Metabolomics as read-across tool: A case study with phenoxy herbicides

ToxProfiler: A novel human-based reporter assay for in vitro chemical safety assessment

In vitro chemical safety assessment often relies on simple and general cytotoxicity endpoint measurements and fails to adequately predict human toxicity. To improve the in vitro chemical safety assessment, it is important to understand the underlying mechanisms of toxicity. Here we introduce ToxProfiler, a novel human-based reporter assay that quantifies the chemical-induced stress responses at a single-cell level and reveals the toxicological mode-of-action (MoA) of novel drugs and chemicals. The assay accurately measures the activation of seven major cellular stress response pathways (oxidative stress, cell cycle stress, endoplasmic reticulum stress, ion stress, protein stress, autophagy and inflammation) that play a role in the adaptive responses prior to cellular toxicity. To assess the applicability of the assay in predicting the toxicity MoA of chemicals, we tested a set of 100 chemicals with well-known in vitro and in vivo toxicological profiles. Concentration response modeling and point-of-departure estimation for each reporter protein allowed for chemical potency ranking and revealed the primary toxicological MoA of chemicals. Furthermore, the assay could effectively group chemicals based on their shared toxicity signatures and link them to specific toxicological targets, e.g. mitochondrial toxicity and genotoxicity, and different human pathologies, including liver toxicity and cardiotoxicity. Overall, ToxProfiler is a quantitative in vitro reporter assay that can accurately provide insight into the toxicological MoA of compounds, thereby assisting in the future mechanism-based safety assessment of chemicals.

Assessment of pulmonary fibrosis using weighted gene co-expression network analysis

For many industrial chemicals toxicological data is sparse regarding several regulatory endpoints, so there is a high and often unmet demand for NAMs that allow for screening and prioritization of these chemicals. In this proof of concept case study we propose multi-gene biomarkers of compounds' ability to induce lung fibrosis and demonstrate their application in vitro. For deriving these biomarkers we used weighted gene co-expression network analysis to reanalyze a study where the time-dependent pulmonary gene-expression in mice treated with bleomycin had been documented. We identified eight modules of 58 to 273 genes each which were particularly activated during the different phases (inflammatory; acute and late fibrotic) of the developing fibrosis. The modules' relation to lung fibrosis was substantiated by comparison to known markers of lung fibrosis from DisGenet. Finally, we show the modules' application as biomarkers of chemical inducers of lung fibrosis based on an in vitro study of four diketones. Clear differences could be found between the lung fibrosis inducing diketones and other compounds with regard to their tendency to induce dose-dependent increases of module activation as determined using a previously proposed differential activation score and the fraction of differentially expressed genes in the modules. Accordingly, this study highlights the potential use of composite biomarkers mechanistic screening for compound-induced lung fibrosis.

Demonstrating the reliability of in vivo metabolomics based chemical grouping: towards best practice

While grouping/read-across is widely used to fill data gaps, chemical registration dossiers are often rejected due to weak category justifications based on structural similarity only. Metabolomics provides a route to robust chemical categories via evidence of shared molecular effects across source and target substances. To gain international acceptance, this approach must demonstrate high reliability, and best-practice guidance is required. The MetAbolomics ring Trial for CHemical groupING (MATCHING), comprising six industrial, government and academic ring-trial partners, evaluated inter-laboratory reproducibility and worked towards best-practice. An independent team selected eight substances (WY-14643, 4-chloro-3-nitroaniline, 17α-methyl-testosterone, trenbolone, aniline, dichlorprop-p, 2-chloroaniline, fenofibrate); ring-trial partners were blinded to their identities and modes-of-action. Plasma samples were derived from 28-day rat tests (two doses per substance), aliquoted, and distributed to partners. Each partner applied their preferred liquid chromatography-mass spectrometry (LC-MS) metabolomics workflows to acquire, process, quality assess, statistically analyze and report their grouping results to the European Chemicals Agency, to ensure the blinding conditions of the ring trial. Five of six partners, whose metabolomics datasets passed quality control, correctly identified the grouping of eight test substances into three categories, for both male and female rats. Strikingly, this was achieved even though a range of metabolomics approaches were used. Through assessing intrastudy quality-control samples, the sixth partner observed high technical variation and was unable to group the substances. By comparing workflows, we conclude that some heterogeneity in metabolomics methods is not detrimental to consistent grouping, and that assessing data quality prior to grouping is essential. We recommend development of international guidance for quality-control acceptance criteria. This study demonstrates the reliability of metabolomics for chemical grouping and works towards best-practice.

Exploring the Relationship Among Lipid Profile Changes, Growth, and Reproduction in Folsomia candida Exposed to Teflubenzuron Over Time

The integration of untargeted lipidomics approaches in ecotoxicology has emerged as a strategy to enhance the comprehensiveness of environmental risk assessment. Although current toxicity tests with soil microarthropods focus on species performance, that is, growth, reproduction, and survival, understanding the mechanisms of toxicity across all levels of biological organization, from molecule to community is essential for informed decision-making. Our study focused on the impacts of sublethal concentrations of the insecticide teflubenzuron on the springtail Folsomia candida. Untargeted lipidomics was applied to link changes in growth, reproduction, and the overall stress response with lipid profile changes over various exposure durations. The accumulation of teflubenzuron in organisms exposed to the highest test concentration (0.035 mg a.s. kg-1 soil dry wt) significantly impacted reproductive output without compromising growth. The results suggested a resource allocation shift from reproduction to size maintenance. This hypothesis was supported by lipid shifts on day 7, at which point reductions in triacylglycerol and diacylglycerol content corresponded with decreased offspring production on day 21. The hypermetabolism of fatty acids and N-acylethanolamines on days 2 and 7 of exposure indicated oxidative stress and inflammation in the animals in response to teflubenzuron bioaccumulation, as measured using high-performance liquid chromatography-tandem mass spectrometry. Overall, the changes in lipid profiles in comparison with phenotypic adverse outcomes highlight the potential of lipid analysis as an early-warning tool for reproductive disturbances caused by pesticides in F. candida. Environ Toxicol Chem 2024;00:1-12. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Utility of in vivo metabolomics to support read-across for UVCB substances under REACH

Structure-based grouping of chemicals for targeted testing and read-across is an efficient way to reduce resources and animal usage. For substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs), structure-based grouping is virtually impossible. Biology-based approaches such as metabolomics could provide a solution. Here, 15 steam-cracked distillates, registered in the EU through the Lower Olefins Aromatics Reach Consortium (LOA), as well as six of the major substance constituents, were tested in a 14-day rat oral gavage study, in line with the fundamental elements of the OECD 407 guideline, in combination with plasma metabolomics. Beyond signs of clinical toxicity, reduced body weight (gain), and food consumption, pathological investigations demonstrated the liver, thyroid, kidneys (males only), and hematological system to be the target organs. These targets were confirmed by metabolome pattern recognition, with no additional targets being identified. While classical toxicological parameters did not allow for a clear distinction between the substances, univariate and multivariate statistical analysis of the respective metabolomes allowed for the identification of several subclusters of biologically most similar substances. These groups were partly associated with the dominant (> 50%) constituents of these UVCBs, i.e., indene and dicyclopentadiene. Despite minor differences in clustering results based on the two statistical analyses, a proposal can be made for the grouping of these UVCBs. Both analyses correctly clustered the chemically most similar compounds, increasing the confidence that this biological approach may provide a solution for the grouping of UVCBs.

Molecular signatures of angiogenesis inhibitors: a single-embryo untargeted metabolomics approach in zebrafish

Angiogenesis is a key process in embryonic development, a disruption of this process can lead to severe developmental defects, such as limb malformations. The identification of molecular perturbations representative of antiangiogenesis in zebrafish embryo (ZFE) may guide the assessment of developmental toxicity from an endpoint- to a mechanism-based approach, thereby improving the extrapolation of findings to humans. Thus, the aim of the study was to discover molecular changes characteristic of antiangiogenesis and developmental toxicity. We exposed ZFEs to two antiangiogenic drugs (SU4312, sorafenib) and two developmental toxicants (methotrexate, rotenone) with putative antiangiogenic action. Molecular changes were measured by performing untargeted metabolomics in single embryos. The metabolome response was accompanied by the occurrence of morphological alterations. Two distinct metabolic effect patterns were observed. The first pattern comprised common effects of two specific angiogenesis inhibitors and the known teratogen methotrexate, strongly suggesting a shared mode of action of antiangiogenesis and developmental toxicity. The second pattern involved joint effects of methotrexate and rotenone, likely related to disturbances in energy metabolism. The metabolites of the first pattern, such as phosphatidylserines, pterines, retinol, or coenzyme Q precursors, represented potential links to antiangiogenesis and related developmental toxicity. The metabolic effect pattern can contribute to biomarker identification for a mechanism-based toxicological testing.

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

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

Application of read-across methods as a framework for the estimation of emissions from chemical processes

The read-across method is a popular data gap filling technique with developed application for multiple purposes, including regulatory. Within the US Environmental Protection Agency's (US EPA) New Chemicals Program under Toxic Substances Control Act (TSCA), read-across has been widely used, as well as within technical guidance published by the Organization for Economic Co-operation and Development, the European Chemicals Agency, and the European Center for Ecotoxicology and Toxicology of Chemicals for filling chemical toxicity data gaps. Under the TSCA New Chemicals Review Program, US EPA is tasked with reviewing proposed new chemical applications prior to commencing commercial manufacturing within or importing into the United States. The primary goal of this review is to identify any unreasonable human health and environmental risks, arising from environmental releases/emissions during manufacturing and the resulting exposure from these environmental releases. The authors propose the application of read-across techniques for the development and use of a framework for estimating the emissions arising during the chemical manufacturing process. This methodology is to utilize available emissions data from a structurally similar analogue chemical or a group of structurally similar chemicals in a chemical family taking into consideration their physicochemical properties under specified chemical process unit operations and conditions. This framework is also designed to apply existing knowledge of read-across principles previously utilized in toxicity estimation for an analogue or category of chemicals and introduced and extended with a concurrent case study.

Toxicometabolomics as a tool for next generation environmental risk assessment

Traditionally applied methodology in environmental risk assessment (ERA) has fallen out of step with technological advancements and regulatory requirements, challenging effectiveness and accuracy of the assessments. Extensive efforts have been focused towards a transition to a more data-driven and mechanistically-based next generation risk assessment. Metabolomics can produce detailed and comprehensive molecular insight into affected biochemical processes. Combining metabolomics with environmental toxicology can help to understand the mechanisms and/or modes of action underlying toxicity of environmental pollutants and inform adverse outcome pathways, as well as facilitate identification of biomarkers to quantify effects and/or exposure. This Technical Report describes the activities and work performed within the frame of the European Food Risk Assessment Fellowship Programme (EU-FORA), implemented at the section 'Environmental Chemistry and Toxicology' at the Department of Environmental Science at Aarhus University in Denmark with synergies to an ongoing H2020 RIA project 'EndocRine Guideline Optimisation' (ERGO). In accordance with the 'training by doing' principles of the EU-FORA, the fellowship project combined the exploration of the status of scientific discussion on methodology in ERA through literature study with hands-on training, using the metabolomics analysis pipeline established at Aarhus University. For the hands-on training, an amphibian metamorphosis assay (OECD test no.231) was used as a proof-of-concept toxicometabolomics study case. Both a targeted biomarker - and an untargeted metabolomics approach was applied.

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

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

Integrated physiological and metabolomic analysis reveals new insights into toxicity pathways of paraquat to Microcystis aeruginosa

Chemical pollutants, such as herbicides, released into the aquatic environment adversely affect the phytoplankton community structure. While majority of herbicides are specifically designed to target photosynthetic processes, they also can be toxic to phytoplankton; however, despite the photosynthetic toxicity, some herbicides can target multiple physiological processes. Therefore, a full picture of toxicity pathway of herbicide to phytoplankton is necessary. In the present study, the cyanobacterium Microcystis aeruginosa was exposed to two levels (17 μg L^-1 (EC₁₀) and 65 μg L^-1 (EC₅₀)) of paraquat for 72 h. The physiological and metabolic responses were analyzed to elucidate the toxicity pathway and establish the adverse outcome pathway of paraquat to M. aeruginosa. The results revealed that enhanced glycolysis (upregulation of pyruvic acid level) and tricarboxylic acid cycle (upregulation of the levels of malic acid, isocitric acid and citric acid) exposed to EC₁₀ level of paraquat, which probably acted as a temporary strategy to maintain a healthy energy status in M. aeruginosa cells. Meanwhile, the expressions of glutathione and benzoic acid were enhanced to scavenge the excessive reactive oxygen species (ROS). Additionally, the accumulation of pigments (chlorophyll a and carotenoid) might play a supplementary role in the acclimation to EC₁₀ level paraquat treatment. In cells exposed to paraquat by EC₅₀ level, the levels of SOD, CAT, glutathione and benzoic acid increased significantly; however, the ROS exceeded the tolerance level of antioxidant system in M. aeruginosa. The adverse effects were revealed by inhibition of chlorophyll a fluorescence, the decreases in several carbohydrates (e.g., glucose 1-phosphate, fructose and galactose) and total protein content. Consequently, paraquat-induced oxidative stress caused the growth inhibition of M. aeruginosa. These findings provide new insights into the mode of action of paraquat in M. aeruginosa.

Connecting Gut Microbial Diversity with Plasma Metabolome and Fecal Bile Acid Changes Induced by the Antibiotics Tobramycin and Colistin Sulfate

The diversity of microbial species in the gut has a strong influence on health and development of the host. Further, there are indications that the variation in expression of gut bacterial metabolic enzymes is less diverse than the taxonomic profile, underlying the importance of microbiome functionality, particularly from a toxicological perspective. To address these relationships, the gut bacterial composition of Wistar rats was altered by a 28 day oral treatment with the antibiotics tobramycin or colistin sulfate. On the basis of 16S marker gene sequencing data, tobramycin was found to cause a strong reduction in the diversity and relative abundance of the microbiome, whereas colistin sulfate had only a marginal impact. Associated plasma and fecal metabolomes were characterized by targeted mass spectrometry-based profiling. The fecal metabolome of tobramycin-treated animals had a high number of significant alterations in metabolite levels compared to controls, particularly in amino acids, lipids, bile acids (BAs), carbohydrates, and energy metabolites. The accumulation of primary BAs and significant reduction of secondary BAs in the feces indicated that the microbial alterations induced by tobramycin inhibit bacterial deconjugation reactions. The plasma metabolome showed less, but still many alterations in the same metabolite groups, including reductions in indole derivatives and hippuric acid, and furthermore, despite marginal effects of colistin sulfate treatment, there were nonetheless systemic alterations also in BAs. Aside from these treatment-based differences, we also uncovered interindividual differences particularly centering on the loss of Verrucomicrobiaceae in the microbiome, but with no apparent associated metabolite alterations. Finally, by comparing the data set from this study with metabolome alterations in the MetaMapTox database, key metabolite alterations were identified as plasma biomarkers indicative of altered gut microbiomes resulting from a wide activity spectrum of antibiotics.

Gut Microbiota as Well as Metabolomes of Wistar Rats Recover within Two Weeks after Doripenem Antibiotic Treatment

An understanding of the changes in gut microbiome composition and its associated metabolic functions is important to assess the potential implications thereof on host health. Thus, to elucidate the connection between the gut microbiome and the fecal and plasma metabolomes, two poorly bioavailable carbapenem antibiotics (doripenem and meropenem), were administered in a 28-day oral study to male and female Wistar rats. Additionally, the recovery of the gut microbiome and metabolomes in doripenem-exposed rats were studied one and two weeks after antibiotic treatment (i.e., doripenem-recovery groups). The 16S bacterial community analysis revealed an altered microbial population in all antibiotic treatments and a recovery of bacterial diversity in the doripenem-recovery groups. A similar pattern was observed in the fecal metabolomes of treated animals. In the recovery group, particularly after one week, an over-compensation was observed in fecal metabolites, as they were significantly changed in the opposite direction compared to previously changed metabolites upon 28 days of antibiotic exposure. Key plasma metabolites known to be diagnostic of antibiotic-induced microbial shifts, including indole derivatives, hippuric acid, and bile acids were also affected by the two carbapenems. Moreover, a unique increase in the levels of indole-3-acetic acid in plasma following meropenem treatment was observed. As was observed for the fecal metabolome, an overcompensation of plasma metabolites was observed in the recovery group. The data from this study provides insights into the connectivity of the microbiome and fecal and plasma metabolomes and demonstrates restoration post-antibiotic treatment not only for the microbiome but also for the metabolomes. The importance of overcompensation reactions for health needs further studies.

Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line

Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B₁, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B₁, benzo[a]pyrene, cyclosporin A: from 0.2048 μM to 50 μM; rotenone: from 0.04096 to 10 μM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.

Comparison of toxicological effects and exposure levels between triclosan and its structurally similar chemicals using in vitro tests for read-across case study

Read-across based on structural and biological similarities is expected to be a promising alternative method for assessing systemic toxicity. A concrete strategy for quantitative chemical risk assessment would be to stack read-across case studies and extract key considerations from them. Thus, we developed a read-across case study by comparing the toxicological effects based on adverse outcome pathways and exposure levels of different structurally similar chemicals for a target organ. In this study, we selected the hepatotoxicity of triclosan and its structurally similar chemicals including diclosan and 1-chloro-3-(4-chlorophenoxy)benzene. The results of in vitro toxicogenomics showed that disorders of cholesterol synthesis were commonly detected with both triclosan and diclosan. The decrease in hepatocellular cholesterol levels was similar in the cells treated with triclosan and diclosan. Furthermore, the exposure levels of triclosan and diclosan for the liver were similar. Collectively, these results suggest that triclosan and diclosan show similar toxicological effects and severity of hepatotoxicity. Considering the existing repeated dose toxicity data, our prediction results are reasonable regarding the toxicological effect and its severity. Thus, the present study demonstrated the usability of comparing toxicological effects and exposure levels using read-across for quantitative chemical risk assessment.

Gut microbiome and plasma metabolome changes in rats after oral gavage of nanoparticles: sensitive indicators of possible adverse health effects

BACKGROUND

The oral uptake of nanoparticles is an important route of human exposure and requires solid models for hazard assessment. While the systemic availability is generally low, ingestion may not only affect gastrointestinal tissues but also intestinal microbes. The gut microbiota contributes essentially to human health, whereas gut microbial dysbiosis is known to promote several intestinal and extra-intestinal diseases. Gut microbiota-derived metabolites, which are found in the blood stream, serve as key molecular mediators of host metabolism and immunity.

RESULTS

Gut microbiota and the plasma metabolome were analyzed in male Wistar rats receiving either SiO2 (1000 mg/kg body weight/day) or Ag nanoparticles (100 mg/kg body weight/day) during a 28-day oral gavage study. Comprehensive clinical, histopathological and hematological examinations showed no signs of nanoparticle-induced toxicity. In contrast, the gut microbiota was affected by both nanoparticles, with significant alterations at all analyzed taxonomical levels. Treatments with each of the nanoparticles led to an increased abundance of Prevotellaceae, a family with gut species known to be correlated with intestinal inflammation. Only in Ag nanoparticle-exposed animals, Akkermansia, a genus known for its protective impact on the intestinal barrier was depleted to hardly detectable levels. In SiO2 nanoparticles-treated animals, several genera were significantly reduced, including probiotics such as Enterococcus. From the analysis of 231 plasma metabolites, we found 18 metabolites to be significantly altered in Ag-or SiO2 nanoparticles-treated rats. For most of these metabolites, an association with gut microbiota has been reported previously. Strikingly, both nanoparticle-treatments led to a significant reduction of gut microbiota-derived indole-3-acetic acid in plasma. This ligand of the arylhydrocarbon receptor is critical for regulating immunity, stem cell maintenance, cellular differentiation and xenobiotic-metabolizing enzymes.

CONCLUSIONS

The combined profiling of intestinal microbiome and plasma metabolome may serve as an early and sensitive indicator of gut microbiome changes induced by orally administered nanoparticles; this will help to recognize potential adverse effects of these changes to the host.

Integrating in vitro metabolomics with a 96-well high-throughput screening platform

INTRODUCTION

High-throughput screening (HTS) is emerging as an approach to support decision-making in chemical safety assessments. In parallel, in vitro metabolomics is a promising approach that can help accelerate the transition from animal models to high-throughput cell-based models in toxicity testing.

OBJECTIVE

In this study we establish and evaluate a high-throughput metabolomics workflow that is compatible with a 96-well HTS platform employing 50,000 hepatocytes of HepaRG per well.

METHODS

Low biomass cell samples were extracted for metabolomics analyses using a newly established semi-automated protocol, and the intracellular metabolites were analysed using a high-resolution spectral-stitching nanoelectrospray direct infusion mass spectrometry (nESI-DIMS) method that was modified for low sample biomass.

RESULTS

The method was assessed with respect to sensitivity and repeatability of the entire workflow from cell culturing and sampling to measurement of the metabolic phenotype, demonstrating sufficient sensitivity (> 3000 features in hepatocyte extracts) and intra- and inter-plate repeatability for polar nESI-DIMS assays (median relative standard deviation < 30%). The assays were employed for a proof-of-principle toxicological study with a model toxicant, cadmium chloride, revealing changes in the metabolome across five sampling times in the 48-h exposure period. To allow the option for lipidomics analyses, the solvent system was extended by establishing separate extraction methods for polar metabolites and lipids.

CONCLUSIONS

Experimental, analytical and informatics workflows reported here met pre-defined criteria in terms of sensitivity, repeatability and ability to detect metabolome changes induced by a toxicant and are ready for application in metabolomics-driven toxicity testing to complement HTS assays.

Automated Sample Preparation and Data Collection Workflow for High-Throughput In Vitro Metabolomics

Regulatory bodies have started to recognise the value of in vitro screening and metabolomics as two types of new approach methodologies (NAMs) for chemical risk assessments, yet few high-throughput in vitro toxicometabolomics studies have been reported. A significant challenge is to implement automated sample preparation of the low biomass samples typically used for in vitro screening. Building on previous work, we have developed, characterised and demonstrated an automated sample preparation and analysis workflow for in vitro metabolomics of HepaRG cells in 96-well microplates using a Biomek i7 Hybrid Workstation (Beckman Coulter) and Orbitrap Elite (Thermo Scientific) high-resolution nanoelectrospray direct infusion mass spectrometry (nESI-DIMS), across polar metabolites and lipids. The experimental conditions evaluated included the day of metabolite extraction, order of extraction of samples in 96-well microplates, position of the 96-well microplate on the instrument's deck and well location within a microplate. By using the median relative standard deviation (mRSD (%)) of spectral features, we have demonstrated good repeatability of the workflow (final mRSD < 30%) with a low percentage of features outside the threshold applied for statistical analysis. To improve the quality of the automated workflow further, small method modifications were made and then applied to a large cohort study (4860 sample infusions across three nESI-DIMS assays), which confirmed very high repeatability of the whole workflow from cell culturing to metabolite measurements, whilst providing a significant improvement in sample throughput. It is envisioned that the automated in vitro metabolomics workflow will help to advance the application of metabolomics (as a part of NAMs) in chemical safety, primarily as an approach for high throughput screening and prioritisation.

A 10-step framework for use of read-across (RAX) in next generation risk assessment (NGRA) for cosmetics safety assessment

This paper presents a 10-step read-across (RAX) framework for use in cases where a threshold of toxicological concern (TTC) approach to cosmetics safety assessment is not possible. RAX builds on established approaches that have existed for more than two decades using chemical properties and in silico toxicology predictions, by further substantiating hypotheses on toxicological similarity of substances, and integrating new approach methodologies (NAM) in the biological and kinetic domains. NAM include new types of data on biological observations from, for example, in vitro assays, toxicogenomics, metabolomics, receptor binding screens and uses physiologically-based kinetic (PBK) modelling to inform about systemic exposure. NAM data can help to substantiate a mode/mechanism of action (MoA), and if similar chemicals can be shown to work by a similar MoA, a next generation risk assessment (NGRA) may be performed with acceptable confidence for a data-poor target substance with no or inadequate safety data, based on RAX approaches using data-rich analogue(s), and taking account of potency or kinetic/dynamic differences.

Analytical and Omics-Based Advances in the Study of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a significant clinical issue, affecting 1-1.5 million patients annually, and remains a major challenge during drug development-toxicity and safety concerns are the second-highest reason for drug candidate failure. The future prevalence of DILI can be minimized by developing a greater understanding of the biological mechanisms behind DILI. Both qualitative and quantitative analytical techniques are vital to characterizing and investigating DILI. In vitro assays are capable of characterizing specific aspects of a drug's hepatotoxic nature and multiplexed assays are capable of characterizing and scoring a drug's association with DILI. However, an even deeper insight into the perturbations to biological pathways involved in the mechanisms of DILI can be gained through the use of omics-based analytical techniques: genomics, transcriptomics, proteomics, and metabolomics. These omics analytical techniques can offer qualitative and quantitative insight into genetic susceptibilities to DILI, the impact of drug treatment on gene expression, and the effect on protein and metabolite abundance. This review will discuss the analytical techniques that can be applied to characterize and investigate the biological mechanisms of DILI and potential predictive biomarkers.

Commentary: Novel strategies and new tools to curtail the health effects of pesticides

BACKGROUND

Flaws in the science supporting pesticide risk assessment and regulation stand in the way of progress in mitigating the human health impacts of pesticides. Critical problems include the scope of regulatory testing protocols, the near-total focus on pure active ingredients rather than formulated products, lack of publicly accessible information on co-formulants, excessive reliance on industry-supported studies coupled with reticence to incorporate published results in the risk assessment process, and failure to take advantage of new scientific opportunities and advances, e.g. biomonitoring and "omics" technologies.

RECOMMENDED ACTIONS

Problems in pesticide risk assessment are identified and linked to study design, data, and methodological shortcomings. Steps and strategies are presented that have potential to deepen scientific knowledge of pesticide toxicity, exposures, and risks. We propose four solutions: (1) End near-sole reliance in regulatory decision-making on industry-supported studies by supporting and relying more heavily on independent science, especially for core toxicology studies. The cost of conducting core toxicology studies at labs not affiliated with or funded directly by pesticide registrants should be covered via fees paid by manufacturers to public agencies. (2) Regulators should place more weight on mechanistic data and low-dose studies within the range of contemporary exposures. (3) Regulators, public health agencies, and funders should increase the share of exposure-assessment resources that produce direct measures of concentrations in bodily fluids and tissues. Human biomonitoring is vital in order to quickly identify rising exposures among vulnerable populations including applicators, pregnant women, and children. (4) Scientific tools across disciplines can accelerate progress in risk assessments if integrated more effectively. New genetic and metabolomic markers of adverse health impacts and heritable epigenetic impacts are emerging and should be included more routinely in risk assessment to effectively prevent disease.

CONCLUSIONS

Preventing adverse public health outcomes triggered or made worse by exposure to pesticides will require changes in policy and risk assessment procedures, more science free of industry influence, and innovative strategies that blend traditional methods with new tools and mechanistic insights.

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.

Grouping of chemicals based on the potential mechanisms of hepatotoxicity of naphthalene and structurally similar chemicals using in vitro testing for read-across and its validation

Integrated Approaches to Testing and Assessment provides a framework to improve the reliability of read-across for chemical risk assessment of systemic toxicity without animal testing. However, the availability of only a few case studies hinders the use of this concept for regulatory purposes. Thus, we compared the biological similarity of structurally similar chemicals using in vitro testing to demonstrate the validity of this concept for grouping chemicals and to extract key considerations in read-across. We analyzed the hepatotoxicity of naphthalene and three chemicals structurally similar to naphthalene (2,7-naphthalenediol, 1,5-naphthalenediol, and 1-naphthol) for which 90-day repeated dose toxicity data are available. To elucidate and compare their potential mechanisms, we conducted in vitro microarray analysis using rat primary hepatocytes and validated the results using a biomarker and metabolic activation analysis. We observed that 2,7-naphthalenediol, 1,5-naphthalenediol, and 1-naphthol had similar potential mechanisms, namely, induction of oxidative stress by their metabolic activation. Conversely, naphthalene did not show a similar toxicity effect. The existing in vivo data confirmed our grouping of chemicals based on this potential mechanism. Thus, our findings suggest that in vitro toxicogenomics and related biochemical assays are useful for comparing biological similarities and grouping chemicals based on their toxicodynamics for read-across.

Approaches in metabolomics for regulatory toxicology applications

Innovative methodological approaches are needed to conduct human health and environmental risk assessments on a growing number of marketed chemicals. Metabolomics is progressively proving its value as an efficient strategy to perform toxicological evaluations of new and existing substances, and it will likely become a key tool to accelerate chemical risk assessments. However, additional guidance with widely accepted and harmonized procedures is needed before metabolomics can be routinely incorporated in decision-making for regulatory purposes. The aim of this review is to provide an overview of metabolomic strategies that have been successfully employed in toxicity assessment as well as the most promising workflows in a regulatory context. First, we provide a general view of the different steps of regulatory toxicology-oriented metabolomics. Emphasis is put on three key elements: robustness of experimental design, choice of analytical platform, and use of adapted data treatment tools. Then, examples in which metabolomics supported regulatory toxicology outputs in different scenarios are reviewed, including chemical grouping, elucidation of mechanisms of toxicity, and determination of points of departure. The overall intention is to provide insights into why and how to plan and conduct metabolomic studies for regulatory toxicology purposes.

Elucidating the Relations between Gut Bacterial Composition and the Plasma and Fecal Metabolomes of Antibiotic Treated Wistar Rats

The gut microbiome is vital to the health and development of an organism, specifically in determining the host response to a chemical (drug) administration. To understand this, we investigated the effects of six antibiotic (AB) treatments (Streptomycin sulfate, Roxithromycin, Sparfloxacin, Vancomycin, Clindamycin and Lincomycin hydrochloride) and diet restriction (–20%) on the gut microbiota in 28-day oral toxicity studies on Wistar rats. The fecal microbiota was determined using 16S rDNA marker gene sequencing. AB-class specific alterations were observed in the bacterial composition, whereas restriction in diet caused no observable difference. These changes associated well with the changes in the LC–MS/MS- and GC–MS-based metabolome profiles, particularly of feces and to a lesser extent of plasma. Particularly strong and AB-specific metabolic alterations were observed for bile acids in both plasma and feces matrices. Although AB-group-specific plasma metabolome changes were observed, weaker associations between fecal and plasma metabolome suggest a profound barrier between them. Numerous correlations between the bacterial families and the fecal metabolites were established, providing a holistic overview of the gut microbial functionality. Strong correlations were observed between microbiota and bile acids, lipids and fatty acids, amino acids and related metabolites. These microbiome–metabolome correlations promote understanding of the functionality of the microbiome for its host.

KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development

Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.

Comparison of the potential mechanisms for hepatotoxicity of p-dialkoxy chlorobenzenes in rat primary hepatocytes for read-across

Read-across based on only structural similarity is considered to have a risk of error in chemical risk assessment. Under these circumstances, considering biological similarity based on adverse outcome pathways using in vitro omics technologies is expected to enhance the accuracy and robustness of conclusions in read-across. However, due to a lack of practical case studies, key considerations and use of these technologies for data gap filling are not well discussed. Here we extracted and compared the potential mechanisms for hepatotoxicity for structural analogs of p-dialkoxy chlorobenzenes including 1,4-dichloro-2,5-dimethoxybenzene (DDMB), 2,5-dichloro-1,4-diethoxybenzene (DDEB), 2-chloro-1,4-dimethoxybenzene (CDMB), and 1-chloro-2,5-diethoxybenzene (CDEB) using in vitro omics technologies for read-across. To reveal the potential mechanisms for hepatotoxicity, we conducted microarray analysis with rat primary hepatocytes. The results showed that three (DDMB, DDEB, CDEB) of the four chemicals affected similar biological pathways such as peroxisome proliferation, oxidative stress, and mitochondrial dysfunction. Furthermore, these biological pathways are consistent with in vivo hepatotoxicity in the source chemical, DDMB. In contrast, CDMB did not affect a specific toxicological pathway. Taken together, these data show the potential mechanisms for hepatotoxicity for three chemicals (DDMB, DDEB, CDEB) and provide novel insights into grouping chemicals using in vitro toxicogenomics for read-across.

Using a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data

Read-across has become a primary approach to fill data gaps for chemical safety assessments. Chemical similarity based on structure, reactivity, and physic-chemical property information is a traditional approach applied for read-across toxicity studies. However, toxicity mechanisms are usually complicated in a biological system, so only using chemical similarity to perform the read-across for new compounds was not satisfactory for most toxicity endpoints, especially when the chemically similar compounds show dissimilar toxicities. This study aims to develop an enhanced read-across method for chemical toxicity predictions. To this end, we used two large toxicity datasets for read-across purposes. One consists of 3979 compounds with Ames mutagenicity data, and the other contains 7332 compounds with rat acute oral toxicity data. First, biological data for all compounds in these two datasets were obtained by querying thousands of PubChem bioassays. The PubChem bioassays with at least five compounds from either of these two datasets showing active responses were selected to generate comprehensive bioprofiles. The read-across studies were performed by using chemical similarity search only and also by using a hybrid similarity search based on both chemical descriptors and bioprofiles. Compared to traditional read-across based on chemical similarity, the hybrid read-across approach showed improved accuracy of predictions for both Ames mutagenicity and acute oral toxicity. Furthermore, we could illustrate potential toxicity mechanisms by analyzing the bioprofiles used for this hybrid read-across study. The results of this study indicate that the new hybrid read-across approach could be an applicable computational tool for chemical toxicity predictions. In this way, the bottleneck of traditional read-across studies can be overcome by introducing public biological data into the traditional process. The incorporation of bioprofiles generated from the additional biological data for compounds can partially solve the "activity cliff" issue and reveal their potential toxicity mechanisms. This study leads to a promising direction to utilize data-driven approaches for computational toxicology studies in the big data era.

Using metabolomics to assess the sub-lethal effects of zinc and boscalid on an estuarine polychaete worm over time

INTRODUCTION

Zinc is a heavy metal commonly detected in urban estuaries around Australia. Boscalid is a fungicide found in estuaries, both in water and sediment, it enters the system predominantly through agricultural run-off. Zinc is persistent while boscalid breaks down, with a half-life of 108 days. Both contaminants are widely distributed and their effects on ecosystems are not well understood.

OBJECTIVES

The aim of this study was to determine the metabolite changes in Simplisetia aequisetis (an estuarine polychaete) following laboratory exposure to a sub-lethal concentration of zinc or boscalid over a 2-week period.

METHODS

Individuals were collected at six time points over a 2-week period. Whole polychaete metabolites were extracted and quantified using a multi-platform approach. Polar metabolites were detected using a semi-targeted GC-MS analysis and amine containing compounds were analysed using a targeted LC-MS analysis. Total lipid energy content was also analysed for Simplisetia aequisetis.

RESULTS

The pathways that responded to zinc and boscalid exposure were alanine, aspartate and glutamate metabolism (AAG); glycine, serine and threonine metabolism (GST) and metabolites associated with the tricarboxylic acid cycle (TCA). Results showed that changes in total abundance of some metabolites could be detected as early as 24-h exposure. Changes were detected in the metabolites before commonly used total lipid energy assays identified effects.

CONCLUSION

A multi-platform approach provided a holistic overview of the metabolomic response to contaminants in polychaetes. This approach shows promise to be used in biomonitoring programs to provide early diagnostic indicators of contamination and exposure.

Use cases, best practice and reporting standards for metabolomics in regulatory toxicology

Metabolomics is a widely used technology in academic research, yet its application to regulatory science has been limited. The most commonly cited barrier to its translation is lack of performance and reporting standards. The MEtabolomics standaRds Initiative in Toxicology (MERIT) project brings together international experts from multiple sectors to address this need. Here, we identify the most relevant applications for metabolomics in regulatory toxicology and develop best practice guidelines, performance and reporting standards for acquiring and analysing untargeted metabolomics and targeted metabolite data. We recommend that these guidelines are evaluated and implemented for several regulatory use cases.

Toxicogenomics - What added Value Do These Approaches Provide for Carcinogen Risk Assessment?

It is still a major challenge to protect humans at workplaces and in the environment. To cope with this task, it is a prerequisite to obtain detailed information on the extent of chemical perturbations of biological pathways, in particular, adaptive vs. adverse effects and the dose-response relationships. This knowledge serves as the basis for the classification of non-carcinogens and carcinogens and for further distinguishing carcinogens in genotoxic (DNA damaging) or non-genotoxic compounds. Basing on quantitative dose-response relationships, points of departures can be derived for chemical risk assessment. In recent years, new methods have shown their capability to support the established rodent models of carcinogenicity testing. In vitro high throughput screening assays assess more comprehensively cell response. In addition, omics technologies were applied to study the mode of action of chemicals whereby the term "toxicogenomics" comprises various technologies such as transcriptomics, epigenomics, or metabolomics. This review aims to summarize the current state of toxicogenomic approaches in risk science and to compare them with established ones. For example, measurement of global transcriptional changes generates meaningful information for toxicological risk assessment such as accurate classification of genotoxic/non-genotoxic carcinogens. Alteration in mRNA expression offers previously unknown insights in the mode of action and enables the definition of key events. Based on these, benchmark doses can be calculated for the transition from an adaptive to an adverse state. In short, this review assesses the potential and challenges of transcriptomics and addresses the impact of other omics technologies on risk assessment in terms of hazard identification and dose-response assessment.

Metabolomics Discovers Early-Response Metabolic Biomarkers that Can Predict Chronic Reproductive Fitness in Individual Daphnia magna

Chemical risk assessment remains entrenched in chronic toxicity tests that set safety thresholds based on animal pathology or fitness. Chronic tests are resource expensive and lack mechanistic insight. Discovering a chemical's mode-of-action can in principle provide predictive molecular biomarkers for a toxicity endpoint. Furthermore, since molecular perturbations precede pathology, early-response molecular biomarkers may enable shorter, more resource efficient testing that can predict chronic animal fitness. This study applied untargeted metabolomics to attempt to discover early-response metabolic biomarkers that can predict reproductive fitness of Daphnia magna, an internationally-recognized test species. First, we measured the reproductive toxicities of cadmium, 2,4-dinitrophenol and propranolol to individual Daphnia in 21-day OECD toxicity tests, then measured the metabolic profiles of these animals using mass spectrometry. Multivariate regression successfully discovered putative metabolic biomarkers that strongly predict reproductive impairment by each chemical, and for all chemicals combined. The non-chemical-specific metabolic biomarkers were then applied to metabolite data from Daphnia 24-h acute toxicity tests and correctly predicted that significant decreases in reproductive fitness would occur if these animals were exposed to cadmium, 2,4-dinitrophenol or propranolol for 21 days. While the applicability of these findings is limited to three chemicals, they provide proof-of-principle that early-response metabolic biomarkers of chronic animal fitness can be discovered for regulatory toxicity testing.

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.

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment

In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework.