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

Investigation of parenteral nutrition-induced hepatotoxicity using human liver spheroid co-cultures

Parenteral nutrition (PN) is typically administered to individuals with gastrointestinal dysfunction, a contraindication for enteral feeding, and a need for nutritional therapy. When PN is the only energy source in patients, it is defined as total parenteral nutrition (TPN). TPN is a life-saving approach for different patient populations, both in infants and adults. However, despite numerous benefits, TPN can cause adverse effects, including metabolic disorders and liver injury. TPN-associated liver injury, known as intestinal failure-associated liver disease (IFALD), represents a significant problem affecting up to 90% of individuals receiving TPN. IFALD pathogenesis is complex, depending on the TPN components as well as on the patient's medical conditions. Despite numerous animal studies and clinical observations, the molecular mechanisms driving IFALD remain largely unknown. The present study was set up to elucidate the mechanisms underlying IFALD. For this purpose, human liver spheroid co-cultures were treated with a TPN mixture, followed by RNA sequencing analysis. Subsequently, following exposure to TPN and its single nutritional components, several key events of liver injury, including mitochondrial dysfunction, endoplasmic reticulum stress, oxidative stress, apoptosis, and lipid accumulation (steatosis), were studied using various techniques. It was found that prolonged exposure to TPN substantially changes the transcriptome profile of liver spheroids and affects multiple metabolic and signaling pathways contributing to liver injury. Moreover, TPN and its main components, especially lipid emulsion, induce changes in all key events measured and trigger steatosis.

The new normal chemical landscape: the future of risk assessment toward optimum consumer safety

The further optimization of consumer safety through risk assessment of chemicals present in food will require adaptability and flexibility to utilize the accelerating developments in safety science and technology. New Approach Methodologies (NAMs) are gaining traction as a systematic approach to support informed decision making in chemical risk assessment. The vision is to be able to predict risk more accurately, rapidly and efficiently. The opportunity exists now to use these approaches which requires a strategy to translate the science into future regulatory implementation. Here we discuss new insights obtained from three recent workshops on how to translate the science into future regulatory implementation. To assist the UK in this endeavor, the Food Standards Agency (FSA) and the scientific advisory committee on chemical toxicity (COT) have been developing a roadmap. In addition, we discuss how these new insights fit into the bigger picture of the new chemical landscape for better consumer safety and the importance of international harmonization.

Building an adverse outcome pathway network for estrogen-, androgen- and steroidogenesis-mediated reproductive toxicity

Introduction: Adverse Outcome Pathways (AOPs) can support both testing and assessment of endocrine disruptors (EDs). There is, however, a need for further development of the AOP framework to improve its applicability in a regulatory context. Here we have inventoried the AOP-wiki to identify all existing AOPs related to mammalian reproductive toxicity arising from disruption to the estrogen, androgen, and steroidogenesis modalities. Core key events (KEs) shared between relevant AOPs were also identified to aid in further AOP network (AOPN) development. Methods: A systematic approach using two different methods was applied to screen and search the entire AOP-wiki library. An AOPN was visualized using Cytoscape. Manual refinement was performed to remove AOPS devoid of any KEs and/or KERs. Results: Fifty-eight AOPs relevant for mammalian reproductive toxicity were originally identified, with 42 AOPs included in the final AOPN. Several of the KEs and KE relationships (KERs) described similar events and were thus merged to optimize AOPN construction. Sixteen sub-networks related to effects on hormone levels or hormone activity, cancer outcomes, male and female reproductive systems, and overall effects on fertility and reproduction were identified within the AOPN. Twenty-six KEs and 11 KERs were identified as core blocks of knowledge in the AOPN, of which 19 core KEs are already included as parameters in current OECD and US EPA test guidelines. Discussion: The AOPN highlights knowledge gaps that can be targeted for further development of a more complete AOPN that can support the identification and assessment of EDs.

Diuron and its metabolites induce mitochondrial dysfunction-mediated cytotoxicity in urothelial cells

In the environment, or during mammalian metabolism, the diuron herbicide (3-(3,4-dichlorophenyl)-1,1-dimethylurea) is transformed mainly into 3-(3,4-dichlorophenyl)-1-methylurea (DCPMU) and 3,4-dichloroaniline (DCA). Previous research suggests that such substances are toxic to the urothelium of Wistar rats where, under specific exposure conditions, they may induce urothelial cell degeneration, necrosis, hyperplasia, and eventually tumors. However, the intimate mechanisms of action associated with such chemical toxicity are not fully understood. In this context, the purpose of the current in vitro study was to analyze the underlying mechanisms involved in the urothelial toxicity of those chemicals, addressing cell death and the possible role of mitochondrial dysfunction. Thus, human 1T1 urothelial cells were exposed to six different concentrations of diuron, DCA, and DCPMU, ranging from 0.5 to 500 µM. The results showed that tested chemicals induced oxidative stress and mitochondrial damage, cell cycle instability, and cell death, which were more expressive at the higher concentrations of the metabolites. These data corroborate previous studies from this laboratory and, collectively, suggest mitochondrial dysfunction as an initiating event triggering urothelial cell degeneration and death.

An adverse outcome pathway for chemical-induced Parkinson's disease: Calcium is key

Exposure to pesticides is associated with an increased risk of developing Parkinson's disease (PD). Currently, rodent-based risk assessment studies cannot adequately capture neurodegenerative effects of pesticides due to a lack of human-relevant endpoints targeted at neurodegeneration. Thus, there is a need for improvement of the risk assessment guidelines. Specifically, a mechanistic assessment strategy, based on human physiology and (patho)biology is needed, which can be applied in next generation risk assessment. The Adverse Outcome Pathway (AOP) framework is particularly well-suited to provide the mechanistic basis for such a strategy. Here, we conducted a semi-systematic review in Embase and MEDLINE, focused on neurodegeneration and pesticides, to develop an AOP network for parkinsonian motor symptoms. Articles were labelled and included/excluded using the online platform Sysrev. Only primary articles, written in English, focused on effects of pesticides or PD model compounds in models for the brain were included. A total of 66 articles, out of the 1700 screened, was included. PD symptoms are caused by loss of function and ultimately death of dopaminergic neurons in the substantia nigra (SN). Our literature review highlights that a unique feature of these cells that increases their vulnerability is their reliance on continuous low-level influx of calcium. As such, excess intracellular calcium was identified as a central early Key Event (KE). This KE can lead to death of dopaminergic neurons of the SN, and eventually parkinsonian motor symptoms, via four distinct pathways: 1) activation of calpains, 2) endoplasmic reticulum stress, 3) impairment of protein degradation, and 4) oxidative damage. Several receptors have been identified that may serve as molecular initiating events (MIEs) to trigger one or more of these pathways. The proposed AOP network provides the biological basis that can be used to develop a mechanistic testing strategy that captures neurodegenerative effects of pesticides.

Multi-omic responses of fish exposed to complex chemical mixtures in the Shenandoah River watershed

To evaluate relationships between different anthropogenic impacts, contaminant occurrence, and fish health, we conducted in situ fish exposures across the Shenandoah River watershed at five sites with different land use. Exposure water was analyzed for over 500 chemical constituents, and organismal, metabolomic, and transcriptomic endpoints were measured in fathead minnows. Adverse reproductive outcomes were observed in fish exposed in the upper watershed at both wastewater treatment plant (WWTP) effluent- and agriculture-impacted sites, including decreased gonadosomatic index and altered secondary sex characteristics. This was accompanied with increased mortality at the site most impacted by agricultural activities. Molecular biomarkers of estrogen exposure were unchanged and consistent with low or non-detectable concentrations of common estrogens, indicating that alternative mechanisms were involved in organismal adverse outcomes. Hepatic metabolomic and transcriptomic profiles were altered in a site-specific manner, consistent with variation in land use and contaminant profiles. Integrated biomarker response data were useful for evaluating mechanistic linkages between contaminants and adverse outcomes, suggesting that reproductive endocrine disruption, altered lipid processes, and immunosuppression may have been involved in these organismal impacts. This study demonstrated linkages between human-impact, contaminant occurrence, and exposure effects in the Shenandoah River watershed and showed increased risk of adverse outcomes in fathead minnows exposed to complex mixtures at sites impacted by municipal wastewater discharges and agricultural practices.

CTD tetramers: a new online tool that computationally links curated chemicals, genes, phenotypes, and diseases to inform molecular mechanisms for environmental health

The molecular mechanisms connecting environmental exposures to adverse endpoints are often unknown, reflecting knowledge gaps. At the Comparative Toxicogenomics Database (CTD), we developed a bioinformatics approach that integrates manually curated, literature-based interactions from CTD to generate a "CGPD-tetramer": a 4-unit block of information organized as a step-wise molecular mechanism linking an initiating Chemical, an interacting Gene, a Phenotype, and a Disease outcome. Here, we describe a novel, user-friendly tool called CTD Tetramers that generates these evidence-based CGPD-tetramers for any curated chemical, gene, phenotype, or disease of interest. Tetramers offer potential solutions for the unknown underlying mechanisms and intermediary phenotypes connecting a chemical exposure to a disease. Additionally, multiple tetramers can be assembled to construct detailed modes-of-action for chemical-induced disease pathways. As well, tetramers can help inform environmental influences on adverse outcome pathways (AOPs). We demonstrate the tool's utility with relevant use cases for a variety of environmental chemicals (eg, perfluoroalkyl substances, bisphenol A), phenotypes (eg, apoptosis, spermatogenesis, inflammatory response), and diseases (eg, asthma, obesity, male infertility). Finally, we map AOP adverse outcome terms to corresponding CTD terms, allowing users to query for tetramers that can help augment AOP pathways with additional stressors, genes, and phenotypes, as well as formulate potential AOP disease networks (eg, liver cirrhosis and prostate cancer). This novel tool, as part of the complete suite of tools offered at CTD, provides users with computational datasets and their supporting evidence to potentially fill exposure knowledge gaps and develop testable hypotheses about environmental health.

Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach

BACKGROUND

Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships.

METHODS

Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship.

RESULTS

This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis.

CONCLUSIONS

This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.

Mechanistic insight into the adverse outcome of tire wear and road particle leachate exposure in zebrafish (Danio rerio) larvae

Tire wear particles (TWP) have become the major microplastic pollution in China. Road runoff containing TWP leachate can decrease the eye size and even induced mortality in the aquatic organisms. However, the toxic mechanism of TWP and road particles (RP) leachate on aquatic organisms is still unclear. In this study, the zebrafish embryos were exposed to TWP or RP leachate for 5 days at both environmental relevant and high concentrations. The adverse outcome pathways (AOPs) were screened from individual to molecular levels. The morphological and behavioral analysis demonstrated that the leachate exposure mainly impaired the eye development of zebrafish larvae and inhibited the larval swim behavior and phototactic response, which are the adverse outcomes. The phototransduction modulated by zebrafish retina was significantly down-regulated through transcriptomics and metabolomics analysis. The eye histopathological analysis showed that the decreased thickness of the retinal outer nuclear layer (ONL) and retinal pigmented epithelium (RPE) after leachate exposure were caused by the decreased photoreceptor cells. Moreover, the expression of NR2E3 and TPO genes showed concentration-dependent down-regulation after leachate exposure. The inhibition of photoreceptor cell proliferation was identified as the main reason for photoreceptor cell decrease in zebrafish larval eye. This study, for the first time, uncovered the underlying toxic mechanism of TWP and RP on zebrafish larval eyes.

Artificial intelligence-driven drug development against autoimmune diseases

Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.

Investigating fatty liver disease-associated adverse outcome pathways of perfluorooctane sulfonate using a systems toxicology approach

Adverse outcome pathway (AOP) frameworks help elucidate toxic mechanisms and support chemical regulation. AOPs link a molecular initiating event (MIE), key events (KEs), and an adverse outcome by key event relationships (KERs), which assess the biological plausibility, essentiality, and empirical evidence involved. Perfluorooctane sulfonate (PFOS), a hazardous poly-fluoroalkyl substance, demonstrates hepatotoxicity in rodents. PFOS may induce fatty liver disease (FLD) in humans; however, the underlying mechanism remains unclear. In this study, we evaluated the toxic mechanisms of PFOS-associated FLD by developing an AOP using publicly available data. We identified MIE and KEs by performing GO enrichment analysis on PFOS- and FLD-associated target genes collected from public databases. The MIEs and KEs were then prioritized by PFOS-gene-phenotype-FLD networks, AOP-helpFinder, and KEGG pathway analyses. Following a comprehensive literature review, an AOP was then developed. Finally, six KEs for the AOP of FLD were identified. This AOP indicated that toxicological processes initiated by SIRT1 inhibition led to SREBP-1c activation, de novo fatty acid synthesis, and fatty acid and triglyceride accumulation, culminating in liver steatosis. Our study provides insights into the toxic mechanism of PFOS-induced FLD and suggests approaches to assessing the risk of toxic chemicals.

Preliminary Toxicological Analysis in a Safe-by-Design and Adverse Outcome Pathway-Driven Approach on Different Silver Nanoparticles: Assessment of Acute Responses in A549 Cells

Silver nanoparticles (Ag NPs) are among the most widely used metal-based nanomaterials (NMs) and their applications in different products, also as antibacterial additives, are increasing. In the present manuscript, according to an adverse outcome pathway (AOP) approach, we tested two safe-by-design (SbD) newly developed Ag NPs coated with hydroxyethyl cellulose (HEC), namely AgHEC powder and AgHEC solution. These novel Ag NPs were compared to two reference Ag NPs (naked and coated with polyvinylpyrrolidone-PVP). Cell viability, inflammatory response, reactive oxygen species, oxidative DNA damage, cell cycle, and cell-particle interactions were analyzed in the alveolar in vitro model, A549 cells. The results show a different toxicity pattern of the novel Ag NPs compared to reference NPs and that between the two novel NPs, the AgHEC solution is the one with the lower toxicity and to be further developed within the SbD framework.

A walk in the PARC: developing and implementing 21st century chemical risk assessment in Europe

Current approaches for the assessment of environmental and human health risks due to exposure to chemical substances have served their purpose reasonably well. Nevertheless, the systems in place for different uses of chemicals are faced with various challenges, ranging from a growing number of chemicals to changes in the types of chemicals and materials produced. This has triggered global awareness of the need for a paradigm shift, which in turn has led to the publication of new concepts for chemical risk assessment and explorations of how to translate these concepts into pragmatic approaches. As a result, next-generation risk assessment (NGRA) is generally seen as the way forward. However, incorporating new scientific insights and innovative approaches into hazard and exposure assessments in such a way that regulatory needs are adequately met has appeared to be challenging. The European Partnership for the Assessment of Risks from Chemicals (PARC) has been designed to address various challenges associated with innovating chemical risk assessment. Its overall goal is to consolidate and strengthen the European research and innovation capacity for chemical risk assessment to protect human health and the environment. With around 200 participating organisations from all over Europe, including three European agencies, and a total budget of over 400 million euro, PARC is one of the largest projects of its kind. It has a duration of seven years and is coordinated by ANSES, the French Agency for Food, Environmental and Occupational Health & Safety.

Editorial: New mechanistic insight into perinatal origins of reproductive disorders caused by chemical exposures

Introducing a Special Issue on mechanism of reproductive disorders in mammals originating from exposure to environmental chemicals during perinatal life.

Nongenomic effects and mechanistic study of butyl benzyl phthalate-induced thyroid disruption: Based on integrated in vitro, in silico assays and proteome analysis

Based on in vitro and in silico assays as well as proteome analysis, this study explored the nongenomic mechanism for butyl benzyl phthalate (BBP)-induced thyroid disruption. Molecular docking simulations showed that BBP could dock into the Arg-Gly-Asp (RGD) domain of integrin α_vβ₃ and form hydrogen bonds with a docking energy of -35.80 kcal/mol. This chemical enhanced rat pituitary tumor cell (GH3) proliferation and exhibited thyroid hormone-disrupting effects at 5-10 μmol/L. Meanwhile, BBP upregulated β₃ gene expression and activated the downstream mitogen-activated protein kinase (MAPK) pathway in GH3 cells. Interestingly, GH3 cell proliferation was attenuated by integrin α_vβ₃ inhibitor (RGD peptide) or ERK1/2 inhibitor (PD98059), suggesting that the disruptions might be partly attributed to its interaction with integrin α_vβ₃ and activation of MAPK. Furthermore, quantitative proteomic analysis of zebrafish embryos exposed to BBP at an environmentally relevant concentration of 0.3 μmol/L revealed that BBP perturbed proteins and pathways related to cell communication (e.g., integrin binding) and signal transduction (e.g., MAPK signaling pathway). Taken together, our results supported that the biological effects of BBP-activated integrin α_vβ₃ mediated by the nongenomic pathway play an important role in its thyroid disruption. CAPSULE: The nongenomic pathway plays a vital role in the thyroid disruption-inducing actions of BBP.

Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study

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

The exposome as the science of social-to-biological transitions

The understanding of disease etiology and pathogenesis has radically changed as a consequence of the new challenges posed by climate change, environmental degradation and emerging infectious diseases. The awareness of the influence of distal causes (e.g. planetary changes at the roots of new pandemics), of the social environment and of early life exposures calls for innovative models of disease onset. Here we propose a scheme for the practice of epidemiology and toxicology that incorporates new recent advancements in both disciplines, under the general umbrella of the "exposome". The exposome approach to disease encompasses a lifecourse perspective from conception onwards, and the investigation of the role played by all exposures individuals undergo in their lives. These include social inequalities and psychosocial influences, in addition to chemical, biological and physical exposures. We stress the role played by social differences and inequalities in the course of life as an overarching factor that influences downstream layers (including behaviours). We show that the idea of "lifecourse exposome" is compatible with the current interpretation of Adverse Outcome Pathways in toxicology, and in fact we propose an extension of the concept towards "lifecourse Adverse Outcome Pathways". We propose to merge different research perspectives and promote an encounter between the sociological perspective of "biography" (using Pierre Bourdieu's conceptual framework) and biology, according to the idea of accumulated biological capital of individuals. We also propose to treat social capital (including inequalities) no longer as a confounding factor but as an overarching determinant, perhaps the most important of all because it is the one that influences all other exposures downstream. The importance of early exposures in a lifecourse perspective leads to policy implications, i.e. investing more in the various forms of capital (social, economic, cultural) in early life.

Using alternative test methods to predict endocrine disruption and reproductive adverse outcomes: do we have enough knowledge?

Endocrine disrupting chemicals (EDCs) are a matter of great concern. They are ubiquitous in the environment, are considered harmful to humans and wildlife, yet remain challenging to identify based on current international test guidelines and regulatory frameworks. For a compound to be identified as an EDC within the EU regulatory system, a plausible link between an endocrine mode-of-action and an adverse effect outcome in an intact organism must be established. This requires in-depth knowledge about molecular pathways regulating normal development and function in animals and humans in order to elucidate causes for disease. Although our knowledge about the role of the endocrine system in animal development and function is substantial, it remains challenging to predict endocrine-related disease outcomes in intact animals based on non-animal test data. A main reason for this is that our knowledge about mechanism-of-action are still lacking for essential causal components, coupled with the sizeable challenge of mimicking the complex multi-organ endocrine system by methodological reductionism. Herein, we highlight this challenge by drawing examples from male reproductive toxicity, which is an area that has been at the forefront of EDC research since its inception. We discuss the importance of increased focus on characterizing mechanism-of-action for EDC-induced adverse health effects. This is so we can design more robust and reliable testing strategies using non-animal test methods for predictive toxicology; both to improve chemical risk assessment in general, but also to allow for considerable reduction and replacement of animal experiments in chemicals testing of the 21st Century.

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.

In Silico Identification of Potential Thyroid Hormone System Disruptors among Chemicals in Human Serum and Chemicals with a High Exposure Index

Data on toxic effects are at large missing the prevailing understanding of the risks of industrial chemicals. Thyroid hormone (TH) system disruption includes interferences of the life cycle of the thyroid hormones and may occur in various organs. In the current study, high-throughput screening data available for 14 putative molecular initiating events of adverse outcome pathways, related to disruption of the TH system, were used to develop 19 in silico models for identification of potential thyroid hormone system-disrupting chemicals. The conformal prediction framework with the underlying Random Forest was used as a wrapper for the models allowing for setting the desired confidence level and controlling the error rate of predictions. The trained models were then applied to two different databases: (i) an in-house database comprising xenobiotics identified in human blood and ii) currently used chemicals registered in the Swedish Product Register, which have been predicted to have a high exposure index to consumers. The application of these models showed that among currently used chemicals, fewer were overall predicted as active compared to chemicals identified in human blood. Chemicals of specific concern for TH disruption were identified from both databases based on their predicted activity.

Investigation of a derived adverse outcome pathway (AOP) network for endocrine-mediated perturbations

An adverse outcome pathway (AOP) is a compact representation of the available mechanistic information on observed adverse effects upon environmental exposure. Sharing of events across individual AOPs has led to the emergence of AOP networks. Since AOP networks are expected to be functional units of toxicity prediction, there is current interest in their development tailored to specific research question or regulatory problem. To this end, we have developed a detailed workflow to construct an endocrine-relevant AOP (ED-AOP) network based on the existing information available in AOP-Wiki. We propose a cumulative weight of evidence (WoE) score for each ED-AOP based on the WoE scores assigned to key event relationships (KERs) by AOP-Wiki, revealing gaps in AOP development. Connectivity analysis of the ED-AOP network comprising 48 AOPs reveals 7 connected components and 12 isolated AOPs. Subsequently, we apply standard network measures to perform an in-depth analysis of the two largest connected components of the ED-AOP network. Notably, the graph-theoretic analyses led to the identification of important events including points of convergence or divergence in the ED-AOP network. These findings can suggest potential adverse outcomes and facilitate the development of new endpoints or assays for chemical risk assessment. Detailed analysis of the largest component in the ED-AOP network gives insights on the systems-level perturbations caused by endocrine disruption, emergent paths, and stressor-event associations. In sum, the derived ED-AOP network can provide a broader view of the biological events disrupted by endocrine disruption, as well as facilitate better risk assessment of environmental chemicals.

Linking Micropollutants to Trait Syndromes across Freshwater Diatom, Macroinvertebrate, and Fish Assemblages

The ecological quality of freshwater ecosystems is endangered by various micropollutants released into the environment by human activities. The cumulative effects of these micropollutants can affect the fitness of organisms and populations and the functional diversity of stream ecosystems. In this study, we investigated the relationships between the joint toxicity of micropollutants and trait syndromes. A trait syndrome corresponds to a combination of traits that could occur together in communities due to the trait selection driven by exposure to these micropollutants. Our objectives were to (i) identify trait syndromes specific to diatom, macroinvertebrate, and fish assemblages and their responses to exposure, taking into account four micropollutant types (mineral micropollutants, pesticides, PAHs, and other organic micropollutants) and nine modes of action (only for pesticides), (ii) explore how these syndromes vary within and among the three biological compartments, (iii) investigate the trait categories driving the responses of syndromes to micropollutant exposure, and (iv) identify specific taxa, so-called paragons, which are highly representative of these syndromes. To achieve these objectives, we analyzed a dataset including the biological and physico-chemical results of 2007 sampling events from a large-scale monitoring survey routinely performed in French wadeable streams. We have identified five (diatoms), eight (macroinvertebrates), and eight (fishes) trait syndromes, either positively or negatively related to an increasing toxicity gradient of different clusters of micropollutant types or modes of action. Our analyses identified several key trait categories and sets of paragons, exhibiting good potential for highlighting exposure by specific micropollutant types and modes of action. Overall, trait syndromes might represent a novel and integrative bioassessment tool, driven by the diversity of trait-based responses to increasing gradients of micropollutant toxic cocktails.

Non-additive gene interactions underpin molecular and phenotypic responses in honey bee larvae exposed to imidacloprid and thymol

Understanding the cumulative risk of chemical mixtures at environmentally realistic concentrations is a key challenge in honey bee ecotoxicology. Ecotoxicogenomics, including transcriptomics, measures responses in individual organisms at the molecular level which can provide insights into the mechanisms underlying phenotypic responses induced by one or more stressors and link impacts on individuals to populations. Here, fifth instar honey bee larvae were sampled from a previously reported field experiment exploring the phenotypic impacts of environmentally realistic chronic exposures of the pesticide imidacloprid (5 μg.kg^-1 for six weeks) and the acaricide thymol (250 g.kg^-1 applied via Apiguard gel in-hive for four weeks), both separately and in combination. RNA-seq was used to discover individual and interactive chemical effects on larval gene expression and to uncover molecular mechanisms linked to reported adult and colony phenotypes. The separate and combined treatments had distinct gene expression profiles which represented differentially affected signaling and metabolic pathways. The molecular signature of the mixture was characterised by additive interactions in canonical stress responses associated with oxidative stress and detoxification, and non-additive interactions in secondary responses including developmental, neurological, and immune pathways. Novel emergent impacts on eye development genes correlated with long-term defects in visual learning performance as adults. This is consistent with these chemicals working through independent modes of action that combine to impact common downstream pathways, and highlights the importance of establishing mechanistic links between molecular and phenotypic responses when predicting effects of chemical mixtures on ecologically relevant population outcomes.

Transcriptomic profiling of clobetasol propionate-induced immunosuppression in challenged zebrafish embryos

In the ecotoxicological hazard assessment of chemicals, the detection of immunotoxicity is currently neglected. This is mainly due to the complexity of the immune system and the consequent lack of standardized procedures and markers for the comprehensive assessment of immunotoxic modes of action. In this study, we present a new approach applying transcriptome profiling to an immune challenge with a mixture of pathogen-associated molecular patterns (PAMPs) in zebrafish embryos, analyzing differential gene expression during acute infection with and without prior exposure to the immunosuppressive drug clobetasol propionate (CP). While PAMP injection itself triggered biological processes associated with immune activation, some of these genes were more differentially expressed upon prior exposure to CP than by immune induction alone, whereas others showed weaker or no differential regulation in response to the PAMP stimulus. All of these genes responding differently to PAMP after prior CP exposure showed additivity of PAMP- and CP-induced effects, indicating independent regulatory mechanisms. The transcriptomic profiles suggest that CP impaired innate immune induction by attenuating the response of genes involved in antigen processing, TLR signaling, NF-КB signaling, and complement activation. We propose this approach as a powerful method for detecting gene biomarkers for immunosuppressive modes of action, as it was able to identify alternatively regulated processes and pathways in a sublethal, acute infection zebrafish embryo model. This allowed to define biomarker candidates for immune-mediated effects and to comprehensively characterize immunosuppression. Ultimately, this work contributes to the development of molecular biomarker-based environmental hazard assessment of chemicals in the future.

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

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

Towards a qAOP framework for predictive toxicology - Linking data to decisions

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Chemical toxicity assessment depends on the quantification of kinetics and dynamics.

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Quantitative AOPs (qAOPs) are toxicodynamic models based on Adverse Outcome Pathways.

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Existing e-resources could form the basis of an e-infrastructure for qAOP modelling.

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Best practices for qAOP development, assessment and application are needed.

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Three qAOP case studies are presented to illustrate a modelling workflow.

The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.

Comparative analysis of transcriptomic points-of-departure (tPODs) and apical responses in embryo-larval fathead minnows exposed to fluoxetine

Current approaches in chemical hazard assessment face significant challenges because they rely on live animal testing, which is time-consuming, expensive, and ethically questionable. These concerns serve as an impetus to develop new approach methodologies (NAMs) that do not rely on live animal tests. This study explored a molecular benchmark dose (BMD) approach using a 7-day embryo-larval fathead minnow (FHM) assay to derive transcriptomic points-of-departure (tPODs) to predict apical BMDs of fluoxetine (FLX), a highly prescribed and potent selective serotonin reuptake inhibitor frequently detected in surface waters. Fertilized FHM embryos were exposed to graded concentrations of FLX (confirmed at < LOD, 0.19, 0.74, 3.38, 10.2, 47.5 μg/L) for 32 days. Subsets of fish were subjected to omics and locomotor analyses at 7 days post-fertilization (dpf) and to histological and biometric measurements at 32 dpf. Enrichment analyses of transcriptomics and proteomics data revealed significant perturbations in gene sets associated with serotonergic and axonal functions. BMD analysis resulted in tPOD values of 0.56 μg/L (median of the 20 most sensitive gene-level BMDs), 5.0 μg/L (tenth percentile of all gene-level BMDs), 7.51 μg/L (mode of the first peak of all gene-level BMDs), and 5.66 μg/L (pathway-level BMD). These tPODs were protective of locomotor and reduced body weight effects (LOEC of 10.2 μg/L) observed in this study and were reflective of chronic apical BMDs of FLX reported in the literature. Furthermore, the distribution of gene-level BMDs followed a bimodal pattern, revealing disruption of sensitive neurotoxic pathways at low concentrations and metabolic pathway perturbations at higher concentrations. This is one of the first studies to derive protective tPODs for FLX using a short-term embryo assay at a life stage not considered to be a live animal under current legislations.

Machine learning-based biomarkers identification from toxicogenomics - Bridging to regulatory relevant phenotypic endpoints

One of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo regulatory-relevant phenotypic toxicity endpoint. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (maximum relevance and minimum redundancy (MRMR)) and classification method (support vector machine (SVM)), an "optimal" number of biomarkers with minimum redundancy can be identified for prediction of phenotypic toxicity endpoints with good accuracy. We included two case studies for in-vivo carcinogenicity and Ames genotoxicity prediction, using 20 selected chemicals including model genotoxic chemicals and negative controls, respectively. The results suggested that, employing the adverse outcome pathway (AOP) concept, molecular endpoints based on a relatively small number of properly selected biomarker-ensemble involved in the conserved DNA-damage and repair pathways among eukaryotes, were able to predict both Ames genotoxicity endpoints and in-vivo carcinogenicity in rats. A prediction accuracy of 76% with AUC = 0.81 was achieved while predicting in-vivo carcinogenicity with the top-ranked five biomarkers. For Ames genotoxicity prediction, the top-ranked five biomarkers were able to achieve prediction accuracy of 70% with AUC = 0.75. However, the specific biomarkers identified as the top-ranked five biomarkers are different for the two different phenotypic genotoxicity assays. The top-ranked biomarkers for the in-vivo carcinogenicity prediction mainly focused on double strand break repair and DNA recombination, whereas the selected top-ranked biomarkers for Ames genotoxicity prediction are associated with base- and nucleotide-excision repair The method developed in this study will help to fill in the knowledge gap in phenotypic anchoring and predictive toxicology, and contribute to the progress in the implementation of tox 21 vision for environmental and health applications.

A 10-step framework for use of read-across (RAX) in next generation risk assessment (NGRA) for cosmetics safety assessment

This paper presents a 10-step read-across (RAX) framework for use in cases where a threshold of toxicological concern (TTC) approach to cosmetics safety assessment is not possible. RAX builds on established approaches that have existed for more than two decades using chemical properties and in silico toxicology predictions, by further substantiating hypotheses on toxicological similarity of substances, and integrating new approach methodologies (NAM) in the biological and kinetic domains. NAM include new types of data on biological observations from, for example, in vitro assays, toxicogenomics, metabolomics, receptor binding screens and uses physiologically-based kinetic (PBK) modelling to inform about systemic exposure. NAM data can help to substantiate a mode/mechanism of action (MoA), and if similar chemicals can be shown to work by a similar MoA, a next generation risk assessment (NGRA) may be performed with acceptable confidence for a data-poor target substance with no or inadequate safety data, based on RAX approaches using data-rich analogue(s), and taking account of potency or kinetic/dynamic differences.

Transient exposure to sublethal concentrations of a pesticide mixture (chlorpyrifos-difenoconazole) caused different responses in fish species from different trophic levels of the same community

The assessment of early effects caused in biota by sublethal exposure to pesticide mixtures should enhance the realism in the ecological risk assessment for agricultural landscapes. This study aimed to evaluate sub-individual responses in fish, which can be linked with outcomes at higher levels of biological organization and affect their trophic relationships. A multilevel biomarker approach was applied to assess the effects of a 48 h exposure of two freshwater mesoamerican fish species (Parachromis dovii and Poecilia gillii) to a mixture of sublethal concentrations of chlorpyrifos (5 μg/L) and difenoconazole (325 μg/L). Transcriptomic induction of cyp1A and the activities of 7-ethoxy-resorufin-O-distillase (EROD) and glutathione S-transferase (GST) were measured as biotransformation-related biomarkers; cholinesterase activity (ChE) was assessed as a neurotoxicity biomarker; resting metabolic rate (RMR) was measured as a physiological biomarker; and the movement of fish in a dark-light environment as a behavior biomarker. The exposure to the mixture had evident effects on P. gillii, with significant induction of cyp1A transcription, increased EROD activity, ChE inhibition in muscle, and increased permanence in the light side of the dark-light environment. Meanwhile, P. dovii only showed significant induction of cyp1A, without evidence of neurotoxicity or changes in behavior. This study demonstrates that the severity of the effects caused by the exposure to a mixture of pesticides can differ among species from the same trophic chain. The potential impairment of predator-prey relationships is a relevant effect that pesticide pollution can cause and it should be considered for the risk assessment of such contaminants.

Retinaldehyde Dehydrogenase Inhibition-Related Adverse Outcome Pathway: Potential Risk of Retinoic Acid Synthesis Inhibition during Embryogenesis

Retinoic acid (RA) is one of the factors crucial for cell growth, differentiation, and embryogenesis; it interacts with the retinoic acid receptor and retinoic acid X receptor to eventually regulate target gene expression in chordates. RA is transformed from retinaldehyde via oxidization by retinaldehyde dehydrogenase (RALDH), which belongs to the family of oxidoreductases. Several chemicals, including disulphiram, diethylaminobenzaldehyde, and SB-210661, can effectively inhibit RALDH activity, potentially causing reproductive and developmental toxicity. The modes of action can be sequentially explained based on the molecular initiating event toward key events, and finally the adverse outcomes. Adverse outcome pathway (AOP) is a conceptual and theoretical framework that describes the sequential chain of casually liked events at different biological levels from molecular events to adverse effects. In the present review, we discussed a recently registered AOP (AOP₂₉₇; inhibition of retinaldehyde dehydrogenase leads to population decline) to explain and support the weight of evidence for RALDH inhibition-related developmental toxicity using the existing knowledge.

Pilot-scale expanded assessment of inorganic and organic tapwater exposures and predicted effects in Puerto Rico, USA

A pilot-scale expanded target assessment of mixtures of inorganic and organic contaminants in point-of-consumption drinking water (tapwater, TW) was conducted in Puerto Rico (PR) to continue to inform TW exposures and corresponding estimations of cumulative human-health risks across the US. In August 2018, a spatial synoptic pilot assessment of than 524 organic and 37 inorganic chemicals was conducted in 14 locations (7 home; 7 commercial) across PR. A follow-up 3-day temporal assessment of TW variability was conducted in December 2018 at two of the synoptic locations (1 home, 1 commercial) and included daily pre- and post-flush samples. Concentrations of regulated and unregulated TW contaminants were used to calculate cumulative in vitro bioactivity ratios and Hazard Indices (HI) based on existing human-health benchmarks. Synoptic results confirmed that human exposures to inorganic and organic contaminant mixtures, which are rarely monitored together in drinking water at the point of consumption, occurred across PR and consisted of elevated concentrations of inorganic contaminants (e.g., lead, copper), disinfection byproducts (DBP), and to a lesser extent per/polyfluoroalkyl substances (PFAS) and phthalates. Exceedances of human-health benchmarks in every synoptic TW sample support further investigation of the potential cumulative risk to vulnerable populations in PR and emphasize the importance of continued broad characterization of drinking-water exposures at the tap with analytical capabilities that better represent the complexity of both inorganic and organic contaminant mixtures known to occur in ambient source waters. Such health-based monitoring data are essential to support public engagement in source water sustainability and treatment and to inform consumer point-of-use treatment decision making in PR and throughout the US.

Assessing the Toxicity of 17α-Ethinylestradiol in Rainbow Trout Using a 4-Day Transcriptomics Benchmark Dose (BMD) Embryo Assay

There is an urgent demand for more efficient and ethical approaches in ecological risk assessment. Using 17α-ethinylestradiol (EE2) as a model compound, this study established an embryo benchmark dose (BMD) assay for rainbow trout (RBT; Oncorhynchus mykiss) to derive transcriptomic points-of-departure (tPODs) as an alternative to live-animal tests. Embryos were exposed to graded concentrations of EE2 (measured: 0, 1.13, 1.57, 6.22, 16.3, 55.1, and 169 ng/L) from hatch to 4 and up to 60 days post-hatch (dph) to assess molecular and apical responses, respectively. Whole proteome analyses of alevins did not show clear estrogenic effects. In contrast, transcriptomics revealed responses that were in agreement with apical effects, including excessive accumulation of intravascular and hepatic proteinaceous fluid and significant increases in mortality at 55.1 and 169 ng/L EE2 at later time points. Transcriptomic BMD analysis estimated the median of the 20th lowest geneBMD to be 0.18 ng/L, the most sensitive tPOD. Other estimates (0.78, 3.64, and 1.63 ng/L for the 10th percentile geneBMD, first peak geneBMD distribution, and median geneBMD of the most sensitive over-represented pathway, respectively) were within the same order of magnitude as empirically derived apical PODs for EE2 in the literature. This 4-day alternative RBT embryonic assay was effective in deriving tPODs that are protective of chronic effects of EE2.

Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach

Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal and organize public high-throughput screening data for compounds with nuclear estrogen receptor α and β (ERα and ERβ) binding potentials. The target activity was rodent uterotrophic bioactivity driven by ERα/ERβ activations. After training, the resultant network successfully inferred critical relationships among ERα/ERβ target bioassays, shown as weights of 6521 edges between 1071 neurons. The resultant network uses an adverse outcome pathway (AOP) framework to mimic the signaling pathway initiated by ERα and identify compounds that mimic endogenous estrogens (i.e., estrogen mimetics). The k-DNN can predict estrogen mimetics by activating neurons representing several events in the ERα/ERβ signaling pathway. Therefore, this virtual pathway model, starting from a compound's chemistry initiating ERα activation and ending with rodent uterotrophic bioactivity, can efficiently and accurately prioritize new estrogen mimetics (AUC = 0.864-0.927). This k-DNN method is a potential universal computational toxicology strategy to utilize public high-throughput screening data to characterize hazards and prioritize potentially toxic compounds.

Application of the adverse outcome pathway framework to predict the toxicity of chemicals in the semiconductor manufacturing industry

Background

To solve current issues using big data, solve current issues related to the semiconductor and electronics industry, I tried to establish the data for each toxicity mechanism for adverse outcome pathway (AOP) for the exposure.

Objective

I planned to increase the efficiency of human hazard assessment by searching, analyzing, and linking test data on the relationship between key events occurred at each level, which are the biological targets of chemicals in semiconductor manufacturing.

Results

It was found that 48 kinds of chemicals had 11 AOPs, while 103 chemicals had multiple AOPs, and 26 had case evidence. As a result of AOP analysis, it was found that a total of 320 chemicals had 42 AOPs, and 190 major chemicals corresponded to 11 AOPs. It was found necessary to develop a complex AOP and secure an (inhalation or dermal) exposure scenario for combined exposure at work. As a comparative search (41 out of 190 chemicals) of biomarkers specific to occupational diseases, 12 biomarkers were found to be related to breast cancer. The AOPs for 50 specific chemicals were presented, together with occupational disease-specific AOPs and key events relationship from 50 chemicals, and taxonomic classification for each AOP analysis could be found. With a comparative search, 41 out of 190 chemicals were associated with specific biomarkers for occupational diseases, and 12 mRNA or protein biomarkers were found to be related to breast cancer by cross-validation with the attached Table 24 of the Enforcement Regulations of the OSHAct and the CTD.

Conclusion

The mechanism of occupational diseases caused by chemicals was presented, together with pathological preventions. I believe that a strategy is needed to expand the target organization for each chemical by linking with activities, such as work environment measurement, and cooperating with screening items and methods suitable for toxic chemicals, like AOP tools.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13273-021-00139-4.

In Vitro Three-Dimensional Liver Models for Nanomaterial DNA Damage Assessment

Whilst the liver possesses the ability to repair and restore sections of damaged tissue following acute injury, prolonged exposure to engineered nanomaterials (ENM) may induce repetitive injury leading to chronic liver disease. Screening ENM cytotoxicity using 3D liver models has recently been performed, but a significant challenge has been the application of such in vitro models for evaluating ENM associated genotoxicity; a vital component of regulatory human health risk assessment. This review considers the benefits, limitations, and adaptations of specific in vitro approaches to assess DNA damage in the liver, whilst identifying critical advancements required to support a multitude of biochemical endpoints, focusing on nano(geno)toxicology (e.g., secondary genotoxicity, DNA damage, and repair following prolonged or repeated exposures).

Toxicogenomic fin(ger)prints for thyroid disruption AOP refinement and biomarker identification in zebrafish embryos

Endocrine disruption (ED) can trigger far-reaching effects on environmental populations, justifying a refusal of market approval for chemicals with ED properties. For the hazard assessment of ED effects on the thyroid system, regulatory decisions mostly rely on amphibian studies. Here, we used transcriptomics and proteomics for identifying molecular signatures of interference with thyroid hormone signaling preceding physiological effects in zebrafish embryos. For this, we analyzed the thyroid hormone 3,3',5-triiodothyronine (T3) and the thyroid peroxidase inhibitor 6-propyl-2-thiouracil (6-PTU) as model substances for increased and repressed thyroid hormone signaling in a modified zebrafish embryo toxicity test. We identified consistent gene expression fingerprints for both modes-of-action (MoA) at sublethal test concentrations. T3 and 6-PTU both significantly target the expression of genes involved in muscle contraction and functioning in an opposing fashion, allowing for a mechanistic refinement of key event relationships in thyroid-related adverse outcome pathways in fish. Furthermore, our fingerprints identify biomarker candidates for thyroid disruption hazard screening approaches. Perspectively, our findings will promote the AOP-based development of in vitro assays for thyroidal ED assessment, which in the long term will contribute to a reduction of regulatory animal tests.

CTD Anatomy: analyzing chemical-induced phenotypes and exposures from an anatomical perspective, with implications for environmental health studies

The Comparative Toxicogenomics Database (CTD) is a freely available public resource that curates and interrelates chemical, gene/protein, phenotype, disease, organism, and exposure data. CTD can be used to address toxicological mechanisms for environmental chemicals and facilitate the generation of testable hypotheses about how exposures affect human health. At CTD, manually curated interactions for chemical-induced phenotypes are enhanced with anatomy terms (tissues, fluids, and cell types) to describe the physiological system of the reported event. These same anatomy terms are used to annotate the human media (e.g., urine, hair, nail, blood, etc.) in which an environmental chemical was assayed for exposure. Currently, CTD uses more than 880 unique anatomy terms to contextualize over 255,000 chemical-phenotype interactions and 167,000 exposure statements. These annotations allow chemical-phenotype interactions and exposure data to be explored from a novel, anatomical perspective. Here, we describe CTD's anatomy curation process (including the construction of a controlled, interoperable vocabulary) and new anatomy webpages (that coalesce and organize the curated chemical-phenotype and exposure data sets). We also provide examples that demonstrate how this feature can be used to identify system- and cell-specific chemical-induced toxicities, help inform exposure data, prioritize phenotypes for environmental diseases, survey tissue and pregnancy exposomes, and facilitate data connections with external resources. Anatomy annotations advance understanding of environmental health by providing new ways to explore and survey chemical-induced events and exposure studies in the CTD framework.

Assessing the Ecological Risks of Per- and Polyfluoroalkyl Substances: Current State-of-the Science and a Proposed Path Forward

Per- and poly-fluoroalkyl substances (PFAS) encompass a large, heterogenous group of chemicals of potential concern to human health and the environment. Based on information for a few relatively well-understood PFAS such as perfluorooctane sulfonate and perfluorooctanoate, there is ample basis to suspect that at least a subset can be considered persistent, bioaccumulative, and/or toxic. However, data suitable for determining risks in either prospective or retrospective assessments are lacking for the majority of PFAS. In August 2019, the Society of Environmental Toxicology and Chemistry sponsored a workshop that focused on the state-of-the-science supporting risk assessment of PFAS. The present review summarizes discussions concerning the ecotoxicology and ecological risks of PFAS. First, we summarize currently available information relevant to problem formulation/prioritization, exposure, and hazard/effects of PFAS in the context of regulatory and ecological risk assessment activities from around the world. We then describe critical gaps and uncertainties relative to ecological risk assessments for PFAS and propose approaches to address these needs. Recommendations include the development of more comprehensive monitoring programs to support exposure assessment, an emphasis on research to support the formulation of predictive models for bioaccumulation, and the development of in silico, in vitro, and in vivo methods to efficiently assess biological effects for potentially sensitive species/endpoints. Addressing needs associated with assessing the ecological risk of PFAS will require cross-disciplinary approaches that employ both conventional and new methods in an integrated, resource-effective manner. Environ Toxicol Chem 2021;40:564-605. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Developmental biology meets toxicology: contributing reproductive mechanisms to build adverse outcome pathways

An adverse outcome pathway (AOP) is a simplified description of the sequence of mechanistic events that lead to a particular toxicological effect, from initial trigger to adverse outcome. Although designed to inform regulatory risk assessors, the AOP framework also provides a platform for innovative collaborations between experts from relevant research fields and the regulatory community. The underpinning for any AOP is basic knowledge about molecular and developmental processes; such knowledge can only be attained by solid bioscientific research. Starting with this fundamental knowledge, the objective is to devise novel testing strategies that focus on key events in a causative pathway. It is anticipated that such a knowledge-based approach will ultimately alleviate many of the burdens associated with classical chemical testing strategies that typically involve large-scale animal toxicity regimens. This hails from the notion that a solid understanding of the underlying mechanisms will allow the development and use of alternative test methods, including both in vitro and in silico approaches. This review is specifically targeted at professionals working in bioscientific fields, such as developmental and reproductive biology, and aims to (i) inform on the existence of the AOP framework and (ii) encourage new cross-disciplinary collaborations. It is hoped that fundamental biological knowledge can thus be better exploited for applied purposes: firstly, an improved understanding of how our perpetual exposure to environmental chemicals is causing human reproductive disease and, secondly, new approaches to screen for harmful chemicals more efficiently. This is not an instructional manual on how to create AOPs; rather, we discuss how to harness fundamental knowledge from the biosciences to assist regulatory toxicologists in their efforts to protect humans against chemicals that harm human reproductive development and function.

Combined In Vitro and In Vivo Approaches to Propose a Putative Adverse Outcome Pathway for Acute Lung Inflammation Induced by Nanoparticles: A Study on Carbon Dots

With the growth of nanotechnologies, concerns raised regarding the potential adverse effects of nanoparticles (NPs), especially on the respiratory tract. Adverse outcome pathways (AOP) have become recently the subject of intensive studies in order to get a better understanding of the mechanisms of NP toxicity, and hence hopefully predict the health risks associated with NP exposure. Herein, we propose a putative AOP for the lung toxicity of NPs using emerging nanomaterials called carbon dots (CDs), and in vivo and in vitro experimental approaches. We first investigated the effect of a single administration of CDs on mouse airways. We showed that CDs induce an acute lung inflammation and identified airway macrophages as target cells of CDs. Then, we studied the cellular responses induced by CDs in an in vitro model of macrophages. We observed that CDs are internalized by these cells (molecular initial event) and induce a series of key events, including loss of lysosomal integrity and mitochondrial disruption (organelle responses), as well as oxidative stress, inflammasome activation, inflammatory cytokine upregulation and macrophage death (cellular responses). All these effects triggering lung inflammation as tissular response may lead to acute lung injury.

Leveraging the Comparative Toxicogenomics Database to Fill in Knowledge Gaps for Environmental Health: A Test Case for Air Pollution-induced Cardiovascular Disease

Environmental health studies relate how exposures (eg, chemicals) affect human health and disease; however, in most cases, the molecular and biological mechanisms connecting an exposure with a disease remain unknown. To help fill in these knowledge gaps, we sought to leverage content from the public Comparative Toxicogenomics Database (CTD) to identify potential intermediary steps. In a proof-of-concept study, we systematically compute the genes, molecular mechanisms, and biological events for the environmental health association linking air pollution toxicants with 2 cardiovascular diseases (myocardial infarction and hypertension) as a test case. Our approach integrates 5 types of curated interactions in CTD to build sets of "CGPD-tetramers," computationally constructed information blocks relating a Chemical- Gene interaction with a Phenotype and Disease. This bioinformatics strategy generates 653 CGPD-tetramers for air pollution-associated myocardial infarction (involving 5 pollutants, 58 genes, and 117 phenotypes) and 701 CGPD-tetramers for air pollution-associated hypertension (involving 3 pollutants, 96 genes, and 142 phenotypes). Collectively, we identify 19 genes and 96 phenotypes shared between these 2 air pollutant-induced outcomes, and suggest important roles for oxidative stress, inflammation, immune responses, cell death, and circulatory system processes. Moreover, CGPD-tetramers can be assembled into extensive chemical-induced disease pathways involving multiple gene products and sequential biological events, and many of these computed intermediary steps are validated in the literature. Our method does not require a priori knowledge of the toxicant, interacting gene, or biological system, and can be used to analyze any environmental chemical-induced disease curated within the public CTD framework. This bioinformatics strategy links and interrelates chemicals, genes, phenotypes, and diseases to fill in knowledge gaps for environmental health studies, as demonstrated for air pollution-associated cardiovascular disease, but can be adapted by researchers for any environmentally influenced disease-of-interest.

Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management

Various types of drug toxicity can halt the development of a drug. Because drugs are xenobiotics, they inherently have the potential to cause injury. Clarifying the mechanisms of toxicity to evaluate and manage drug safety during drug development is extremely important. However, toxicity mechanisms, especially hepatotoxic mechanisms, are very complex. The significant exposure of liver cells to drugs can cause dysfunction, cell injury, and organ failure in the liver. To clarify potential risks in drug safety management, it is necessary to systematize knowledge from a consistent viewpoint. In this study, we adopt an ontological approach. Ontology provides a controlled vocabulary for sharing and reusing of various data with a computer-friendly manner. We focus on toxic processes, especially hepatotoxic processes, and construct the toxic process ontology (TXPO). The TXPO systematizes knowledge concerning hepatotoxic courses with consistency and no ambiguity. In our application study, we developed a toxic process interpretable knowledge system (TOXPILOT) to bridge the gaps between basic science and medicine for drug safety management. Using semantic web technology, TOXPILOT supports the interpretation of toxicity mechanisms and provides visualizations of toxic courses with useful information based on ontology. Our system will contribute to various applications for drug safety evaluation and management.

Polyps of the Jellyfish Aurelia aurita Are Unaffected by Chronic Exposure to a Combination of Pesticides

Pesticides are a major contaminant in coastal waters and can cause adverse effects in marine invertebrates such as jellyfish. Most studies have investigated short-term responses of organisms to unrealistically high concentrations of pesticides; however, chronic exposure to persistent low concentrations, which are more likely to occur in the environment, are rarely analyzed. We tested the response of polyps of the moon jellyfish Aurelia aurita to environmental concentrations of the herbicide atrazine and the insecticide chlorpyrifos, individually and in combination, over 9 wk. We hypothesized that exposure to individual pesticides would reduce rates of asexual reproduction and alter polyps' metabolite profiles, and that the results would be more severe when polyps were exposed to the combined pesticides. Polyps survived and reproduced (through budding) in all treatments, and no differences among treatments were observed. Proton nuclear magnetic resonance spectroscopy revealed no difference in profiles of polar metabolites of polyps exposed to the individual or combined pesticides. Our results suggest that A. aurita polyps are unaffected by chronic exposure to atrazine and chlorpyrifos at concentrations recommended as being protective by current Australian water quality guidelines. Environ Toxicol Chem 2020;39:1685-1692. © 2020 SETAC.

Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels

New approach methodologies (NAMs) for chemical hazard assessment are often evaluated via comparison to animal studies; however, variability in animal study data limits NAM accuracy. The US EPA Toxicity Reference Database (ToxRefDB) enables consideration of variability in effect levels, including the lowest effect level (LEL) for a treatment-related effect and the lowest observable adverse effect level (LOAEL) defined by expert review, from subacute, subchronic, chronic, multi-generation reproductive, and developmental toxicity studies. The objectives of this work were to quantify the variance within systemic LEL and LOAEL values, defined as potency values for effects in adult or parental animals only, and to estimate the upper limit of NAM prediction accuracy. Multiple linear regression (MLR) and augmented cell means (ACM) models were used to quantify the total variance, and the fraction of variance in systemic LEL and LOAEL values explained by available study descriptors (e.g., administration route, study type). The MLR approach considered each study descriptor as an independent contributor to variance, whereas the ACM approach combined categorical descriptors into cells to define replicates. Using these approaches, total variance in systemic LEL and LOAEL values (in log₁₀-mg/kg/day units) ranged from 0.74 to 0.92. Unexplained variance in LEL and LOAEL values, approximated by the residual mean square error (MSE), ranged from 0.20-0.39. Considering subchronic, chronic, or developmental study designs separately resulted in similar values. Based on the relationship between MSE and R-squared for goodness-of-fit, the maximal R-squared may approach 55 to 73% for a NAM-based predictive model of systemic toxicity using these data as reference. The root mean square error (RMSE) ranged from 0.47 to 0.63 log₁₀-mg/kg/day, depending on dataset and regression approach, suggesting that a two-sided minimum prediction interval for systemic effect levels may have a width of 58 to 284-fold. These findings suggest quantitative considerations for building scientific confidence in NAM-based systemic toxicity predictions.

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.

Putative adverse outcome pathways for female reproductive disorders to improve testing and regulation of chemicals

Modern living challenges female reproductive health. We are witnessing a rise in reproductive disorders and drop in birth rates across the world. The reasons for these manifestations are multifaceted and most likely include continuous exposure to an ever-increasing number of chemicals. The cause–effect relationships between chemical exposure and female reproductive disorders, however, have proven problematic to determine. This has made it difficult to assess the risks chemical exposures pose to a woman’s reproductive development and function. To address this challenge, this review uses the adverse outcome pathway (AOP) concept to summarize current knowledge about how chemical exposure can affect female reproductive health. We have a special focus on effects on the ovaries, since they are essential for lifelong reproductive health in women, being the source of both oocytes and several reproductive hormones, including sex steroids. The AOP framework is widely accepted as a new tool for toxicological safety assessment that enables better use of mechanistic knowledge for regulatory purposes. AOPs equip assessors and regulators with a pragmatic network of linear cause–effect relationships, enabling the use of a wider range of test method data in chemical risk assessment and regulation. Based on current knowledge, we propose ten putative AOPs relevant for female reproductive disorders that can be further elaborated and potentially be included in the AOPwiki. This effort is an important step towards better safeguarding the reproductive health of all girls and women.