The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions

Relevance and feasibility of principles for health and environmental risk decision-making

Globally, national regulatory authorities are both responsible and accountable for health and environmental decisions related to diverse products and risk decision contexts. These authorities provided regulatory oversight and expedited market authorizations of vaccines and other therapeutic products during the COVID-19 pandemic. Regulatory decisions regarding such products and situations depend upon well-established risk assessment and management steps. The underlying processes supporting such decisions were outlined in frameworks describing the complex interactions between factors including risk assessment and management steps as well as principles which help guide risk decision-making. In 2022, experts in risk science proposed a set of 10 guiding principles, further examining the intersection and utility of these principles using 10 diverse risk contexts, and inviting a broader discourse on the application of these principles in risk decision-making. To add to this information, Canadian regulatory practitioners responsible for evaluating health and environmental risks and establishing policies convened at a Health Canada workshop on Principles for Risk Decision-Making. This review reports the results derived from this interactive engagement and provides a first pragmatic analysis of the relevance, importance, and feasibility of such principles for health and environmental risk decision-making within the Canadian regulatory context.

The NIH pediatric/young adult chordoma clinic and natural history study: Making advances in a very rare tumor

BACKGROUND

Chordomas are rare tumors arising from the skull base and spine, with approximately 20 pediatric chordoma cases in the Unitedn States per year. The natural history and optimal treatment of pediatric chordomas, especially poorly differentiated and dedifferentiated subtypes, is incompletely understood. Herein, we present findings from our first National Cancer Institute (NCI) chordoma clinic and a retrospective analysis of published cases of pediatric poorly differentiated chordomas (PDC) and dedifferentiated chordomas (DC).

METHODS

Patients less than 40 years old with chordoma were enrolled on the NCI Natural History and Biospecimens Acquisitions Study for Children and Adults with Rare Solid Tumors protocol (NCT03739827). Chordoma experts reviewed patient records, evaluated patients, and provided treatment recommendations. Patient-reported outcomes, biospecimens, and volumetric tumor analyses were collected. A literature review for pediatric PDC and DC was conducted.

RESULTS

Twelve patients (median age: 14 years) attended the clinic, including four patients with active disease and three patients with PDC responsive to systemic therapy. Consensus treatment, management, and recommendations were provided to patients. Literature review returned 45 pediatric cases of PDC or DC with variable treatments and outcomes.

CONCLUSIONS

A multidisciplinary expert clinic was feasible and successful in improving understanding of pediatric chordoma. While multimodal approaches have all been employed, treatment for PDC has been inconsistent and a recommended standardized treatment approach has not been defined. Centralized efforts, inclusive of specialized chordoma-focused clinics, natural history studies, and prospective analyses will help in the standardization of care for this challenging disease.

Investigating the adverse outcome pathways (AOP) of neurotoxicity induced by DBDPE with a combination of in vitro and in silico approaches

Current studies have shown an association between DBDPE and neurotoxicity. In this study, the adverse outcome pathway (AOP) and mechanistic analysis of DBDPE-induced neurotoxicity were explored by a combination of in vitro and in silico approaches in SK-N-SH cells. DBDPE-induced oxidative stress caused DNA strand breaks, resulting in the activation of poly (ADP-ribose) (PAR) polymerase-1 (PARP-1). Activation of PARP1 could cause toxic damage in various organ systems, especially in the nervous system. DBDPE-induced apoptosis via the caspase-dependent intrinsic mitochondrial pathway and the PARP1-dependent pathway. Activation of PARP1 by DBDPE was deemed the initiating event, thereby affecting the key downstream biochemical events (e.g., ROS production, DNA damage, membrane potential changes, and ATP reduction), which induced apoptosis. Furthermore, excessive activation of PARP1 was accompanied by the translocation of the apoptosis-inducing factor (AIF), which was associated with PARP1-dependent cell death. The inhibition of PARP1 by PJ34 reduced DBDPE-induced apoptosis and maintained cellular ATP levels. PJ34 also prevented the translocation of AIF from the mitochondria to the nucleus. These findings improve the understanding of the mechanism of DBDPE-induced neurotoxic effects and provide a theoretical basis for the ecological risk of DBDPE.

Current practice and recommendations for advancing how human variability and susceptibility are considered in chemical risk assessment

A key element of risk assessment is accounting for the full range of variability in response to environmental exposures. Default dose-response methods typically assume a 10-fold difference in response to chemical exposures between average (healthy) and susceptible humans, despite evidence of wider variability. Experts and authoritative bodies support using advanced techniques to better account for human variability due to factors such as in utero or early life exposure and exposure to multiple environmental, social, and economic stressors.This review describes: 1) sources of human variability and susceptibility in dose-response assessment, 2) existing US frameworks for addressing response variability in risk assessment; 3) key scientific inadequacies necessitating updated methods; 4) improved approaches and opportunities for better use of science; and 5) specific and quantitative recommendations to address evidence and policy needs.Current default adjustment factors do not sufficiently capture human variability in dose-response and thus are inadequate to protect the entire population. Susceptible groups are not appropriately protected under current regulatory guidelines. Emerging tools and data sources that better account for human variability and susceptibility include probabilistic methods, genetically diverse in vivo and in vitro models, and the use of human data to capture underlying risk and/or assess combined effects from chemical and non-chemical stressors.We recommend using updated methods and data to improve consideration of human variability and susceptibility in risk assessment, including the use of increased default human variability factors and separate adjustment factors for capturing age/life stage of development and exposure to multiple chemical and non-chemical stressors. Updated methods would result in greater transparency and protection for susceptible groups, including children, infants, people who are pregnant or nursing, people with disabilities, and those burdened by additional environmental exposures and/or social factors such as poverty and racism.

Impact of endocrine disrupting chemicals and pharmaceuticals on Sertoli cell development and functions

Sertoli cells play essential roles in male reproduction, from supporting fetal testis development to nurturing male germ cells from fetal life to adulthood. Dysregulating Sertoli cell functions can have lifelong adverse effects by jeopardizing early processes such as testis organogenesis, and long-lasting processes such as spermatogenesis. Exposure to endocrine disrupting chemicals (EDCs) is recognized as contributing to the rising incidence of male reproductive disorders and decreasing sperm counts and quality in humans. Some drugs also act as endocrine disruptors by exerting off-target effects on endocrine tissues. However, the mechanisms of toxicity of these compounds on male reproduction at doses compatible with human exposure are still not fully resolved, especially in the case of mixtures, which remain understudied. This review presents first an overview of the mechanisms regulating Sertoli cell development, maintenance, and functions, and then surveys what is known on the impact of EDCs and drugs on immature Sertoli cells, including individual compounds and mixtures, and pinpointing at knowledge gaps. Performing more studies on the impact of mixtures of EDCs and drugs at all ages is crucial to fully understand the adverse outcomes these chemicals may induce on the reproductive system.

Establishment of a human cell-based in vitro battery to assess developmental neurotoxicity hazard of chemicals

Developmental neurotoxicity (DNT) is a major safety concern for all chemicals of the human exposome. However, DNT data from animal studies are available for only a small percentage of manufactured compounds. Test methods with a higher throughput than current regulatory guideline methods, and with improved human relevance are urgently needed. We therefore explored the feasibility of DNT hazard assessment based on new approach methods (NAMs). An in vitro battery (IVB) was assembled from ten individual NAMs that had been developed during the past years to probe effects of chemicals on various fundamental neurodevelopmental processes. All assays used human neural cells at different developmental stages. This allowed us to assess disturbances of: (i) proliferation of neural progenitor cells (NPC); (ii) migration of neural crest cells, radial glia cells, neurons and oligodendrocytes; (iii) differentiation of NPC into neurons and oligodendrocytes; and (iv) neurite outgrowth of peripheral and central neurons. In parallel, cytotoxicity measures were obtained. The feasibility of concentration-dependent screening and of a reliable biostatistical processing of the complex multi-dimensional data was explored with a set of 120 test compounds, containing subsets of pre-defined positive and negative DNT compounds. The battery provided alerts (hit or borderline) for 24 of 28 known toxicants (82% sensitivity), and for none of the 17 negative controls. Based on the results from this screen project, strategies were developed on how IVB data may be used in the context of risk assessment scenarios employing integrated approaches for testing and assessment (IATA).

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

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

Understanding and overcoming the technical challenges in using in silico predictions in regulatory decisions of complex toxicological endpoints - A pesticide perspective for regulatory toxicologists with a focus on machine learning models

There are many challenges that must be overcome before in silico toxicity predictions are ripe for regulatory decision-making. Today, mandates in the United States of America and the European Union to avoid animal usage in toxicity testing is driving the need to consider alternative technologies, including Quantitative Structure Activity Relationship (QSAR) models, and read across approaches. However, when adopting new methods, it is critical that both new approach developers as well as regulatory users understand the strengths and challenges with these new approaches. In this paper, we identify potential sources of bias in machine learning methods specific to toxicity predictions, that may impact the overall performance of in silico models. We also discuss ways to mitigate these biases. Based on our experiences, the most prevalent sources of bias include class imbalance (differing numbers of "toxic" vs "nontoxic" compounds), limited numbers of chemicals within a particular chemistry, and biases within the studies that make up the database used for model building, as well as model evaluation biases. While this is already complex for repeated dose toxicity, in reproduction and developmental toxicity a further level of complexity is introduced by the need to evaluate effects on individual animal and litter basis (e.g., a hierarchal structure). We also discuss key considerations developers and regulators need to make when they use machine learning models to predict chemical safety. Our objective is for our paper to serve as a desk reference for model developers and regulators as they evaluate machine learning models and as they make decisions using these models.

Development of an Evidence-Based Risk Assessment Framework

Assessment of potential human health risks associated with environmental and other agents requires careful evaluation of all available and relevant evidence for the agent of interest, including both data-rich and data-poor agents. With the advent of new approach methodologies in toxicological risk assessment, guidance on integrating evidence from mul-tiple evidence streams is needed to ensure that all available data is given due consideration in both qualitative and quantitative risk assessment. The present report summarizes the discussions among academic, government, and private sector participants from North America and Europe in an international workshop convened to explore the development of an evidence-based risk assessment framework, taking into account all available evidence in an appropriate manner in order to arrive at the best possible characterization of potential human health risks and associated uncertainty. Although consensus among workshop participants was not a specific goal, there was general agreement on the key consider-ations involved in evidence-based risk assessment incorporating 21st century science into human health risk assessment. These considerations have been embodied into an overarching prototype framework for evidence integration that will be explored in more depth in a follow-up meeting.

Toxicoepigenetics for Risk Assessment: Bridging the Gap Between Basic and Regulatory Science

Toxicoepigenetics examines the health effects of environmental exposure associated with, or mediated by, changes in the epigenome. Despite high expectations, toxicoepigenomic data and methods have yet to become significantly utilized in chemical risk assessment. This article draws on a social science framework to highlight hitherto overlooked structural barriers to the incorporation of toxicoepigenetics in risk assessment and to propose ways forward. The present barriers stem not only from the lack of maturity of the field but also from differences in constraints and standards between the data produced by toxicoepigenetics and the regulatory science data that risk assessment processes require. Criteria and strategies that frame the validation of knowledge used for regulatory purposes limit the application of basic research in toxicoepigenetics toward risk assessment. First, the need in regulatory toxicology for standardized methods that form a consensus between regulatory agencies, basic research, and the industry conflicts with the wealth of heterogeneous data in toxicoepigenetics. Second, molecular epigenetic data do not readily translate into typical toxicological endpoints. Third, toxicoepigenetics investigates new forms of toxicity, in particular low-dose and long-term effects, that do not align well with the traditional framework of regulatory toxicology. We propose that increasing the usefulness of epigenetic data for risk assessment will require deliberate efforts on the part of the toxicoepigenetics community in 4 areas: fostering the understanding of epigenetics among risk assessors, developing knowledge infrastructure to demonstrate applicability, facilitating the normalization and exchange of data, and opening the field to other stakeholders.

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.

Microphysiological Systems Evaluation: Experience of TEX-VAL Tissue Chip Testing Consortium

Much has been written and said about the promise and excitement of microphysiological systems, miniature devices that aim to recreate aspects of human physiology on a chip. The rapid explosion of the offerings and persistent publicity placed high expectations on both product manufacturers and regulatory agencies to adopt the data. Inevitably, discussions of where this technology fits in chemical testing paradigms are ongoing. Some end-users became early adopters, while others have taken a more cautious approach because of the high cost and uncertainties of their utility. Here, we detail the experience of a public-private collaboration established for testing of diverse microphysiological systems. Collectively, we present a number of considerations on practical aspects of using microphysiological systems in the context of their applications in decision-making. Specifically, future end-users need to be prepared for extensive on-site optimization and have access to a wide range of imaging and other equipment. We reason that cells, related reagents and the technical skills of the research staff, not the devices themselves, are the most critical determinants of success. Extrapolation from concentration-response effects in microphysiological systems to human blood or oral exposures, difficulties with replicating the whole organ, and long-term functionality remain as critical challenges. Overall, we conclude that it is unlikely that a rodent- or human-equivalent model is achievable through a finite number of microphysiological systems in the near future; therefore, building consensus and promoting the gradual incorporation of these models into tiered approaches for safety assessment and decision-making is the sensible path to wide adoption.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Integration of Adverse Outcome Pathways, Causal Networks and ‘Omics to Support Chemical Hazard Assessment

Several approaches have been used in an attempt to simplify and codify the events that lead to adverse effects of chemicals including systems biology, ‘omics, in vitro assays and frameworks such as the Adverse Outcome Pathway (AOP). However, these approaches are generally not integrated despite their complementary nature. Here we propose to integrate toxicogenomics data, systems biology information and AOPs using causal biological networks to define Key Events in AOPs. We demonstrate this by developing a causal subnetwork of 28 nodes that represents the Key Event of regenerative proliferation – a critical event in AOPs for liver cancer. We then assessed the effects of three chemicals known to cause liver injury and cell proliferation (carbon tetrachloride, aflatoxin B₁, thioacetamide) and two with no known cell proliferation effects (diazepam, simvastatin) on the subnetwork using rat liver gene expression data from the toxicogenomic database Open TG-GATEs. Cyclin D1 (Ccnd1), a gene both causally linked to and sufficient to infer regenerative proliferation activity, was overexpressed after exposures to carbon tetrachloride, aflatoxin B₁ and thioacetamide, but not in exposures to diazepam and simvastatin. These results were consistent with known effects on rat livers and liver pathology of exposed rats. Using these approaches, we demonstrate that transcriptomics, AOPs and systems biology can be applied to examine the presence and progression of AOPs in order to better understand the hazards of chemical exposure.

Toxicogenomic profiling after sublethal exposure to nerve- and muscle-targeting insecticides reveals cardiac and neuronal developmental effects in zebrafish embryos

For specific primary modes of action (MoA) in environmental non-target organisms, EU legislation restricts the usage of active substances of pesticides or biocides. Corresponding regulatory hazard assessments are costly, time consuming and require large numbers of non-human animal studies. Currently, predictive toxicology of development compounds relies on their chemical structure and provides little insights into toxicity mechanisms that precede adverse effects. Using the zebrafish embryo model, we characterized transcriptomic responses to a range of sublethal concentrations of six nerve- and muscle-targeting insecticides with different MoA (abamectin, carbaryl, chlorpyrifos, fipronil, imidacloprid & methoxychlor). Our aim was to identify affected biological processes and suitable biomarker candidates for MoA-specific signatures. Abamectin showed the most divergent signature among the tested insecticides, linked to lipid metabolic processes. Differentially expressed genes (DEGs) after imidacloprid exposure were primarily associated with immune system and inflammation. In total, 222 early responsive genes to either MoA were identified, many related to three major processes: (1) cardiac muscle cell development and functioning (tcap, desma, bag3, hspb1, hspb8, flnca, myoz3a, mybpc2b, actc2, tnnt2c), (2) oxygen transport and hypoxic stress (alas2, hbbe1.1, hbbe1.3, hbbe2, hbae3, igfbp1a, hif1al) and (3) neuronal development and plasticity (npas4a, egr1, btg2, ier2a, vgf). The thyroidal function related gene dio3b was upregulated by chlorpyrifos and downregulated by higher abamectin concentrations. Important regulatory genes for cardiac muscle (tcap) and forebrain development (npas4a) were the most frequently ifferentially expressed across all insecticide treatments. We consider the identified gene sets as useful early warning biomarker candidates, i.e. for developmental toxicity targeting heart and brain in aquatic vertebrates. Our findings provide a better understanding about early molecular events in response to the analyzed MoA. Perceptively, this promotes the development for sensitive and informative biomarker-based in vitro assays for toxicological MoA prediction and AOP refinement, without the suffering of adult fish.

3D Microtissues Mimic the Architecture, Estradiol Synthesis, and Gap Junction Intercellular Communication of the Avascular Granulosa

Humans are consistently exposed to thousands of untested chemicals that have been detected in the follicular fluid of the ovaries, and can disrupt reproductive health. Human granulosa cells (GCs) are the functional unit of the ovarian follicle with steroidogenic and signaling activities, and play a pivotal role in oocyte development. During follicle progression, GCs multiply to form a 3D avascular structure, and establish gap junction intercellular communication (GJIC) that is critical to maintaining optimal viability and function. We developed a high-throughput in vitro platform of human GCs for the screening of chemicals that can impact GJIC and estradiol (E2) production of human granulosa. Our granulosa 3D microtissues fabricated with human ovarian granulosa-like tumor KGN cells are multicell-layered structures that mimic the avascular granulosa layers surrounding the oocyte. These microtissues robustly expressed the steroidogenic CYP19 aromatase enzyme and GJIC intercellular membrane channel, connexin 43. Granulosa microtissues produced E2 at rates comparable to primary human GCs as previously reported. E2 production was suppressed by the CYP19 inhibitor, letrozole, and induced by CYP19 activators, bisphenol A at 100 µM, and genistein at 100 µM. Granulosa microtissues displayed active GJIC function, as demonstrated by the connexin 43-dependent diffusion of calcein fluorescent dye from microtissue surface to the core using high-throughput confocal microscopy in conjunction with our open-sourced automated image analysis tool. Overall, our 3D human granulosa screening platform is highly promising for predictive and efficient in vitro toxicity testing to screen for chemicals that contaminate follicular fluid and may affect fertility.

A Modern Genotoxicity Testing Paradigm: Integration of the High-Throughput CometChip® and the TGx-DDI Transcriptomic Biomarker in Human HepaRG™ Cell Cultures

Higher-throughput, mode-of-action-based assays provide a valuable approach to expedite chemical evaluation for human health risk assessment. In this study, we combined the high-throughput alkaline DNA damage-sensing CometChip^® assay with the TGx-DDI transcriptomic biomarker (DDI = DNA damage-inducing) using high-throughput TempO-Seq^®, as an integrated genotoxicity testing approach. We used metabolically competent differentiated human HepaRG™ cell cultures to enable the identification of chemicals that require bioactivation to cause genotoxicity. We studied 12 chemicals (nine DDI, three non-DDI) in increasing concentrations to measure and classify chemicals based on their ability to damage DNA. The CometChip^® classified 10/12 test chemicals correctly, missing a positive DDI call for aflatoxin B1 and propyl gallate. The poor detection of aflatoxin B1 adducts is consistent with the insensitivity of the standard alkaline comet assay to bulky lesions (a shortcoming that can be overcome by trapping repair intermediates). The TGx-DDI biomarker accurately classified 10/12 agents. TGx-DDI correctly identified aflatoxin B1 as DDI, demonstrating efficacy for combined used of these complementary methodologies. Zidovudine, a known DDI chemical, was misclassified as it inhibits transcription, which prevents measurable changes in gene expression. Eugenol, a non-DDI chemical known to render misleading positive results at high concentrations, was classified as DDI at the highest concentration tested. When combined, the CometChip^® assay and the TGx-DDI biomarker were 100% accurate in identifying chemicals that induce DNA damage. Quantitative benchmark concentration (BMC) modeling was applied to evaluate chemical potencies for both assays. The BMCs for the CometChip^® assay and the TGx-DDI biomarker were highly concordant (within 4-fold) and resulted in identical potency rankings. These results demonstrate that these two assays can be integrated for efficient identification and potency ranking of DNA damaging agents in HepaRG™ cell cultures.

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization

Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.

Identification of Microbiome Etiology Associated With Drug Resistance in Pleural Empyema

Identification of the offending organism and appropriate antimicrobial therapy are crucial for treating empyema. Diagnosis of empyema is largely obscured by the conventional bacterial cultivation and PCR process that has relatively low sensitivity, leading to limited understanding of the etiopathogenesis, microbiology, and role of antibiotics in the pleural cavity. To expand our understanding of its pathophysiology, we have carried out a metagenomic snapshot of the pleural effusion from 45 empyema patients by Illumina sequencing platform to assess its taxonomic, and antibiotic resistome structure. Our results showed that the variation of microbiota in the pleural effusion is generally stratified, not continuous. There are two distinct microbiome clusters observed in the forty-five samples: HA-SA type and LA-SA type. The categorization is mostly driven by species composition: HA-SA type is marked by Staphylococcus aureus as the core species, with other enriched 6 bacteria and 3 fungi, forming a low diversity and highly stable microbial community; whereas the LA-SA type has a more diverse microbial community with a distinct set of bacterial species that are assumed to be the oral origin. The microbial community does not shape the dominant antibiotic resistance classes which were common in the two types, while the increase of microbial diversity was correlated with the increase in antibiotic resistance genes. The existence of well-balanced microbial symbiotic states might respond differently to pathogen colonization and drug intake. This study provides a deeper understanding of the pathobiology of pleural empyema and suggests that potential resistance genes may hinder the antimicrobial therapy of empyema.

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

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

Evidence-based assessment on environmental mixture using a concentration-dependent transcriptomics approach

Development of new approach methodologies is urgently needed to characterize the likelihood that complex mixtures of chemicals affect water quality. Omics advances in ecotoxicology allow assessment on a broadest coverage of disrupted biological pathway by mixtures. Here the usefulness of transcriptomic analyses for evaluation of combined effects and identification of main effect components are explored. Two artificial mixtures (Mix 1 and Mix 2) were tested by a concentration-dependent reduced zebrafish transcriptome (CRZT) approach and toxicity bioassays using zebrafish embryos. Then, the toxicities and transcriptomic effects of 12 component chemicals on embryos were incorporated into additivity models to characterize the combined effects of chemicals in mixtures and to identify the main bioactive compounds. Mix 1 and Mix 2 displayed similar embryo toxicities (LD₅₀: 6.6 μM and 8.7 μM, respectively), however, Mix 2 elicited broader biological process perturbations and 5-fold higher transcriptome potency (point of departure eliciting a 20% pathway response, POD_path20) than Mix 1. The predicted mixture toxicities derived from additivity expectations deviated by 2-fold or less from the measured embryo toxicities except for the Jaw defect endpoint; most biological processes deviated by 3-fold or less. Finally, diclofenac (DFC) and propiconazole (PCZ) were identified as the main contributing components (≥80% explanation) to the embryo toxicity and biological process perturbations by Mix 1. While DFC and chlorophene (CLP) explained up to 80% of the embryo toxicities and biological effects of Mix 2 associated with development and Metabolism processes. The CRZT approach provides a powerful tool for assessment of biological pathways perturbed by chemicals in mixtures and for identification of main bioactive compounds.

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.

Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

In 2016, the European Commission launched the EU-ToxRisk research project to develop and promote animal-free approaches in toxicology. The 36 partners of this consortium used in vitro and in silico methods in the context of case studies (CSs). These CSs included both compounds with a highly defined target (e.g. mitochondrial respiratory chain inhibitors) as well as compounds with poorly defined molecular initiation events (e.g. short-chain branched carboxylic acids). The initial project focus was on developing a science-based strategy for read-across (RAx) as an animal-free approach in chemical risk assessment. Moreover, seamless incorporation of new approach method (NAM) data into this process (= NAM-enhanced RAx) was explored. Here, the EU-ToxRisk consortium has collated its scientific and regulatory learnings from this particular project objective. For all CSs, a mechanistic hypothesis (in the form of an adverse outcome pathway) guided the safety evaluation. ADME data were generated from NAMs and used for comprehensive physiological-based kinetic modelling. Quality assurance and data management were optimized in parallel. Scientific and Regulatory Advisory Boards played a vital role in assessing the practical applicability of the new approaches. In a next step, external stakeholders evaluated the usefulness of NAMs in the context of RAx CSs for regulatory acceptance. For instance, the CSs were included in the OECD CS portfolio for the Integrated Approach to Testing and Assessment project. Feedback from regulators and other stakeholders was collected at several stages. Future chemical safety science projects can draw from this experience to implement systems toxicology-guided, animal-free next-generation risk assessment.

Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens

Traditional methods for cancer risk assessment are resource-intensive, retrospective, and not feasible for the vast majority of environmental chemicals. In this study, we investigated whether quantitative genomic data from short-term studies may be used to set protective thresholds for potential tumorigenic effects. We hypothesized that gene expression biomarkers measuring activation of the key early events in established pathways for rodent liver cancer exhibit cross-chemical thresholds for tumorigenesis predictive for liver cancer risk. We defined biomarker thresholds for 6 major liver cancer pathways using training sets of chemicals with short-term genomic data (3-29 days of exposure) from the TG-GATES (n = 77 chemicals) and DrugMatrix (n = 86 chemicals) databases and then tested these thresholds within and between datasets. The 6 pathway biomarkers represented genotoxicity, cytotoxicity, and activation of xenobiotic, steroid, and lipid receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Thresholds were calculated as the maximum values derived from exposures without detectable liver tumor outcomes. We identified clear response values that were consistent across training and test sets. Thresholds derived from the TG-GATES training set were highly predictive (97%) in a test set of independent chemicals, whereas thresholds derived from the DrugMatrix study were 96%-97% predictive for the TG-GATES study. Threshold values derived from an abridged gene list (2/biomarker) also exhibited high predictive accuracy (91%-94%). These findings support the idea that early genomic changes can be used to establish threshold estimates or "molecular tipping points" that are predictive of later-life health outcomes.

The integration of the adverse outcome pathway framework to radiation risk assessment

BACKGROUND

A large body of radiobiological data has been generated over the past century using in vitro, animal and epidemiological models. This information represents global efforts to understand the mechanistic basis of radiation-induced health effects. However, it has been difficult to effectively integrate this data to derive meaningful information for refining the guidance on chronic, low dose radiation exposure for workers and the public.

METHODS

To increase our understanding of radiation risks and the biological processes that contribute to those risks, a paradigm shift is needed that will enable integration of information across levels of biological organization from a 'stressor' centric to an 'adverse outcome' approach to risk assessment. In chemical and ecological toxicity, a framework has been developed that captures available biologically-based knowledge in the literature and links it to outcomes of relevance to chemical toxicity, resulting in an adverse outcome pathway (AOP).

RESULTS

In this paper, we discuss the AOP concept, how it can be applied to the radiation field, and our vision for the next steps. For the approach to be successful, the radiation research community will need to work collaboratively to vet the vast amount of literature and harness the data in a systematic manner for incorporation into a framework based on the AOP approach.

CONCLUSION

We anticipate that AOPs could be adopted as a method to synthesize current available information to facilitate the identification of knowledge gaps, better co-ordinate research and qualitatively and quantitatively link key events to an adverse outcome. This can further assist in identifying biomarkers relevant to radiation exposures, refining risk from co-exposures and understanding critical events at the molecular, cellular, tissue, organ and whole animal level related to low dose/dose-rate exposures.

Probabilistic human health risk assessment of perfluorooctane sulfonate (PFOS) by integrating in vitro, in vivo toxicity, and human epidemiological studies using a Bayesian-based dose-response assessment coupled with physiologically based pharmacokinetic (PBPK) modeling approach

BACKGROUND

Environmental exposure to perfluorooctane sulfonate (PFOS) is associated with various adverse outcomes in humans. However, risk assessment for PFOS with the traditional risk estimation method is faced with multiple challenges because there are high variabilities and uncertainties in its toxicokinetics and toxicity between species and among different types of studies.

OBJECTIVES

This study aimed to develop a robust probabilistic risk assessment framework accounting for interspecies and inter-experiment variabilities and uncertainties to derive the human equivalent dose (HED) and reference dose for PFOS.

METHODS

A Bayesian dose-response model was developed to analyze selected 34 critical studies, including human epidemiological, animal in vivo, and ToxCast in vitro toxicity datasets. The dose-response results were incorporated into a multi-species physiologically based pharmacokinetic (PBPK) model to reduce the toxicokinetic/toxicodynamic variabilities. In addition, a population-based probabilistic risk assessment of PFOS was performed for Asian, Australian, European, and North American populations, respectively, based on reported environmental exposure levels.

RESULTS

The 5th percentile of HEDs derived from selected studies was estimated to be 21.5 (95% CI: 10.6-36.3) ng/kg/day. After exposure to environmental levels of PFOS, around 50% of the population in all studied populations would likely have >20% of increase in serum cholesterol, but the effects on other endpoints were estimated to be minimal (<10% changes). There was a small population (~10% of the population) that was highly sensitive to endocrine disruption and cellular response by environmental PFOS exposure.

CONCLUSION

Our results provide insights into a complete risk characterization of PFOS and may help regulatory agencies in the reevaluation of PFOS risk. Our new probabilistic approach can conduct dose-response analysis of different types of toxicity studies simultaneously and this method could be used to improve risk assessment for other perfluoroalkyl substances (PFAS).

Flow cytometric micronucleus assay and TGx-DDI transcriptomic biomarker analysis of ten genotoxic and non-genotoxic chemicals in human HepaRG™ cells

Background

Modern testing paradigms seek to apply human-relevant cell culture models and integrate data from multiple test systems to accurately inform potential hazards and modes of action for chemical toxicology. In genetic toxicology, the use of metabolically competent human hepatocyte cell culture models provides clear advantages over other more commonly used cell lines that require the use of external metabolic activation systems, such as rat liver S9. HepaRG™ cells are metabolically competent cells that express Phase I and II metabolic enzymes and differentiate into mature hepatocyte-like cells, making them ideal for toxicity testing. We assessed the performance of the flow cytometry in vitro micronucleus (MN) test and the TGx-DDI transcriptomic biomarker to detect DNA damage-inducing (DDI) chemicals in human HepaRG™ cells after a 3-day repeat exposure. The biomarker, developed for use in human TK6 cells, is a panel of 64 genes that accurately classifies chemicals as DDI or non-DDI. Herein, the TGx-DDI biomarker was analyzed by Ion AmpliSeq whole transcriptome sequencing to assess its classification accuracy using this more modern gene expression technology as a secondary objective.

Methods

HepaRG™ cells were exposed to increasing concentrations of 10 test chemicals (six genotoxic chemicals, including one aneugen, and four non-genotoxic chemicals). Cytotoxicity and genotoxicity were measured using the In Vitro MicroFlow® kit, which was run in parallel with the TGx-DDI biomarker.

Results

A concentration-related decrease in relative survival and a concomitant increase in MN frequency were observed for genotoxic chemicals in HepaRG™ cells. All five DDI and five non-DDI agents were correctly classified (as genotoxic/non-genotoxic and DDI/non-DDI) by pairing the test methods. The aneugenic agent (colchicine) yielded the expected positive result in the MN test and negative (non-DDI) result by TGx-DDI.

Conclusions

This next generation genotoxicity testing strategy is aligned with the paradigm shift occurring in the field of genetic toxicology. It provides mechanistic insight in a human-relevant cell-model, paired with measurement of a conventional endpoint, to inform the potential for adverse health effects. This work provides support for combining these assays in an integrated test strategy for accurate, higher throughput genetic toxicology testing in this metabolically competent human progenitor cell line.

The IARC Monographs: Updated Procedures for Modern and Transparent Evidence Synthesis in Cancer Hazard Identification

The Monographs produced by the International Agency for Research on Cancer (IARC) apply rigorous procedures for the scientific review and evaluation of carcinogenic hazards by independent experts. The Preamble to the IARC Monographs, which outlines these procedures, was updated in 2019, following recommendations of a 2018 expert advisory group. This article presents the key features of the updated Preamble, a major milestone that will enable IARC to take advantage of recent scientific and procedural advances made during the 12 years since the last Preamble amendments. The updated Preamble formalizes important developments already being pioneered in the Monographs program. These developments were taken forward in a clarified and strengthened process for identifying, reviewing, evaluating, and integrating evidence to identify causes of human cancer. The advancements adopted include the strengthening of systematic review methodologies; greater emphasis on mechanistic evidence, based on key characteristics of carcinogens; greater consideration of quality and informativeness in the critical evaluation of epidemiological studies, including their exposure assessment methods; improved harmonization of evaluation criteria for the different evidence streams; and a single-step process of integrating evidence on cancer in humans, cancer in experimental animals, and mechanisms for reaching overall evaluations. In all, the updated Preamble underpins a stronger and more transparent method for the identification of carcinogenic hazards, the essential first step in cancer prevention.

Towards Sustainable Environmental Quality: Priority Research Questions for the Australasian Region of Oceania

Environmental challenges persist across the world, including the Australasian region of Oceania, where biodiversity hotspots and unique ecosystems such as the Great Barrier Reef are common. These systems are routinely affected by multiple stressors from anthropogenic activities, and increasingly influenced by global megatrends (e.g., the food-energy-water nexus, demographic transitions to cities) and climate change. Here we report priority research questions from the Global Horizon Scanning Project, which aimed to identify, prioritize, and advance environmental quality research needs from an Australasian perspective, within a global context. We employed a transparent and inclusive process of soliciting key questions from Australasian members of the Society of Environmental Toxicology and Chemistry. Following submission of 78 questions, 20 priority research questions were identified during an expert workshop in Nelson, New Zealand. These research questions covered a range of issues of global relevance, including research needed to more closely integrate ecotoxicology and ecology for the protection of ecosystems, increase flexibility for prioritizing chemical substances currently in commerce, understand the impacts of complex mixtures and multiple stressors, and define environmental quality and ecosystem integrity of temporary waters. Some questions have specific relevance to Australasia, particularly the uncertainties associated with using toxicity data from exotic species to protect unique indigenous species. Several related priority questions deal with the theme of how widely international ecotoxicological data and databases can be applied to regional ecosystems. Other timely questions, which focus on improving predictive chemistry and toxicology tools and techniques, will be important to answer several of the priority questions identified here. Another important question raised was how to protect local cultural and social values and maintain indigenous engagement during problem formulation and identification of ecosystem protection goals. Addressing these questions will be challenging, but doing so promises to advance environmental sustainability in Oceania and globally.

Key characteristics of 86 agents known to cause cancer in humans

Since the inception of the International Agency for Research on Cancer (IARC) in the early 1970s, the IARC Monographs Programme has evaluated more than 1000 agents with respect to carcinogenic hazard; of these, up to and including Volume 119 of the IARC Monographs, 120 agents met the criteria for classification as carcinogenic to humans (Group 1). Volume 100 of the IARC Monographs provided a review and update of Group 1 carcinogens. These agents were divided into six broad categories: (I) pharmaceuticals; (II) biological agents; (III) arsenic, metals, fibers, and dusts; (IV) radiation; (V) personal habits and indoor combustions; and (VI) chemical agents and related occupations. Data on biological mechanisms of action (MOA) were extracted from the Monographs to assemble a database on the basis of ten key characteristics attributed to human carcinogens. After some grouping of similar agents, the characteristic profiles were examined for 86 Group 1 agents for which mechanistic information was available in the IARC Monographs up to and including Volume 106, based upon data derived from human in vivo, human in vitro, animal in vivo, and animal in vitro studies. The most prevalent key characteristic was "is genotoxic", followed by "alters cell proliferation, cell death, or nutrient supply" and "induces oxidative stress". Most agents exhibited several of the ten key characteristics, with an average of four characteristics per agent, a finding consistent with the notion that cancer development in humans involves multiple pathways. Information on the key characteristics was often available from multiple sources, with many agents demonstrating concordance between human and animal sources, particularly with respect to genotoxicity. Although a detailed comparison of the characteristics of different types of agents was not attempted here, the overall characteristic profiles for pharmaceutical agents and for chemical agents and related occupations appeared similar. Further in-depth analyses of this rich database of characteristics of human carcinogens are expected to provide additional insights into the MOA of human cancer development.

Triclosan Affects Ca2+ Regulatory Module and Musculature Development in Skeletal Myocyte during Early Life Stages of Zebrafish (Danio rerio)

Advanced technologies for toxicity tests are designed to identify biomarkers with superior predictive power or end points of the complex web of biological pathways. However, the data obtained need to be fully characterized for dose-response, physiological systems, and relevance to a system or (sub) population before biological interpretation and decision making. In this study, the toxicity of triclosan (TCS) on zebrafish was selected as a case study to correlate the observed morphological effects with existing data and identify the critical events by receptor activity sensitivity analysis. Triclosan exhibited weak acute toxicity against zebrafish and significantly affected the development of trunk muscles at 0.52, 1.04, and 1.73 μM. Through receptor-mediated screening, we found that the adverse effects of TCS induce Ryanodine receptor 1 (RyR1) activity and distort Ca²⁺ signaling. The trunk skeletal muscle abnormalities occurred only when the dihydropyridine receptor (DHPR) was blocked, demonstrating that TCS mainly influenced the Ca²⁺ regulatory module associated with signaling between DHPRs and RyR1; DHPRs mainly regulated the orthograde and retrograde signaling in skeletal muscles. This unexpected result could integrate the mode of action of TCS and provide insight for high-throughput screening and toxicity prediction using zebrafish.

Determination of chemical-disease risk values to prioritize connections between environmental factors, genetic variants, and human diseases

Traditional methods for chemical risk assessment are too time-consuming and resource-intensive to characterize either the diversity of chemicals to which humans are exposed or how that diversity may manifest in population susceptibility differences. The advent of novel toxicological data sources and their integration with bioinformatic databases affords opportunities for modern approaches that consider gene-environment (GxE) interactions in population risk assessment. Here, we present an approach that systematically links multiple data sources to relate chemical risk values to diseases and gene-disease variants. These data sources include high-throughput screening (HTS) results from Tox21/ToxCast, chemical-disease relationships from the Comparative Toxicogenomics Database (CTD), hazard data from resources like the Integrated Risk Information System, exposure data from the ExpoCast initiative, and gene-variant-disease information from the DisGeNET database. We use these integrated data to identify variants implicated in chemical-disease enrichments and develop a new value that estimates the risk of these associations toward differential population responses. Finally, we use this value to prioritize chemical-disease associations by exploring the genomic distribution of variants implicated in high-risk diseases. We offer this modular approach, termed DisQGOS (Disease Quotient Genetic Overview Score), for relating overall chemical-disease risk to potential for population variable responses, as a complement to methods aiming to modernize aspects of risk assessment.

Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment

An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Development of a Novel AOP for Cyp2F2-Mediated Lung Cancer in Mice

Traditional methods for carcinogenicity testing rely heavily on the rodent bioassay as the standard for identification of tumorigenic risk. As such, identification of species-specific outcomes and/or metabolism are a frequent argument for regulatory exemption. One example is the association of tumor formation in the mouse lung after exposure to Cyp2F2 ligands. The adverse outcome pathway (AOP) framework offers a theoretical platform to address issues of species specificity that is consistent, transparent, and capable of integrating data from new approach methodologies as well as traditional data streams. A central premise of the AOP concept is that pathway progression from the molecular initiating event (MIE) implies a definable “response-response” (R-R) relationship between each key event (KE) that drives the pathway towards a specific adverse outcome (AO). This article describes an AOP for lung cancer in the mouse from an MIE of Cyp2F2-specific reactive metabolite formation, advancing through KE that include protein and/or nucleic acid adducts, diminished Club Cell 10 kDa (CC10) protein expression, hyperplasia of CC10 deficient Club cells, and culminating in the AO of mixed-cell tumor formation in the distal airways. This tumor formation is independent of route of exposure and our AOP construct is based on overlapping mechanistic events for naphthalene, styrene, ethyl benzene, isoniazid, and fluensulfone in the mouse. This AOP is intended to accelerate the explication of an apparent mouse-specific outcome and serve as a starting point for a quantitative analysis of mouse-human differences in susceptibility to the tumorigenic effects of Cyp2F2 ligands.

Development and implementation of evidence-based laboratory safety management tools for a public health laboratory

We developed an evidence-based continuous quality improvement (CQI) cycle for laboratory safety as a method of utilizing survey data to improve safety in a public health laboratory setting. • Expert Opinion: The CQI cycle begins with the solicitation of laboratory staff input via an annual survey addressing potential chemical, physical and radiological hazards associated with multiple laboratory activities. The survey collects frequency, severity and exposure data related to these activities in the context of the most pathogenic organisms handled at least weekly. • Gap Analysis: Step 2 of the CQI cycle used survey data to identify areas needing improvement. Typically, the traditional two-dimensional risk assessment matrix is used to prioritize mitigations. However, we added an additional dimension - frequency of exposure - to create three-dimensional risk maps to better inform and communicate risk priorities. • Mitigation Measures: Step 3 of the CQI cycle was to use these results to develop mitigations. This included evaluating the identified risks to determine what risk control measures (elimination, substitution, engineering, administrative or PPE) were needed. In the 2016 iteration of the CQI cycle described here, all mitigations were based on administrative controls. • Evaluation and Feedback: The last step of the CQI cycle was to evaluate the inferred effects of interventions through subsequent surveys, allowing for qualitative assessment of intervention effectiveness while simultaneously restarting the cycle by identifying new hazards. Here we describe the tools used to drive this CQI cycle, including the survey tool, risk analysis method, design of interventions and inference of mitigation effectiveness.

Integrating -Omics Approaches into Human Population-Based Studies of Prenatal and Early-Life Exposures

PURPOSE OF REVIEW

We present the study design and methodological suggestions for population-based studies that integrate molecular -omics data and highlight recent studies that have used such data to examine the potential impacts of prenatal environmental exposures on fetal health.

RECENT FINDINGS

Epidemiologic studies have observed numerous relationships between prenatal exposures (smoking, toxic metals, endocrine disruptors) and fetal and early-life molecular profiles, though such investigations have so far been dominated by epigenomic association studies. However, recent transcriptomic, proteomic, and metabolomic studies have demonstrated their promise for the identification of exposure and response biomarkers. Molecular -omics have opened new avenues of research in environmental health that can improve our understanding of disease etiology and contribute to the development of exposure and response biomarkers. Studies that incorporate multiple -omics data from different molecular domains in longitudinally collected samples hold particular promise.

Homology models of mouse and rat estrogen receptor-α ligand-binding domain created by in silico mutagenesis of a human template: molecular docking with 17ß-estradiol, diethylstilbestrol, and paraben analogs

Crystal structures exist for human, but not rodent, estrogen receptor-α ligand-binding domain (ERα-LBD). Consequently, rodent studies involving binding of compounds to ERα-LBD are limited in their molecular-level interpretation and extrapolation to humans. Because the sequences of rodent and human ERα-LBDs are > 95% identical, we expected their 3D structures and ligand binding to be highly similar. To test this hypothesis, we used the human ERα-LBD structure (PDB 3UUD) as a template to produce rat and mouse homology models. Employing the rodent models and human structure, we generated docking poses of 23 Group A ligands (17ß-estradiol, diethylstilbestrol, and 21 paraben analogs) in AutoDock Vina for interspecies comparisons. Ligand RMSDs (Å) (median, 95% CI) were 0.49 (0.21-1.82) (human-mouse) and 1.19 (0.22-1.82) (human-rat), well below the 2.0-2.5 Å range for equivalent docking poses. Numbers of interspecies ligand-receptor residue contacts were highly similar, with Sorensen Sc (%) = 96.8 (90.0-100) (human-mouse) and 97.7 (89.5-100) (human-rat). Likewise, numbers of interspecies ligand-receptor residue contacts were highly correlated: Pearson r = 0.913 (human-mouse) and 0.925 (human-rat). Numbers of interspecies ligand-receptor atom contacts were even more tightly correlated: r = 0.979 (human-mouse) and 0.986 (human-rat). Pyramid plots of numbers of ligand-receptor atom contacts by residue exhibited high interspecies symmetry and had Spearman r s = 0.977 (human-mouse) and 0.966 (human-rat). Group B ligands included 15 ring-substituted parabens recently shown experimentally to exhibit decreased binding to human ERα and to exert increased antimicrobial activity. Ligand efficiencies calculated from docking ligands into human ERα-LBD were well correlated with those derived from published experimental data (Pearson partial r p = 0.894 and 0.918; Groups A and B, respectively). Overall, the results indicate that our constructed rodent ERα-LBDs interact with ligands in like manner to the human receptor, thus providing a high level of confidence in extrapolations of rodent to human ligand-receptor interactions.

New insights into antibiotic resistome in drinking water and management perspectives: A metagenomic based study of small-sized microbes

The proliferation of antibiotic resistance genes (ARGs) in drinking water and their potential horizontal transfer to pathogenic microbes may cause failure of antibiotics. However, antimicrobial resistome monitoring in drinking water is not currently routine. The bacterial hosts of ARGs, especially small-sized microbes in drinking water, may not be effectively removed by membrane filtration disinfection and thus pose threats to human health. In the present study, using metagenomic based approach, we investigated antibiotic resistome of small-sized microbes (0.2-0.45 μm) in 20 household drinking water samples from 12 cities in Mainland China, Hong Kong and Singapore. A total of 265 ARG subtypes belonging to 17 ARG types were detected at abundances ranging from 4.0 × 10^-2 to 1.0 × 10⁰ copies/cell. Multidrug, bacitracin and aminoglycoside resistance genes are dominant, and 43 ARG subtypes were specifically carried by small-size microbes. Metagenomic assembly strategy revealed fragments of three opportunistic pathogen, i.e. Pseudomonas alcaligenes, Pseudomonas aeruginosa and Mycobacterium gordonae, carried mexW, aph(3')-I and aac(2')-I, respectively. Drinking water samples were classified into three groups based on the presence of ARG, pathogen and ARG-carrying pathogen. These new insights into the antibiotic resistome of small microbes in drinking water over a broad scale indicate the need for more comprehensive ARGs monitoring and surveillance of drinking water supplies. These findings, together with the perspectives and strategies proposed in this study, could support initiatives to improve drinking water safety.

Mechanisms of DNA-reactive and epigenetic chemical carcinogens: applications to carcinogenicity testing and risk assessment

Chemicals with carcinogenic activity in either animals or humans produce increases in neoplasia through diverse mechanisms. One mechanism is reaction with nuclear DNA. Other mechanisms consist of epigenetic effects involving either modifications of regulatory macromolecules or perturbation of cellular regulatory processes. The basis for distinguishing between carcinogens that have either DNA reactivity or an epigenetic activity as their primary mechanism of action is detailed in this review. In addition, important applications of information on these mechanisms of action to carcinogenicity testing and human risk assessment are discussed.

The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives

The field of experimental toxicology is rapidly advancing by incorporating novel techniques and methods that provide a much more granular view into the mechanisms of potential adverse effects of chemical exposures on human health. The data from various in vitro assays and computational models are useful not only for increasing confidence in hazard and risk decisions, but also are enabling better, faster and cheaper assessment of a greater number of compounds, mixtures, and complex products. This is of special value to the field of green chemistry where design of new materials or alternative uses of existing ones is driven, at least in part, by considerations of safety. This article reviews the state of the science and decision-making in scenarios when little to no data may be available to draw conclusions about which choice in green chemistry is "safer." It is clear that there is no "one size fits all" solution and multiple data streams need to be weighed in making a decision. Moreover, the overall level of familiarity of the decision-makers and scientists alike with new assessment methodologies, their validity, value and limitations is evolving. Thus, while the "impact" of the new developments in toxicology on the field of green chemistry is great already, it is premature to conclude that the data from new assessment methodologies have been widely accepted yet.

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.

Effect of aflatoxin B1, benzo[a]pyrene, and methapyrilene on transcriptomic and epigenetic alterations in human liver HepaRG cells

The increasing number of man-made chemicals in the environment that may pose a carcinogenic risk highlights the need for developing reliable time- and cost-effective approaches for carcinogen detection and identification. To address this issue, we investigated the utility of high-throughput microarray gene expression and next-generation genome-wide DNA methylation sequencing for the in vitro identification of genotoxic and non-genotoxic carcinogens. Terminally differentiated and metabolically competent human liver HepaRG cells were treated at minimally cytotoxic concentrations of (i) the genotoxic human liver carcinogen aflatoxin B₁ (AFB1) and its structural non-carcinogenic analog aflatoxin B₂ (AFB2); (ii) the genotoxic human lung carcinogen benzo[a]pyrene (B[a]P) and its non-carcinogenic isomer benzo[e]pyrene (B[e]P); and (iii) the non-genotoxic liver carcinogen methapyrilene for 72 h and transcriptomic and DNA methylation profiles were examined. Treatment of HepaRG cells with the liver carcinogens AFB1 and methapyrilene generated distinct gene-expression profiles, whereas B[a]P had only a slight effect on gene expression. In contrast to transcriptomic alterations, treatment of HepaRG cells with the carcinogenic and non-carcinogenic chemicals resulted in profound changes in the DNA methylation footprint; however, the correlation between gene-specific DNA methylation and gene expression changes was minimal. Among the carcinogen-altered genes, transferrin (TF) emerged as sensitive marker for an initial screening of chemicals for their potential liver carcinogenicity. Potential liver carcinogens (i.e., chemicals causing altered TF gene expression) could then be subjected to gene-expression analyses to differentiate genotoxic from non-genotoxic liver carcinogens. This approach may substantially enhance the identification and assessment of potential liver carcinogens.

High-Content Assay Multiplexing for Muscle Toxicity Screening in Human-Induced Pluripotent Stem Cell-Derived Skeletal Myoblasts

Skeletal muscle-associated toxicity is an underresearched area in the field of high-throughput toxicity screening; hence, the potential adverse effects of drugs and chemicals on skeletal muscle are largely unknown. Novel organotypic microphysiological in vitro models are being developed to replicate the contractile function of skeletal muscle; however, the throughput and a need for specialized equipment may limit the utility of these tissue chip models for screening. In addition, recent developments in stem cell biology have resulted in the generation of induced pluripotent stem cell (iPSC)-derived skeletal myoblasts that enable high-throughput in vitro screening. This study set out to develop a high-throughput multiplexed assay using iPSC-derived skeletal myoblasts that can be used as a first-pass screen to assess the potential for chemicals to affect skeletal muscle. We found that cytotoxicity and cytoskeletal integrity are most useful and reproducible assays for the skeletal myoblasts when evaluating overall cellular health or gauging disruptions in actin polymerization following 24 h of exposure. Both assays are based on high-content imaging and quantitative image processing to derive quantitative phenotypes. Both assays showed good to excellent assay robustness and reproducibility measured by interplate and interday replicability, coefficients of variation of negative controls, and Z'-factors for positive control chemicals. Concentration response assessment of muscle-related toxicants showed specificity of the observed effects compared to the general cytotoxicity. Overall, this study establishes a high-throughput multiplexed assay using skeletal myoblasts that may be used for screening and prioritization of chemicals for more complex tissue chip-based and in vivo evaluation.