An integrative data mining approach to identifying adverse outcome pathway signatures

Novel perspective on particulate matter and Alzheimer's disease: Insights from adverse outcome pathway framework

Alzheimer's disease (AD) is a neurodegenerative disease, that accounts for 50-75% of all dementia cases. Evidence demonstrates the link between particulate matter (PM) exposure and AD. However, there are still considerable research gaps. This review aims to clarify the mechanism between PM and AD from different levels (subcellular/cellular/system/population) by using an adverse outcome pathway (AOP) framework. We applied a chemical-phenotype interaction network-based workflow to integrate diverse genes and phenotypes. The interactions among PM, genes, phenotypes, and AD were retrieved from the Comparative Toxicogenomics Database (CTD), DisGeNET, MalaCards, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), which are publicly available databases. The filtered genes and phenotypes were assembled as molecular initiating events (MIEs) and key events (KEs) according to the upstream and downstream relationships, generating a predictive PM-Gene-Phenotype-AD AOP network. According to the Organization for Economic Co-operation and Development handbook (OECD), a verified AOP network was assessed and applied to determine the effects of PM on AD. PM could increase APP and GSK3B, increase apoptosis, impair cognition and memory, and ultimately lead to AD. Overall, chemical-phenotype interactions are expressed in a formal structured notation using controlled terms for chemicals, phenotypes, taxons, and anatomical descriptors. To our knowledge, this is the first AOP framework focusing on the underlying mechanism of exposure to PM on AD. Our network-based approach not only fills mechanism gaps in PM and AD but sheds light on constructing AOP frameworks for new chemicals.

Oxidative stress mediates the association of organophosphate flame retardants with metabolic obesity in U.S. adults: A combined epidemiologic and bioinformatic study

Obesity is a global public health issue, with limited epidemiologic studies on the relationship and mechanisms between organophosphate flame retardants (OPFRs) and metabolic obesity phenotypes (MOPs). We aimed to explore the link between OPFRs metabolite (m-OPFRs) and MOPs using a combined epidemiologic and bioinformatic approach. We used cross-sectional survey data from the U.S. National Health and Nutrition Examination Survey (2011-2018) to analyze the relationship between m-OPFRs and metabolic health obesity (MHO), as well as metabolic unhealthy obesity (MUO). The dataset encompasses eligible adults to assess the impact of individual, mixed, and mediated effects on the outcome variables through multivariate logistic regression, Bayesian kernel machine regression (BKMR), and mediation analysis. Multiple logistic regression models, stratified by tertiles of exposure showed that bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) levels in the body significantly increased the risk of MHO, with OR and 95%CI of 1.454 (1.082, 1.953) for the second tertile (T2) and 1.598 (1.126, 2.268) for the third tertile (T3), compared to the first tertile (T1). Increased levels of BDCIPP in T3 (1.452(1.013, 2.081)) are associated with MUO, compared to T1. Mixed m-OPFRs and MHO risk in BMKR were positively correlated, with BDCIPP being the primary contributor. We found that the serum uric acid (SUA) and white blood cell count (WBC) indicators significantly mediated the association between BDCIPP and MHO (P < 0.05). Our study suggests that OPFRs, either individual or mixed, are associated with two distinct MOPs, with oxidative stress playing an important role. In addition, in silico analysis was used to screen for shared genes, and eight shared genes and eleven biological pathways identified during the screening process were used to construct the adverse outcome pathway, which suggests that exposure to OPFRs may activate the peroxisome proliferator-activated receptor (PPAR) pathway, thereby increasing the risk of obesity. Further studies are needed to validate our findings.

Investigating the relationship of co-exposure to multiple metals with chronic kidney disease: An integrated perspective from epidemiology and adverse outcome pathways

Systematic studies on the associations between co-exposure to multiple metals and chronic kidney disease (CKD), as well as the underlying mechanisms, remain insufficient. This study aimed to provide a comprehensive perspective on the risk of CKD induced by multiple metal co-exposures through the integration of occupational epidemiology and adverse outcome pathway (AOP). The study participants included 401 male mine workers whose blood metal, β2-microglobulin (β2-MG), and cystatin C (Cys-C) levels were measured. Generalized linear models (GLMs), quantile g-computation models (qgcomp), least absolute shrinkage and selection operator (LASSO), and bayesian kernel machine regression (BKMR) were utilized to identify critical nephrotoxic metals. The mean concentrations of lead, cadmium, mercury, arsenic, and manganese were 191.93, 3.92, 4.66, 3.11, 11.35, and 16.33 µg/L, respectively. GLM, LASSO, qgcomp, and BKMR models consistently identified lead, cadmium, mercury, and arsenic as the primary contributors to kidney toxicity. Based on our epidemiological analysis, we used a computational toxicology method to construct a chemical-genetic-phenotype-disease network (CGPDN) from the Comparative Toxicogenomics Database (CTD), DisGeNET, and GeneCard databases, and further linked key events (KEs) related to kidney toxicity from the AOP-Wiki and PubMed databases. Finally, an AOP framework of multiple metals was constructed by integrating the common molecular initiating events (reactive oxygen species) and KEs (MAPK signaling pathway, oxidative stress, mitochondrial dysfunction, DNA damage, inflammation, hypertension, cell death, and kidney toxicity). This is the first AOP network to elucidate the internal association between multiple metal co-exposures and CKD, providing a crucial basis for the risk assessment of multiple metal co-exposures.

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 with 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.

Hepatic toxicity prediction of bisphenol analogs by machine learning strategy

Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevalent type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for human health. However, the lack of in vitro and in vivo data for the emerging BPs has limited the hazard assessment of these synthetic chemicals. Here, we aimed to develop a new strategy to rapidly predict BPs' hepatotoxicity using network analysis coupled with machine learning models. Considering the structural and functional similarities shared by BPs with Bisphenol A (BPA), we first integrated hepatic disease related genes from multiple databases into BPA-Gene-Phenotype-hepatic toxicity network and subjected it to the computational AOP (cAOP). Through cAOP network and conventional machine learning approaches, we scored the hepatotoxicity of 20 emerging BPs and provided new insights into how BPs' structure features contributed to biologic functions with limited experimental data. Additionally, we assessed the interactions between emerging BPs and ESR1 using molecular docking and proposed an AOP framework wherein ESR1 was a molecular initiating event. Overall, our study provides a computational approach to predict the hepatotoxicity of emerging BPs.

Identification of neural-relevant toxcast high-throughput assay intended gene targets: Applicability to neurotoxicity and neurotoxicant putative molecular initiating events

The US EPA's Toxicity Forecaster (ToxCast) is a suite of high-throughput in vitro assays to screen environmental toxicants and predict potential toxicity of uncharacterized chemicals. This work examines the relevance of ToxCast assay intended gene targets to putative molecular initiating events (MIEs) of neurotoxicants. This effort is needed as there is growing interest in the regulatory and scientific communities about developing new approach methodologies (NAMs) to screen large numbers of chemicals for neurotoxicity and developmental neurotoxicity. Assay gene function (GeneCards, NCBI-PUBMED) was used to categorize gene target neural relevance (1 = neural, 2 = neural development, 3 = general cellular process, 3 A = cellular process critical during neural development, 4 = unlikely significance). Of 481 unique gene targets, 80 = category 1 (16.6 %); 16 = category 2 (3.3 %); 303 = category 3 (63.0 %); 97 = category 3 A (20.2 %); 82 = category 4 (17.0 %). A representative list of neurotoxicants (548) was researched (ex. PUBMED, PubChem) for neurotoxicity associated MIEs/Key Events (KEs). MIEs were identified for 375 compounds, whereas only KEs for 173. ToxCast gene targets associated with MIEs were primarily neurotransmitter (ex. dopaminergic, GABA)receptors and ion channels (calcium, sodium, potassium). Conversely, numerous MIEs associated with neurotoxicity were absent. Oxidative stress (OS) mechanisms were 79.1 % of KEs. In summary, 40 % of ToxCast assay gene targets are relevant to neurotoxicity mechanisms. Additional receptor and ion channel subtypes and increased OS pathway coverage are identified for potential future assay inclusion to provide more complete coverage of neural and developmental neural targets in assessing neurotoxicity.

Neurotoxicity of fine and ultrafine particulate matter: A comprehensive review using a toxicity pathway-oriented adverse outcome pathway framework

Fine particulate matter (PM2.5) can cause brain damage and diseases. Of note, ultrafine particles (UFPs) with an aerodynamic diameter less than or equal to 100 nm are a growing concern. Evidence has suggested toxic effects of PM2.5 and UFPs on the brain and links to neurological diseases. However, the underlying mechanism has not yet been fully illustrated due to the variety of the study models, different endpoints, etc. The adverse outcome pathway (AOP) framework is a pathway-based approach that could systematize mechanistic knowledge to assist health risk assessment of pollutants. Here, we constructed AOPs by collecting molecular mechanisms in PM-induced neurotoxicity assessments. We chose particulate matter (PM) as a stressor in the Comparative Toxicogenomics Database (CTD) and identified the critical toxicity pathways based on Ingenuity Pathway Analysis (IPA). We found 65 studies investigating the potential mechanisms linking PM2.5 and UFPs to neurotoxicity, which contained 2, 675 genes in all. IPA analysis showed that neuroinflammation signaling and glucocorticoid receptor signaling were the common toxicity pathways. The upstream regulator analysis (URA) of PM2.5 and UFPs demonstrated that the neuroinflammation signaling was the most initially triggered upstream event. Therefore, neuroinflammation was recognized as the MIE. Strikingly, there is a clear sequence of activation of downstream signaling pathways with UFPs, but not with PM2.5. Moreover, we found that inflammation response and homeostasis imbalance were key cellular events in PM2.5 and emphasized lipid metabolism and mitochondrial dysfunction, and blood-brain barrier (BBB) impairment in UFPs. Previous AOPs, which only focused on phenotypic changes in neurotoxicity upon PM exposure, we for the first time propose AOP framework in which PM2.5 and UFPs may activate pathway cascade reactions, resulting in adverse outcomes associated with neurotoxicity. Our toxicity pathway-based approach not only advances risk assessment for PM-induced neurotoxicity but shines a spotlight on constructing AOP frameworks for new chemicals.

The relationship of organophosphate flame retardants with hyperuricemia and gout via the inflammatory response: An integrated approach

BACKGROUND

Evidence regarding the relationships between organophosphate flame retardants (OPFRs) and hyperuricemia and gout as well as the underlying mechanisms remains scarce, but some evidence indicates that inflammation might play a key role.

OBJECTIVES

Using an integrated approach, we aim to elucidate the associations of urinary metabolite OPFRs (m-OPFRs) with hyperuricemia and gout.

METHODS

Cross-sectional analyses using data from the National Health and Nutrition Examination Survey were performed to reveal the associations. Adults with complete data on five m-OPFRs with high detection frequencies and outcomes were enrolled. We used multivariate logistic regression, restricted cubic spline (RCS), and Bayesian kernel machine regression (BKMR) methods to account for single, nonlinear, and joint effects. The mediating effect of the inflammatory response was also estimated. Moreover, adverse outcome pathways (AOPs) based on network analysis were further constructed to reveal the underlying mechanism.

RESULTS

Multivariate logistic models revealed that bis(2-chloroethyl) phosphate (BCEP) significantly increased risk of hyperuricemia (OR [95 % CI]: 1.165 [1.047, 1.296]) in the fully adjusted model. Elevated levels of bis(1-chloro-2-propyl) phosphate were associated with gout (OR [95 % CI]: 1.293 [1.015, 1.647]). No nonlinear relationship was observed in RCS. There was a positive association between mixed m-OPFRs and hyperuricemia risk in BMKR, with bis(1,3-dichloro-2-propyl) phosphate and BCEP being the main contributors (PIP > 0.5). We found that the inflammatory response significantly mediated the association between BCEP and hyperuricemia (P < 0.05). Network topology analysis identified seven genes and six phenotypes related to OPFR exposure and hyperuricemia. The AOP framework suggested that the inflammatory response, especially the activation of the TNF pathway, played a core role in the above relationships.

CONCLUSION

Our results first revealed that individual and mixed OPFRs were associated with hyperuricemia, in which the inflammatory response plays an important role. Further longitudinal studies are warranted to consolidate or refute our main findings.

Benzo[b]fluoranthene induces male reproductive toxicity and apoptosis via Akt-Mdm2-p53 signaling axis in mouse Leydig cells: Integrating computational toxicology and experimental approaches

This study aims to explore the male reproductive toxicity of Benzo[b]fluoranthene (BbF) and related mechanisms. The results of computational toxicology analysis indicated male reproductive toxicity of BbF was related to apoptosis of Leydig cells and that Akt/p53 pathway might play a key role. In experiments, BbF induced testosterone decline, decreased concentration and motility of sperm and aggravated testicular pathological injury in mice. Besides, BbF led to apoptosis in Leydig cells, and decreased expressions of p-Akt and Bcl2, while improving the expressions of p53, Bax and Cleaved Caspase-3 in vivo and in vitro. Further, compared with BbF group, Akt activator SC79 significantly reduced cell apoptosis rate, improved cell viability, promoted the expressions of p-Akt and p-Mdm2, and reversed the above molecular expressions. Similarly, p53 inhibitor Pifithrin-α also significantly enhanced the cell vitality, alleviated the apoptosis of TM3 cells induced by BbF, and decreased the expressions of Bax and Cleaved Caspase-3, with the up-regulation of Bcl2. To sum up, by inhibiting Akt-Mdm2 signaling, BbF activated the p53-mediated mitochondrial apoptosis pathway, further inducing the apoptosis of Leydig cells, therefore resulting in testosterone decline and male reproductive damage. Besides, this study provided a valid mode integrating computational toxicology and experimental approaches in toxicity testing.

AOP-helpFinder 2.0: Integration of an event-event searches module

To support the use of alternative methods in regulatory assessment of chemical risks, the concept of adverse outcome pathway (AOP) constitutes an important toxicological tool. AOP represents a structured representation of existing knowledge, linking molecular initiating event (MIE) initiated by a prototypical stressor that leads to a cascade of biological key event (KE) to an adverse outcome (AO). Biological information to develop such AOP is very dispersed in various data sources. To increase the chance of capturing relevant existing data to develop a new AOP, the AOP-helpFinder tool was recently implemented to assist researchers to design new AOP. Here, an updated version of AOP-helpFinder proposes novel functionalities. The main one being the implementation of an automatic screening of the abstracts from the PubMed database to identify and extract event-event associations. In addition, a new scoring system was created to classify the identified co-occurred terms (stressor-event or event-event (which represent key event relationships) to help prioritization and support the weight of evidence approach, allowing a global assessment of the strength and reliability of the AOP. Moreover, to facilitate interpretation of the results, visualization options are also proposed. The AOP-helpFinder source code are fully accessible via GitHub, and searches can be performed via a web interface at http://aop-helpfinder-v2.u-paris-sciences.fr/.

Using chemical and biological data to predict drug toxicity

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

The path via pathway-based approaches towards safety assessment: A concise review

For decades, chemical safety assessment has been proposed to shift from animal testing to in vitro testing systems in response to the call for the 3R. In Europe, the answer was to combine various information sources in integrated testing strategies (ITS); In the US, it was in 2007 when the landmark report by the National Research Council put forward a vision of in vitro toxicity testing paradigm. Since then, efforts to develop pathway-based assessment framework have been on the track. In 2010, systems biology brought out a conceptual framework called adverse outcome pathway (AOP), which took one step further from toxicity pathway to regulatory toxicology. Computational modeling, high-throughput screening, high-content omics have all been approached to facilitate this progress. This paper briefly reviewed the achievement of pathway-based chemical assessment since 2007, discussed potential pitfalls and challenges that mechanism-driven chemical assessment may undergo, and presented future perspectives of safety assessment that is to be based on computational system biology.

Aryl Hydrocarbon Receptor in Oxidative Stress as a Double Agent and Its Biological and Therapeutic Significance

The aryl hydrocarbon receptor (AhR) has long been implicated in the induction of a battery of genes involved in the metabolism of xenobiotics and endogenous compounds. AhR is a ligand-activated transcription factor necessary for the launch of transcriptional responses important in health and disease. In past decades, evidence has accumulated that AhR is associated with the cellular response to oxidative stress, and this property of AhR must be taken into account during investigations into a mechanism of action of xenobiotics that is able to activate AhR or that is susceptible to metabolic activation by enzymes encoded by the genes that are under the control of AhR. In this review, we examine various mechanisms by which AhR takes part in the oxidative-stress response, including antioxidant and prooxidant enzymes and cytochrome P450. We also show that AhR, as a participant in the redox balance and as a modulator of redox signals, is being increasingly studied as a target for a new class of therapeutic compounds and as an explanation for the pathogenesis of some disorders.

Associating diethylhexyl phthalate to gestational diabetes mellitus via adverse outcome pathways using a network-based approach

Gestational diabetes mellitus (GDM) is a common pregnancy complication that is harmful to both the woman and fetus. Several epidemiological studies have found that exposure to diethylhexyl phthalate (DEHP), an endocrine disruptor ubiquitous in the environment, may be associated with GDM. This study aims to investigate the mechanism between DEHP and GDM using the adverse outcome pathway (AOP) framework, which can integrate information from different sources to elucidate the causal pathways between chemicals and adverse outcomes. We applied a network-based workflow to integrate diverse information to generate computational AOPs and accelerate the AOP development. The interactions among DEHP, genes, phenotypes, and GDM were retrieved from several publicly available databases, including the Comparative Toxicogenomics Database (CTD), Computational Toxicology (CompTox) Chemicals Dashboard, DisGeNET, MalaCards, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Based on the above interactions, a DEHP-Gene-Phenotype-GDM network consisting of 52 nodes and 227 edges was formed to support AOP construction. The filtered genes and phenotypes were assembled as molecular initiating events (MIEs) and key events (KEs) according to the upstream and downstream relationships, generating a computational AOP (cAOP) network. Based on the Organization for Economic Co-operation and Development handbook of AOPs, a cAOP was assessed and applied to determine the effects of DEHP on GDM. DEHP could increase TNF-α, downregulate the glucose uptake process, and lead to GDM. Overall, this study revealed the utility of computational methods in integrating a variety of datasets, supporting AOP development, and facilitating a better understanding of the underlying mechanism of exposure to chemicals on human health.

Obesity III: Obesogen assays: Limitations, strengths, and new directions

There is increasing evidence of a role for environmental contaminants in disrupting metabolic health in both humans and animals. Despite a growing need for well-understood models for evaluating adipogenic and potential obesogenic contaminants, there has been a reliance on decades-old in vitro models that have not been appropriately managed by cell line providers. There has been a quick rise in available in vitro models in the last ten years, including commercial availability of human mesenchymal stem cell and preadipocyte models; these models require more comprehensive validation but demonstrate real promise in improved translation to human metabolic health. There is also progress in developing three-dimensional and co-culture techniques that allow for the interrogation of a more physiologically relevant state. While diverse rodent models exist for evaluating putative obesogenic and/or adipogenic chemicals in a physiologically relevant context, increasing capabilities have been identified for alternative model organisms such as Drosophila, C. elegans, zebrafish, and medaka in metabolic health testing. These models have several appreciable advantages, including most notably their size, rapid development, large brood sizes, and ease of high-resolution lipid accumulation imaging throughout the organisms. They are anticipated to expand the capabilities of metabolic health research, particularly when coupled with emerging obesogen evaluation techniques as described herein.

More than Meets the Eye: The Aryl Hydrocarbon Receptor is an Environmental Sensor, Physiological Regulator and a Therapeutic Target in Ocular Disease

The aryl hydrocarbon receptor (AHR) is a ligand activated transcription factor originally identified as an environmental sensor of xenobiotic chemicals. However, studies have revealed that the AHR regulates crucial aspects of cell growth and metabolism, development and the immune system. The importance of the AHR and AHR signaling in eye development, toxicology and disease is now being uncovered. The AHR is expressed in many ocular tissues including the retina, choroid, cornea and the orbit. A significant role for the AHR in age-related macular degeneration (AMD), autoimmune uveitis, and other ocular diseases has been identified. Ligands for the AHR are structurally diverse organic molecules from exogenous and endogenous sources. Natural AHR ligands include metabolites of tryptophan and byproducts of the microbiome. Xenobiotic AHR ligands include persistent environmental pollutants such as dioxins, benzo (a) pyrene [B (a) P] and polychlorinated biphenyls (PCBs). Pharmaceutical agents including the proton pump inhibitors, esomeprazole and lansoprazole, and the immunosuppressive drug, leflunomide, activate the AHR. In this review, we highlight the role of the AHR in the eye and discuss how AHR signaling is involved in responding to endogenous and environmental stimuli. We also present the emerging concept that the AHR is a promising therapeutic target for eye disease.

Providing Adverse Outcome Pathways from the AOP-Wiki in a Semantic Web Format to Increase Usability and Accessibility of the Content

Introduction:

The AOP-Wiki is the main platform for the development and storage of adverse outcome pathways (AOPs). These AOPs describe mechanistic information about toxicodynamic processes and can be used to develop effective risk assessment strategies. However, it is challenging to automatically and systematically parse, filter, and use its contents. We explored solutions to better structure the AOP-Wiki content, and to link it with chemical and biological resources. Together, this allows more detailed exploration, which can be automated.

Materials and Methods:

We converted the complete AOP-Wiki content into resource description framework (RDF) triples. We used >20 ontologies for the semantic annotation of property–object relations, including the Chemical Information Ontology, Dublin Core, and the AOP Ontology.

Results:

The resulting RDF contains >122,000 triples describing 158 unique properties of >15,000 unique subjects. Furthermore, >3500 link-outs were added to 12 chemical databases, and >7500 link-outs to 4 gene and protein databases. The AOP-Wiki RDF has been made available at https://aopwiki.rdf.bigcat-bioinformatics.org

Discussion:

SPARQL queries can be used to answer biological and toxicological questions, such as listing measurement methods for all Key Events leading to an Adverse Outcome of interest. The full power that the use of this new resource provides becomes apparent when combining the content with external databases using federated queries.

Conclusion:

Overall, the AOP-Wiki RDF allows new ways to explore the rapidly growing AOP knowledge and makes the integration of this database in automated workflows possible, making the AOP-Wiki more FAIR.

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.

'In silico' toxicology methods in drug safety assessment

While experimental animal investigation has historically been the most conventional approach conducted to assess drug safety and is currently considered the main method for determining drug toxicity, these studies are constricted by cost, time, and ethical approvals. Over the last 20 years, there have been significant advances in computational sciences and computer data processing, while knowledge of alternative techniques and their application has developed into a valuable skill in toxicology. Thus, the application of in silico methods in drug safety assessment is constantly increasing. They are very complex and are grounded on accumulated knowledge from toxicology, bioinformatics, biochemistry, statistics, mathematics, as well as molecular biology. This review will summarize current state-of-the-art scientific data on the use of in silico methods in toxicity testing, taking into account their shortcomings, and highlighting the strategies that should deliver consistent results, while covering the applications of in silico methods in preclinical trials and drug impurities toxicity testing.

A toxicity pathway-oriented approach to develop adverse outcome pathway: AHR activation as a case study

With numerous new chemicals introduced into the environment everyday, identification of their potential hazards to the environment and human health is a considerable challenge. Developing adverse outcome pathway (AOP) framework is promising in helping to achieve this goal as it can bring In Vitro testing into toxicity measurement and understanding. To explore the toxic mechanism underlying environmental chemicals via the AOP approach, an integration of adequate experimental data with systems biology understanding is preferred. Here, we describe a novel method to develop reliable and sensible AOPs that relies on chemical-gene interactions, toxicity pathways, molecular regulations, phenotypes, and outcomes information obtained from comparative toxicogenomics database (CTD) and Ingenuity Pathway Analysis (IPA). Using Benzo(a)pyrene (BaP), a highly studied chemical as a stressor, we identified the pivotal IPA toxicity pathways, the molecular initiating event (MIE), and candidate key events (KEs) to structure AOPs in the liver and lung, respectively. Further, we used the corresponding CTD information of multiple typical AHR-ligands, including 2,3,7,8-tetrachlorodibenzoparadioxin (TCDD), valproic acid, quercetin, and particulate matter, to validate our AOP networks. Our approach is likely to speed up AOP development as providing a time- and cost-efficient way to collect all fragmented bioinformation in published studies. It also facilitates a better understanding of the toxic mechanism of environmental chemicals, and potentially brings new insights into the screening of critical paths in the AOP network.

Generating adverse outcome pathway (AOP) of inorganic arsenic-induced adult male reproductive impairment via integration of phenotypic analysis in comparative toxicogenomics database (CTD) and AOP wiki

BACKGROUND

Inorganic arsenic (iAs) is a worldwide environmental pollutant which exerts complicated and various toxic effects in organisms. Increasingly epidemic studies have revealed the association between iAs exposure and adult male reproductive impairment. Consistent with the proposal for toxicity testing in the 21st century (TT21C), the adverse outcome pathway (AOP) framework may help unravel the iAs-caused molecular and functional changes leading to male reproductive impairment.

METHOD

Combining CTD's phenotype-disease inference data, iAs-phenotypes were anchored to five male reproductive diseases induced by iAs, and local network topological algorithm was applied in prioritizing their interference significance. Through integrating analysis in AOP Wiki knowledge base, filtered phenotypes were linked to key events consisting of AOPs and assembled together based on evidentially upstream and downstream relationships.

RESULTS

A subset of 655 phenotypes were filtered from CTD as potential key events and showed a significant coherence in five reproductive diseases wherein 39 significant phenotypes showed a good clustering features involving cell cycle, ROS and mitochondria function. Two AOP subnetworks were enriched in AOP Wiki where testosterone reduction and apoptosis of sperm served as focus events respectively. Besides, a candidates list of molecular initialing events was provided of which glucocorticoid receptor activation was overall assessed as an example.

CONCLUSION

This study applied computational and bioinformatics methods in generating AOPs for arsenic reproductive toxicity, which identified the imperative roles of testosterone reduction, response to ROS, spermatogenesis and provided a global view about their internal association. Furthermore, this study helped address the existing knowledge gaps for future experimental verification.

Identification of novel activators of the metal responsive transcription factor (MTF-1) using a gene expression biomarker in a microarray compendium

Environmental exposure to metals is known to cause a number of human toxicities including cancer. Metal-responsive transcription factor 1 (MTF-1) is an important component of metal regulation systems in mammalian cells. Here, we describe a novel method to identify chemicals that activate MTF-1 based on microarray profiling data. MTF-1 biomarker genes were identified that exhibited consistent, robust expression across 10 microarray comparisons examining the effects of metals (zinc, nickel, lead, arsenic, mercury, and silver) on gene expression in human cells. A subset of the resulting 81 biomarker genes was shown to be altered by knockdown of the MTF1 gene including metallothionein family members and a zinc transporter. The ability to correctly identify treatment conditions that activate MTF-1 was determined by comparing the biomarker to microarray comparisons from cells exposed to reference metal activators of MTF-1 using the rank-based Running Fisher algorithm. The balanced accuracy for prediction was 93%. The biomarker was then used to identify organic chemicals that activate MTF-1 from a compendium of 11 725 human gene expression comparisons representing 2582 chemicals. There were 700 chemicals identified that included those known to interact with cellular metals, such as clioquinol and disulfiram, as well as a set of novel chemicals. All nine of the novel chemicals selected for validation were confirmed to activate MTF-1 biomarker genes in MCF-7 cells and to lesser extents in MTF1-null cells by qPCR and targeted RNA-Seq. Overall, our work demonstrates that the biomarker for MTF-1 coupled with the Running Fisher test is a reliable strategy to identify novel chemical modulators of metal homeostasis using gene expression profiling.

A semi-automated workflow for adverse outcome pathway hypothesis generation: The use case of non-genotoxic induced hepatocellular carcinoma

The utility of the Adverse Outcome Pathway (AOP) concept has been largely recognized by scientists, however, the AOP generation is still mainly done manually by screening through evidence and extracting probable associations. To accelerate this process and increase the reliability, we have developed an semi-automated workflow for AOP hypothesis generation. In brief, association mining methods were applied to high-throughput screening, gene expression, in vivo and disease data present in ToxCast and Comparative Toxicogenomics Database. This was supplemented by pathway mapping using Reactome to fill in gaps and identify events occurring at the cellular/tissue levels. Furthermore, in vivo data from TG-Gates was integrated to finally derive a gene, pathway, biochemical, histopathological and disease network from which specific disease sub-networks can be queried. To test the workflow, non-genotoxic-induced hepatocellular carcinoma (HCC) was selected as a case study. The implementation resulted in the identification of several non-genotoxic-specific HCC-connected genes belonging to cell proliferation, endoplasmic reticulum stress and early apoptosis. Biochemical findings revealed non-genotoxic-specific alkaline phosphatase increase. The explored non-genotoxic-specific histopathology was associated with early stages of hepatic steatosis, transforming into cirrhosis. This work illustrates the utility of computationally predicted constructs in supporting development by using pre-existing knowledge in a fast and unbiased manner.

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Chemoinformatics

Deciphering Adverse Outcome Pathway Network Linked to Bisphenol F Using Text Mining and Systems Toxicology Approaches

Bisphenol F (BPF) is one of several Bisphenol A (BPA) substituents that is increasingly used in manufacturing industry leading to detectable human exposure. Whereas a large number of studies have been devoted to decipher BPA effects, much less is known about its substituents. To support decision making on BPF's safety, we have developed a new computational approach to rapidly explore the available data on its toxicological effects, combining text mining and integrative systems biology, and aiming at connecting BPF to adverse outcome pathways (AOPs). We first extracted from different databases BPF-protein associations that were expanded to protein complexes using protein-protein interaction datasets. Over-representation analysis of the protein complexes allowed to identify the most relevant biological pathways putatively targeted by BPF. Then, automatic screening of scientific abstracts from literature using the text mining tool, AOP-helpFinder, combined with data integration from various sources (AOP-wiki, CompTox, etc.) and manual curation allowed us to link BPF to AOP events. Finally, we combined all the information gathered through those analyses and built a comprehensive complex framework linking BPF to an AOP network including, as adverse outcomes, various types of cancers such as breast and thyroid malignancies. These results which integrate different types of data can support regulatory assessment of the BPA substituent, BPF, and trigger new epidemiological and experimental studies.

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

New approach methodologies (NAMs) in chemical safety evaluation are being explored to address the current public health implications of human environmental exposures to chemicals with limited or no data for assessment. For over a decade since a push toward "Toxicity Testing in the 21st Century," the field has focused on massive data generation efforts to inform computational approaches for preliminary hazard identification, adverse outcome pathways that link molecular initiating events and key events to apical outcomes, and high-throughput approaches to risk-based ratios of bioactivity and exposure to inform relative priority and safety assessment. Projects like the interagency Tox21 program and the US EPA ToxCast program have generated dose-response information on thousands of chemicals, identified and aggregated information from legacy systems, and created tools for access and analysis. The resulting information has been used to develop computational models as viable options for regulatory applications. This progress has introduced challenges in data management that are new, but not unique, to toxicology. Some of the key questions require critical thinking and solutions to promote semantic interoperability, including: (1) identification of bioactivity information from NAMs that might be related to a biological process; (2) identification of legacy hazard information that might be related to a key event or apical outcomes of interest; and, (3) integration of these NAM and traditional data for computational modeling and prediction of complex apical outcomes such as carcinogenesis. This work reviews a number of toxicology-related efforts specifically related to bioactivity and toxicological data interoperability based on the goals established by Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles. These efforts are essential to enable better integration of NAM and traditional toxicology information to support data-driven toxicology applications.

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.

Public data sources to support systems toxicology applications

Public databases provide a wealth of freely available information about chemicals, genes, proteins, biological networks, phenotypes, diseases, and exposure science that can be integrated to construct pathways for systems toxicology applications. Relating this disparate information from public repositories, however, can be challenging since databases use a variety of ways to represent, describe, and make available their content. The use of standard vocabularies to annotate key data concepts, however, allows the information to be more easily exchanged and combined for discovery of new findings. We explore some of the many public data sources currently available to support systems toxicology, and demonstrate the value of standardizing data to help construct chemical-induced outcome pathways.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach

BACKGROUND

Available toxicity data can be optimally interpreted if they are integrated using computational approaches such as systems biology modeling. Such approaches are particularly warranted in cases where regulatory decisions have to be made rapidly.

OBJECTIVES

The study aims at developing and applying a new integrative computational strategy to identify associations between bisphenol S (BPS), a substitute for bisphenol A (BPA), and components of adverse outcome pathways (AOPs).

METHODS

The proposed approach combines a text mining (TM) procedure and integrative systems biology to comprehensively analyze the scientific literature to enrich AOPs related to environmental stressors. First, to identify relevant associations between BPS and different AOP components, a list of abstracts was screened using the developed text-mining tool AOP-helpFinder, which calculates scores based on the graph theory to prioritize the findings. Then, to fill gaps between BPS, biological events, and adverse outcomes (AOs), a systems biology approach was used to integrate information from the AOP-Wiki and ToxCast databases, followed by manual curation of the relevant publications.

RESULTS

Links between BPS and 48 AOP key events (KEs) were identified and scored via 31 references. The main outcomes were related to reproductive health, endocrine disruption, impairments of metabolism, and obesity. We then explicitly analyzed co-mention of the terms BPS and obesity by data integration and manual curation of the full text of the publications. Several molecular and cellular pathways were identified, which allowed the proposal of a biological explanation for the association between BPS and obesity.

CONCLUSIONS

By analyzing dispersed information from the literature and databases, our novel approach can identify links between stressors and AOP KEs. The findings associating BPS and obesity illustrate the use of computational tools in predictive toxicology and highlight the relevance of the approach to decision makers assessing substituents to toxic chemicals. https://doi.org/10.1289/EHP4200.

Information-Derived Mechanistic Hypotheses for Structural Cardiotoxicity

Adverse events resulting from drug therapy can be a cause of drug withdrawal, reduced and or restricted clinical use, as well as a major economic burden for society. To increase the safety of new drugs, there is a need to better understand the mechanisms causing the adverse events. One way to derive new mechanistic hypotheses is by linking data on drug adverse events with the drugs' biological targets. In this study, we have used data mining techniques and mutual information statistical approaches to find associations between reported adverse events collected from the FDA Adverse Event Reporting System and assay outcomes from ToxCast, with the aim to generate mechanistic hypotheses related to structural cardiotoxicity (morphological damage to cardiomyocytes and/or loss of viability). Our workflow identified 22 adverse event-assay outcome associations. From these associations, 10 implicated targets could be substantiated with evidence from previous studies reported in the literature. For two of the identified targets, we also describe a more detailed mechanism, forming putative adverse outcome pathways associated with structural cardiotoxicity. Our study also highlights the difficulties deriving these type of associations from the very limited amount of data available.

A Mechanistic Framework for Integrating Chemical Structure and High-Throughput Screening Results to Improve Toxicity Predictions

Adverse Outcome Pathways (AOPs) establish a connection between a molecular initiating event (MIE) and an adverse outcome. Detailed understanding of the MIE provides the ideal data for determining chemical properties required to elicit the MIE. This study utilized high-throughput screening data from the ToxCast program, coupled with chemical structural information, to generate chemical clusters using three similarity methods pertaining to nine MIEs within an AOP network for hepatic steatosis. Three case studies demonstrate the utility of the mechanistic information held by the MIE for integrating biological and chemical data. Evaluation of the chemical clusters activating the glucocorticoid receptor identified activity differences in chemicals within a cluster. Comparison of the estrogen receptor results with previous work showed that bioactivity data and structural alerts can be combined to improve predictions in a customizable way where bioactivity data are limited. The aryl hydrocarbon receptor (AHR) highlighted that while structural data can be used to offset limited data for new screening efforts, not all ToxCast targets have sufficient data to define robust chemical clusters. In this context, an alternative to additional receptor assays is proposed where assays for proximal key events downstream of AHR activation could be used to enhance confidence in active calls. These case studies illustrate how the AOP framework can support an iterative process whereby in vitro toxicity testing and chemical structure can be combined to improve toxicity predictions. In vitro assays can inform the development of structural alerts linking chemical structure to toxicity. Consequently, structurally related chemical groups can facilitate identification of assays that would be informative for a specific MIE. Together, these activities form a virtuous cycle where the mechanistic basis for the in vitro results and the breadth of the structural alerts continually improve over time to better predict activity of chemicals for which limited toxicity data exist.

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.

Identification of potential aryl hydrocarbon receptor ligands by virtual screening of industrial chemicals

We have developed a virtual screening procedure to identify potential ligands to the aryl hydrocarbon receptor (AhR) among a set of industrial chemicals. AhR is a key target for dioxin-like compounds, which is related to these compounds' potential to induce cancer and a wide range of endocrine and immune system-related effects. The virtual screening procedure included an initial filtration aiming at identifying chemicals with structural similarities to 66 known AhR binders, followed by 3 enrichment methods run in parallel. These include two ligand-based methods (structural fingerprints and nearest neighbor analysis) and one structure-based method using an AhR homology model. A set of 6445 commonly used industrial chemicals was processed, and each step identified unique potential ligands. Seven compounds were identified by all three enrichment methods, and these compounds included known activators and suppressors of AhR. Only approximately 0.7% (41 compounds) of the studied industrial compounds was identified as potential AhR ligands and among these, 28 compounds have to our knowledge not been tested for AhR-mediated effects or have been screened with low purity. We suggest assessment of AhR-related activities of these compounds and in particular 2-chlorotrityl chloride, 3-p-hydroxyanilino-carbazole, and 3-(2-chloro-4-nitrophenyl)-5-(1,1-dimethylethyl)-1,3,4-oxadiazol-2(3H)-one.

The aryl hydrocarbon receptor in the crossroad of signalling networks with therapeutic value

The aryl hydrocarbon receptor (AhR) is well-known for its major contributions to the cellular responses against environmental toxins and carcinogens. Notably, AhR has also emerged as a key transcription factor controlling many physiological processes including cell proliferation and apoptosis, differentiation, adhesion and migration, pluripotency and stemness. These novel functions have broadened our understanding of the signalling pathways and molecular intermediates interacting with AhR under both homeostatic and pathological conditions. Recent discoveries link AhR with the function of essential organs such as liver, skin and gonads, and with complex organismal structures including the immune and cardiovascular systems. The identification of potential endogenous ligands able to regulate AhR activity, opens the possibility of designing ad hoc molecules with pharmacological and/or therapeutic value to treat human diseases in which AhR may have a causal role. Integration of experimental data from in vitro and in vivo studies with "omic" analyses of human patients affected with cancer, immune diseases, inflammation or neurological disorders will likely contribute to validate the clinical relevance of AhR and the possible benefits of modulating its activity by pharmacologically-driven strategies. In this review, we will highlight signalling pathways involved in human diseases that could be targetable by AhR modulators and discuss the feasibility of using such molecules in therapy. The pros and cons of AhR-aimed approaches will be also mentioned.

Adverse outcome pathways: a concise introduction for toxicologists

Adverse outcome pathways (AOPs) are designed to provide a clear-cut mechanistic representation of critical toxicological effects that propagate over different layers of biological organization from the initial interaction of a chemical with a molecular target to an adverse outcome at the individual or population level. Adverse outcome pathways are currently gaining momentum, especially in view of their many potential applications as pragmatic tools in the fields of human toxicology, ecotoxicology, and risk assessment. A number of guidance documents, issued by the Organization for Economic Cooperation and Development, as well as landmark papers, outlining best practices to develop, assess and use AOPs, have been published in the last few years. The present paper provides a synopsis of the main principles related to the AOP framework for the toxicologist less familiar with this area, followed by two case studies relevant for human toxicology and ecotoxicology.

Advancing the adverse outcome pathway framework-An international horizon scanning approach

Our ability to conduct whole-organism toxicity tests to understand chemical safety has been outpaced by the synthesis of new chemicals for a wide variety of commercial applications. As a result, scientists and risk assessors are turning to mechanistically based studies to increase efficiencies in chemical risk assessment and making greater use of in vitro and in silico methods to evaluate potential environmental and human health hazards. In this context, the adverse outcome pathway (AOP) framework has gained traction in regulatory science because it offers an efficient and effective means for capturing available knowledge describing the linkage between mechanistic data and the apical toxicity end points required for regulatory assessments. A number of international activities have focused on AOP development and various applications to regulatory decision-making. These initiatives have prompted dialogue between research scientists and regulatory communities to consider how best to use the AOP framework. Although expert-facilitated discussions and AOP development have been critical in moving the science of AOPs forward, it was recognized that a survey of the broader scientific and regulatory communities would aid in identifying current limitations while guiding future initiatives for the AOP framework. To that end, a global horizon scanning exercise was conducted to solicit questions concerning the challenges or limitations that must be addressed to realize the full potential of the AOP framework in research and regulatory decision-making. The questions received fell into several broad topical areas: AOP networks, quantitative AOPs, collaboration on and communication of AOP knowledge, AOP discovery and development, chemical and cross-species extrapolation, exposure/toxicokinetics considerations, and AOP applications. Expert ranking was then used to prioritize questions for each category, where 4 broad themes emerged that could help inform and guide future AOP research and regulatory initiatives. In addition, frequently asked questions were identified and addressed by experts in the field. Answers to frequently asked questions will aid in addressing common misperceptions and will allow for clarification of AOP topics. The need for this type of clarification was highlighted with surprising frequency by our question submitters, indicating that improvements are needed in communicating the AOP framework among the scientific and regulatory communities. Overall, horizon scanning engaged the global scientific community to help identify key questions surrounding the AOP framework and guide the direction of future initiatives. Environ Toxicol Chem 2017;36:1411-1421. © 2017 SETAC.