Aggregate Exposure Pathways in Support of Risk Assessment

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Chemical-induced liver cancer: an adverse outcome pathway perspective

INTRODUCTION

The evaluation of the potential carcinogenicity is a key consideration in the risk assessment of chemicals. Predictive toxicology is currently switching toward non-animal approaches that rely on the mechanistic understanding of toxicity.

AREAS COVERED

Adverse outcome pathways (AOPs) present toxicological processes, including chemical-induced carcinogenicity, in a visual and comprehensive manner, which serve as the conceptual backbone for the development of non-animal approaches eligible for hazard identification. The current review provides an overview of the available AOPs leading to liver cancer and discusses their use in advanced testing of liver carcinogenic chemicals. Moreover, the challenges related to their use in risk assessment are outlined, including the exploitation of available data, the need for semantic ontologies, and the development of quantitative AOPs.

EXPERT OPINION

To exploit the potential of liver cancer AOPs in the field of risk assessment, 3 immediate prerequisites need to be fulfilled. These include developing human relevant AOPs for chemical-induced liver cancer, increasing the number of AOPs integrating quantitative toxicodynamic and toxicokinetic data, and developing a liver cancer AOP network. As AOPs and other areas in the field continue to evolve, liver cancer AOPs will progress into a reliable and robust tool serving future risk assessment and management.

Human biomonitoring and toxicokinetics as key building blocks for next generation risk assessment

Human health risk assessment is historically built upon animal testing, often following Organisation for Economic Co-operation and Development (OECD) test guidelines and exposure assessments. Using combinations of human relevant in vitro models, chemical analysis and computational (in silico) approaches bring advantages compared to animal studies. These include a greater focus on the human species and on molecular mechanisms and kinetics, identification of Adverse Outcome Pathways and downstream Key Events as well as the possibility of addressing susceptible populations and additional endpoints. Much of the advancement and progress made in the Next Generation Risk Assessment (NGRA) have been primarily focused on new approach methodologies (NAMs) and physiologically based kinetic (PBK) modelling without incorporating human biomonitoring (HBM). The integration of toxicokinetics (TK) and PBK modelling is an essential component of NGRA. PBK models are essential for describing in quantitative terms the TK processes with a focus on the effective dose at the expected target site. Furthermore, the need for PBK models is amplified by the increasing scientific and regulatory interest in aggregate and cumulative exposure as well as interactions of chemicals in mixtures. Since incorporating HBM data strengthens approaches and reduces uncertainties in risk assessment, here we elaborate on the integrated use of TK, PBK modelling and HBM in chemical risk assessment highlighting opportunities as well as challenges and limitations. Examples are provided where HBM and TK/PBK modelling can be used in both exposure assessment and hazard characterization shifting from external exposure and animal dose/response assays to animal-free, internal exposure-based NGRA.

Evidence integration on health damage for humidifier disinfectant exposure and legal presumption of causation

OBJECTIVES

Inhalation exposure to humidifier disinfectants has resulted to various types of health damages in Korea. To determine the epidemiological correlation necessary for presuming the legal causation, we aimed to develop a method to synthesize the entire evidence.

METHODS

Epidemiological and toxicological studies are systematically reviewed. Target health problems are selected by criteria such as frequent complaints of claimants. Relevant epidemiologic studies are reviewed and the risk of bias and confidence level of the total evidence are evaluated. Toxicological literature reviews are conducted on three lines of evidence including hazard information, animal studies, and mechanistic studies, considering the source-to-exposure-to-outcome continuum. The confidence level of the body of evidence is then translated into the toxicological evidence levels for the causality between humidifier disinfectant exposure and health effects. Finally, the levels of epidemiological and toxicological evidence are synthesized.

RESULTS

Under the Special Act revised in 2020, if the history of exposure and the disease occurred/worsened after exposure were approved, and the epidemiological correlation between the exposure and disease was verified, the legal causation is presumed unless the company proves the evidence against it. The epidemiological correlation can be verified through epidemiological investigations, health monitoring, cohort investigations and/or toxicological studies. It is not simply as statistical association as understood in judicial precedents, but a general causation established by the evidence as a whole, i.e., through weight-of-the-evidence approach.

CONCLUSIONS

The weight-of-the-evidence approach differs from the conclusive single study approach and this systematic evidence integration can be used in presumption of causation.

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

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

Neurotoxicity of diesel exhaust extracts in zebrafish and its implications for neurodegenerative disease

Long-term air pollution (AP) exposure, including diesel exhaust exposure, is increasingly being recognized as a major contributor to the development of neurodegenerative diseases such as Parkinson's and Alzheimer's disease. How AP increases the risk of neurodegeneration is not well understood but might include direct neurotoxicity and CNS inflammation. We investigated the impact of diesel exhaust particulate extract (DEPe) exposure on the brain and the mechanisms by which microglia and astroglia might mediate neuronal changes. Zebrafish (ZF) were utilized to determine neuronal toxicity of and microglial response to DEPe and single cell RNA sequencing was employed to study cell type-specific transcriptomic responses within the ZF brain. DEPe exposure induced neuronal injury and microglial activation in vivo. However, preventing the development of microglia did not attenuate DEPe-induced neuron loss, leading us to investigate microglial, astroglial, and neuronal response to DEPe exposure at single-cell resolution. Differentially expressed genes and disease-relevant pathways were identified within glial and neuronal clusters after DEPe exposure. Microglia and astroglia existed in multiple states, some of which appear toxic and others protective to neurons. Neuronal transcriptomic analysis revealed that DEPe exposure reduced expression of autophagy-related genes consistent with direct neurotoxicity. In summary, DEPe exposure was neurotoxic in developing ZF larvae and induced neuroinflammation. The microglial inflammatory response did not contribute to neurotoxicity of DEPe and in fact, some glial clusters upregulated transcriptional pathways that are likely protective. Furthermore, DEPe exposure led to reduced expression of autophagy-related genes in neurons that likely contribute to its toxicity.

In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.

Diesel Exhaust Extract Exposure Induces Neuronal Toxicity by Disrupting Autophagy

The vast majority of neurodegenerative disease cannot be attributed to genetic causes alone and as a result, there is significant interest in identifying environmental modifiers of disease risk. Epidemiological studies have supported an association between long-term exposure to air pollutants and disease risk. Here, we investigate the mechanisms by which diesel exhaust, a major component of air pollution, induces neurotoxicity. Using a zebrafish model, we found that exposure to diesel exhaust particulate extract caused behavioral deficits and a significant decrease in neuron number. The neurotoxicity was due, at least in part, to reduced autophagic flux, which is a major pathway implicated in neurodegeneration. This neuron loss occurred alongside an increase in aggregation-prone neuronal protein. Additionally, the neurotoxicity induced by diesel exhaust particulate extract in zebrafish was mitigated by co-treatment with the autophagy-inducing drug nilotinib. This study links environmental exposure to altered proteostasis in an in vivo model system. These results shed light on why long-term exposure to traffic-related air pollution increases neurodegenerative disease risk and open up new avenues for exploring therapies to mitigate environmental exposures and promote neuroprotection.

Evolving Science and Practice of Risk Assessment

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

Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling

The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data-demanding systems biology models. Here, we propose a less data-demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, "Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition," using a small experimental data set from exposure of Lemna minor to the pesticide 3,5-dichlorophenol. The AOP-BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose-response and response-response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose-response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP-BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP-BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP-BN is limited by the small data set, our study demonstrates a proof-of-concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2021;17:147-164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Studying the Pathophysiology of Parkinson's Disease Using Zebrafish

Parkinson's disease is a common neurodegenerative disorder leading to severe disability. The clinical features reflect progressive neuronal loss, especially involving the dopaminergic system. The causes of Parkinson's disease are slowly being uncovered and include both genetic and environmental insults. Zebrafish have been a valuable tool in modeling various aspects of human disease. Here, we review studies utilizing zebrafish to investigate both genetic and toxin causes of Parkinson's disease. They have provided important insights into disease mechanisms and will be of great value in the search for disease-modifying therapies.

Exposure to New Emerging Bisphenols Among Young Children in Switzerland

Restrictions on the use of bisphenol A (BPA) in consumer products led to its replacement by various bisphenol (BP) analogues, yet young children's exposure to these analogues has been poorly characterized so far. This study aimed to characterize infants' and toddlers' exposure to BPA and 14 emerging BP analogues (i.e., bisphenol AF, bisphenol AP, bisphenol B, bisphenol BP, bisphenol C (BPC), bisphenol E, bisphenol F (BPF), bisphenol G, bisphenol M (BPM), bisphenol P, bisphenol PH, bisphenol S (BPS), bisphenol TMC, and bisphenol Z). We extracted infants' and toddlers' urine from diapers (n = 109) collected in Swiss daycare centers as a practical and noninvasive alternative approach to urinary biomonitoring. Bisphenols were present in 47% of the samples, with BPC and BPM being the most frequently detected (23% and 25% of all samples, respectively). The mean concentrations of urinary BPS and BPF were greater than that of BPA. This contrasts with data reported previously. Furthermore, statistical analysis revealed a significant and negative correlation between urinary BPM concentration and the population's age. Our results provide a first characterization of infants' and toddlers' exposure to bisphenols in Switzerland. This knowledge can be used to support ongoing biomonitoring studies and to prioritize exposure reduction and prevention strategies.

A case study applying pathway-oriented thinking to problem formulation for planning a systematic review

The use of evidence-based methods in chemical risk assessment (CRA) is still in its infancy. Novel approaches exploring how to implement Systematic Review (SR) principles and methods for evaluating human health risks from environmental chemical exposures are needed. This paper reports and comments on a conceptual model that was developed as part of a mapping exercise for planning a SR, using aluminium-containing antiperspirants (Al-AP) and female breast cancer risk as a case study. The work explores how knowledge-assembly tools and pathway-oriented thinking developed in systems toxicology can be applied to support problem formulation (PF) in the context of SR. A conceptual model was developed to map out key research questions, working hypotheses, routes of exposure, toxicity pathways and endpoints, and related health outcomes. The model draws on the analytic framework for screening topics of the U.S. Preventive Services Task Force and builds on the concept of a "source-to-outcome continuum", integrating knowledge gained from exposure pathway concepts such as the Aggregate Exposure Pathway and Adverse Outcome Pathways. The model can be used as a central decision and prioritization tool for scoping and framing Population, Exposure, Control, Outcome (PECO) questions in a transparent and iterative manner; as a supporting tool to guide the whole SR process; and to lay down the methodological foundation of a SR on the Al-AP breast cancer topic. Logic modelling can be easily combined with systems or pathway-oriented thinking, and allows for a more structured, objective and transparent approach to PF when applying SR methods to the CRA context.

Organizing mechanism-related information on chemical interactions using a framework based on the aggregate exposure and adverse outcome pathways

This paper presents a framework for organizing and accessing mechanistic data on chemical interactions. The framework is designed to support the assessment of risks from combined chemical exposures. The framework covers interactions between chemicals that occur over the entire source-to-outcome continuum including interactions that are studied in the fields of chemical transport, environmental fate, exposure assessment, dosimetry, and individual and population-based adverse outcomes. The framework proposes to organize data using a semantic triple of a chemical (subject), has impact (predicate), and a causal event on the source-to-outcome continuum of a second chemical (object). The location of the causal event on the source-to-outcome continuum and the nature of the impact are used as the basis for a taxonomy of interactions. The approach also builds on concepts from the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP). The framework proposes the linking of AEPs of multiple chemicals and the AOP networks relevant to those chemicals to form AEP-AOP networks that describe chemical interactions that cannot be characterized using AOP networks alone. Such AEP-AOP networks will aid the construction of workflows for both experimental design and the systematic review or evaluation performed in risk assessments. Finally, the framework is used to link the constructs of existing component-based approaches for mixture toxicology to specific categories in the interaction taxonomy.

Application of a combined aggregate exposure pathway and adverse outcome pathway (AEP-AOP) approach to inform a cumulative risk assessment: A case study with phthalates

Advancements in measurement and modeling capabilities are providing unprecedented access to estimates of chemical exposure and bioactivity. With this influx of new data, there is a need for frameworks that help organize and disseminate information on chemical hazard and exposure in a manner that is accessible and transparent. A case study approach was used to demonstrate integration of the Adverse Outcome Pathway (AOP) and Aggregate Exposure Pathway (AEP) frameworks to support cumulative risk assessment of co-exposure to two phthalate esters that are ubiquitous in the environment and that are associated with disruption of male sexual development in the rat: di(2-ethylhexyl) phthalate (DEHP) and di-n-butyl phthalate (DnBP). A putative AOP was developed to guide selection of an in vitro assay for derivation of bioactivity values for DEHP and DnBP and their metabolites. AEPs for DEHP and DnBP were used to extract key exposure data as inputs for a physiologically based pharmacokinetic (PBPK) model to predict internal metabolite concentrations. These metabolite concentrations were then combined using in vitro-based relative potency factors for comparison with an internal dose metric, resulting in an estimated margin of safety of ~13,000. This case study provides an adaptable workflow for integrating exposure and toxicity data by coupling AEP and AOP frameworks and using in vitro and in silico methodologies for cumulative risk assessment.

A Quantitative Source-to-Outcome Case Study To Demonstrate the Integration of Human Health and Ecological End Points Using the Aggregate Exposure Pathway and Adverse Outcome Pathway Frameworks

Exposure to environmental contaminants can lead to adverse outcomes in both human and nonhuman receptors. The Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) frameworks can mechanistically inform cumulative risk assessment for human health and ecological end points by linking together environmental transport and transformation, external exposure, toxicokinetics, and toxicodynamics. This work presents a case study of a hypothetical contaminated site to demonstrate a quantitative approach for implementing the AEP framework and linking this framework to AOPs. We construct an AEP transport and transformation model and then quantify external exposure pathways for humans, fishes, and small herbivorous mammals at the hypothetical site. A Monte Carlo approach was used to address parameter variability. Source apportionment was quantified for each species, and published pharmacokinetic models were used to estimate internal target site exposure from external exposures. Published dose-response data for a multispecies AOP network were used to interpret AEP results in the context of species-specific effects. This work demonstrates (1) the construction, analysis, and application of a quantitative AEP model, (2) the utility of AEPs for organizing mechanistic exposure data and highlighting data gaps, and (3) the advantages provided by a source-to-outcome construct for leveraging exposure data and to aid transparency regarding assumptions.

Public data sources to support systems toxicology applications

Public databases provide a wealth of freely available information about chemicals, genes, proteins, biological networks, phenotypes, diseases, and exposure science that can be integrated to construct pathways for systems toxicology applications. Relating this disparate information from public repositories, however, can be challenging since databases use a variety of ways to represent, describe, and make available their content. The use of standard vocabularies to annotate key data concepts, however, allows the information to be more easily exchanged and combined for discovery of new findings. We explore some of the many public data sources currently available to support systems toxicology, and demonstrate the value of standardizing data to help construct chemical-induced outcome pathways.

A proposal for creating a taxonomy of chemical interactions using concepts from the aggregate exposure and adverse outcome pathways

Currently, there is no single taxonomy for organizing data on the various types of chemical interactions that may affect risks from combined exposures. A taxonomy of chemical interactions is proposed that is based on a combination of the aggregate exposure pathways (AEPs) and adverse outcome pathways (AOPs) (AEP-AOP framework). The AEP-AOP framework organizes data on the causal events that ocur over the entire source-exposure-response continuum of a chemical's release. The proposed taxonomy uses this framework in two ways. First, four top-level categories are established based on the location in the continuum where a chemical interaction occurs. Second, each top-level category has two or more subcategories that are based on concepts taken from AEPs and AOPs. The categories and subcategories are potentially useful in developing standardized definitions for interaction terms and improving our understanding of the impacts of chemical interactions on risk to human health and the environment.