Adverse outcome pathway networks II: Network analytics

Single-cell and multi-omics analyses highlight cancer-associated fibroblasts-induced immune evasion and epithelial mesenchymal transition for smoking bladder cancer

Tobacco carcinogens are recognized as critical hazard factors for bladder tumorigenesis, affecting the prognosis of patients through aromatic amines components. However, the specific function of tobacco carcinogens and systematic assessment models in the prognosis of bladder cancer remains poorly elucidated. We retrieved bladder cancer specific tobacco carcinogens-related genes from Comparative Toxicogenomic Database, our Nanjing Bladder Cancer cohort and TCGA database. Gene×Gene interaction method was utilized to establish a prognostic signature. Integrative assessment of immunogenomics, tumor microenvironments and single-cell RNA-sequencing were performed to illustrate the internal relations of key events from different levels. Finally, we comprehensively identified 33 essential tobacco carcinogens-related genes to construct a novel prognostic signature, and found that high-risk patients were characterized by significantly worse overall survival (HR=2.25; Plog-rank < 0.01). Single-cell RNA-sequencing and multi-omics analysis demonstrated that cancer-associated fibroblasts mediated the crosstalk between epithelial-mesenchymal transition progression and immune evasion. Moreover, an adverse outcome pathway framework was established to facilitate our understanding to the tobacco carcinogens-triggered bladder tumorigenesis. Our study systematically provided immune microenvironmental alternations for smoking-induced adverse survival outcomes in bladder cancer. These findings facilitated the integrative multi-omics insights into risk assessment and toxic mechanisms of tobacco carcinogens.

An integrative data-centric approach to derivation and characterization of an adverse outcome pathway network for cadmium-induced toxicity

Cadmium is a prominent toxic heavy metal that contaminates both terrestrial and aquatic environments. Owing to its high biological half-life and low excretion rates, cadmium causes a variety of adverse biological outcomes. Adverse outcome pathway (AOP) networks were envisioned to systematically capture toxicological information to enable risk assessment and chemical regulation. Here, we leveraged AOP-Wiki and integrated heterogeneous data from four other exposome-relevant resources to build the first AOP network relevant for inorganic cadmium-induced toxicity. From AOP-Wiki, we filtered 309 high confidence AOPs, identified 312 key events (KEs) associated with inorganic cadmium from five exposome-relevant databases using a data-centric approach, and thereafter, curated 30 cadmium relevant AOPs (cadmium-AOPs). By constructing the undirected AOP network, we identified a large connected component of 18 cadmium-AOPs. Further, we analyzed the directed network of 59 KEs and 82 key event relationships (KERs) in the largest component using graph-theoretic approaches. Subsequently, we mined published literature using artificial intelligence-based tools to provide auxiliary evidence of cadmium association for all KEs in the largest component. Finally, we performed case studies to verify the rationality of cadmium-induced toxicity in humans and aquatic species. Overall, cadmium-AOP network constructed in this study will aid ongoing research in systems toxicology and chemical exposome.

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.

Combined toxicity and adverse outcome pathways of common pesticides on Chlorella pyrenoidosa

Pesticides due to their extensive use have entered the soil and water environment through various pathways, causing great harm to the environment. Herbicides and insecticides are common pesticides with long-term biological toxicity and bioaccumulation, which can harm the human body. The concept of the adverse outcome pathway (AOP) involves systematically analyzing the response levels of chemical mixtures to health-related indicators at the molecular and cellular levels. The AOP correlates the structures of chemical pollutants, toxic molecular initiation events and adverse outcomes of biological toxicity, providing a new model for toxicity testing, prediction, and evaluation of pollutants. Therefore, typical pesticides including diquat (DIQ), cyanazine (CYA), dipterex (DIP), propoxur (PRO), and oxamyl (OXA) were selected as research objects to explore the combined toxicity of typical pesticides on Chlorella pyrenoidosa (C. pyrenoidosa) and their adverse outcome pathways (AOPs). The mixture systems of pesticides were designed by the direct equipartition ray (EquRay) method and uniform design ray (UD-Ray) method. The toxic effects of single pesticides and their mixtures were systematically investigated by the time-dependent microplate toxicity analysis (t-MTA) method. The interactions of their mixtures were analyzed by the concentration addition model (CA) and the deviation from the CA model (dCA). The toxicity data showed a good concentration-effect relationship; the toxicities of five pesticides were different and the order was CYA > DIQ > OXA > PRO > DIP. Binary, ternary and quaternary mixture systems exhibited antagonism, while quinary mixture systems exhibited an additive effect. The AOP of pesticides showed that an excessive accumulation of peroxide in green algae cells led to a decline in stress resistance, inhibition of the synthesis of chlorophyll and protein in algal cells, destruction of the cellular structure, and eventually led to algal cell death.

Causal diagramming for assessing human system risk in spaceflight

For over a decade, the National Aeronautics and Space Administration (NASA) has tracked and configuration-managed approximately 30 risks that affect astronaut health and performance before, during and after spaceflight. The Human System Risk Board (HSRB) at NASA Johnson Space Center is responsible for setting the official risk posture for each of the human system risks and determining-based on evaluation of the available evidence-when that risk posture changes. The ultimate purpose of tracking and researching these risks is to find ways to reduce spaceflight-induced risk to astronauts. The adverse effects of spaceflight begin at launch and continue throughout the duration of the mission, and in some cases, across the lifetime of the astronaut. Historically, research has been conducted in individual risk "silos" to characterize risk, however, astronauts are exposed to all risks simultaneously. In January of 2020, the HSRB at NASA began assessing the potential value of causal diagramming as a tool to facilitate understanding of the complex causes and effects that contribute to spaceflight-induced human system risk. Causal diagrams in the form of directed acyclic graphs (DAGs) are used to provide HSRB stakeholders with a shared mental model of the causal flow of risk. While primarily improving communication among those stakeholders, DAGs also allow a composite risk network to be created that can be tracked and configuration managed. This paper outlines the HSRB's pilot process for this effort, the lessons learned, and future goals for data-driven risk management approaches.

Comprehensive mapping of the AOP-Wiki database: identifying biological and disease gaps

Introduction: The Adverse Outcome Pathway (AOP) concept facilitates rapid hazard assessment for human health risks. AOPs are constantly evolving, their number is growing, and they are referenced in the AOP-Wiki database, which is supported by the OECD. Here, we present a study that aims at identifying well-defined biological areas, as well as gaps within the AOP-Wiki for future research needs. It does not intend to provide a systematic and comprehensive summary of the available literature on AOPs but summarizes and maps biological knowledge and diseases represented by the already developed AOPs (with OECD endorsed status or under validation). Methods: Knowledge from the AOP-Wiki database were extracted and prepared for analysis using a multi-step procedure. An automatic mapping of the existing information on AOPs (i.e., genes/proteins and diseases) was performed using bioinformatics tools (i.e., overrepresentation analysis using Gene Ontology and DisGeNET), allowing both the classification of AOPs and the development of AOP networks (AOPN). Results: AOPs related to diseases of the genitourinary system, neoplasms and developmental anomalies are the most frequently investigated on the AOP-Wiki. An evaluation of the three priority cases (i.e., immunotoxicity and non-genotoxic carcinogenesis, endocrine and metabolic disruption, and developmental and adult neurotoxicity) of the EU-funded PARC project (Partnership for the Risk Assessment of Chemicals) are presented. These were used to highlight under- and over-represented adverse outcomes and to identify and prioritize gaps for further research. Discussion: These results contribute to a more comprehensive understanding of the adverse effects associated with the molecular events in AOPs, and aid in refining risk assessment for stressors and mitigation strategies. Moreover, the FAIRness (i.e., data which meets principles of findability, accessibility, interoperability, and reusability (FAIR)) of the AOPs appears to be an important consideration for further development.

Development of an adverse outcome pathway network for nephrotoxicity

Adverse outcome pathways (AOPs) were introduced in modern toxicology to provide evidence-based representations of the events and processes involved in the progression of toxicological effects across varying levels of the biological organisation to better facilitate the safety assessment of chemicals. AOPs offer an opportunity to address knowledge gaps and help to identify novel therapeutic targets. They also aid in the selection and development of existing and new in vitro and in silico test methods for hazard identification and risk assessment of chemical compounds. However, many toxicological processes are too intricate to be captured in a single, linear AOP. As a result, AOP networks have been developed to aid in the comprehension and placement of associated events underlying the emergence of related forms of toxicity-where complex exposure scenarios and interactions may influence the ultimate adverse outcome. This study utilised established criteria to develop an AOP network that connects thirteen individual AOPs associated with nephrotoxicity (as sourced from the AOP-Wiki) to identify several key events (KEs) linked to various adverse outcomes, including kidney failure and chronic kidney disease. Analysis of the modelled AOP network and its topological features determined mitochondrial dysfunction, oxidative stress, and tubular necrosis to be the most connected and central KEs. These KEs can provide a logical foundation for guiding the selection and creation of in vitro assays and in silico tools to substitute for animal-based in vivo experiments in the prediction and assessment of chemical-induced nephrotoxicity in human health.

Specific effects on the thyroid relevant for performing a dietary cumulative risk assessment of pesticide residues: 2024 update

EFSA updated its previous work on the establishment of specific effects that are considered relevant for grouping pesticide residues targeting the thyroid and for performing the retrospective assessment of dietary cumulative risk (CRA). The two specific effects already selected in 2019 leading to the two cumulative assessment groups (CAGs) 'hypothyroidism' and 'C-cell hypertrophy, hyperplasia and neoplasia' were reconfirmed. Compared to 2019, the list of indicators that can be used to identify these specific effects was refined to only include histopathological changes. In a second phase of the work, data will be extracted on indicators of the specific effects from the dossiers on active substances (a.s.) used as plant protection products. The criteria for including a.s. into CAGs were also updated, together with the hazard characterisation methodology and the lines of evidence for assessing CAG-membership probabilities. The tasks related to the data extraction and the establishment of the CAGs on hypothyroidism and on C-cell hypertrophy, hyperplasia and neoplasia are beyond the scope of this report. This part of the CRA process has been outsourced and will be the subject of a separate report.

In utero exposure to ionizing radiation and metabolic regulation: perspectives for future multi- and trans-generation effects studies

PURPOSE

The radiation protection community has been particularly attentive to the risks of delayed effects on offspring from low dose or low dose-rate exposures to ionizing radiation. Despite this, the current epidemiologic studies and scientific data are still insufficient to provide the necessary evidence for improving risk assessment guidelines. This literature review aims to inform future studies on multigenerational and transgenerational effects. It primarily focuses on animal studies involving in utero exposure and discusses crucial elements for interpreting the results. These elements include in utero exposure scenarios relative to the developmental stages of the embryo/fetus, and the primary biological mechanisms responsible for transmitting heritable or hereditary effects to future generations. The review addresses several issues within the contexts of both multigenerational and transgenerational effects, with a focus on hereditary perspectives.

CONCLUSIONS

Knowledge consolidation in the field of Developmental Origins of Health and Disease (DOHaD) has led us to propose a new study strategy. This strategy aims to address the transgenerational effects of in utero exposure to low dose and low dose-rate radiation. Within this concept, there is a possibility that disruption of epigenetic programming in embryonic and fetal cells may occur. This disruption could lead to metabolic dysfunction, which in turn may cause abnormal responses to future environmental challenges, consequently increasing disease risk. Lastly, we discuss methodological limitations in our studies. These limitations are related to cohort size, follow-up time, model radiosensitivity, and analytical techniques. We propose scientific and analytical strategies for future research in this field.

The adverse outcome pathway for breast cancer: a knowledge management framework bridging biomedicine and toxicology

Breast cancer is the most common cancer worldwide, with an estimated 2.3 million new cases diagnosed every year. Effective measures for cancer prevention and cancer therapy require a detailed understanding of the individual key disease mechanisms involved and their interactions at the molecular, cellular, tissue, organ, and organism level. In that regard, the rapid progress of biomedical and toxicological research in recent years now allows the pursuit of new approaches based on non-animal methods that provide greater mechanistic insight than traditional animal models and therefore facilitate the development of Adverse Outcome Pathways (AOPs) for human diseases. We performed a systematic review of the current state of published knowledge with regard to breast cancer to identify relevant key mechanisms for inclusion into breast cancer AOPs, i.e. decreased cell stiffness and decreased cell adhesion, and to concurrently map non-animal methods addressing these key events. We conclude that the broader sharing of expertise and methods between biomedical research and toxicology enabled by the AOP knowledge management framework can help to coordinate global research efforts and accelerate the transition to advanced non-animal methods, which, when combined into powerful method batteries, closely mimic human physiology and disease states without the need for animal testing.

Advancing food safety risk assessment in China: development of new approach methodologies (NAMs)

Novel techniques and methodologies are being developed to advance food safety risk assessment into the next-generation. Considering the shortcomings of traditional animal testing, new approach methodologies (NAMs) will be the main tools for the next-generation risk assessment (NGRA), using non-animal methodologies such as in vitro and in silico approaches. The United States Environmental Protection Agency and the European Food Safety Authority have established work plans to encourage the development and application of NAMs in NGRA. Currently, NAMs are more commonly used in research than in regulatory risk assessment. China is also developing NAMs for NGRA but without a comprehensive review of the current work. This review summarizes major NAM-related research articles from China and highlights the China National Center for Food Safety Risk Assessment (CFSA) as the primary institution leading the implementation of NAMs in NGRA in China. The projects of CFSA on NAMs such as the Food Toxicology Program and the strategies for implementing NAMs in NGRA are outlined. Key issues and recommendations, such as discipline development and team building, are also presented to promote NAMs development in China and worldwide.

Advancing the use of new approach methodologies for assessing teratogenicity: Building a tiered approach

Many New Approach Methodologies (NAMs) have been developed for the safety assessment of new ingredients. Research into reproductive toxicity and teratogenicity is a particularly high priority, especially given their mechanistic complexity. Forty-six non-teratogenic and 39 teratogenic chemicals were screened for teratogenic potential using the in silico DART model from the OECD QSAR Toolbox; the devTox quickPredict™ (devTox assay) test and the Zebrafish Embryotoxicity Test (ZET). The sensitivity and specificity were 94.7% and 84.1%, respectively, for the DART tree (83 chemicals), 86.1% and 35.6% for the devTox (81 chemicals) and 77.8% and 76.7% for the ZET (57 chemicals). Fifty-three chemicals were tested in all three assays and when results were combined and based on a "2 out of 3 rule", the sensitivity and specificity were 96.0% and 71.4%, respectively. The specificity of the devTox assay for a sub-set of 43 chemicals was increased from 26.1% to 82.6% by incorporating human plasma concentrations into the assay interpretation. When all 85 chemicals were assessed in a decision tree approach, there was an excellent predictivity and assay robustness of 90%. In conclusion, all three models exhibited a good sensitivity and specificity, especially when outcomes from all three were combined or used in "2 out of 3" or a tiered decision tree approach. The latter is an interesting predictive approach for evaluating the teratogenic potential of new chemicals. Future investigations will extend the number of chemicals tested, as well as explore ways to refine the results and obtain a robust Integrated Testing Strategy to evaluate teratogenic potential.

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.

Cross-species applicability of an adverse outcome pathway network for thyroid hormone system disruption

Thyroid hormone system disrupting compounds are considered potential threats for human and environmental health. Multiple adverse outcome pathways (AOPs) for thyroid hormone system disruption (THSD) are being developed in different taxa. Combining these AOPs results in a cross-species AOP network for THSD which may provide an evidence-based foundation for extrapolating THSD data across vertebrate species and bridging the gap between human and environmental health. This review aimed to advance the description of the taxonomic domain of applicability (tDOA) in the network to improve its utility for cross-species extrapolation. We focused on the molecular initiating events (MIEs) and adverse outcomes (AOs) and evaluated both their plausible domain of applicability (taxa they are likely applicable to) and empirical domain of applicability (where evidence for applicability to various taxa exists) in a THSD context. The evaluation showed that all MIEs in the AOP network are applicable to mammals. With some exceptions, there was evidence of structural conservation across vertebrate taxa and especially for fish and amphibians, and to a lesser extent for birds, empirical evidence was found. Current evidence supports the applicability of impaired neurodevelopment, neurosensory development (eg, vision) and reproduction across vertebrate taxa. The results of this tDOA evaluation are summarized in a conceptual AOP network that helps prioritize (parts of) AOPs for a more detailed evaluation. In conclusion, this review advances the tDOA description of an existing THSD AOP network and serves as a catalog summarizing plausible and empirical evidence on which future cross-species AOP development and tDOA assessment could build.

Towards a science-based testing strategy to identify maternal thyroid hormone imbalance and neurodevelopmental effects in the progeny - Part IV: the ECETOC and CLE Proposal for a Thyroid Function-Related Neurodevelopmental Toxicity Testing and Assessment Scheme (Thyroid-NDT-TAS)

Following the European Commission Endocrine Disruptor Criteria, substances shall be considered as having endocrine disrupting properties if they (a) elicit adverse effects, (b) have endocrine activity, and (c) the two are linked by an endocrine mode-of-action (MoA) unless the MoA is not relevant for humans. A comprehensive, structured approach to assess whether substances meet the Endocrine Disruptor Criteria for the thyroid modality (EDC-T) is currently unavailable. Here, the European Centre for Ecotoxicology and Toxicology of Chemicals Thyroxine Task Force and CropLife Europe propose a Thyroid Function-Related Neurodevelopmental Toxicity Testing and Assessment Scheme (Thyroid-NDT-TAS). In Tier 0, before entering the Thyroid-NDT-TAS, all available in vivo, in vitro and in silico data are submitted to weight-of-evidence (WoE) evaluations to determine whether the substance of interest poses a concern for thyroid disruption. If so, Tier 1 of the Thyroid-NDT-TAS includes an initial MoA and human relevance assessment (structured by the key events of possibly relevant adverse outcome pathways) and the generation of supportive in vitro/in silico data, if relevant. Only if Tier 1 is inconclusive, Tier 2 involves higher-tier testing to generate further thyroid- and/or neurodevelopment-related data. Tier 3 includes the final MoA and human relevance assessment and an overarching WoE evaluation to draw a conclusion on whether, or not, the substance meets the EDC-T. The Thyroid-NDT-TAS is based on the state-of-the-science, and it has been developed to minimise animal testing. To make human safety assessments more accurate, it is recommended to apply the Thyroid-NDT-TAS during future regulatory assessments.

A curated gene and biological system annotation of adverse outcome pathways related to human health

Adverse outcome pathways (AOPs) are emerging as a central framework in modern toxicology and other fields in biomedicine. They serve as an extension of pathway-based concepts by depicting biological mechanisms as causally linked sequences of key events (KEs) from a molecular initiating event (MIE) to an adverse outcome. AOPs guide the use and development of new approach methodologies (NAMs) aimed at reducing animal experimentation. While AOPs model the systemic mechanisms at various levels of biological organisation, toxicogenomics provides the means to study the molecular mechanisms of chemical exposures. Systematic integration of these two concepts would improve the application of AOP-based knowledge while also supporting the interpretation of complex omics data. Hence, we established this link through rigorous curation of molecular annotations for the KEs of human relevant AOPs. We further expanded and consolidated the annotations of the biological context of KEs. These curated annotations pave the way to embed AOPs in molecular data interpretation, facilitating the emergence of new knowledge in biomedicine.

Prediction of zebrafish embryonic developmental toxicity by integrating omics with adverse outcome pathway

New approach methodologies (NAMs), especially omics-based high-throughput bioassays have been developed rapidly, providing rich mechanistic information such as molecular initiation events (MIEs) and (sub)cellular key events (KEs) in adverse outcome pathways (AOPs). However, how to apply the knowledge of MIEs/KEs to predict adverse outcomes (AOs) induced by chemicals represents a new challenge for computational toxicology. Here, an integrated method named ScoreAOP was developed and evaluated to predict chemicals' developmental toxicity for zebrafish embryos by integrating four related AOPs and dose-dependent reduced zebrafish transcriptome (RZT). The rules of ScoreAOP included 1) sensitivity of responsive KEs demonstrated by point of departure of KEs (POD_KE), 2) evidence reliability and 3) distance between KEs and AOs. Moreover, eleven chemicals with different modes of action (MoAs) were tested to evaluate ScoreAOP. Results showed that eight of the eleven chemicals caused developmental toxicity at tested concentration in apical tests. All the tested chemicals' developmental defects were predicted using ScoreAOP, whereas eight out of the eleven chemicals predicted by ScoreMIE which was developed to score MIEs disturbed by chemicals based on in vitro bioassays data. Finally, in terms of mechanism explanation, ScoreAOP clustered chemicals with different MoAs while ScoreMIE failed, and ScoreAOP revealed the activation of aryl hydrocarbon receptor (AhR) plays a significant role in dysfunction of cardiovascular system, resulting in zebrafish developmental defects and mortality. In conclusion, ScoreAOP represents a promising approach to apply mechanism information obtained from omics to predict AOs induced by chemicals.

AOP Report: Adverse Outcome Pathways for Aromatase Inhibition or Androgen Receptor Agonism Leading to Male-Biased Sex Ratio and Population Decline in Fish

Screening and testing of potential endocrine-disrupting chemicals for ecological effects are examples of risk assessment/regulatory activities that can employ adverse outcome pathways (AOPs) to establish linkages between readily measured alterations in endocrine function and whole organism- and population-level responses. Of particular concern are processes controlled by the hypothalamic-pituitary-gonadal/thyroidal (HPG/T) axes. However, the availability of AOPs suitable to meet this need is currently limited in terms of species and life-stage representation relative to the diversity of endpoints influenced by HPG/T function. In our report we describe two novel AOPs that comprise a simple AOP network focused on the effects of chemicals on sex differentiation during early development in fish. The first AOP (346) documents events starting with inhibition of cytochrome P450 aromatase (CYP19), resulting in decreased availability of 17β-estradiol during gonad differentiation, which increases the occurrence of testis formation, resulting in a male-biased sex ratio and consequent population-level declines. The second AOP (376) is initiated by activation of the androgen receptor (AR), also during sexual differentiation, again resulting in a male-biased sex ratio and population-level effects. Both AOPs are strongly supported by existing physiological and toxicological evidence, including numerous fish studies with model CYP19 inhibitors and AR agonists. Accordingly, AOPs 346 and 376 provide a basis for more focused screening and testing of chemicals with the potential to affect HPG function in fish during early development. Environ Toxicol Chem 2023;42:747-756. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target

In the real world, individuals are exposed to chemicals from sources that vary over space and time. However, traditional risk assessments based on in vivo animal studies typically use a chemical-by-chemical approach and apical disease endpoints. New approach methodologies (NAMs) in toxicology, such as in vitro high-throughput (HTS) assays generated in Tox21 and ToxCast, can more readily provide mechanistic chemical hazard information for chemicals with no existing data than in vivo methods. In this paper, we establish a workflow to assess the joint action of 41 modeled ambient chemical exposures in the air from the USA-wide National Air Toxics Assessment by integrating human exposures with hazard data from curated HTS (cHTS) assays to identify counties where exposure to the local chemical mixture may perturb a common biological target. We exemplify this proof-of-concept using CYP1A1 mRNA up-regulation. We first estimate internal exposure and then convert the inhaled concentration to a steady state plasma concentration using physiologically based toxicokinetic modeling parameterized with county-specific information on ages and body weights. We then use the estimated blood plasma concentration and the concentration-response curve from the in vitro cHTS assay to determine the chemical-specific effects of the mixture components. Three mixture modeling methods were used to estimate the joint effect from exposure to the chemical mixture on the activity levels, which were geospatially mapped. Finally, a Monte Carlo uncertainty analysis was performed to quantify the influence of each parameter on the combined effects. This workflow demonstrates how NAMs can be used to predict early-stage biological perturbations that can lead to adverse health outcomes that result from exposure to chemical mixtures. As a result, this work will advance mixture risk assessment and other early events in the effects of chemicals.

Adverse effects and underlying mechanism of amorphous silica nanoparticles in liver

Amorphous silica nanoparticles (SiNPs) have been widely used and mass-producted due to its unique properties. With the life cycle of SiNPs-based products, SiNPs are further released into the air, soil, surface water and sediment, resulting in an increasing risk to humans. SiNPs could enter into the human body through vein, respiratory tract, digestive tract or skin. Moreover, recent evidences have showed that, regardless of exposure pathways, SiNPs could even be traced in liver, which is gradually considered as one of the main organs that SiNPs accumulate. Increasing evidences supported the link between SiNPs exposure and adverse liver effects. However, the research models are diverse and the molecular mechanisms have not been well integrated. In this review, the liver-related studies of SiNPs in vivo and in vitro were screened from the PubMed database by systematic retrieval method. We explored the interaction between SiNPs and the liver, and especially proposed a framework of SiNPs-caused liver toxicity, considering AOP Wiki and existing studies. We identified increased reactive oxygen species (ROS) as a molecular initiating event (MIE), oxidative stress, endoplasmic reticulum stress, lysosome disruption and mitochondrial dysfunction as subsequent key events (KEs), which gradually led to adverse outcomes (AOs) containing liver dysfunction and liver fibrosis through a series of key events about cell inflammation and death such as hepatocyte apoptosis/pyroptosis, hepatocyte autophagy dysfuncton and hepatic macrophages pyroptosis. To our best knowledge, this is the first AOP proposed on SiNPs-related liver toxicity. In the future, more epidemiological studies need to be performed and more biomarkers need to be explored to improve the AOP framework for SiNPs-associated liver toxicity.

Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach

Mechanistic toxicology provides a powerful approach to inform on the safety of chemicals and the development of safe-by-design compounds. Although toxicogenomics supports mechanistic evaluation of chemical exposures, its implementation into the regulatory framework is hindered by uncertainties in the analysis and interpretation of such data. The use of mechanistic evidence through the adverse outcome pathway (AOP) concept is promoted for the development of new approach methodologies (NAMs) that can reduce animal experimentation. However, to unleash the full potential of AOPs and build confidence into toxicogenomics, robust associations between AOPs and patterns of molecular alteration need to be established. Systematic curation of molecular events to AOPs will create the much-needed link between toxicogenomics and systemic mechanisms depicted by the AOPs. This, in turn, will introduce novel ways of benefitting from the AOPs, including predictive models and targeted assays, while also reducing the need for multiple testing strategies. Hence, a multi-step strategy to annotate AOPs is developed, and the resulting associations are applied to successfully highlight relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, a panel of AOP-derived in vitro biomarkers for pulmonary fibrosis (PF) is identified and experimentally validated.

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

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

Use of omics analysis for low-dose radiotoxicology and health risk assessment: the case of uranium

Exposure to environmental pollution and the increase in the incidence of multifactorial diseases in the population have become health problems for industrialized countries. In this context, the question of the health impact of exposure to these pollutants is not clearly identified in the low-dose range. This article looks at this problem using the example of preclinical studies of the effects of chronic low-dose exposure to uranium in rats. These studies demonstrate the value of molecular screening analyses (omics) and multimodal integrative approaches, of which the extreme sensitivity and breadth of observation spectrum make it possible to observe all the biological processes affected and the mechanisms of action triggered at the molecular level by exposure to low doses. They also show the value of these analytical approaches for finding diagnostic biomarkers or indicators of prognosis, which can be necessary to evaluate a risk. Finally, the results of these studies raise the question of the health risk caused by epigenomic deregulations occurring during critical developmental phases and their potential contribution to the development of chronic diseases that are metabolic in origin or to the development of certain cancer liable in the long term to affect the exposed adult and possibly its progeny.

The Adverse Outcome Pathway Framework Applied to Neurological Symptoms of COVID-19

Several reports have shown that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has the potential to also be neurotropic. However, the mechanisms by which SARS-CoV-2 induces neurologic injury, including neurological and/or psychological symptoms, remain unclear. In this review, the available knowledge on the neurobiological mechanisms underlying COVID-19 was organized using the AOP framework. Four AOPs leading to neurological adverse outcomes (AO), anosmia, encephalitis, stroke, and seizure, were developed. Biological key events (KEs) identified to induce these AOs included binding to ACE2, blood–brain barrier (BBB) disruption, hypoxia, neuroinflammation, and oxidative stress. The modularity of AOPs allows the construction of AOP networks to visualize core pathways and recognize neuroinflammation and BBB disruption as shared mechanisms. Furthermore, the impact on the neurological AOPs of COVID-19 by modulating and multiscale factors such as age, psychological stress, nutrition, poverty, and food insecurity was discussed. Organizing the existing knowledge along an AOP framework can represent a valuable tool to understand disease mechanisms and identify data gaps and potentially contribute to treatment, and prevention. This AOP-aligned approach also facilitates synergy between experts from different backgrounds, while the fast-evolving and disruptive nature of COVID-19 emphasizes the need for interdisciplinarity and cross-community research.

Toxicokinetic Modeling of Per- and Polyfluoroalkyl Substance Concentrations within Developing Zebrafish (Danio rerio) Populations

Per- and polyfluoroalkyl substances (PFAS) are pervasive environmental contaminants, and their relative stability and high bioaccumulation potential create a challenging risk assessment problem. Zebrafish (Danio rerio) data, in principle, can be synthesized within a quantitative adverse outcome pathway (qAOP) framework to link molecular activity with individual or population level hazards. However, even as qAOP models are still in their infancy, there is a need to link internal dose and toxicity endpoints in a more rigorous way to further not only qAOP models but adverse outcome pathway frameworks in general. We address this problem by suggesting refinements to the current state of toxicokinetic modeling for the early development zebrafish exposed to PFAS up to 120 h post-fertilization. Our approach describes two key physiological transformation phenomena of the developing zebrafish: dynamic volume of an individual and dynamic hatching of a population. We then explore two different modeling strategies to describe the mass transfer, with one strategy relying on classical kinetic rates and the other incorporating mechanisms of membrane transport and adsorption/binding potential. Moving forward, we discuss the challenges of extending this model in both timeframe and chemical class, in conjunction with providing a conceptual framework for its integration with ongoing qAOP modeling efforts.

Towards a science-based testing strategy to identify maternal thyroid hormone imbalance and neurodevelopmental effects in the progeny-part III: how is substance-mediated thyroid hormone imbalance in pregnant/lactating rats or their progeny related to neurodevelopmental effects?

This review investigated which patterns of thyroid- and brain-related effects are seen in rats upon gestational/lactational exposure to 14 substances causing thyroid hormone imbalance by four different modes-of-action (inhibition of thyroid peroxidase, sodium-iodide symporter and deiodinase activities, enhancement of thyroid hormone clearance) or to dietary iodine deficiency. Brain-related parameters included motor activity, cognitive function, acoustic startle response, hearing function, periventricular heterotopia, electrophysiology and brain gene expression. Specific modes-of-action were not related to specific patterns of brain-related effects. Based upon the rat data reviewed, maternal serum thyroid hormone levels do not show a causal relationship with statistically significant neurodevelopmental effects. Offspring serum thyroxine together with offspring serum triiodothyronine and thyroid stimulating hormone appear relevant to predict the likelihood for neurodevelopmental effects. Based upon the collated database, thresholds of ≥60%/≥50% offspring serum thyroxine reduction and ≥20% and statistically significant offspring serum triiodothyronine reduction indicate an increased likelihood for statistically significant neurodevelopmental effects; accuracies: 83% and 67% when excluding electrophysiology (and gene expression). Measurements of brain thyroid hormone levels are likely relevant, too. The extent of substance-mediated thyroid hormone imbalance appears more important than substance mode-of-action to predict neurodevelopmental impairment in rats. Pertinent research needs were identified, e.g. to determine whether the phenomenological offspring thyroid hormone thresholds are relevant for regulatory toxicity testing. The insight from this review shall be used to suggest a tiered testing strategy to determine whether gestational/lactational substance exposure may elicit thyroid hormone imbalance and potentially also neurodevelopmental effects.

Mapping DEHP to the adverse outcome pathway network for human female reproductive toxicity

Adverse outcome pathways (AOPs) and AOP networks are tools for mechanistic presentation of toxicological effects across different levels of biological organization. These tools are used to better understand how chemicals impact human health. In this study, a four-step workflow was used to derive the AOP network of human female reproductive toxicity (HFRT-AOP) from five AOPs available in the AOP-Wiki and ten AOPs obtained from the literature. Standard network analysis identified key events (KEs) that are point of convergence and divergence, upstream and downstream KEs, and bottlenecks across the network. To map di-(2-ethylhexyl) phthalate (DEHP) to the HFRT-AOP network, we extracted DEHP target genes and proteins from the Comparative Toxicogenomic and the CompTox Chemicals Dashboard databases. Enriched GO terms analysis was used to identify relevant biological processes in the ovary that are DEHP targets, whereas screening of scientific literature was performed manually and automatically using AOP-helpFinder. We combined this information to map DEHP to HFRT-AOP network to provide insight on the KEs and system-level perturbations caused by this endocrine disruptor and the emergent paths. This approach can enable better understanding of the toxic mechanism of DEHP-induced human female reproductive toxicity and reveal potential novel DEHP female reproductive targets for experimental studies.

First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability

Quantitative structure-activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of read-across structure-activity relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and finally develops a supervised learning model (like QSAR). The RASAR method has so far been used only in case of graded predictions or classification modeling. In this work, we attempt, for the first time, to apply RASAR for quantitative predictions (q-RASAR) using a case study of androgen receptor binding affinity data. We have computed a number of error-based and similarity-based measures such as weighted standard deviation of the predicted values, coefficient of variation of the computed predictions, average similarity level of close training compounds for each query molecule, standard deviation and coefficient of variation of similarity levels, maximum similarity levels to positive and negative close training compounds, a concordance measure indicating similarity to positive, negative or both classes of close training compounds, etc. We have clubbed these additional measures along with the selected chemical descriptors from the previously developed QSAR model and redeveloped new partial least squares models from the training set, and predicted the endpoint using the query data set. Interestingly, these new models outperform the internal and external validation quality of the original QSAR model. In this study, we have also introduced a new similarity-based concordance measure (Banerjee-Roy coefficient) that can significantly contribute to the model quality. A q-RASAR model also has the advantage over read-across predictions in providing easy interpretation and indicating quantitative contributions of important chemical features. The strategy described here should be applicable to other biological/toxicological/property data modeling for enhanced quality of predictions, easy interpretability, and efficient transferability.

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.

Building a Network of Adverse Outcome Pathways (AOPs) Incorporating the Tau-Driven AOP Toward Memory Loss (AOP429)

The adverse outcome pathway (AOP) concept was first proposed as a tool for chemical hazard assessment facilitating the regulatory decision-making in toxicology and was more recently recommended during the BioMed21 workshops as a tool for the characterization of crucial endpoints in the human disease development. This AOP framework represents mechanistically based approaches using existing data, more realistic and relevant to human biological systems. In principle, AOPs are described by molecular initiating events (MIEs) which induce key events (KEs) leading to adverse outcomes (AOs). In addition to the individual AOPs, the network of AOPs has been also suggested to beneficially support the understanding and prediction of adverse effects in risk assessment. The AOP-based networks can capture the complexity of biological systems described by different AOPs, in which multiple AOs diverge from a single MIE or multiple MIEs trigger a cascade of KEs that converge to a single AO. Here, an AOP network incorporating a recently proposed tau-driven AOP toward memory loss (AOP429) related to sporadic (late-onset) Alzheimer’s disease is constructed. This proposed AOP network is an attempt to extract useful information for better comprehending the interactions among existing mechanistic data linked to memory loss as an early phase of sporadic Alzheimer’s disease pathology.

Adverse outcome pathway of fine particulate matter leading to increased cardiovascular morbidity and mortality: An integrated perspective from toxicology and epidemiology

Fine particulate matter (PM_2.5) exposure is a major threat to public health, and is listed as one of the leading factors associated with global premature mortality. Among the adverse health effects on multiple organs or tissues, the influence of PM_2.5 exposure on cardiovascular system has drawn more and more attention. Although numerous studies have investigated the mechanisms responsible for the cardiovascular toxicity of PM_2.5, the various mechanisms have not been integrated due to the variety of the study models, different levels of toxicity assessment endpoints, etc. Adverse Outcome Pathway (AOP) framework is a useful tool to achieve this goal so as to facilitate comprehensive understanding of toxicity assessment of PM_2.5 on cardiovascular system. This review aims to illustrate the causal mechanistic relationships of PM_2.5-triggered cardiovascular toxicity from different levels (from molecular/cellular/organ to individual/population) by using AOP framework. Based on the AOP Wiki and published literature, we propose an AOP framework focusing on the cardiovascular toxicity induced by PM_2.5 exposure. The molecular initiating event (MIE) is identified as reactive oxygen species generation, followed by the key events (KEs) of oxidative damage and mitochondria dysfunction, which induces vascular endothelial dysfunction via vascular endothelial cell autophagy dysfunction, vascular fibrosis via vascular smooth muscle cell activation, cardiac dysregulation via myocardial apoptosis, and cardiac fibrosis via fibroblast proliferation and myofibroblast differentiation, respectively; all of the above cardiovascular injuries ultimately elevate cardiovascular morbidity and mortality in the general population. As far as we know, this is the first work on PM_2.5-related cardiovascular AOP construction. In the future, more work needs to be done to explore new markers in the safety assessment of cardiovascular toxicity induced by PM_2.5.

Identification of the hormetic dose-response and regulatory network of multiple metals co-exposure-related hypertension via integration of metallomics and adverse outcome pathways

Environmental stressors, including heavy metals, can be associated with hypertension development. However, little information regarding the dose-response relationship and toxicity mechanisms of metal mixtures with hypertension development is currently available. Therefore, we recruited 940 participants from six factories in northeastern China and measured the urinary concentrations of 19 metals. Then, we used Bayesian kernel machine regression (BKMR) to explore associations between metals co-exposure and hypertension. The BKMR model indicated a hermetic dose-response relationship between eight urinary metals (Co, Cr, Ni, Cd, As, Fe, Zn, and Pb) and hypertension risk. Moreover, heterogeneous and non-linear association patterns were detected across different metals/metalloids concentrations. Next, for the first time, we analyzed data of chemicals containing specific metal elements in the Comparative Toxicogenomics Database (CTD) from a disease perspective and provided insights from various biological levels to explain heavy metal co-exposure-related hypertension. On the molecular scale, 43 chemical components and 112 potential target genes were detected for metal exposure-related hypertension. Further, the network topology analysis indicated that target genes such as insulin (INS, degree = 78), albumin (ALB, degree = 74), renin (REN, degree = 71), interleukin-6 (IL6, degree = 70), endothelin 1 (EDN1, degree = 70), and endothelial nitric oxide synthase (NOS3, degree = 69) have a strong correlation with heavy metals co-exposure. Finally, we used integrative analyses in the adverse outcome pathway (AOP) wiki to analyze the co-exposure of heavy metals and hypertension and support an integrated metallomics approach. We selected the AOP 149 as the framework and found that the molecular initiating events (MIEs) of hypertension stems from the oxidation of AA residues on critical peptides of the NO pathway. The NOS3 was particularly promising since its subunit has three metal ion cross-linking domains with Zn²⁺, Fe²⁺, and Ga³⁺, which might serve as a binding site for heavy metal ions.

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

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

Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network

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A developmental neurotoxicity Adverse Outcome Pathway network was simplified.

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Common key events were chosen based on topology analysis and expert judgement.

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Quantification of causal relationships was informed by key event relationships.

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Various types of information were integrated for probability prediction.

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Bayesian hierarchical modelling was applied for hazard identification.

In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as in silico and in vitro information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.

The Eco-Exposome Concept: Supporting an Integrated Assessment of Mixtures of Environmental Chemicals

Organisms are exposed to ever-changing complex mixtures of chemicals over the course of their lifetime. The need to more comprehensively describe this exposure and relate it to adverse health effects has led to formulation of the exposome concept in human toxicology. Whether this concept has utility in the context of environmental hazard and risk assessment has not been discussed in detail. In this Critical Perspective, we propose-by analogy to the human exposome-to define the eco-exposome as the totality of the internal exposure (anthropogenic and natural chemicals, their biotransformation products or adducts, and endogenous signaling molecules that may be sensitive to an anthropogenic chemical exposure) over the lifetime of an ecologically relevant organism. We describe how targeted and nontargeted chemical analyses and bioassays can be employed to characterize this exposure and discuss how the adverse outcome pathway concept could be used to link this exposure to adverse effects. Available methods, their limitations, and/or requirement for improvements for practical application of the eco-exposome concept are discussed. Even though analysis of the eco-exposome can be resource-intensive and challenging, new approaches and technologies make this assessment increasingly feasible. Furthermore, an improved understanding of mechanistic relationships between external chemical exposure(s), internal chemical exposure(s), and biological effects could result in the development of proxies, that is, relatively simple chemical and biological measurements that could be used to complement internal exposure assessment or infer the internal exposure when it is difficult to measure. Environ Toxicol Chem 2022;41:30-45. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Immunotoxicity of nanomaterials in health and disease: Current challenges and emerging approaches for identifying immune modifiers in susceptible populations

Nanosafety assessment has experienced an intense era of research during the past decades driven by a vivid interest of regulators, industry, and society. Toxicological assays based on in vitro cellular models have undergone an evolution from experimentation using nanoparticulate systems on singular epithelial cell models to employing advanced complex models more realistically mimicking the respective body barriers for analyzing their capacity to alter the immune state of exposed individuals. During this phase, a number of lessons were learned. We have thus arrived at a state where the next chapters have to be opened, pursuing the following objectives: (1) to elucidate underlying mechanisms, (2) to address effects on vulnerable groups, (3) to test material mixtures, and (4) to use realistic doses on (5) sophisticated models. Moreover, data reproducibility has become a significant demand. In this context, we studied the emerging concept of adverse outcome pathways (AOPs) from the perspective of immune activation and modulation resulting in pro-inflammatory versus tolerogenic responses. When considering the interaction of nanomaterials with biological systems, protein corona formation represents the relevant molecular initiating event (e.g., by potential alterations of nanomaterial-adsorbed proteins). Using this as an example, we illustrate how integrated experimental-computational workflows combining in vitro assays with in silico models aid in data enrichment and upon comprehensive ontology-annotated (meta)data upload to online repositories assure FAIRness (Findability, Accessibility, Interoperability, Reusability). Such digital twinning may, in future, assist in early-stage decision-making during therapeutic development, and hence, promote safe-by-design innovation in nanomedicine. Moreover, it may, in combination with in silico-based exposure-relevant dose-finding, serve for risk monitoring in particularly loaded areas, for example, workplaces, taking into account pre-existing health conditions. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials.

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.

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

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

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

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

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

Opinion on the impact of non-monotonic dose responses on EFSA's human health risk assessments

This Opinion assesses the biological relevance of the non-monotonic dose responses (NMDR) identified in a previous EFSA External Report (Beausoleil et al., 2016) produced under GP/EFSA/SCER/2014/01 and the follow-up probabilistic assessment (Chevillotte et al., 2017a,b), focusing on the in vivo data sets fulfilling most of the checkpoints of the visual/statistical-based analysis identified in Beausoleil et al. (2016). The evaluation was completed with cases discussed in EFSA assessments and the update of the scientific literature. Observations of NMDR were confirmed in certain studies and are particularly relevant for receptor-mediated effects. Based on the results of the evaluation, the Opinion proposes an approach to be applied during the risk assessment process when apparent non-monotonicity is observed, also providing advice on specific elements to be considered to facilitate the assessment of NMDR in EFSA risk assessments. The proposed approach was applied to two case studies, Bisphenol A and bis(2-ethylhexyl phthalate (DEHP) and these evaluations are reported in dedicated annexes. Considering the potential impact of NMDRs in regulatory risk assessment, the Scientific Committee recommends a concerted international effort on developing internationally agreed guidance and harmonised frameworks for identifying and addressing NMDRs in the risk assessment process.

Applicability of organ-on-chip systems in toxicology and pharmacology

Organ-on-chip (OoC) systems are microfabricated cell culture devices designed to model functional units of human organs by harboring an in vitro generated organ surrogate. In the present study, we reviewed issues and opportunities related to the application of OoC in the safety and efficacy assessment of chemicals and pharmaceuticals, as well as the steps needed to achieve this goal. The relative complexity of OoC over simple in vitro assays provides advantages and disadvantages in the context of compound testing. The broader biological domain of OoC potentially enhances their predictive value, whereas their complexity present issues with throughput, standardization and transferability. Using OoCs for regulatory purposes requires detailed and standardized protocols, providing reproducible results in an interlaboratory setting. The extent to which interlaboratory standardization of OoC is feasible and necessary for regulatory application is a matter of debate. The focus of applying OoCs in safety assessment is currently directed to characterization (the biology represented in the test) and qualification (the performance of the test). To this aim, OoCs are evaluated on a limited scale, especially in the pharmaceutical industry, with restricted sets of reference substances. Given the low throughput of OoC, it is questionable whether formal validation, in which many reference substances are extensively tested in different laboratories, is feasible for OoCs. Rather, initiatives such as open technology platforms, and collaboration between OoC developers and risk assessors may prove an expedient strategy to build confidence in OoCs for application in safety and efficacy assessment.

Development of a putative adverse outcome pathway network for male rat reproductive tract abnormalities with specific considerations for the androgen sensitive window of development

Structured approaches like the adverse outcome pathway (AOP) framework offer great potential for depicting complex toxicological processes in a manner that can facilitate informed integration of mechanistic information in regulatory decisions. While this concept provides a structure for organizing evidence and facilitates consistency in evidence integration; the process, inputs, and manner in which AOPs and AOP networks are developed is still evolving. Following the OECD guiding principles of AOP development, we propose three AOPs for male reproductive tract abnormalities and derive a putative AOP network. The AOPs were developed using a fundamental understanding of the developmental biology of the organs of interest, paying close attention to the gestational timing of key events (KEs) to very specifically inform the domain of life stage applicability for the key event relationships (KERs). Chemical stressor data primarily from studies on low molecular weight phthalates (LMWPs) served to 'bound' the pathways of focus in this dynamic period of development and were integrated with the developmental biology data through an iterative process to define KEs and conclude on the extent of evidence in support of the KERs. The AOPs developed describe the linkage between 1) a decrease in Insl3 gene expression and cryptorchidism, 2) the sustained expression of Coup-tfII and hypospadias and 3) the sustained expression of Coup-tfII and altered Wolffian duct development/ epididymal agenesis. A putative AOP network linking AOP2 and AOP3 through decreased steroidogenic biosynthetic protein expression and converging of all AOPS at the population level impaired fertility adverse outcome is proposed. The network depiction specifies and displays the KEs aligned with their occurrence in gestational time. The pathways and network described herein are intended to catalyze collaborative initiatives for expansion into a larger network to enable effective data collection and inform alternative approaches for identifying stressors impacting this sensitive period of male reproductive tract development.

Derivation, characterisation and analysis of an adverse outcome pathway network for human hepatotoxicity

Adverse outcome pathways (AOPs) and their networks are important tools for the development of mechanistically based non-animal testing approaches, such as in vitro and/or in silico assays, to assess toxicity induced by chemicals. In the present study, an AOP network connecting 14 linear AOPs related to human hepatotoxicity, currently available in the AOP-Wiki, was derived according to established criteria. The derived AOP network was characterised and analysed with regard to its structure and topological features. In-depth analysis of the AOP network showed that cell injury/death, oxidative stress, mitochondrial dysfunction and accumulation of fatty acids are the most highly connected and central key events. Consequently, these key events may be considered as the rational and mechanistically anchored basis for selecting, developing and/optimising in vitro and/or in silico assays to predict hepatotoxicity induced by chemicals in view of animal-free hazard identification.

Signposts for Aquatic Toxicity Evaluation in China: Text Mining using Event-Driven Taxonomy within and among Regions

Selection of toxicity endpoints affects outcomes of risk assessment. Scientific decisions based on more holistic evidence is preferable for designing bioassay batteries rather than subjective selections, particularly when systems are poorly understood. Here, we propose a novel event-driven taxonomy (EDT)-based text mining tool to prioritize stressors likely to elicit water quality deterioration. The tool integrated automated literature collection, natural language processing using adverse outcome pathway-based toxicological terminologies and machine learning to classify event drivers (EDs). From aquatic toxicity assessments within China over the past decade, we gathered over 14 000 sources of information. With a dictionary that included 1039 toxicological terms, 15 bioassay-related modes of actions were mapped, yet less than half of the bioassays could be elucidated by available adverse outcome pathways. To fill these mechanistic knowledge gaps, we developed a Naïve Bayesian ED-classifier to annotate apical responses. The classifier's 4-fold cross-validation reached 74% accuracy and labeled 85% bioassays as 26 EDs. Narcosis, estrogen receptor-, and aryl hydrogen receptor-mediators were the major EDs in aquatic systems across China, whereas individual regions had distinct ED fingerprints. The EDT-based tool provides a promising diagnostic strategy to inform region-specific bioassay design and selection for water quality assessments in a big data era.

Cross-Model Comparison of Transcriptomic Dose-Response of Short-Chain Chlorinated Paraffins

Short-chain chlorinated paraffins (SCCPs) have attracted attention because of their toxicological potential in humans and wildlife at environmentally relevant doses. However, limited information is available regarding mechanistic differences across species in terms of the biological pathways that are impacted by SCCP exposure. Here, a concentration-dependent reduced human transcriptome (RHT) approach was conducted to evaluate 15 SCCPs in HepG2 cells and compared with our previous results using a reduced zebrafish transcriptome (RZT) approach in zebrafish embryos (ZFEs). Generally, SCCPs induced a broader suite of biological pathways in ZFEs than HepG2 cells, and all of the 15 SCCPs were more potent in HepG2 cells compared to ZFEs. Despite these general differences, the transcriptional potency of SCCPs in both model systems showed a significant linear relationship (p = 0.0017, r² = 0.57), and the average ratios of transcriptional potency for each SCCP in RZT to that in RHT were ∼100,000. C₁₀H₁₄Cl₈ was the most potent SCCP, while C₁₀H₁₇Cl₅ was the least potent in both ZFEs and HepG2 cells. An adverse outcome pathway network-based analysis demonstrated model-specific responses, such as xenobiotic metabolism that may be mediated by different nuclear receptor-mediated pathways between HepG2 cells (e.g., CAR and AhR activation) and ZFEs (e.g., PXR activation). Moreover, induced transcriptional changes in ZFEs associated with pathways and molecular initiating events (e.g., activation of nicotinic acetylcholine receptor) suggest that SCCPs may disrupt neural development processes. The cross-model comparison of concentration-dependent transcriptomics represents a promising approach to assess and prioritize SCCPs.