How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

Beyond Encapsulation: Exploring Macrophage-Fibroblast Cross Talk in Implant-Induced Fibrosis

The foreign body response (FBR) and organ fibrosis are complex biological processes involving the interaction between macrophages and fibroblasts. Understanding the molecular mechanisms underlying macrophage-fibroblast cross talk is crucial for developing strategies to mitigate implant encapsulation, a major cause of implant failure. This article reviews the current knowledge on the role of macrophages and fibroblasts in the FBR and organ fibrosis, highlighting the similarities between these processes. The FBR is characterized by the formation of a fibrotic tissue capsule around the implant, leading to functional impairment. Various factors, including material properties such as surface chemistry, stiffness, and topography, influence the degree of encapsulation. Cross talk between macrophages and fibroblasts plays a critical role in both the FBR and organ fibrosis. However, the precise molecular mechanisms remain poorly understood. Macrophages secrete a wide range of cytokines that modulate fibroblast behavior such as abundant collagen deposition and myofibroblast differentiation. However, the heterogeneity of macrophages and fibroblasts and their dynamic behavior in different tissue environments add complexity to this cross talk. Experimental evidence from in vitro studies demonstrates the impact of material properties on macrophage cytokine secretion and fibroblast physiology. However, the correlation between in vitro response and in vivo encapsulation outcomes is not robust. Adverse outcome pathways (AOPs) offer a potential framework to understand and predict process complexity. AOPs describe causal relationships between measurable events leading to adverse outcomes, providing mechanistic insights for in vitro testing and predictive modeling. However, the development of an AOP for the FBR does require a comprehensive understanding of the molecular initiating events and key event relationships to identify which events are essential. In this article, we describe the current knowledge on macrophage-fibroblast cross talk in the FBR and discuss how targeted research can help build an AOP for implant-related fibrosis.

Combination of computational new approach methodologies for enhancing evidence of biological pathway conservation across species

The ability to predict which chemicals are of concern for environmental safety is dependent, in part, on the ability to extrapolate chemical effects across many species. This work investigated the complementary use of two computational new approach methodologies to support cross-species predictions of chemical susceptibility: the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and Unilever's recently developed Genes to Pathways - Species Conservation Analysis (G2P-SCAN) tool. These stand-alone tools rely on existing biological knowledge to help understand chemical susceptibility and biological pathway conservation across species. The utility and challenges of these combined computational approaches were demonstrated using case examples focused on chemical interactions with peroxisome proliferator activated receptor alpha (PPARα), estrogen receptor 1 (ESR1), and gamma-aminobutyric acid type A receptor subunit alpha (GABRA1). Overall, the biological pathway information enhanced the weight of evidence to support cross-species susceptibility predictions. Through comparisons of relevant molecular and functional data gleaned from adverse outcome pathways (AOPs) to mapped biological pathways, it was possible to gain a toxicological context for various chemical-protein interactions. The information gained through this computational approach could ultimately inform chemical safety assessments by enhancing cross-species predictions of chemical susceptibility. It could also help fulfill a core objective of the AOP framework by potentially expanding the biologically plausible taxonomic domain of applicability of relevant AOPs.

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.

Advances and applications of machine learning and deep learning in environmental ecology and health

Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.

Putative adverse outcome pathways of the male reproductive toxicity derived from toxicological studies of perfluoroalkyl acids

Adverse outcome pathway (AOP) as a conceptual framework is a powerful tool in the field of toxicology to connect seemingly discrete events at different levels of biological organizations into an organized pathway from molecular interactions to whole organism toxicity. Based on numerous toxicological studies, eight AOPs for reproductive toxicity have been endorsed by the Organization for Economic Co-operation and Development (OECD) Task Force on Hazard Assessment. We have conducted a literature survey on the mechanistic studies on male reproductive toxicity of perfluoroalkyl acids (PFAAs), a class of global environmental contaminants with high persistence, bioaccumulation and toxicity. Using the AOP development strategy, five new AOPs for male reproductive toxicity were proposed here, namely (1) changes in membrane permeability leading to reduced sperm motility, (2) disruption of mitochondrial function leading to sperm apoptosis, (3) decreased gonadotropin-releasing hormone (GnRH) expression in hypothalamus leading to reduced testosterone production in male rats, (4) activation of the p38 signaling pathway leading to disruption of BTB in mice, (5) inhibition of p-FAK-Tyr407 activity leading to the destruction of BTB. The molecular initiating events in the proposed AOPs are different from those in the endorsed AOPs, which are either receptor activation or enzyme inhibition. Although some of the AOPs are still incomplete, they can serve as a building block upon which full AOPs can be developed and applied to not only PFAAs but also other chemical toxicants with male reproductive toxicity.

Using Pop-GUIDE to Assess the Applicability of MCnest for Relative Risk of Pesticides to Hummingbirds

Hummingbirds are charismatic fauna that provide important pollination services, including in the continental US, where 15 species regularly breed. Compared to other birds in North America, hummingbirds (family Trochilidae) have a unique exposure route to pesticides because they forage on nectar. Therefore, hummingbirds may be exposed to systemic pesticides borne in nectar. They also may be particularly vulnerable to pesticide exposure due to their small size and extreme metabolic demands. We review relevant factors including hummingbird life history, nectar residue uptake, and avian bioenergetic considerations with the goal of clearly identifying and articulating the specific modeling challenges that must be overcome to develop and/or adapt existing modeling approaches. To help evaluate these factors, we developed a dataset for ruby-throated hummingbirds (Archilochus colubris) and other avian species potentially exposed to pesticides. We used the systemic neonicotinoid pesticide imidacloprid as an illustration and compared results to five other common current use pesticides. We use the structure of Pop-GUIDE to provide a conceptual modeling framework for implementation of MCnest and to compile parameter values and relevant algorithms to predict the effects of pesticide exposure on avian pollinators. Conservative screening assessments suggest the potential for adverse effects from imidacloprid, as do more refined assessments, though many important limitations and uncertainties remain. Our review found many areas in which current USEPA avian models must be improved in order to conduct a full higher-tier risk assessment for avian pollinators exposed to neonicotinoid insecticides, including addition of models suitable for soil and seed treatments within the MCnest environment, ability to include empirical residue data in both nectar and invertebrates rather than relying on existing nomograms, expansion of MCnest to a full annual cycle, and increased representation of spatial heterogeneity. Although this work focuses on hummingbirds, the methods and recommendations may apply more widely to other vertebrate pollinators.

Application of AOPs to assist regulatory assessment of chemical risks - Case studies, needs and recommendations

While human regulatory risk assessment (RA) still largely relies on animal studies, new approach methodologies (NAMs) based on in vitro, in silico or non-mammalian alternative models are increasingly used to evaluate chemical hazards. Moreover, human epidemiological studies with biomarkers of effect (BoE) also play an invaluable role in identifying health effects associated with chemical exposures. To move towards the next generation risk assessment (NGRA), it is therefore crucial to establish bridges between NAMs and standard approaches, and to establish processes for increasing mechanistically-based biological plausibility in human studies. The Adverse Outcome Pathway (AOP) framework constitutes an important tool to address these needs but, despite a significant increase in knowledge and awareness, the use of AOPs in chemical RA remains limited. The objective of this paper is to address issues related to using AOPs in a regulatory context from various perspectives as it was discussed in a workshop organized within the European Union partnerships HBM4EU and PARC in spring 2022. The paper presents examples where the AOP framework has been proven useful for the human RA process, particularly in hazard prioritization and characterization, in integrated approaches to testing and assessment (IATA), and in the identification and validation of BoE in epidemiological studies. Nevertheless, several limitations were identified that hinder the optimal usability and acceptance of AOPs by the regulatory community including the lack of quantitative information on response-response relationships and of efficient ways to map chemical data (exposure and toxicity) onto AOPs. The paper summarizes suggestions, ongoing initiatives and third-party tools that may help to overcome these obstacles and thus assure better implementation of AOPs in the NGRA.

A comprehensive understanding of ambient particulate matter and its components on the adverse health effects based from epidemiological and laboratory evidence

The impacts of air pollution on public health have become a great concern worldwide. Ambient particulate matter (PM) is a major air pollution that comprises a heterogeneous mixture of different particle sizes and chemical components. The chemical composition and physicochemical properties of PM change with space and time, which may cause different impairments. However, the mechanisms of the adverse effects of PM on various systems have not been fully elucidated and systematically integrated. The Adverse Outcome Pathway (AOP) framework was used to comprehensively illustrate the molecular mechanism of adverse effects of PM and its components, so as to clarify the causal mechanistic relationships of PM-triggered toxicity on various systems. The main conclusions and new insights of the correlation between public health and PM were discussed, especially at low concentrations, which points out the direction for further research in the future. With the deepening of the study on its toxicity mechanism, it was found that PM can still induce adverse health effects with low-dose exposure. And the recommended Air Quality Guideline level of PM_2.5 was adjusted to 5 μg/m³ by World Health Organization, which meant that deeper and more complex mechanisms needed to be explored. Traditionally, oxidative stress, inflammation, autophagy and apoptosis were considered the main mechanisms of harmful effects of PM. However, recent studies have identified several emerging mechanisms involved in the toxicity of PM, including pyroptosis, ferroptosis and epigenetic modifications. This review summarized the comprehensive evidence on the health effects of PM and the chemical components of it, as well as the combined toxicity of PM with other air pollutants. Based on the AOP Wiki and the mechanisms of PM-induced toxicity at different levels, we first constructed the PM-related AOP frameworks on various systems.

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

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

A high-level overview of the Organisation for Economic Co-operation and Development Adverse Outcome Pathway Programme

Background The Organisation for Economic Co-operation and Development (OECD), through its Chemical Safety Programme, is delegated to ensure the safety of humans and wildlife from harmful toxicants. To support these needs, initiatives to increase the efficiency of hazard identification and risk management are under way. Amongst these, the adverse outcome pathway (AOP) approach integrates information on biological knowledge and test methodologies (both established and new) to support regulatory decision making. AOPs collate biological knowledge from different sources, assess lines of evidence through considerations of causality and undergo rigorous peer-review before being subsequently endorsed by the OECD. It is envisioned that the OECD AOP Development Programme will transform the toxicity testing paradigm by leveraging the strengths of mechanistic and modelling based approaches and enhance the utility of high throughput screening assays. Since its launch, in 2012, the AOP Development Programme has matured with a greater number of AOPs endorsed since inception, and the attraction of new scientific disciplines (e.g. the radiation field). Recently, a Radiation and Chemical (Rad/Chem) AOP Joint Topical Group has been formed by the OECD Nuclear Energy Agency High-Level Group on Low-Dose Research (HLG-LDR) under the auspices of the Committee on Radiological Protection and Public Health (CRPPH). The topical group will work to evolve the development and use of the AOP framework in radiation research and regulation. As part of these efforts, the group will bring awareness and understanding on the programme, as it has matured from the chemical perspective. In this context, this paper provides the radiation community with a high-level overview of the OECD AOP Development Programme, including examples of application using knowledge gleaned from the field of chemical toxicology, and their work towards regulatory implementation. Conclusion: Although the drivers for developing AOPs in chemical sector differ from that of the radiation field, the principles and transparency of the approach can benefit both scientific disciplines. By providing perspectives and an understanding of the evolution of the OECD AOP Development Programme including case examples and work towards quantitative AOP development, it may motivate the expansion and implementation of AOPs in the radiation field.

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.

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

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

Current Therapeutic Landscape and Safety Roadmap for Targeting the Aryl Hydrocarbon Receptor in Inflammatory Gastrointestinal Indications

Target modulation of the AhR for inflammatory gastrointestinal (GI) conditions holds great promise but also the potential for safety liabilities both within and beyond the GI tract. The ubiquitous expression of the AhR across mammalian tissues coupled with its role in diverse signaling pathways makes development of a “clean” AhR therapeutically challenging. Ligand promiscuity and diversity in context-specific AhR activation further complicates targeting the AhR for drug development due to limitations surrounding clinical translatability. Despite these concerns, several approaches to target the AhR have been explored such as small molecules, microbials, PROTACs, and oligonucleotide-based approaches. These various chemical modalities are not without safety liabilities and require unique de-risking strategies to parse out toxicities. Collectively, these programs can benefit from in silico and in vitro methodologies that investigate specific AhR pathway activation and have the potential to implement thresholding parameters to categorize AhR ligands as “high” or “low” risk for sustained AhR activation. Exploration into transcriptomic signatures for AhR safety assessment, incorporation of physiologically-relevant in vitro model systems, and investigation into chronic activation of the AhR by structurally diverse ligands will help address gaps in our understanding regarding AhR-dependent toxicities. Here, we review the role of the AhR within the GI tract, novel therapeutic modality approaches to target the AhR, key AhR-dependent safety liabilities, and relevant strategies that can be implemented to address drug safety concerns. Together, this review discusses the emerging therapeutic landscape of modalities targeting the AhR for inflammatory GI indications and offers a safety roadmap for AhR drug development.

A Case Study on Integrating a New Key Event Into an Existing Adverse Outcome Pathway on Oxidative DNA Damage: Challenges and Approaches in a Data-Rich Area

Adverse outcome pathways (AOPs) synthesize toxicological information to convey and weigh evidence in an accessible format. AOPs are constructed in modules that include key events (KEs) and key event relationships (KERs). This modular structure facilitates AOP expansion and network development. AOP development requires finding relevant information to evaluate the weight of evidence supporting each KER. To do this, the use of transparent/reproducible search methods, such as systematic review (SR), have been proposed. Applying SR to AOP development in a data-rich area is difficult as SR requires screening each article returned from a search. Here we describe a case study to integrate a single new KE into an existing AOP. We explored the use of SR concepts and software to conduct a transparent and documented literature search to identify empirical data supporting the incorporation of a new KE, increase in cellular reactive oxygen species (ROS), upstream of an existing AOP: “Oxidative DNA Damage Leading to Chromosomal Aberrations and Mutations”. Connecting this KE to the AOP is supported by the development of five new KERs, the most important being the first adjacent KER (increase in ROS leading to oxidative DNA damage). We initially searched for evidence of all five KERs and screened 100 papers to develop a preliminary evidence map. After removing papers not containing relevant data based on our Population, Exposure, Comparator and Outcome statement, 39 articles supported one or more KERs; these primarily addressed temporal or dose concordance of the non-adjacent KERs with limited evidence supporting the first adjacent KER. We thus conducted a second focused set of searches using search terms for specific methodologies to measure these first two KEs. After screening, 12 articles were identified that contained quantitative evidence supporting the first adjacent KER. Given that integrating a new KE into an existing AOP requires the development of multiple KERs, this approach of building a preliminary evidence map, focusing evidence gathering on the first adjacent KER, and applying reproducible search strategies using specific methodologies for the first adjacent KER, enabled us to prioritize studies to support expansion of this data-rich AOP.

Pluripotent Stem Cell Assays: Modalities and Applications For Predictive Developmental Toxicity

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A systematic scoping review of the literature evaluated the embryonic stem cell test (EST).

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1533 publications included 18 publications testing 10 or more compounds in human or mouse EST.

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Selected case examples included 5-fluorouracil, thalidomide, and caffeine.

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Applicability, limitations, and recommendations for further work are discussed.

This manuscript provides a review focused on embryonic stem cell-based models and their place within the landscape of alternative developmental toxicity assays. Against the background of the principles of developmental toxicology, the wide diversity of alternative methods using pluripotent stem cells developed in this area over the past half century is reviewed. In order to provide an overview of available models, a systematic scoping review was conducted following a published protocol with inclusion criteria, which were applied to select the assays. Critical aspects including biological domain, readout endpoint, availability of standardized protocols, chemical domain, reproducibility and predictive power of each assay are described in detail, in order to review the applicability and limitations of the platform in general and progress moving forward to implementation. The horizon of innovative routes of promoting regulatory implementation of alternative methods is scanned, and recommendations for further work are given.

Argumentation Analysis of Risk Assessments: The Case of Perfluorooctanoic Acid

Risk assessment of chemicals can be based on toxicology and/or epidemiology. The choice of toxicological or epidemiological data can result in different health-based guidance values (HBGVs). Communicating the underlying argumentation is important to explain these differences to the public and policymakers. In this article, we explore the argumentation used to justify the use of toxicological or epidemiological data in the derivation of HBGVs in four different risk assessments for the chemical Perfluorooctanoic acid (PFOA). The pragma-dialectical argumentation theory (PDAT) is hereby applied. The argumentations to select relevant health endpoints or certain studies to infer causality appeared mainly based on "symptomatic relations," that is, study results are used as characteristic of what was claimed to be a causal relation without delving into the actual causal argumentation that preceded it. Starting points that are at the basis of the chain of arguments remained implicit. Argumentation to use epidemiological and/or toxicological data was only briefly mentioned and the underlying argumentative foundation that led to the conclusion was seldom found or not addressed at all. The decision to include/exclude information was made based on the availability of data, or the motives for the choice remained largely unclear. We conclude that more depth in argumentation and a subordinative chain of arguments is needed to better disclose the underlying reasoning leading to a certain health-based guidance value (HBGV). More explicit identification and discussion of starting points could be a valuable addition to general risk assessment frameworks for maximum use of toxicological and epidemiological data and shared conclusions of the assessment.

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

INTRODUCTION

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

MATERIALS AND METHODS

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

RESULTS

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

DISCUSSION

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

CONCLUSION

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

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

Introduction:

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

Materials and Methods:

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

Results:

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

Discussion:

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

Conclusion:

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

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.

Toxicity to bronchial cells and endocrine disruptive potentials of indoor air and dust extracts and their association with multiple chemical classes

Pollution of indoor environment, where people spend much of their time, comprises complex mixtures of compounds with vastly understudied hazard potential. This study examined several important specific toxic effects and pollutant levels (177 compounds) of indoor samples (air gas phase, PM₁₀ and dust) from different microenvironments after two extractions with focus on their gas/particle/dust distribution and polarity. The endocrine disruptive (ED) potential was assessed by human cell-based in vitro bioassays addressing anti-/estrogenicity, anti-/androgenicity, aryl hydrocarbon, thyroid and peroxisome proliferator-activated receptor-mediated activities. Potential toxicity to respiratory tract tissue was assessed using human bronchial cell line. The toxicological analyses pointed out the relevance of both inhalation and ingestion exposure, with significant effects detected after exposure to extracts from all three studied matrices with distinct gas/particle distribution patterns. Chemical analyses document the high complexity of indoor pollutant mixtures with greatest levels of phthalates, their emerging alternatives, and PAHs in dust. Despite the detection of up to 108 chemicals, effects were explained only to low extent. This emphasizes data gaps regarding ED potencies of many detected abundant indoor contaminants, but also potential presence of other unidentified ED compounds. The omnipresent ED potentials in indoor environment rise concern regarding associated human health risk.

Adverse Outcome Pathway: A Path towards better Data Consolidation and Global Co-ordination of Radiation Research

Background The purpose of toxicology is to protect human health and the environment. To support this, the Organisation for Economic Co-operation and Development (OECD), operating via its Extended Advisory Group for Molecular Screening and Toxicogenomics (EAGMST), has been developing the Adverse Outcome Pathway (AOP) approach to consolidate evidence for chemical toxicity spanning multiple levels of biological organization. The knowledge transcribed into AOPs provides a structured framework to transparently organize data, examine the weight of evidence of the AOP, and identify causal relationships between exposure to stressors and adverse effects of regulatory perspective. The AOP framework has undergone substantial maturation in the field of hazard characterization of chemicals over the last decade, and has also recently gained attention from the radiation community as a means to advance the mechanistic understanding of human and ecological health effects from exposure to ionizing radiation at low dose and low dose-rates. To fully exploit the value of such approaches for facilitating risk assessment and management in the field of radiation protection, solicitation of experiences and active cooperation between chemical and radiation communities are needed. As a result, the Radiation and Chemical (Rad/Chem) AOP joint topical group was formed on June 1, 2021 as part of the initiative from the High Level Group on Low Dose Research (HLG-LDR). HLG-LDR is overseen by the OECD Nuclear Energy Agency (NEA) Committee on Radiation Protection and Public Health (CRPPH). The main aims of the joint AOP topical group are to advance the use of AOPs in radiation research and foster broader implementation of AOPs into hazard and risk assessment. With global representation, it serves as a forum to discuss, identify and develop joint initiatives that support research and take on regulatory challenges. Conclusion: The Rad/Chem AOP joint topical group will specifically engage, promote, and implement the use of the AOP framework to: a) organize and evaluate mechanistic knowledge relevant to the protection of human and ecosystem health from radiation; b) identify data gaps and research needs pertinent to expanding knowledge of low dose and low dose-rate radiation effects; and c) demonstrate utility to support risk assessment by developing radiation-relevant case studies. It is envisioned that the Rad/Chem AOP joint topical group will actively liaise with the OECD EAGMST AOP developmental program to collectively advance areas of common interest and, specifically, provide recommendations for harmonization of the AOP framework to accommodate non-chemical stressors, such as radiation.

Endogenous Lifecycle Models for Chemical Risk Assessment

Despite over 50 years of research on the use of population models in chemical risk assessment, their practical utility has remained elusive. A novel application and interpretation of ecotoxicological models, Endogenous Lifecycle Models (ELM), is proposed that offers some of the benefits sought from population models, at much lower cost of design, parametrization, and verification. ELMs capture the endogenous lifecycle processes of growth, development, survival, and reproduction and integrate these to estimate and predict expected fitness. Two measures of fitness are proposed as natural model predictions in the context of chemical risk assessment, lifetime reproductive success, and the expected annual propagation of genetic descendants, including self (intrinsic fitness). Six characteristics of the ELM approach are reviewed and illustrated with two ELM examples, the first for a general passerine lifecycle and the second for bald eagle (Haliaeetus leucocephalus). Throughout, the focus is on development of robust qualitative model predictions that depend as little as possible on specific parameter values. Thus, ELMs sacrifice precision to optimize generality in understanding the effects of chemicals across the diversity of avian lifecycles. Notably, the ELM approach integrates naturally with the adverse outcome pathway framework; this integration can be employed as a midtier risk assessment tool when lower tier analyses suggest potential risk.

Extrapolating from acute to chronic toxicity in vitro

Chemical safety assessment requires information on both chronic and acute effects of toxicants. Traditionally, such information has been provided by a set of animal studies conducted over different durations, ranging from a single dose with observation of effects over a few days, to repeat daily dosing and observations made over many months. With the advent of modern mechanistic approaches to toxicology, the role of in vitro studies within alternative approaches has never been more prominent. Typical in vitro experiments are conducted over short durations with measurements of response at a single time point, with a focus on providing effect and concentration-response information as input to hazard and risk assessment. This limits the usefulness of such data since potential chronic effects that cumulate over time are not usually considered. To address this, an experimental design is presented to characterise the toxicodynamics of a response not only in terms of concentration, but also as a function of time. Generation of concentration-time-effect responses allows both the extrapolation of points of departure from an acute to chronic exposure, and the determination of a chronicity index that provides a quantitative measure of a chemical's potential to cause cumulative effects over time. In addition, the approach provides a means to characterise the dynamics of key event relationships for the development of quantitative adverse outcome pathways.

A pragmatic approach to Adverse Outcome Pathway (AOP) development and evaluation

The adverse outcome pathway (AOP) framework provides a practical means for organizing scientific knowledge that can be used to infer cause-effect relationships between stressor events and toxicity outcomes in intact organisms. It has reached wide acceptance as a tool to aid chemical safety assessment and regulatory toxicology by supporting a systematic way of predicting adverse health outcomes based on accumulated mechanistic knowledge. A major challenge for broader application of the AOP concept in regulatory toxicology, however, has been developing robust AOPs to a level where they are peer reviewed and accepted. This is because the amount of work required to substantiate the modular units of a complete AOP is considerable, to the point where it can take years from start to finish. To help alleviate this bottleneck, we propose a more pragmatic approach to AOP development whereby the focus becomes on smaller blocks. First, we argue that the key event relationship (KER) should be formally recognized as the core building block of knowledge assembly within the AOP knowledge base (AOP-KB), albeit framing them within full AOPs to ensure regulatory utility. Second, we argue that KERs should be developed using systematic review approaches, but only in cases where the underlying concept does not build on what is considered canonical knowledge. In cases where knowledge is considered canonical, rigorous systematic review approaches should not be required. It is our hope that these approaches will contribute to increasing the pace at which the AOP-KB is populated with AOPs with utility for chemical safety assessors and regulators.

Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language

Harmonized language is critical for helping researchers to find data, collecting scientific data to facilitate comparison, and performing pooled and meta-analyses. Using standard terms to link data to knowledge systems facilitates knowledge-driven analysis, allows for the use of biomedical knowledge bases for scientific interpretation and hypothesis generation, and increasingly supports artificial intelligence (AI) and machine learning. Due to the breadth of environmental health sciences (EHS) research and the continuous evolution in scientific methods, the gaps in standard terminologies, vocabularies, ontologies, and related tools hamper the capabilities to address large-scale, complex EHS research questions that require the integration of disparate data and knowledge sources. The results of prior workshops to advance a harmonized environmental health language demonstrate that future efforts should be sustained and grounded in scientific need. We describe a community initiative whose mission was to advance integrative environmental health sciences research via the development and adoption of a harmonized language. The products, outcomes, and recommendations developed and endorsed by this community are expected to enhance data collection and management efforts for NIEHS and the EHS community, making data more findable and interoperable. This initiative will provide a community of practice space to exchange information and expertise, be a coordination hub for identifying and prioritizing activities, and a collaboration platform for the development and adoption of semantic solutions. We encourage anyone interested in advancing this mission to engage in this community.

Wild Zebrafish Sentinels: Biological Monitoring of Site Differences Using Behavior and Morphology

Environmental change poses a devastating risk to human and environmental health. Rapid assessment of water conditions is necessary for monitoring, evaluating, and addressing this global health danger. Sentinels or biological monitors can be deployed in the field using minimal resources to detect water quality changes in real time, quickly and cheaply. Zebrafish (Danio rerio) are ideal sentinels for detecting environmental changes due to their biomedical tool kit, widespread geographic distribution, and well-characterized phenotypic responses to environmental disturbances. Here, we demonstrate the utility of zebrafish sentinels by characterizing phenotypic differences in wild zebrafish between two field sites in India. Site 1 was a rural environment with flowing water, low-hypoxic conditions, minimal human-made debris, and high iron and lead concentrations. Site 2 was an urban environment with still water, hypoxic conditions, plastic pollution, and high arsenic, iron, and chromium concentrations. We found that zebrafish from Site 2 were smaller, more cohesive, and less active than Site 1 fish. We also found sexually dimorphic body shapes within the Site 2, but not the Site 1, population. Advancing zebrafish sentinel research and development will enable rapid detection, evaluation, and response to emerging global health threats.

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.

Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity

Introduction: Genotoxicity is an imperative component of the human health safety assessment of chemicals. Its secure forecast is of the utmost importance for all health prevention strategies and regulations.Areas covered: We surveyed several types of alternative, animal-free approaches ((quantitative) structure-activity relationship (Q)SAR, read-across, Adverse Outcome Pathway, Integrated Approaches to Testing and Assessment) for genotoxicity prediction within the needs of regulatory frameworks, putting special emphasis on data quality and uncertainties issues.Expert opinion: (Q)SAR models and read-across approaches for in vitro bacterial mutagenicity have sufficient reliability for use in prioritization processes, and as support in regulatory decisions in combination with other types of evidence. (Q)SARs and read-across methodologies for other genotoxicity endpoints need further improvements and should be applied with caution. It appears that there is still large room for improvement of genotoxicity prediction methods. Availability of well-curated high-quality databases, covering a broader chemical space, is one of the most important needs. Integration of in silico predictions with expert knowledge, weight-of-evidence-based assessment, and mechanistic understanding of genotoxicity pathways are other key points to be addressed for the generation of more accurate and trustable results.

Deep Learning-Based Conformal Prediction of Toxicity

Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models.

The Promise of AI for DILI Prediction

Drug-induced liver injury (DILI) is a common reason for the withdrawal of a drug from the market. Early assessment of DILI risk is an essential part of drug development, but it is rendered challenging prior to clinical trials by the complex factors that give rise to liver damage. Artificial intelligence (AI) approaches, particularly those building on machine learning, range from random forests to more recent techniques such as deep learning, and provide tools that can analyze chemical compounds and accurately predict some of their properties based purely on their structure. This article reviews existing AI approaches to predicting DILI and elaborates on the challenges that arise from the as yet limited availability of data. Future directions are discussed focusing on rich data modalities, such as 3D spheroids, and the slow but steady increase in drugs annotated with DILI risk labels.

Evolving Science and Practice of Risk Assessment

Managing public health risks from environmental contaminants has historically relied on a risk assessment process defined by the regulatory context in which these risks are assessed. Risk assessment guidance follows a straightforward, chemical-by-chemical approach to inform regulatory decisions around the question "what is the risk-based concentration protective of human and ecological health outcomes?" Here we briefly summarize regulatory risk assessment in the context of innovative risk assessment approaches based on an evolving understanding of the underlying scientific disciplines that support risk analysis more broadly. We discuss scientific versus regulatory tensions in the application of these approaches for future risk assessments, and challenges in translating our improved understanding of the underlying scientific complexity to the regulatory landscape to better inform decision making that extends beyond conventional regulatory mandates.

Case Study in 21st Century Ecotoxicology: Using In Vitro Aromatase Inhibition Data to Predict Short-Term In Vivo Responses in Adult Female Fish

The present study evaluated whether in vitro measures of aromatase inhibition as inputs into a quantitative adverse outcome pathway (qAOP) construct could effectively predict in vivo effects on 17β-estradiol (E2) and vitellogenin (VTG) concentrations in female fathead minnows. Five chemicals identified as aromatase inhibitors in mammalian-based ToxCast assays were screened for their ability to inhibit fathead minnow aromatase in vitro. Female fathead minnows were then exposed to 3 of those chemicals: letrozole, epoxiconazole, and imazalil in concentration-response (5 concentrations plus control) for 24 h. Consistent with AOP-based expectations, all 3 chemicals caused significant reductions in plasma E2 and hepatic VTG transcription. Characteristic compensatory upregulation of aromatase and follicle-stimulating hormone receptor (fshr) transcripts in the ovary were observed for letrozole but not for the other 2 compounds. Considering the overall patterns of concentration-response and temporal concordance among endpoints, data from the in vivo experiments strengthen confidence in the qualitative relationships outlined by the AOP. Quantitatively, the qAOP model provided predictions that fell within the standard error of measured data for letrozole but not for imazalil and epoxiconazole. However, the inclusion of measured plasma concentrations of the test chemicals as inputs improved model predictions, with all predictions falling within the range of measured values. Results highlight both the utility and limitations of the qAOP and its potential use in 21st century ecotoxicology. Environ Toxicol Chem 2021;40:1155-1170. © 2020 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

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.

Identification of toxicity pathway of diesel particulate matter using AOP of PPARγ inactivation leading to pulmonary fibrosis

Diesel particulate matter (DPM), a major subset of urban fine particulate matter (PM2.5), raises huge concerns for human health and has therefore been classified as a group 1 carcinogen by the International Agency for Research on Cancer (IARC). However, as DPM is a complex mixture of various chemicals, understanding of DPM's toxicity mechanism remains limited. As the major exposure route of DPM is through inhalation, we herein investigated its toxicity mechanism based on the Adverse Outcome Pathway (AOP) of pulmonary fibrosis, which we previously submitted to AOPWiki as AOP ID 206 (AOP206). We first screened whether individual chemicals in DPM have the potential to exert their toxicity through AOP206 by using the ToxCast database and deep learning models approach, then confirmed this by examining whether DPM as a mixture alters the expression of the molecular initiating event (MIE) and key events (KEs) of AOP206. For identifying the activeness of the component chemicals of DPM, we used 24 ToxCast assays potentially related to AOP206 and deep learning models based on these assays, which were identified and developed in our previous study. Of the 100 individual chemicals in DPM, 34 were active in PPARγ (MIE)-related assay, of which 17 were active in one or more KEs. To further identify whether individual chemicals in DPM are related to the MIE of AOP206, we performed molecular docking simulation on PPARγ for the chemicals showing activeness. Benzo[e]pyrene, benzo[a]pyrene and other related chemicals were the most likely to bind to PPARγ. In in vitro experiments, PPARγ activity increased with exposure of the DPM mixture, and the protein expression of PPARγ (MIE), and fibronectin (AO) also tended to be increased. Overall, we have demonstrated that AOP206 can be applied to identify the toxicity pathway of DPM. Further, we suggest that applying the AOP approach using ToxCast and deep learning models is useful for identifying potential toxicity pathways of chemical mixtures, such as DPM, by determining the activity of individual chemicals.

Collaborative efforts are needed among the scientific community to advance the adverse outcome pathway concept in areas of radiation risk assessment

Disease prevention and prediction have led to the generation of phenotypically based methods for deriving the limits of safety across toxicological disciplines. In the ionizing radiation field, human data has formed the basis of the linear-no-threshold (LNT) model for risk estimates. However, uncertainties around its accuracy at low doses and low dose-rates have led to passionate debates on its effectiveness to derive radiation risk estimates under these conditions. Concerns arise from the linear extrapolation of data from high doses to low doses, below 0.1 Gy where there is considerable variability in the scientific literature. Efforts to address these controversies have led to a mountain of mechanistic data to improve the understanding of molecular and cellular effects related to phenotypic changes. These data provide fragments of information that have yet to be combined and used effectively to improve modeling, reduce uncertainties, and update radiation protection approaches. This paper suggests a better consolidation of mechanistic research may serve to guide priority research and facilitate translation to risk assessment. An effective approach that may be implemented is the organization of data using the adverse outcome pathway (AOP) framework, a programme that has been launched by the Organization for Economic Cooperation and Development in the chemical toxicology field. The AOP concept has proved beneficial to human health and ecological toxicological fields, demonstrating possibilities for better linkages of mechanistic data to phenotypic effects. A similar approach may be beneficial to the field of radiation research. However, for this to work effectively, collaborative efforts are needed among the scientific communities in the area of AOP development and documentation. Studies will need to be evaluated, re-organized and integrated into AOPs. Here, details of the AOP approach and areas it could support in the radiation field are discussed. In addition, challenges are highlighted and steps to integration are outlined. Organizing studies in this manner will facilitate a better understanding of our current knowledge in the radiation field and help identify areas where more focused work can be undertaken. This will, in turn, allow for improved linkage of mechanistic data to human relevance and better support radiation risk assessments.

In vitro Label Free Raman Microspectroscopic Analysis to Monitor the Uptake, Fate and Impacts of Nanoparticle Based Materials

The continued emergence of nanoscale materials for nanoparticle-based therapy, sensing and imaging, as well as their more general adoption in a broad range of industrial applications, has placed increasing demands on the ability to assess their interactions and impacts at a cellular and subcellular level, both in terms of potentially beneficial and detrimental effects. Notably, however, many such materials have been shown to interfere with conventional in vitro cellular assays that record only a single colorimetric end-point, challenging the ability to rapidly screen cytological responses. As an alternative, Raman microspectroscopy can spatially profile the biochemical content of cells, and any changes to it as a result of exogenous agents, such as toxicants or therapeutic agents, in a label free manner. In the confocal mode, analysis can be performed at a subcellular level. The technique has been employed to confirm the cellular uptake and subcellular localization of polystyrene nanoparticles (PSNPs), graphene and molybdenum disulfide micro/nano plates (MoS₂), based on their respective characteristic spectroscopic signatures. In the case of PSNPs it was further employed to identify their local subcellular environment in endosomes, lysosomes and endoplasmic reticulum, while for MoS₂ particles, it was employed to monitor subcellular degradation as a function of time. For amine functionalized PSNPs, the potential of Raman microspectroscopy to quantitatively characterize the dose and time dependent toxic responses has been explored, in a number of cell lines. Comparing the responses to those of poly (amidoamine) nanoscale polymeric dendrimers, differentiation of apoptotic and necrotic pathways based on the cellular spectroscopic responses was demonstrated. Drawing in particular from the experience of the authors, this paper details the progress to date in the development of applications of Raman microspectroscopy for in vitro, label free analysis of the uptake, fate and impacts of nanoparticle based materials, in vitro, and the prospects for the development of a routine, label free high content spectroscopic analysis technique.

Thresholds Derived From Common Measures in Rat Studies Are Predictive of Liver Tumorigenic Chemicals

We hypothesized that typical tissue and clinical chemistry (ClinChem) end points measured in rat toxicity studies exhibit chemical-independent biological thresholds beyond which cancer occurs. Using the rat in vivo TG-GATES study, 75 chemicals were examined across chemical-dose-time comparisons that could be linked to liver tumor outcomes. Thresholds for liver weight to body weight (LW/BW) and 21 serum ClinChem end points were defined as the maximum and minimum values for those exposures that did not lead to liver tumors in rats. Upper thresholds were identified for LW/BW (117%), aspartate aminotransferase (195%), alanine aminotransferase (141%), alkaline phosphatase (152%), and total bilirubin (115%), and lower thresholds were identified for phospholipids (82%), relative albumin (93%), total cholesterol (82%), and total protein (94%). Thresholds derived from the TG-GATES data set were consistent across other acute and subchronic rat studies. A training set of ClinChem and LW/BW thresholds derived from a 38 chemical training set from TG-GATES was predictive of liver tumor outcomes for a test set of 37 independent TG-GATES chemicals (91%). The thresholds were most predictive when applied to 7d treatments (98%). These findings provide support that biological thresholds for common end points in rodent studies can be used to predict chemical tumorigenic potential.

Drivers of and Obstacles to the Adoption of Toxicogenomics for Chemical Risk Assessment: Insights from Social Science Perspectives

BACKGROUND

Some 20 y ago, scientific and regulatory communities identified the potential of omics sciences (genomics, transcriptomics, proteomics, metabolomics) to improve chemical risk assessment through development of toxicogenomics. Recognizing that regulators adopt new scientific methods cautiously given accountability to diverse stakeholders, the scope and pace of adoption of toxicogenomics tools and data have nonetheless not met the ambitious, early expectations of omics proponents.

OBJECTIVE

Our objective was, therefore, to inventory, investigate, and derive insights into drivers of and obstacles to adoption of toxicogenomics in chemical risk assessment. By invoking established social science frameworks conceptualizing innovation adoption, we also aimed to develop recommendations for proponents of toxicogenomics and other new approach methodologies (NAMs).

METHODS

We report findings from an analysis of 56 scientific and regulatory publications from 1998 through 2017 that address the adoption of toxicogenomics for chemical risk assessment. From this purposeful sample of toxicogenomics discourse, we identified major categories of drivers of and obstacles to adoption of toxicogenomics tools and data sets. We then mapped these categories onto social science frameworks for conceptualizing innovation adoption to generate actionable insights for proponents of toxicogenomics.

DISCUSSION

We identify the most salient drivers and obstacles. From 1998 through 2017, adoption of toxicogenomics was understood to be helped by drivers such as those we labeled Superior scientific understanding, New applications, and Reduced cost & increased efficiency but hindered by obstacles such as those we labeled Insufficient validation, Complexity of interpretation, and Lack of standardization. Leveraging social science frameworks, we find that arguments for adoption that draw on the most salient drivers, which emphasize superior and novel functionality of omics as rationales, overlook potential adopters' key concerns: simplicity of use and compatibility with existing practices. We also identify two perspectives-innovation-centric and adopter-centric-on omics adoption and explain how overreliance on the former may be undermining efforts to promote toxicogenomics. https://doi.org/10.1289/EHP6500.

Challenges in the quantification approach to a radiation relevant adverse outcome pathway for lung cancer

PURPOSE

Adverse outcome pathways (AOPs) provide a modular framework for describing sequences of biological key events (KEs) and key event relationships (KERs) across levels of biological organization. Empirical evidence across KERs can support construction of quantified AOPs (qAOPs). Using an example AOP of energy deposition from ionizing radiation onto DNA leading to lung cancer incidence, we investigate the feasibility of quantifying data from KERs supported by all types of stressors. The merits and challenges of this process in the context of AOP construction are discussed.

MATERIALS AND METHODS

Empirical evidence across studies of dose-response from four KERs of the AOP were compiled independently for quantification. Three upstream KERs comprised of evidence from various radiation types in line with AOP guidelines. For these three KERs, a focused analysis of data from alpha-particle studies was undertaken to better characterize the process to the adverse outcome (AO) for a radon gas stressor. Numerical information was extracted from tables and graphs to plot and tabulate the response of KEs. To complement areas of the AOP quantification process, Monte Carlo (MC) simulations in TOPAS-nBio were performed to model exposure conditions relevant to the AO for an example bronchial compartment of the lung with secretory cell nuclei targets.

RESULTS

Quantification of AOP KERs highlighted the relevance of radiation types under the stressor-agnostic intent of AOP design, motivating a focus on specific types. For a given type, significant differences of KE response indicate meaningful data to derive linkages from the MIE to the AO is lacking and that better response-response focused studies are required. The MC study estimates the linear energy transfer (LET) of alpha-particles emitted by radon-222 and its progeny in the secretory cell nuclei of the example lung compartment to range from 94 - 5 + 5 to 192 - 18 + 15 keV/µm.

CONCLUSION

Quantifying AOP components provides a means to assemble empirical evidence across different studies. This highlights challenges in the context of studies examining similar endpoints using different radiation types. Data linking KERs to a MIE of 'deposition of energy' is shown to be non-compatible with the stressor-agnostic principles of AOP design. Limiting data to that describing response-response relationships between adjacent KERs may better delineate studies relevant to the damage that drives a pathway to the next KE and still support an 'all hazards' approach. Such data remains limited and future investigations in the radiation field may consider this approach when designing experiments and reporting their results and outcomes.

Differential Sensitivity to In Vitro Inhibition of Cytochrome P450 Aromatase (CYP19) Activity Among 18 Freshwater Fishes

There is significant concern regarding potential impairment of fish reproduction associated with endocrine disrupting chemicals. Aromatase (CYP19) is a steroidogenic enzyme involved in the conversion of androgens to estrogens. Inhibition of aromatase by chemicals can result in reduced concentrations of estrogens leading to adverse reproductive effects. These effects have been extensively investigated in a small number of laboratory model fishes, such as fathead minnow (Pimephales promelas), Japanese medaka (Oryzias latipes), and zebrafish (Danio rerio). But, differences in sensitivity among species are largely unknown. Therefore, this study took a first step toward understanding potential differences in sensitivity to aromatase inhibitors among fishes. Specifically, a standard in vitro aromatase inhibition assay using subcellular fractions of whole tissue homogenates was used to evaluate the potential sensitivity of 18 phylogenetically diverse species of freshwater fish to the nonsteroidal aromatase inhibitor fadrozole. Sensitivity to fadrozole ranged by more than 52-fold among these species. Five species were further investigated for sensitivity to up to 4 additional nonsteroidal aromatase inhibitors, letrozole, imazalil, prochloraz, and propiconazole. Potencies of each of these chemicals relative to fadrozole ranged by up to 2 orders of magnitude among the 5 species. Fathead minnow, Japanese medaka, and zebrafish were among the least sensitive to all the investigated chemicals; therefore, ecological risks of aromatase inhibitors derived from these species might not be adequately protective of more sensitive native fishes. This information could guide more objective ecological risk assessments of native fishes to chemicals that inhibit aromatase.

Mechanisms of pH-Dependent Uptake of Ionizable Organic Chemicals by Fish from Oil Sands Process-Affected Water (OSPW)

Uptake and effects of ionizable organic chemicals (IOCs) that are weak acids in aqueous solution by fish can differ as a function of pH. While the pH-dependent behavior of select IOCs is well-understood, complex mixtures of IOCs, e.g., from oil sands process-affected water (OSPW), have not yet been studied systematically. Here, we established an in vitro screening method using the rainbow trout gill cell line, RTgill-W1, to investigate pH-dependent cytotoxicity and permeation of IOCs across cultured epithelia using ultra-high-performance liquid chromatography with high-resolution mass spectrometry (UPLC-HRMS). The assay was benchmarked using model chemicals and technical mixtures, and then used to characterize fractions and reconstituted extracts of field-collected OSPW. Significant pH-dependent cytotoxicity of individual IOCs, acidic fractions, and reconstituted extracts of OSPW was observed. In vitro data were in good agreement with data from a 96 h in vivo exposure experiment with juvenile rainbow trout. Permeation of some IOCs from OSPW was mediated by active transport, as revealed by studies in which inhibitors of these active transport mechanisms were applied. We conclude that the RTgill-W1 in vitro assay is useful for the screening of pH-dependent uptake of IOCs in fish, and has applications for in vitro-in vivo extrapolation, and prioritization of chemicals in nontarget screenings.

Spatiotemporal variation in occurrence and co-occurrence of pesticides, hormones, and other organic contaminants in rivers in the Chesapeake Bay Watershed, United States

Investigating the spatiotemporal dynamics of contaminants in surface water is crucial to better understand how introduced chemicals are interacting with and potentially influencing aquatic organisms and environments. Within the Chesapeake Bay Watershed, United States, there are concerns about the potential role of contaminant exposure on fish health. Evidence suggests that exposure to contaminants in surface water is causing immunosuppression and intersex in freshwater fish species. Despite these concerns, there is a paucity of information regarding the complex dynamics of contaminant occurrence and co-occurrence in surface water across both space and time. To address these concerns, we applied a Bayesian hierarchical joint-contaminant model to describe the occurrence and co-occurrence patterns of 28 contaminants and total estrogenicity across six river sites and over three years. We found that seasonal occurrence patterns varied by contaminant, with the highest occurrence probabilities during the spring and summer months. Additionally, we found that the proportion of agricultural landcover in the immediate catchment, as well as stream discharge, did not have a significant effect on the occurrence probabilities of most compounds. Four pesticides (atrazine, metolachlor, fipronil and simazine) co-occurred across sites after accounting for environmental covariates. These results provide baseline information on the contaminant occurrence patterns of several classes of compounds within the Chesapeake Bay Watershed. Understanding the spatiotemporal dynamics of contaminants in surface water is the first step in investigating the effects of contaminant exposure on fisheries and aquatic environments.

Extended Robust Boolean Network of Budding Yeast Cell Cycle

Background:

How to explore the dynamics of transition probabilities between phases of budding yeast cell cycle (BYCC) network based on the dynamics of protein activities that control this network? How to identify the robust structure of protein interactions of BYCC Boolean network (BN)? Budding yeast allows scientists to put experiments into effect in order to discover the intracellular cell cycle regulating structures which are well simulated by mathematical modeling.

Methods:

We extended an available deterministic BN of proteins responsible for the cell cycle to a Markov chain model containing apoptosis besides G1, S, G2, M, and stationary G1. Using genetic algorithm (GA), we estimated the kinetic parameters of the extended BN model so that the subsequent transition probabilities derived using Markov chain model of cell states as normal cell cycle becomes the maximum while the structure of chemical interactions of extended BN of cell cycle becomes more stable.

Results:

Using kinetic parameters optimized by GA, the probability of the subsequent transitions between cell cycle phases is maximized. The relative basin size of stationary G1 increased from 86% to 96.48% while the number of attractors decreased from 7 in the original model to 5 in the extended one. Hence, an increase in the robustness of the system has been achieved.

Conclusion:

The structure of interacting proteins in cell cycle network affects its robustness and probabilities of transitions between different cell cycle phases. Markov chain and BN are good approaches to study the stability and dynamics of the cell cycle network.

QSAR without borders

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

In-plate toxicometabolomics of single zebrafish embryos

Toxicometabolomic studies involving zebrafish embryos have become increasingly popular for linking apical endpoints to biochemical perturbations as part of adverse outcome pathway determination. These experiments involve pooling embryos to generate sufficient biomass for metabolomic measurement, which adds both time and cost. To address this limitation, we developed a high-throughput toxicometabolomic assay involving single zebrafish embryos. Incubation, microscopy, embryo extraction, and instrumental metabolomic analysis were all performed in the same 96-well plate, following acquisition of conventional toxicological endpoints. The total time for the assay (including testing of 6 doses/n = 12 embryos per dose plus positive and negative controls, assessing conventional endpoints, instrumental analysis, data processing and multivariate statistics) is <14 days. Metabolomic perturbations at low dose were linked statistically to those observed at high dose and in the presence of an adverse effect, thereby contextualizing omic data amongst apical endpoints. Overall, this assay enables collection of high resolution metabolomic data in a high throughput manner, suitable for mode of action hypothesis generation in the context of pharmaceutical or toxicological screening.

Quantitative adverse outcome pathway (qAOP) models for toxicity prediction

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