Framework for the quantitative weight-of-evidence analysis of 'omics data for regulatory purposes

Empirical analysis of lead neurotoxicity mode of action and its application in health risk assessment

The mode of action (MOA) framework is proposed to inform a biological link between chemical exposures and adverse health effects. Despite a significant increase in knowledge and awareness, the application of MOA in human health risk assessment (RA) remains limited. This study aims to discuss the adoption of MOA for health RA within a regulatory context, taking our previously proposed but not yet validated MOA for lead neurotoxicity as an example. We first conducted a quantitative weight of evidence (qWOE) assessment, which revealed that the MOA has a moderate confidence. Then, targeted bioassays were performed within an in vitro blood-brain barrier (BBB) model to quantitatively validate the scientific validity of key events (KEs) in terms of essentiality and concordance of empirical support (dose/temporal concordance), which increases confidence in utilizing the MOA for RA. Building upon the quantitative validation data, we further conducted benchmark dose (BMD) analysis to map dose-response relationships for the critical toxicity pathways, and the lower limit of BMD at a 5% response (BMDL5) was identified as the point of departure (POD) value for adverse health effects. Notably, perturbation of the Aryl Hydrocarbon Receptor (AHR) signaling pathway exhibited the lowest POD value, measured at 0.0062 μM. Considering bioavailability, we further calculated a provisional health-based guidance value (HBGV) for children's lead intake, determining it to be 2.56 μg/day. Finally, the health risk associated with the HBGV was assessed using the hazard quotient (HQ) approach, which indicated that the HBGV established in this study is a relative safe reference value for lead intake. In summary, our study described the procedure for utilizing MOA in health RA and set an example for MOA-based human health risk regulation.

Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction

Assessing chemical safety is essential to evaluate the potential risks of chemical exposure to human health and the environment. Traditional methods relying on animal testing are being replaced by 3R (reduction, refinement, and replacement) principle-based alternatives, mainly depending on in vitro test methods and the Adverse Outcome Pathway framework. However, these approaches often focus on the properties of the compound, missing the broader chemical-biological interaction perspective. Currently, the lack of comprehensive molecular characterization of the in vitro test system results in limited real-world representation and contextualization of the toxicological effect under study. Leveraging omics data strengthens the understanding of the responses of different biological systems, emphasizing holistic chemical-biological interactions when developing in vitro methods. Here, we discuss the relevance of meticulous test system characterization on two safety assessment relevant scenarios and how omics-based, data-driven approaches can improve the future generation of alternative methods.

Evidential requirements for the regulatory hazard and risk assessment of respiratory sensitisers: methyl methacrylate as an example

This review addresses the need for a framework to increase the consistency, objectivity and transparency in the regulatory assessment of respiratory sensitisers and associated uncertainties. Principal issues are considered and illustrated through a case study (with methyl methacrylate). In the absence of test methods validated for regulatory use, formal documentation of the weight-of-evidence for hazard classification both at the level of integration of individual studies within lines of evidence and across a broad range of data streams was agreed to be critical for such a framework. An integrated approach is proposed to include not only occupational studies and clinical evidence for the regulatory assessment of respiratory sensitisers, but also information on structure and physical and chemical factors, predictive approaches such as structure activity analysis and in vitro and in vivo mechanistic and toxicokinetic findings. A weight-of-evidence protocol, incorporating integration of these sources of data based on predefined considerations, would contribute to transparency and consistency in the outcome of the assessment. In those cases where a decision may need to be taken on the basis of occupational findings alone, conclusions should be based on transparent weighting of relevant data on the observed prevalence of occupational asthma in various studies taking into account all relevant information including the range and nature of workplace exposures to the substance of interest, co-exposure to other chemicals and study quality.

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.

Semi-automated harmonization and selection of chemical data for risk and impact assessment

Chemical data for thousands of substances are available for safety, risk, life cycle and substitution assessments, as submitted for example under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation. However, to widely disseminate reported physicochemical properties as well as human and ecological exposure and toxicological data for use in various science and policy fields, systematic methods for data harmonization and selection are necessary. In response to this need, we developed a semi-automated method for deriving appropriate substance property values as input for various assessment frameworks with different requirements for resolution and data quality. Starting with data reported for a given substance and property, we propose a set of aligned data selection and harmonization criteria to obtain a representative mean value and related confidence intervals per chemical-property combination. The proposed method was tested on a set of octanol-water partition coefficients (K_ow) for an illustrative set of 20 substances, reported under the REACH regulation as example data source. Our method is generally applicable to any set of substances, and can assess specific distributions in quality and variability across reported data. Further research can likely extend our method for mining information from text fields and adapt it to available data reported or collected from other sources and other substance properties to improve the reliability of input data for risk and impact assessments.

Application of radiation omics in the development of adverse outcome pathway networks: an example of radiation-induced cardiovascular disease

BACKGROUND

Epidemiological studies have indicated that exposure of the heart to doses of ionizing radiation as low as 0.5 Gy increases the risk of cardiac morbidity and mortality with a latency period of decades. The damaging effects of radiation to myocardial and endothelial structures and functions have been confirmed radiobiologically at high dose, but much less is known at low dose. Integration of radiation biology and epidemiology data is a recommended approach to improve the radiation risk assessment process. The adverse outcome pathway (AOP) framework offers a comprehensive tool to compile and translate mechanistic information into pathological endpoints which may be relevant for risk assessment at the different levels of a biological system. Omics technologies enable the generation of large volumes of biological data at various levels of complexity, from molecular pathways to functional organisms. Given the quality and quantity of available data across levels of biology, omics data can be attractive sources of information for use within the AOP framework. It is anticipated that radiation omics studies could improve our understanding of the molecular mechanisms behind the adverse effects of radiation on the cardiovascular system. In this review, we explored the available omics studies on radiation-induced cardiovascular disease (CVD) and their applicability to the proposed AOP for CVD.

RESULTS

The results of 80 omics studies published on radiation-induced CVD over the past 20 years have been discussed in the context of the AOP of CVD proposed by Chauhan et al. Most of the available omics data on radiation-induced CVD are from proteomics, transcriptomics, and metabolomics, whereas few datasets were available from epigenomics and multi-omics. The omics data presented here show great promise in providing information for several key events of the proposed AOP of CVD, particularly oxidative stress, alterations of energy metabolism, extracellular matrix and vascular remodeling.

CONCLUSIONS

The omics data presented here shows promise to inform the various levels of the proposed AOP of CVD. However, the data highlight the urgent need of designing omics studies to address the knowledge gap concerning different radiation scenarios, time after exposure and experimental models. This review presents the evidence to build a qualitative omics-informed AOP and provides views on the potential benefits and challenges in using omics data to assess risk-related outcomes.

Clearly weighing the evidence in read-across can improve assessments of data-poor chemicals

This paper provides a systematic weight-of-evidence method for read-across analyses of data-poor chemicals. The read-across technique extrapolates toxicity from analogous chemicals for which suitable test data are available to a target chemical. To determine that a candidate analogue is the 'best' and is sufficiently similar, the evidence for similarity of each candidate analogue to the target is weighed. We present a systematic weight of evidence method that provides transparency and imposes a consistent and rigorous inferential process. The method assembles relevant information concerning structure, physicochemical attributes, toxicokinetics, and toxicodynamics of the target and analogues. The information is then organized by evidence types and subtypes and weighted in terms of properties: relevance, strength, and reliability into weight levels, expressed as symbols. After evidence types are weighted, the bodies of evidence are weighted for collective properties: number, diversity, and coherence. Finally, the weights for the types and bodies of evidence are weighed for each analogue, and, if the overall weight of evidence is sufficient for one or more analogues, the analogue with the greatest weight is used to estimate the endpoint effect. We illustrate this WoE approach with a read-across analysis for screening the organochlorine contaminant, p,p'-dichlorodiphenyldichloroethane (DDD), for noncancer oral toxicity.

Open Science in regulatory environmental risk assessment

A possible way to alleviate the public skepticism toward regulatory science is to increase transparency by making all data and value judgments used in regulatory decision making accessible for public interpretation, ideally early on in the process, and following the concepts of Open Science. This paper discusses the opportunities and challenges in strengthening Open Science initiatives in regulatory environmental risk assessment (ERA). In this discussion paper, we argue that the benefits associated with Open Science in regulatory ERA far outweigh its perceived risks. All stakeholders involved in regulatory ERA (e.g., governmental regulatory authorities, private sector, academia, and nongovernmental organizations), as well as professional organizations like the Society of Environmental Toxicology and Chemistry, can play a key role in supporting the Open Science initiative, by promoting the use of recommended reporting criteria for reliability and relevance of data and tools used in ERA, and by developing a communication strategy for both professionals and nonprofessionals to transparently explain the socioeconomic value judgments and scientific principles underlying regulatory ERA. Integr Environ Assess Manag 2021;17:1229-1242. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Improved Algal Toxicity Test System for Robust Omics-Driven Mode-of-Action Discovery in Chlamydomonas reinhardtii

Algae are key components of aquatic food chains. Consequently, they are internationally recognised test species for the environmental safety assessment of chemicals. However, existing algal toxicity test guidelines are not yet optimized to discover molecular modes of action, which require highly-replicated and carefully controlled experiments. Here, we set out to develop a robust, miniaturised and scalable Chlamydomonas reinhardtii toxicity testing approach tailored to meet these demands. We primarily investigated the benefits of synchronised cultures for molecular studies, and of exposure designs that restrict chemical volatilisation yet yield sufficient algal biomass for omics analyses. Flow cytometry and direct-infusion mass spectrometry metabolomics revealed significant and time-resolved changes in sample composition of synchronised cultures. Synchronised cultures in sealed glass vials achieved adequate growth rates at previously unachievably-high inoculation cell densities, with minimal pH drift and negligible chemical loss over 24-h exposures. Algal exposures to a volatile test compound (chlorobenzene) yielded relatively high reproducibility of metabolic phenotypes over experimental repeats. This experimental test system extends existing toxicity testing formats to allow highly-replicated, omics-driven, mode-of-action discovery.

An Integrative Multi-Omics Workflow to Address Multifactorial Toxicology Experiments

Toxicology studies can take advantage of omics approaches to better understand the phenomena underlying the phenotypic alterations induced by different types of exposure to certain toxicants. Nevertheless, in order to analyse the data generated from multifactorial omics studies, dedicated data analysis tools are needed. In this work, we propose a new workflow comprising both factor deconvolution and data integration from multiple analytical platforms. As a case study, 3D neural cell cultures were exposed to trimethyltin (TMT) and the relevance of the culture maturation state, the exposure duration, as well as the TMT concentration were simultaneously studied using a metabolomic approach combining four complementary analytical techniques (reversed-phase LC and hydrophilic interaction LC, hyphenated to mass spectrometry in positive and negative ionization modes). The ANOVA multiblock OPLS (AMOPLS) method allowed us to decompose and quantify the contribution of the different experimental factors on the outcome of the TMT exposure. Results showed that the most important contribution to the overall metabolic variability came from the maturation state and treatment duration. Even though the contribution of TMT effects represented the smallest observed modulation among the three factors, it was highly statistically significant. The MetaCore™ pathway analysis tool revealed TMT-induced alterations in biosynthetic pathways and in neuronal differentiation and signaling processes, with a predominant deleterious effect on GABAergic and glutamatergic neurons. This was confirmed by combining proteomic data, increasing the confidence on the mechanistic understanding of such a toxicant exposure.

Deciphering the Combined Effects of Environmental Stressors on Gene Transcription: A Conceptual Approach

The use of classical mixture toxicity models to predict the combined effects of environmental stressors based on toxicogenomics (OMICS) data is still in its infancy. Although several studies have made attempts to implement mixture modeling in OMICS analysis to understand the low-dose interactions of stressors, it is not clear how interactions occur at the molecular level and how results generated from such approaches can be better used to inform future studies and cumulative hazard assessment of multiple stressors. The present work was therefore conducted to propose a conceptual approach for combined effect assessment using global gene expression data, as illustrated by a case study on assessment of combined effects of gamma radiation and depleted uranium (DU) on Atlantic salmon ( Salmo salar). Implementation of the independent action (IA) model in reanalysis of a previously published microarray gene expression dataset was performed to describe gene expression patterns of combined effects and identify key gene sets and pathways that were relevant for understanding the interactive effects of these stressors. By using this approach, 3120 differentially expressed genes (DEGs) were found to display additive effects, whereas 279 (273 synergistic, 6 antagonistic) were found to deviate from additivity. Functional analysis further revealed that multiple toxicity pathways, such as oxidative stress responses, cell cycle regulation, lipid metabolism, and immune responses were enriched by DEGs showing synergistic gene expression. A key toxicity pathway of DNA damage leading to enhanced tumorigenesis signaling is highlighted and discussed in detail as an example of how to take advantage of the approach. Furthermore, a conceptual workflow describing the integration of combined effect modeling, OMICS analysis, and bioinformatics is proposed. The present study presents a conceptual framework for utilizing OMICS data in combined effect assessment and may provide novel strategies for dealing with data analysis and interpretation of molecular responses of multiple stressors.

Framework for the quality assurance of 'omics technologies considering GLP requirements

'Omics technologies are gaining importance to support regulatory toxicity studies. Prerequisites for performing 'omics studies considering GLP principles were discussed at the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) Workshop Applying 'omics technologies in Chemical Risk Assessment. A GLP environment comprises a standard operating procedure system, proper pre-planning and documentation, and inspections of independent quality assurance staff. To prevent uncontrolled data changes, the raw data obtained in the respective 'omics data recording systems have to be specifically defined. Further requirements include transparent and reproducible data processing steps, and safe data storage and archiving procedures. The software for data recording and processing should be validated, and data changes should be traceable or disabled. GLP-compliant quality assurance of 'omics technologies appears feasible for many GLP requirements. However, challenges include (i) defining, storing, and archiving the raw data; (ii) transparent descriptions of data processing steps; (iii) software validation; and (iv) ensuring complete reproducibility of final results with respect to raw data. Nevertheless, 'omics studies can be supported by quality measures (e.g., GLP principles) to ensure quality control, reproducibility and traceability of experiments. This enables regulators to use 'omics data in a fit-for-purpose context, which enhances their applicability for risk assessment.

Applying 'omics technologies in chemicals risk assessment: Report of an ECETOC workshop

Prevailing knowledge gaps in linking specific molecular changes to apical outcomes and methodological uncertainties in the generation, storage, processing, and interpretation of 'omics data limit the application of 'omics technologies in regulatory toxicology. Against this background, the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) convened a workshop Applying 'omics technologies in chemicals risk assessment that is reported herein. Ahead of the workshop, multi-expert teams drafted frameworks on best practices for (i) a Good-Laboratory Practice-like context for collecting, storing and curating 'omics data; (ii) the processing of 'omics data; and (iii) weight-of-evidence approaches for integrating 'omics data. The workshop participants confirmed the relevance of these Frameworks to facilitate the regulatory applicability and use of 'omics data, and the workshop discussions provided input for their further elaboration. Additionally, the key objective (iv) to establish approaches to connect 'omics perturbations to phenotypic alterations was addressed. Generally, it was considered promising to strive to link gene expression changes and pathway perturbations to the phenotype by mapping them to specific adverse outcome pathways. While further work is necessary before gene expression changes can be used to establish safe levels of substance exposure, the ECETOC workshop provided important incentives towards achieving this goal.