Adverse outcome pathways: opportunities, limitations and open questions

Construction of an adverse outcome pathway framework for arsenic-induced lung cancer using a network-based approach

Environmental pollutants are considered as a cause of tumorigenesis, but approaches to assess their risk of causing tumors remain insufficient. As an alternative approach, the adverse outcome pathway (AOP) framework is used to assess the risk of tumors caused by environmental pollutants. Arsenic is a pollutant associated with lung cancer, but early assessment of lung cancer risk is lacking. Therefore, we applied the AOP framework to arsenic-induced lung cancer. A systematic review revealed increased risks of lung cancer following exposure to a range of arsenic concentrations in drinking water (OR = 1.83, 95 % CI = 1.46-2.30). We obtained, from public databases, genes related to risk of arsenic-induced lung cancer. Then, Cox and LASSO regressions were used to screen target genes from the risk genes. Subsequently, target genes, phenotypes, and pathways were used to construct the computational AOP network, which was determined by Cytoscape to have 156 edges and 45 nodes. Further, target genes, phenotypes, and pathways were used as molecular initiating events and key events to construct the AOP framework depending on upstream and downstream relationships. In the AOP framework, by Weight of Evidence, arsenic exposure increased levels of EGFR, activated the PI3K/AKT pathway, regulated cell proliferation by promoting the G1/S phase transition, and caused generation of lung cancers. External validation was achieved through arsenite-induced, malignant transformed human bronchial epithelial (HBE) cells. Overall, these results, by integration into existing data to construct an AOP framework, provide insights into the assessment of lung cancer risk for arsenic exposure. Special attention needs to be focused on populations with low-dose arsenic exposure.

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

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

Combined Exposures and Mixtures Research: An Enduring NIEHS Priority

BACKGROUND

The National Institute of Environmental Health Sciences (NIEHS) continues to prioritize research to better understand the health effects resulting from exposure to mixtures of chemical and nonchemical stressors. Mixtures research activities over the last decade were informed by expert input during the development and deliberations of the 2011 NIEHS Workshop "Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects." NIEHS mixtures research efforts since then have focused on key themes including a) prioritizing mixtures for study, b) translating mixtures data from in vitro and in vivo studies, c) developing cross-disciplinary collaborations, d) informing component-based and whole-mixture assessment approaches, e) developing sufficient similarity methods to compare across complex mixtures, f) using systems-based approaches to evaluate mixtures, and g) focusing on management and integration of mixtures-related data.

OBJECTIVES

We aimed to describe NIEHS driven research on mixtures and combined exposures over the last decade and present areas for future attention.

RESULTS

Intramural and extramural mixtures research projects have incorporated a diverse array of chemicals (e.g., polycyclic aromatic hydrocarbons, botanicals, personal care products, wildfire emissions) and nonchemical stressors (e.g., socioeconomic factors, social adversity) and have focused on many diseases (e.g., breast cancer, atherosclerosis, immune disruption). We have made significant progress in certain areas, such as developing statistical methods for evaluating multiple chemical associations in epidemiology and building translational mixtures projects that include both in vitro and in vivo models.

DISCUSSION

Moving forward, additional work is needed to improve mixtures data integration, elucidate interactions between chemical and nonchemical stressors, and resolve the geospatial and temporal nature of mixture exposures. Continued mixtures research will be critical to informing cumulative impact assessments and addressing complex challenges, such as environmental justice and climate change. https://doi.org/10.1289/EHP14340.

Controversy on health-based guidance values for bisphenol A-the need of criteria for studies that serve as a basis for risk assessment

Since 2006, the responsible regulatory bodies have proposed five health-based guidance values (HBGV) for bisphenol A (BPA) that differ by a factor of 250,000. This range of HBGVs covers a considerable part of the range from highly toxic to relatively non-toxic substances. As such heterogeneity of regulatory opinions is a challenge not only for scientific risk assessment but also for all stakeholders, the Senate Commission on Food Safety (SKLM) of the German Research Foundation (DFG) analyzed the reasons for the current discrepancy and used this example to suggest improvements for the process of HBGV recommendations. A key aspect for deriving a HBGV is the selection of appropriate studies that allow the identification of a point of departure (PoD) for risk assessment. In the case of BPA, the HBGV derived in the 2023 EFSA assessment was based on a study that reported an increase of Th17 cells in mice with a benchmark dose lower bound (BMDL40) of 0.53 µg/kg bw/day. However, this study does not comply with several criteria that are important for scientific risk assessment: (1) the selected end-point, Th17 cell frequency in the spleen of mice, is insufficiently understood with respect to health outcomes. (2) It is unclear, by which mechanism BPA may cause an increase in Th17 cell frequency. (3) It is unknown, if an increase of Th17 cell frequency in rodents is comparably observed in humans. (4) Toxicokinetics were not addressed. (5) Neither the raw data nor the experimental protocols are available. A further particularly important criterion (6) is independent data confirmation which is not available in the present case. Previous studies using other readouts did not observe immune-related adverse effects such as inflammation, even at doses orders of magnitude higher than in the Th17 cell-based study. The SKLM not only provides here key criteria for the use of such studies, but also suggests that the use of such a "checklist" requires a careful and comprehensive scientific judgement of each item. It is concluded that the Th17 cell-based study data do not represent an adequate basis for risk assessment of BPA.

Microplastics: Omnipresent and an ongoing challenge for medical science

Micro- and nanoplastics are omnipresent not only in the environment, but have also been detected in human body fluids and tissue. The subsequent commentary provides a perspective about potential risks for human health as well as resulting challenges for medical science.

From hazard to risk prioritization: a case study to predict drug-induced cholestasis using physiologically based kinetic modeling

Cholestasis is characterized by hepatic accumulation of bile acids. Clinical manifestation of cholestasis only occurs in a small proportion of exposed individuals. The present study aims to develop a new approach methodology (NAM) to predict drug-induced cholestasis as a result of drug-induced hepatic bile acid efflux inhibition and the resulting bile acid accumulation. To this end, hepatic concentrations of a panel of drugs were predicted by a generic physiologically based kinetic (PBK) drug model. Their effects on hepatic bile acid efflux were incorporated in a PBK model for bile acids. The predicted bile acid accumulation was used as a measure for a drug's cholestatic potency. The selected drugs were known to inhibit hepatic bile acid efflux in an assay with primary suspension-cultured hepatocytes and classified as common, rare, or no for cholestasis incidence. Common cholestasis drugs included were atorvastatin, chlorpromazine, cyclosporine, glimepiride, ketoconazole, and ritonavir. The cholestasis incidence of the drugs appeared not to be adequately predicted by their Ki for inhibition of hepatic bile acid efflux, but rather by the AUC of the PBK model predicted internal hepatic drug concentration at therapeutic dose level above this Ki. People with slower drug clearance, a larger bile acid pool, reduced bile salt export pump (BSEP) abundance, or given higher than therapeutic dose levels were predicted to be at higher risk to develop drug-induced cholestasis. The results provide a proof-of-principle of using a PBK-based NAM for cholestasis risk prioritization as a result of transporter inhibition and identification of individual risk factors.

Radiation Adverse Outcome pathways (AOPs): examining priority questions from an international horizon-style exercise

PURPOSE

The Organisation for Economic Co-operation and Development (OECD) Adverse Outcome Pathway (AOP) Development Programme is being explored in the radiation field, as an overarching framework to identify and prioritize research needs that best support strengthening of radiation risk assessment and risk management strategies. To advance the use of AOPs, an international horizon-style exercise (HSE) was initiated through the Radiation/Chemical AOP Joint Topical Group (JTG) formed by the OECD Nuclear Energy Agency (NEA) High-Level Group on Low Dose Research (HLG-LDR) under the auspices of the Committee on Radiological Protection and Public Health (CRPPH). The intent of the HSE was to identify key research questions for consideration in AOP development that would help to reduce uncertainties in estimating the health risks following exposures to low dose and low dose-rate ionizing radiation. The HSE was conducted in several phases involving the solicitation of relevant questions, a collaborative review of open-ended candidate questions and an elimination exercise that led to the selection of 25 highest priority questions for the stated purpose. These questions were further ranked by over 100 respondents through an international survey. This final set of questions was judged to provide insights into how the OECD's AOP approach can be put into practice to meet the needs of hazard and risk assessors, regulators, and researchers. This paper examines the 25 priority questions in the context of hazard/risk assessment framework for ionizing radiation.

CONCLUSION

By addressing the 25 priority questions, it is anticipated that constructed AOPs will have a high level of specificity, making them valuable tools for simplifying and prioritizing complex biological processes for use in developing revised radiation hazard and risk assessment strategies.

New approach methods to assess developmental and adult neurotoxicity for regulatory use: a PARC work package 5 project

In the European regulatory context, rodent in vivo studies are the predominant source of neurotoxicity information. Although they form a cornerstone of neurotoxicological assessments, they are costly and the topic of ethical debate. While the public expects chemicals and products to be safe for the developing and mature nervous systems, considerable numbers of chemicals in commerce have not, or only to a limited extent, been assessed for their potential to cause neurotoxicity. As such, there is a societal push toward the replacement of animal models with in vitro or alternative methods. New approach methods (NAMs) can contribute to the regulatory knowledge base, increase chemical safety, and modernize chemical hazard and risk assessment. Provided they reach an acceptable level of regulatory relevance and reliability, NAMs may be considered as replacements for specific in vivo studies. The European Partnership for the Assessment of Risks from Chemicals (PARC) addresses challenges to the development and implementation of NAMs in chemical risk assessment. In collaboration with regulatory agencies, Project 5.2.1e (Neurotoxicity) aims to develop and evaluate NAMs for developmental neurotoxicity (DNT) and adult neurotoxicity (ANT) and to understand the applicability domain of specific NAMs for the detection of endocrine disruption and epigenetic perturbation. To speed up assay time and reduce costs, we identify early indicators of later-onset effects. Ultimately, we will assemble second-generation developmental neurotoxicity and first-generation adult neurotoxicity test batteries, both of which aim to provide regulatory hazard and risk assessors and industry stakeholders with robust, speedy, lower-cost, and informative next-generation hazard and risk assessment tools.

Microplastics may induce food dilution and endocrine disrupting effects in fathead minnows (Pimephales promelas), and decrease offspring quality

Microplastics are a complex environmental contaminant that have been reported to cause a variety of impacts, although the mechanism of these impacts remains unclear. Many studies have investigated either sub-organismal or apical endpoints, while very few have attempted to integrate and link endpoints seen at multiple levels of organization. Here, we exposed fathead minnows to microplastics for their entire lifecycle, from the egg stage through to reproduction, and raised a subset of the offspring in clean water. We show that both preconsumer and environmentally sourced microplastics impact adult growth, lipid storage, and external colouration, suggesting a potential food dilution effect. Environmentally sourced microplastics, but not preconsumer microplastics, had further endocrine disrupting impacts on the parental generation and their offspring in the low concentration treatments such that egg production began later, eggs were less viable, and the offspring had higher rates of malformation. Low dose effects are a typical dose-response for endocrine disrupting contaminants. These results suggest that microplastic exposure, at concentrations relevant to what is being found in the environment, has potential implications for forage fish populations. Our findings also highlight the importance of using an integrative approach to understanding the mechanisms behind how and why microplastics impact organisms.

Ferroptosis participated in inhaled polystyrene nanoplastics-induced liver injury and fibrosis

The emerging contaminant nanoplastics (NPs) have received considerable attention. Due to their tiny size and unique colloidal properties, NPs could more easily enter the body and cross biological barriers with inhalation exposure. While NPs-induced hepatotoxicity has been reported, the hepatic impact of inhaled NPs was still unknown. To close this gap, a 40 nm polystyrene NPs (PS-NPs) inhalation exposure mice model was developed to explore the hepatotoxicity during acute (1 week), subacute (4 weeks), and subchronic period (12 weeks), with four exposure doses (0, 16, 40, and 100 μg/day). Results showed that inhaled PS-NPs caused a remarkable increase of ALT, AST, and ALP with a decrease of CHE, indicating liver dysfunction. Various histological abnormalities and significantly higher levels of inflammation in a dose- and time-dependent manner were observed. Moreover, after 4 weeks and 12 weeks of exposure, Masson staining and upregulated expression of TGF-β, α-SMA, and Col1a1 identified that inhaled PS-NPs exposure triggered the progression of liver fibrosis with the exposure time prolonged. From the mechanistic perspective, transcriptome analysis revealed that ferroptosis was involved in PS-NPs-induced liver hepatotoxicity, and key features of ferroptosis were detected, including persistent oxidative stress, iron overload, increased LPO, mitochondria damage, and the expression changes of GPX4, TFRC, and Ferritin. And in vitro and in vivo recovery tests showed that ferroptosis inhibitor Fer-1 treatment alleviated liver injury and fibrosis. The above results confirmed the critical role of ferroptosis in PS-NPs-induced hepatotoxicity. Furthermore, to better conclude our findings and understand the mechanistic causality within it, an adverse outcome pathway (AOP) framework was established. In total, this present study conducted the first experimental assessment of inhalation exposure to PS-NPs on the liver, identified that continuous inhaled PS-NPs could cause liver injury and fibrosis, and PS-NPs- ferroptosis provided a novel mechanistic explanation.

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.

Polystyrene micro- and nanoplastics induce gastric toxicity through ROS mediated oxidative stress and P62/Keap1/Nrf2 pathway

Microplastics (MPs) exist widely in the environment and can enter the human body indirectly through the food chain or directly through inhalation or ingestion. The primary organ that MPs contaminated food or water enters the human body through the digestive tract is the stomach. However, at present, the effects of MPs on the stomach and the related mechanism remain unclear. In this study, our results indicated that 50 nm and 250 nm polystyrene MPs (PS-MPs) at environmental related dose significantly decreased stomach organ coefficient, inhibited gastric juice secretion and mucus secretion, disrupted gastric barrier function and suppressed antioxidant ability in mice. In vitro experiments showed that PS-MPs inhibited cell viability, increased ROS generation, and induced apoptosis through mitochondria-dependent pathway. Simultaneously, PS-MPs also decreased mitochondrial membrane potential, ATP level, disrupted mitochondrial kinetic homeostasis, and activated P62 / Nrf2 / Keap1 pathway. Furthermore, blocking ROS (NAC) partially alleviated ROS and apoptosis caused by PS-MPs. Based on above findings, the potential adverse outcome pathway (AOP) of PS-MPs-caused gastric toxicity was proposed which provides a new insight into the risk assessment of MP related gastric damage. Our study unveils the gastric injury induced by PS MPs is dependent on ROS - mediated P62 / Nrf2 / Keap1 signaling pathway, and provides scientific basis for further exploration the mechanism of gastric toxicity of PS MPs.

Metabolomics in Preclinical Drug Safety Assessment: Current Status and Future Trends

Metabolomics is emerging as a powerful systems biology approach for improving preclinical drug safety assessment. This review discusses current applications and future trends of metabolomics in toxicology and drug development. Metabolomics can elucidate adverse outcome pathways by detecting endogenous biochemical alterations underlying toxicity mechanisms. Furthermore, metabolomics enables better characterization of human environmental exposures and their influence on disease pathogenesis. Metabolomics approaches are being increasingly incorporated into toxicology studies and safety pharmacology evaluations to gain mechanistic insights and identify early biomarkers of toxicity. However, realizing the full potential of metabolomics in regulatory decision making requires a robust demonstration of reliability through quality assurance practices, reference materials, and interlaboratory studies. Overall, metabolomics shows great promise in strengthening the mechanistic understanding of toxicity, enhancing routine safety screening, and transforming exposure and risk assessment paradigms. Integration of metabolomics with computational, in vitro, and personalized medicine innovations will shape future applications in predictive toxicology.

Adverse outcome pathways of PBDEs inducing male reproductive toxicity

Polybrominated diphenyl ethers (PBDEs) are widely used brominated flame retardants, they are easily released into environment and causing adverse effects to the ecosystem and human health. This review aims to summarize the research status of PBDEs-induced male reproductive toxicity and its mechanisms at various levels such as molecular/cellular, tissue/organ and individual/population. The Adverse Outcome Pathways (AOPs) diagram showed that PBDEs-induced reactive oxygen species (ROS) production, disruptions of estrogen receptor-α (ERα) and antagonism of androgen receptor (AR) were defined as critical molecular initiating events (MIEs). They caused key events (KEs) at the molecular and cellular levels, including oxidative stress, increased DNA damage, damaging mitochondria, increased glycolipid levels and apoptosis, depletion of ectoplasmic specialization and decreased Leydig cells numbers. These in turn lead to followed KEs at the tissue or organ levels, such as the impaired spermatogenesis, impaired blood-testis barrier and reduced testosterone synthesis and function. As a result, reproductive system-related adverse outcomes (AOs) were reported, such as the decreased sperm quantity or quality, shorten male anogenital distance and cryptorchidism in individual and reduced reproduction of the population. This review assembled information on the mechanisms of male reproductive toxicity induced by PBDEs, and constructed a causal mechanism relationship diagram from different levels using the an AOP framework to provide theoretical basis for ecological risk assessment and environmental management of PBDEs. The AOP framework makes it possible to develop risk management strategies based on toxicity mechanisms and support for development of Integrated Approach to Testing and Assessment (IATA) which are available for regulatory purposes.

The hepatotoxicity assessment of micro/nanoplastics: A preliminary study to apply the adverse outcome pathways

Plastic pollution has become a significant global problem over the years, leading to the continuous decomposition and accumulation of micro/nanoplastics (MNPLs) in the environment. As a result, human exposure to these MNPLs is inevitable. The liver, in particular, is highly susceptible to potential MNPL toxicity. In this study, we systematically reviewed the current literature on MNPLs-induced hepatotoxicity and collected data on toxic events occurring at different biological levels. Then, to better understand the cause-mechanism causality, we developed an Adverse Outcome Pathway (AOP) framework for MNPLs-induced hepatotoxicity. The AOP framework provided insights into the mechanism of MNPL-induced hepatotoxicity and highlighted potential health risks such as liver dysfunction and inflammation, metabolism disorders and liver fibrosis. Moreover, we discussed the potential application of emerging toxicological models in the hepatotoxicity study. Liver organoids and liver-on-chips, which can simulate the structure and function of the liver in vitro, offer a promising alternative platform for toxicity testing and risk assessment. We proposed combining the AOP framework with these emerging toxicological models to improve our understanding of the hepatotoxic effects of MNPLs. Overall, this study performed a preliminary exploration of novel toxicological methodologies to assess the hepatotoxicity of MNPLs, providing a deeper understanding of environmental toxicology.

Advancing Computational Toxicology by Interpretable Machine Learning

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

ELIXIR and Toxicology: a community in development

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Predicting chemicals' toxicity pathway of female reproductive disorders using AOP7 and deep neural networks

Experimental evidence shows that certain chemicals, particularly endocrine disrupting chemicals, may negatively affect the female reproductive system, thereby lowering women's fertility. However, humans are constantly exposed to a number of different chemicals with limited or no experimental data regarding their effect and the mechanism of action in the female reproductive system. To predict chemical hazards to the female reproductive system, we used a previously defined adverse outcome pathway (AOP) that links activation of the peroxisome proliferator-activated receptor γ to the reproductive toxicity in adult females (AOP7) and the Convolutional Deep Neural Network models that produce meaningful predictions when trained on a significant amount of data. The models trained using CompTox assays with intended molecular and biological targets corresponding to AOP7 achieved high performance (over 90% validation accuracy). The integration of AOP7 and Deep Neural Network identified chemicals that could negatively affect female reproduction through the mechanism described in AOP7. We provide a solution to quickly analyze the data and produce machine learning models to identify potentially active chemicals in the female reproductive system. Although we focused on the female reproductive system, this approach could be valid for a number of other chemicals and AOPs if the right data exist.

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.

New approach methodologies in human regulatory toxicology - Not if, but how and when!

The predominantly animal-centric approach of chemical safety assessment has increasingly come under pressure. Society is questioning overall performance, sustainability, continued relevance for human health risk assessment and ethics of this system, demanding a change of paradigm. At the same time, the scientific toolbox used for risk assessment is continuously enriched by the development of "New Approach Methodologies" (NAMs). While this term does not define the age or the state of readiness of the innovation, it covers a wide range of methods, including quantitative structure-activity relationship (QSAR) predictions, high-throughput screening (HTS) bioassays, omics applications, cell cultures, organoids, microphysiological systems (MPS), machine learning models and artificial intelligence (AI). In addition to promising faster and more efficient toxicity testing, NAMs have the potential to fundamentally transform today's regulatory work by allowing more human-relevant decision-making in terms of both hazard and exposure assessment. Yet, several obstacles hamper a broader application of NAMs in current regulatory risk assessment. Constraints in addressing repeated-dose toxicity, with particular reference to the chronic toxicity, and hesitance from relevant stakeholders, are major challenges for the implementation of NAMs in a broader context. Moreover, issues regarding predictivity, reproducibility and quantification need to be addressed and regulatory and legislative frameworks need to be adapted to NAMs. The conceptual perspective presented here has its focus on hazard assessment and is grounded on the main findings and conclusions from a symposium and workshop held in Berlin in November 2021. It intends to provide further insights into how NAMs can be gradually integrated into chemical risk assessment aimed at protection of human health, until eventually the current paradigm is replaced by an animal-free "Next Generation Risk Assessment" (NGRA).

G × E interactions as a basis for toxicological uncertainty

To transfer toxicological findings from model systems, e.g. animals, to humans, standardized safety factors are applied to account for intra-species and inter-species variabilities. An alternative approach would be to measure and model the actual compound-specific uncertainties. This biological concept assumes that all observed toxicities depend not only on the exposure situation (environment = E), but also on the genetic (G) background of the model (G × E). As a quantitative discipline, toxicology needs to move beyond merely qualitative G × E concepts. Research programs are required that determine the major biological variabilities affecting toxicity and categorize their relative weights and contributions. In a complementary approach, detailed case studies need to explore the role of genetic backgrounds in the adverse effects of defined chemicals. In addition, current understanding of the selection and propagation of adverse outcome pathways (AOP) in different biological environments is very limited. To improve understanding, a particular focus is required on modulatory and counter-regulatory steps. For quantitative approaches to address uncertainties, the concept of "genetic" influence needs a more precise definition. What is usually meant by this term in the context of G × E are the protein functions encoded by the genes. Besides the gene sequence, the regulation of the gene expression and function should also be accounted for. The widened concept of past and present "gene expression" influences is summarized here as Ge. Also, the concept of "environment" needs some re-consideration in situations where exposure timing (Et) is pivotal: prolonged or repeated exposure to the insult (chemical, physical, life style) affects Ge. This implies that it changes the model system. The interaction of Ge with Et might be denoted as Ge × Et. We provide here general explanations and specific examples for this concept and show how it could be applied in the context of New Approach Methodologies (NAM).

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.

Thermal performance of the electron transport system Complex III in seven Alabama fishes

Management of fish populations for conservation in thermally variable systems requires an understanding of the fish's underlying physiology and responses to thermal stress. Physiological research at the organismal level provides information on the overall effects of stressors such as extreme temperature fluctuations. While experiments with whole organisms provide information as to the overall effects of temperature fluctuations, biochemical assays of thermal stress provide direct results of exposure that are both sensitive and specific. Electron transport system (ETS; Complex III) assays quantify a rate-limiting step of respiratory enzymes. Parameters that can be estimated via this approach include optimum thermal temperature (T_opt ) and optimal breadth of thermal performance (T_breadth ), which can both be related to organismal-level temperature thresholds. We exposed enzymes of seven fish species (native fish chosen to represent a typical community in Alabama streams) to temperatures in the range 11-44°C. The resultant enzymatic thermal performance curves showed that T_opt , the lower temperature for enzyme optimal thermal performance (T_low ), the upper temperature for enzyme optimal thermal performance (T_up ), and T_breadth differed among species. Relationships between enzymatic activity and temperature for all fish followed a pattern of steadily increasing enzyme activity to T_opt before gradually decreasing with increasing temperature. A comparison of our enzyme optimum and upper-temperature limit results versus published critical thermal maxima values supports that ETS Complex III assays may be useful for assessing organismal-level thermal tolerance.

Impact of endocrine disruptors from mother's diet on immuno-hormonal orchestration of brain development and introduction of the virtual human twin tool

Diet has long been known to modify physiology during development and adulthood. However, due to a growing number of manufactured contaminants and additives over the last few decades, diet has increasingly become a source of exposure to chemicals that has been associated with adverse health risks. Sources of food contaminants include the environment, crops treated with agrochemicals, inappropriate storage (e.g., mycotoxins) and migration of xenobiotics from food packaging and food production equipment. Hence, consumers are exposed to a mixture of xenobiotics, some of which are endocrine disruptors (EDs). The complex interactions between immune function and brain development and their orchestration by steroid hormones are insufficiently understood in human populations, and little is known about the impact on immune-brain interactions by transplacental fetal exposure to EDs via maternal diet. To help to identify the key data gaps, this paper aims to present (a) how transplacental EDs modify immune system and brain development, and (b) how these mechanisms may correlate with diseases such as autism and disturbances of lateral brain development. Attention is given to disturbances of the subplate, a transient structure of crucial significance in brain development. Additionally, we describe cutting edge approaches to investigate the developmental neurotoxicity of EDs, such as the application of artificial intelligence and comprehensive modelling. In the future, highly complex investigations will be performed using virtual brain models constructed using sophisticated multi-physics/multi-scale modelling strategies based on patient and synthetic data, which will enable a greater understanding of healthy or disturbed brain development.

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

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

The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

In vitro to in vivo acetaminophen hepatotoxicity extrapolation using classical schemes, pharmacodynamic models and a multiscale spatial-temporal liver twin

In vitro to in vivo extrapolation represents a critical challenge in toxicology. In this paper we explore extrapolation strategies for acetaminophen (APAP) based on mechanistic models, comparing classical (CL) homogeneous compartment pharmacodynamic (PD) models and a spatial-temporal (ST), multiscale digital twin model resolving liver microarchitecture at cellular resolution. The models integrate consensus detoxification reactions in each individual hepatocyte. We study the consequences of the two model types on the extrapolation and show in which cases these models perform better than the classical extrapolation strategy that is based either on the maximal drug concentration (Cmax) or the area under the pharmacokinetic curve (AUC) of the drug blood concentration. We find that an CL-model based on a well-mixed blood compartment is sufficient to correctly predict the in vivo toxicity from in vitro data. However, the ST-model that integrates more experimental information requires a change of at least one parameter to obtain the same prediction, indicating that spatial compartmentalization may indeed be an important factor.

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.

Deltamethrin-Induced Respiratory and Behavioral Effects and Adverse Outcome Pathways (AOP) in Short-Term Exposed Mozambique Tilapia, Oreochromis mossambicus

Disrupted behavior and respiratory distress effects of 96-h acute deltamethrin exposures in adult Mozambique tilapia, Oreochromis mossambicus, were investigated using behavioral indices and opercular movement, respectively. Deltamethrin concentrations were found to be associated with toxicological (lethal and sublethal) responses. At 24, 48, 72, and 96 h, the LC50 values and 95% confidence limits were 12.290 (11.174-14.411 µg/L), 12.671 (11.334-15.649 µg/L), 10.172 (9.310-11.193 µg/L), and 8.639 (7.860-9.417 µg/L), respectively. The GUTS-model analysis showed that GUTS-SD (stochastic death) with a narrow tolerance distribution in deltamethrin exposed O. mossambicus populations was more sensitive than the GUTS-IT (individual tolerance) model. Prior to death, exposed fish demonstrated concentration-dependent mortality and disturbed behavioral responses, including uncoordinated swim motions, increased mucus secretion, unbalanced and unpredictable swimming patterns, and inactivity. The altered behavioral patterns and increased opercular movement with increased deltamethrin levels and exposure time are strongly suggestive of neurotoxicity and respiratory distress, respectively. Adverse Outcome Pathways (AOPs), describing biological mechanisms and plausible pathways, highlighted oxidative stress and cholinergic effects as intermediate steps linked to respiratory distress and behavioral toxicity.

Modified Mycotoxins, a Still Unresolved Issue

Mycotoxins are toxic secondary metabolites produced by filamentous microfungi on almost every agricultural commodity worldwide. After the infection of crop plants, mycotoxins are modified by plant enzymes or other fungi and often conjugated to more polar substances, like sugars. The formed—often less toxic—metabolites are stored in the vacuole in soluble form or bound to macromolecules. As these substances are usually not detected during routine analysis and no maximum limits are in force, they are called modified mycotoxins. While, in most cases, modified mycotoxins have lower intrinsic toxicity, they might be reactivated during mammalian metabolism. In particular, the polar group might be cleaved off (e.g., by intestinal bacteria), releasing the native mycotoxin. This review aims to provide an overview of the critical issues related to modified mycotoxins. The main conclusion is that analytical aspects, toxicological evaluation, and exposure assessment merit more investigation.

UV-B filter octylmethoxycinnamate-induced vascular endothelial disruption on rat aorta: In silico and in vitro approach

Throughout human life, an extensive and varied range of emerging environmental contaminants, called endocrine disruptors (EDCs), cause adverse health effects, including in the cardiovascular (CV) system. Cardiovascular diseases (CVD) are worryingly one of the leading causes of all mortality and mobility worldwide. The UV-B filter octylmethoxycinnamate (also designated octinoxate, or ethylhexyl methoxycinnamate (CAS number: 5466-77-3)) is an EDC widely present in all personal care products. However, to date, there are no studies evaluating the OMC-induced effects on vasculature using animal models to improve human cardiovascular health. This work analysed the effects of OMC on rat aorta vasculature and explored the modes of action implicated in these effects. Our results indicated that OMC relaxes the rat aorta by endothelium-dependent mechanisms through the signaling pathways of cyclic nucleotides and by endothelium-independent mechanisms involving inhibition of L-Type voltage-operated Ca²⁺ channels (L-Type VOCC). Overall, OMC toxicity on rat aorta may produce hypotension via vasodilation due to excessive NO release and blockade of L-Type VOCC. Moreover, the OMC-induced endothelial dysfunction may also occur by promoting the endothelial release of endothelin-1. Therefore, our findings demonstrate that exposure to OMC alters the reactivity of the rat aorta and highlight that long-term OMC exposure may increase the risk of human CV diseases.

A new perspective on endocrine disrupting effects of triphenyltin on marine medaka: From brain transcriptome, gut content metabolome and behavior

Triphenyltin (TPT) is an endocrine contaminant that is often detected in the environment. However, the mechanism of the effects of TPT on biological systems is not fully understood. Here we exposed marine medaka (Oryzias melastigma) to TPT for 21 days. Brain transcriptome, intestinal content metabolism group, and behavior analysis were carried out. Through the comprehensive analysis of multiomics for the in-depth understanding of the ways related to health improvement, we determined that the glycine-serine-threonine metabolic axis was most perturbed by TPT. Through behavioral analysis, it was found that there was behavioral hyperactivity in the exposed group; behavioral hyperactivity may be caused by the interference of TPT with the neuroendocrine system. In order to gain a full understanding of the impacts of TPT on human health, transcriptomic screening of differential genes and an impartial attitude based on bioinformatics were used. Gene-disease interaction analysis using the Comparative Toxicogenomics Database (CTD) revealed the possible effects of TPT on human health. Finally, based on these findings, the relevant adverse outcome pathway (AOP), which is the "epigenetic modification of PPARG leading to adipogenesis," was identified from AOP Wiki. Further research is required to validate the potential AOP of TPT.

Combining DELs and machine learning for toxicology prediction

DNA-encoded libraries (DELs) allow starting chemical matter to be identified in drug discovery. The volume of experimental data generated also makes DELs an attractive resource for machine learning (ML). ML allows modeling complex relationships between compounds and numerical endpoints, such as the binding to a target measured by DELs. DELs could also empower other areas of drug discovery. Here, we propose that DELs and ML could be combined to model binding to off-targets, enabling better predictive toxicology. With enough data, ML models can make accurate predictions across a vast chemical space, and they can be reused and expanded across projects. Although there are limitations, more general toxicology models could be applied earlier during drug discovery, illuminating safety liabilities at a lower cost.

The influence of global climate change on accumulation and toxicity of persistent organic pollutants and chemicals of emerging concern in Arctic food webs

This review summarizes current understanding of how climate change-driven physical and ecological processes influence the levels of persistent organic pollutants (POPs) and contaminants of emerging Arctic concern (CEACs) in Arctic biota and food webs. The review also highlights how climate change may interact with other stressors to impact contaminant toxicity, and the utility of modeling and newer research tools in closing knowledge gaps on climate change-contaminant interactions. Permafrost thaw is influencing the concentrations of POPs in freshwater ecosystems. Physical climate parameters, including climate oscillation indices, precipitation, water salinity, sea ice age, and sea ice quality show statistical associations with POPs concentrations in multiple Arctic biota. Northward range-shifting species can act as biovectors for POPs and CEACs into Arctic marine food webs. Shifts in trophic position can alter POPs concentrations in populations of Arctic species. Reductions in body condition are associated with increases in levels of POPs in some biota. Although collectively understudied, multiple stressors, including contaminants and climate change, may act to cumulatively impact some populations of Arctic biota. Models are useful for predicting the net result of various contrasting climate-driven processes on POP and CEAC exposures; however, for some parameters, especially food web changes, insufficient data exists with which to populate such models. In addition to the impact of global regulations on POP levels in Arctic biota, this review demonstrates that there are various direct and indirect mechanisms by which climate change can influence contaminant exposure, accumulation, and effects; therefore, it is important to attribute POP variations to the actual contributing factors to inform future regulations and policies. To do so, a broad range of habitats, species, and processes must be considered for a thorough understanding and interpretation of the consequences to the distribution, accumulation, and effects of environmental contaminants. Given the complex interactions between climate change, contaminants, and ecosystems, it is important to plan for long-term, integrated pan-Arctic monitoring of key biota and ecosystems, and to collect ancillary data, including information on climate-related parameters, local meteorology, ecology, and physiology, and when possible, behavior, when carrying out research on POPs and CEACs in biota and food webs of the Arctic.

Mixture Math: Deciding What to Add in a Cumulative Risk Assessment

Component-based approaches for cumulative risk assessment provide an important tool for informing public health policy. While current quantitative cumulative risk assessments focus narrowly on pesticides that share a mechanism of action, growing scientific evidence supports expansion of their application to encompass stressors that target a common disease. Case studies have demonstrated dose additive effects of chemicals with different mechanisms of action on liver steatosis, craniofacial malformations, and male reproductive tract developmental disruption. Evidence also suggests that nonchemical stressors such as noise or psychosocial stress can modify effects of chemicals. Focused research attention is required before nonchemical stressors can routinely be included in quantitative cumulative risk assessments.

In vitro/in silico prediction of drug induced steatosis in relation to oral doses and blood concentrations by the Nile Red assay

The accumulation of lipid droplets in hepatocytes is a key feature of drug-induced liver injury (DILI) and can be induced by a subset of hepatotoxic compounds. In the present study, we optimized and evaluated an in vitro technique based on the fluorescent dye Nile Red, further named Nile Red assay to quantify lipid droplets induced by the exposure to chemicals. The Nile Red assay and a cytotoxicity test (CTB assay) were then performed on cells exposed concentration-dependently to 60 different compounds. Of these, 31 were known to induce hepatotoxicity in humans, and 13 were reported to also cause steatosis. In order to compare in vivo relevant blood concentrations, pharmacokinetic models were established for all compounds to simulate the maximal blood concentrations (C_max) at therapeutic doses. The results showed that several hepatotoxic compounds induced an increase in lipid droplets at sub-cytotoxic concentrations. To compare how well (1) the cytotoxicity test alone, (2) the Nile Red assay alone, and (3) the combination of the cytotoxicity test and the Nile Red assay (based on the lower EC₁₀ of both assays) allow the differentiation between hepatotoxic and non-hepatotoxic compounds, a previously established performance metric, the Toxicity Separation Index (TSI) was calculated. In addition, the Toxicity Estimation Index (TEI) was calculated to determine how well blood concentrations that cause an increased DILI risk can be estimated for hepatotoxic compounds. Our findings indicate that the combination of both assays improved the TSI and TEI compared to each assay alone. In conclusion, the study demonstrates that inclusion of the Nile Red assay into in vitro test batteries may improve the prediction of DILI compounds.

Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay

Traditional experimental approaches to evaluate hepatotoxicity are expensive and time-consuming. As an advanced framework of risk assessment, adverse outcome pathways (AOPs) describe the sequence of molecular and cellular events underlying chemical toxicities. We aimed to develop an AOP that can be used to predict hepatotoxicity by leveraging computational modeling and in vitro assays. We curated 869 compounds with known hepatotoxicity classifications as a modeling set and extracted assay data from PubChem. The antioxidant response element (ARE) assay, which quantifies transcriptional responses to oxidative stress, showed a high correlation to hepatotoxicity (PPV=0.82). Next, we developed quantitative structure-activity relationship (QSAR) models to predict ARE activation for compounds lacking testing results. Potential toxicity alerts were identified and used to construct a mechanistic hepatotoxicity model. For experimental validation, 16 compounds in the modeling set and 12 new compounds were selected and tested using an in-house ARE-luciferase assay in HepG2-C8 cells. The mechanistic model showed good hepatotoxicity predictivity (accuracy = 0.82) for these compounds. Potential false positive hepatotoxicity predictions by only using ARE results can be corrected by incorporating structural alerts and vice versa. This mechanistic model illustrates a potential toxicity pathway for hepatotoxicity, and this strategy can be expanded to develop predictive models for other complex toxicities.

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.

Deploying elements of scoping review methods for Adverse Outcome Pathway development: A space travel case example

Purpose Health protection agencies require scientific information for evidence-based decision-making and guideline development. However, vetting and collating large quantities of published research to identify relevant high-quality studies is a challenge. One approach to address this issue is the use of Adverse Outcome Pathways (AOPs) that provide a framework to assemble toxicological knowledge into causally linked chains of key events across levels of biological organization to culminate in an adverse health outcome of significance. Traditionally, AOPs have been constructed using a narrative review approach where the collection of evidence that supports each pathway is based on prior knowledge of influential studies that can also be supplemented by individually selecting and reviewing relevant references. Objectives: We aimed to create a protocol for AOP weight of evidence gathering that harnesses elements of both scoping review methods and artificial intelligence tools to increase transparency while reducing bias and workload of human screeners. Methods: To develop this protocol, an existing space-health AOP in the workplan of the Organisation for Economic Co-operation and Development (OECD) AOP program was used as a case example. To balance the benefits of both scoping review tools and narrative approaches, a study protocol outlining a screening and search strategy was developed, and three reference collection workflows were tested to identify the most efficient method to inform weight of evidence. The workflows differed in their literature search strategies, and combinations of software tools used. Results: Across the three tested workflows, over 59 literature searches were completed, retrieving over 34000 references of which over 3300 were human reviewed. The most effective of the three methods used a search strategy with searches across each component of the AOP network, SWIFT Review as a pre-filtering software, and DistillerSR to create structured screening and data extraction forms. This methodology effectively retrieved relevant studies while balancing efficiency in data retrieval without compromising transparency, leading to a well-synthesized evidence base to support the AOP. Conclusions: The workflow is still exploratory in the context of AOP development, and we anticipate adaptations to the protocol with further experience. To further the systematicity, future iterations of the workflow could include structured quality assessment and risk of bias analysis. Overall, the workflow provides a transparent and unbiased approach to support AOP development, which in turn will support the need for rigorous methods to identify relevant scientific evidence while being practical to allow uptake by the broader community.

Potential of the zebrafish (Danio rerio) embryo test to discriminate between chemicals of similar molecular structure-a study with valproic acid and 14 of its analogues

Valproic acid is a frequently used antiepileptic drug and known pediatric hepatotoxic agent. In search of pharmaceuticals with increased effectiveness and reduced toxicity, analogue chemicals came into focus. So far, toxicity and teratogenicity data of drugs and metabolites have usually been collected from mammalian model systems such as mice and rats. However, in an attempt to reduce mammalian testing while maintaining the reliability of toxicity testing of new industrial chemicals and drugs, alternative test methods are being developed. To this end, the potential of the zebrafish (Danio rerio) embryo to discriminate between valproic acid and 14 analogues was investigated by exposing zebrafish embryos for 120 h post fertilization in the extended version of the fish embryo acute toxicity test (FET; OECD TG 236), and analyzing liver histology to evaluate the correlation of liver effects and the molecular structure of each compound. Although histological evaluation of zebrafish liver did not identify steatosis as the prominent adverse effect typical in human and mice, the structure–activity relationship (SAR) derived was comparable not only to human HepG2 cells, but also to available in vivo mouse and rat data. Thus, there is evidence that zebrafish embryos might serve as a tool to bridge the gap between subcellular, cell-based systems and vertebrate models.

Factors Modulating COVID-19: A Mechanistic Understanding Based on the Adverse Outcome Pathway Framework

Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. Here, we describe how eleven selected factors (age, sex, genetic factors, lipid disorders, heart failure, gut dysbiosis, diet, vitamin D deficiency, air pollution and exposure to chemicals) influence COVID-19 by applying the Adverse Outcome Pathway (AOP), which is well-established in regulatory toxicology. This framework aims to model the sequence of events leading to an adverse health outcome. Several linear AOPs depicting pathways from the binding of the virus to ACE2 up to clinical outcomes observed in COVID-19 have been developed and integrated into a network offering a unique overview of the mechanisms underlying the disease. As SARS-CoV-2 infectibility and ACE2 activity are the major starting points and inflammatory response is central in the development of COVID-19, we evaluated how those eleven intrinsic and extrinsic factors modulate those processes impacting clinical outcomes. Applying this AOP-aligned approach enables the identification of current knowledge gaps orientating for further research and allows to propose biomarkers to identify of high-risk patients. This approach also facilitates expertise synergy from different disciplines to address public health issues.

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.

Adverse outcome pathway from activation of the AhR to breast cancer-related death

Adverse outcome pathways (AOPs) are formalized and structured linear concepts that connect one molecular initiating event (MIE) to an adverse outcome (AO) via different key events (KE) through key event relationships (KER). They are mainly used in eco-toxicology toxicology, and regulatory health issues. AOPs must respond to specific guidelines from the Organization for Economic Co-operation and Development (OECD) to weight the evidence between each KE. Breast cancer is the deadliest cancer in women with a poor prognosis in case of metastatic breast cancer. The role of the environments in the formation of metastasis has been suggested. We hypothesized that activation of the AhR (MIE), a xenobiotic receptor, could lead to breast cancer related death (AO), through different KEs, constituting a new AOP. An artificial intelligence tool (AOP-helpfinder), which screens the available literature, was used to collect all existing scientific abstracts to build a novel AOP, using a list of key words. Four hundred and seven abstracts were found containing at least a word from our MIE list and either one word from our AO or KE list. A manual curation retained 113 pertinent articles, which were also screened using PubTator. From these analyses, an AOP was created linking the activation of the AhR to breast cancer related death through decreased apoptosis, inflammation, endothelial cell migration, angiogenesis, and invasion. These KEs promote an increased tumor growth, angiogenesis and migration which leads to breast cancer metastasis and breast cancer related death. The evidence of the proposed AOP was weighted using the tailored Bradford Hill criteria and the OECD guidelines. The confidence in our AOP was considered strong. An in vitro validation must be carried out, but our review proposes a strong relationship between AhR activation and breast cancer-related death with an innovative use of an artificial intelligence literature search.

Evaluation of dioxin induced transcriptomic responses in a 3D human liver microtissue model

Three-dimensional human liver microtissue model provides a promising method for predicting the human hepatotoxicity of environmental chemicals. However, the dynamics of transcriptional responses of 3D human liver microtissue model to dioxins exposure remain unclear. Herein, time-series transcriptomic analysis was used to characterize modulation of gene expression over 14 days in 3D human liver microtissues exposed to 2,3,7,8-tetra-chlorodibenzo-p-dioxin (TCDD, 31 nM, 10 ng/ml). Changes in gene expression and modulation of biological pathways were evaluated at several time points. The results showed that microtissues stably expressed genes related to toxicological pathways (e.g. highly of genes involved in external stimuli and maintenance of cell homeostasis pathways) during the 14-day culture period. Furthermore, a weekly phenomenon pattern was observed for the number of the differentially expressed genes in microtissues exposed to TCDD at each time point. TCDD led to an induction of genes involved in cell cycle regulation at day three. Metabolic pathways were the main significantly induced pathways during the subsequent days, with the immune/inflammatory response enriched on the fifth day, and the cellular response to DNA damage was identified at the end of the exposure. Finally, relevant transcription patterns identified in microtissues were compared with published data on rodent and human cell-line studies to elucidate potential species-specific responses to TCDD over time. Cell development and cytochrome P450 pathway were mainly affected after a 3-day exposure, with the DNA damage response identified at the end of exposure in the human microtissue system but not in mouse/rat primary hepatocytes models. Overall, the 3D human liver microtissue model is a valuable tool to predict the toxic effects of environmental chemicals with a relatively long exposure.

The Evolution of Regulatory Toxicology: Where is the Gardener?

There is a need for paradigm change in the methodology employed for toxicological testing and assessment. It could be said that this change is well on its way, through an evolutionary progress analogous to that of natural selection. Darwin's Theory of Evolution has defined the idea of evolution and descendancy since the last third of the 19th century. Increasingly, this concept of 'evolution' is being applied beyond the field of biology. This Comment article discusses the progress of toxicological testing in the context of 'evolutionary pressure' and deliberates how this process can help foster the development, implementation and acceptance of mechanistic and human-relevant methods in this field. By comparing the current regulatory landscape in toxicity testing and assessment to specific elements in Charles Darwin's evolutionary theory, we aim to better understand the needs and requirements for the future.

Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)

Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level. In toxicology, Adverse Outcome Pathways (AOPs) aim to formalize this as temporal sequences of events, in which event relationships should be supported by causal evidence according to the tailored Bradford-Hill criteria. One of the criteria is whether events are consistently observed in a certain temporal order and, in this work, we study this time concordance using the concept of “first activation” as data-driven means to generate hypotheses on potentially causal mechanisms. As a case study, we analysed liver data from repeat-dose studies in rats from the TG-GATEs database which comprises measurements across eight timepoints, ranging from 3 hours to 4 weeks post-treatment. We identified time-concordant gene expression-derived events preceding adverse histopathology, which serves as surrogate readout for Drug-Induced Liver Injury (DILI). We find known mechanisms in DILI to be time-concordant, and show further that significance, frequency and log fold change (logFC) of differential expression are metrics which can additionally prioritize events although not necessary to be mechanistically relevant. Moreover, we used the temporal order of transcription factor (TF) expression and regulon activity to identify transcriptionally regulated TFs and subsequently combined this with prior knowledge on functional interactions to derive detailed gene-regulatory mechanisms, such as reduced Hnf4a activity leading to decreased expression and activity of Cebpa. At the same time, also potentially novel events are identified such as Sox13 which is highly significantly time-concordant and shows sustained activation over time. Overall, we demonstrate how time-resolved transcriptomics can derive and support mechanistic hypotheses by quantifying time concordance and how this can be combined with prior causal knowledge, with the aim of both understanding mechanisms of toxicity, as well as potential applications to the AOP framework. We make our results available in the form of a Shiny app (https://anikaliu.shinyapps.io/dili_cascades), which allows users to query events of interest in more detail.

Understanding mechanisms from systems-scale biological data is of great relevance in toxicology as well as drug discovery; however how to generate causal hypotheses instead of correlations is by no means clear. In this work, we study the conserved temporal order of events and present an automatable framework to quantify and characterize time concordance across a large set of time-series. We apply this concept to events derived from time-resolved gene expression and histopathology from the TG-GATEs in vivo liver data as a case study. We were able to recover known events involved in the pathogenesis of Drug-Induced Liver Injury (DILI), and identify potentially novel pathway and transcription factors (TFs) which precede adverse histopathology. As complementary sources of evidence for causality, we additionally show how time concordance and prior knowledge on plausible interactions between TFs can be combined to derive causal hypotheses on the TFs’ mode of regulation and interaction partners. Overall, the results derived in our case study can serve as valuable hypothesis-free starting points for the development of Adverse Outcome Pathways for DILI, and demonstrate that our approach provides a novel angle to prioritize mechanistically relevant events.

Establishing a Communication and Engagement Strategy to Facilitate the Adoption of the Adverse Outcome Pathways in Radiation Research and Regulation

BACKGROUND

Studies on human health and ecological effects of ionizing radiation are rapidly evolving as innovative technologies arise and the body of scientific knowledge grows. Structuring this information could effectively support the development of decision making tools and health risk models to complement current system of radiation protection. To this end, the adverse outcome pathway (AOP) approach is being explored as a means to consolidate the most relevant research to identify causation between exposure to a chemical or non-chemical stressor and disease or adverse effect progression. This tool is particularly important for low dose and low dose rate radiation exposures because of the latency and uncertainties in the biological responses at these exposure levels. To progress this aspect, it is essential to build a community of developers, facilitators, risk assessors (in the private sector and in government), policy-makers, and regulators who understand the strengths and weaknesses of, and how to appropriately utilize AOPs for consolidating our knowledge on the impact of low dose ionizing radiation. Through co-ordination with the Organisation of Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) High-Level Group on Low-Dose Research (HLG-LDR) and OECD's AOP Programme, initiatives are under way to demonstrate this approach in radiation research and regulation. Among these, a robust communications strategy and stakeholder engagement will be essential. It will help establish best practices for AOPs in institutional project development and aid in dissemination for more efficient and timely uptake and use of AOPs. In this regard, on June 1, 2021, the Radiation and Chemical (Rad/Chem) AOP Joint Topical Group was formed as part of the initiative from the NEA's HLG-LDR. The topical group will work to develop a communication and engagement strategy to define the target audiences, establish the clear messages and identify the delivery and engagement platforms.

CONCLUSION

The incorporation of the best science and better decision-making should motive the radiation protection community to develop, refine and use AOPs, recognizing that their incorporation into radiation health risk assessments is critical for public health and environmental protection in the 21st century.

From Seeing to Simulating: A Survey of Imaging Techniques and Spatially-Resolved Data for Developing Multiscale Computational Models of Liver Regeneration

Liver regeneration, which leads to the re-establishment of organ mass, follows a specifically organized set of biological processes acting on various time and length scales. Computational models of liver regeneration largely focused on incorporating molecular and signaling detail have been developed by multiple research groups in the recent years. These modeling efforts have supported a synthesis of disparate experimental results at the molecular scale. Incorporation of tissue and organ scale data using noninvasive imaging methods can extend these computational models towards a comprehensive accounting of multiscale dynamics of liver regeneration. For instance, microscopy-based imaging methods provide detailed histological information at the tissue and cellular scales. Noninvasive imaging methods such as ultrasound, computed tomography and magnetic resonance imaging provide morphological and physiological features including volumetric measures over time. In this review, we discuss multiple imaging modalities capable of informing computational models of liver regeneration at the organ-, tissue- and cellular level. Additionally, we discuss available software and algorithms, which aid in the analysis and integration of imaging data into computational models. Such models can be generated or tuned for an individual patient with liver disease. Progress towards integrated multiscale models of liver regeneration can aid in prognostic tool development for treating liver disease.

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.