tubular cells, changes in AKR7A1 and GSTP1 levels correlated well with the extent of cytotoxicity induced by Hg, cadmium, or cisplatin. AKR7A1 and GSTP1 were identified as new candidates for Hg-induced nephrotoxicity, suggesting that these biomarkers have potential for evaluating or predicting nephrotoxicity.

Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: use case in breast carcinogenesis

Advances in technology within biomedical sciences have led to an inundation of data across many fields, raising new challenges in how best to integrate and analyze these resources. For example, rapid chemical screening programs like the US Environmental Protection Agency's ToxCast and the collaborative effort, Tox21, have produced massive amounts of information on putative chemical mechanisms where assay targets are identified as genes; however, systematically linking these hypothesized mechanisms with in vivo toxicity endpoints like disease outcomes remains problematic. Herein we present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. Resources that tag genes to articles were integrated, then cross-species orthologs were identified using UniRef50 clusters. MeSH term frequency was normalized to reflect the MeSH tree structure, and then the resulting GeneID-MeSH associations were ranked using NPMI. The resulting network, called Entity MeSH Co-occurrence Network (EMCON), is a scalable resource for the identification and ranking of genes for a given topic of interest. The utility of EMCON was evaluated with the use case of breast carcinogenesis. Topics relevant to breast carcinogenesis were used to query EMCON and retrieve genes important to each topic. A breast cancer gene set was compiled through expert literature review (ELR) to assess performance of the search results. We found that the results from EMCON ranked the breast cancer genes from ELR higher than randomly selected genes with a recall of 0.98. Precision of the top five genes for selected topics was calculated as 0.87. This work demonstrates that EMCON can be used to link in vitro results to possible biological outcomes, thus aiding in generation of testable hypotheses for furthering understanding of biological function and the contribution of chemical exposures to disease.

The Adverse Outcome Pathway: A Multifaceted Framework Supporting 21st Century Toxicology

The adverse outcome pathway (AOP) framework serves as a knowledge assembly, interpretation, and communication tool designed to support the translation of pathway-specific mechanistic data into responses relevant to assessing and managing risks of chemicals to human health and the environment. As such, AOPs facilitate the use of data streams often not employed by risk assessors, including information from in silico models, in vitro assays and short-term in vivo tests with molecular/biochemical endpoints. This translational capability can increase the capacity and efficiency of safety assessments both for single chemicals and chemical mixtures. Our mini-review describes the conceptual basis of the AOP framework and aspects of its current status relative to use by toxicologists and risk assessors, including four illustrative applications of the framework to diverse assessment scenarios.

In silico cancer research towards 3R

BACKGROUND

Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost.

MAIN BODY

We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell's functional organization up to model building for predictive systems.

CONCLUSION

Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

The future trajectory of adverse outcome pathways: a commentary

The advent of adverse outcome pathways (AOPs) has provided a new lexicon for description of mechanistic toxicology, and a renewed enthusiasm for exploring modes of action resulting in adverse health and environmental effects. In addition, AOPs have been used successfully as a framework for the design and development of non-animal approaches to toxicity testing. Although the value of AOPs is widely recognised, there remain challenges and opportunities associated with their use in practise. The purpose of this article is to consider specifically how the future trajectory of AOPs may provide a basis for addressing some of those challenges and opportunities.

Linking in vitro estrogenicity to adverse effects in the inland silverside (Menidia beryllina)

High-throughput cell assays that detect and integrate the response of multiple chemicals acting via a common mode of action have the potential to enhance current environmental monitoring practices. Establishing the linkage between in vitro and in vivo responses is key to demonstrating that in vitro cell assays can be predictive of ecologically relevant outcomes. The present study investigated the potency of 17β-estradiol (E2), estrone (E1), nonylphenol (NP), and treated wastewater effluent using the readily available GeneBLAzer^® estrogen receptor transactivation assay and 2 life stages of the inland silverside (Menidia beryllina). In vitro estrogenic potencies were ranked as follows: E2 > E1 >> NP. All 3 model estrogens induced vitellogenin and choriogenin expression in a dose-dependent manner in larvae and juveniles. However, apical effects were only found for E2 and E1 exposures of juveniles, which resulted in female-skewed sex ratios. Wastewater effluent samples exhibiting low in vitro estrogenicity (below the 10% effective concentration [EC10]), did not cause significant changes in M. beryllina. Significant induction of estrogen-responsive genes was observed at concentrations 6 to 26 times higher than in vitro responses. Gonadal feminization occurred at concentrations at least 19 to 26 times higher than the in vitro responses. These findings indicated that in vitro cell assays were more sensitive than the fish assays, making it possible to develop in vitro effect thresholds protective of aquatic organisms. Environ Toxicol Chem 2018;37:884-892. © 2017 SETAC.

Development of an adverse outcome pathway for chemically induced hepatocellular carcinoma: case study of AFB1, a human carcinogen with a mutagenic mode of action

Adverse outcome pathways (AOPs) are frameworks starting with a molecular initiating event (MIE), followed by key events (KEs) linked by KE relationships (KERs), ultimately resulting in a specific adverse outcome. Relevant data for the pathway and each KE/KER are evaluated to assess biological plausibility, weight-of-evidence, and confidence. We aimed to describe an AOP relevant to chemicals directly inducing mutation in cancer critical gene(s), via the formation of chemical-specific pro-mutagenic DNA adduct(s), as an early critical step in tumor etiology. Such chemicals have mutagenic modes-of-action (MOA) for tumor induction. To assist with developing this AOP, Aflatoxin B1 (AFB1) was selected as a case study because it has a rich database and is considered to have a mutagenic MOA. AFB1 information was used to define specific KEs, KERs, and to inform development of a generic AOP for mutagen-induced hepatocellular carcinoma (HCC). In assessing the AFB1 information, it became clear that existing data are, in fact, not optimal and for some KEs/KERs, the definitive data are not available. In particular, while there is substantial information that AFB1 can induce mutations (based on a number of mutation assays), the definitive evidence - the ability to induce mutation in the cancer critical gene(s) in the tumor target tissue - is not available. Thus, it is necessary to consider the patterns of results in the weight-of-evidence for KEs and KERs. It was important to determine whether there was sufficient evidence that AFB1 can induce the necessary critical mutations early in the carcinogenic process, which was the case.

Creating a Structured AOP Knowledgebase via Ontology-Based Annotations

Introduction

The Adverse Outcome Pathway framework is increasingly used to integrate data generated based on traditional and emerging toxicity testing paradigms. As the number of AOP descriptions has increased, so has the need to define the AOP in computable terms.

Materials and Methods

Herein, we present a comprehensive annotation of 172 AOPs housed in the AOP-Wiki as of December 4, 2016 using terms from existing biological ontologies.

Results

AOP Key Events (KEs) were assigned ontology terms using a concept called the Event Component, which consists of a Process, an Object, and an Action term, with each term originating from ontologies and other controlled vocabularies. Annotation of KEs with ontology classes from fourteen ontologies and controlled vocabularies resulted in a total of 685 KEs being annotated with a total of 809 Event Components. A set of seven conventions resulted, defining the annotation of KEs via Event Components.

Discussion

This expanded annotation of AOPs allows computational reasoners to aid in both AOP development and applications. In addition, the incorporation of explicit biological objects will reduce the time required for converting a qualitative AOP description into a conceptual model that can support computational modeling. As high throughput genomics becomes a more important part of the high throughput toxicity testing landscape, the new approaches described here for annotating key events will also promote the visualization and analysis of genomics data in an AOP context.