Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA)

Defining clinical outcome pathways

Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: molecular initiating event (MIE) → intermediate event(s) → clinical outcome. We illustrate the concept with COP examples both for primary and alternative (i.e., drug repurposing) therapeutic applications. We also describe the elucidation of COPs for several drugs of interest using the publicly accessible Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP) biomedical knowledge graph-mining tool. We propose that broader use of COP uncovered with the help of biomedical knowledge graph mining will likely accelerate drug discovery and repurposing efforts.

In vitro toxicity screening of amorphous silica nanoparticles using mitochondrial fraction exposure followed by MS-based proteomic analysis

Application of mitochondrial proteomic analysis in toxicity screening of amorphous silica nanoforms. Concordance between SiNP exposure-related perturbations in mitochondrial proteins and cellular ATP responses.

Probabilistic risk assessment - the keystone for the future of toxicology

Safety sciences must cope with uncertainty of models and results as well as information gaps. Acknowledging this uncer-tainty necessitates embracing probabilities and accepting the remaining risk. Every toxicological tool delivers only probable results. Traditionally, this is taken into account by using uncertainty / assessment factors and worst-case / precautionary approaches and thresholds. Probabilistic methods and Bayesian approaches seek to characterize these uncertainties and promise to support better risk assessment and, thereby, improve risk management decisions. Actual assessments of uncertainty can be more realistic than worst-case scenarios and may allow less conservative safety margins. Most importantly, as soon as we agree on uncertainty, this defines room for improvement and allows a transition from traditional to new approach methods as an engineering exercise. The objective nature of these mathematical tools allows to assign each methodology its fair place in evidence integration, whether in the context of risk assessment, sys-tematic reviews, or in the definition of an integrated testing strategy (ITS) / defined approach (DA) / integrated approach to testing and assessment (IATA). This article gives an overview of methods for probabilistic risk assessment and their application for exposure assessment, physiologically-based kinetic modelling, probability of hazard assessment (based on quantitative and read-across based structure-activity relationships, and mechanistic alerts from in vitro studies), indi-vidual susceptibility assessment, and evidence integration. Additional aspects are opportunities for uncertainty analysis of adverse outcome pathways and their relation to thresholds of toxicological concern. In conclusion, probabilistic risk assessment will be key for constructing a new toxicology paradigm - probably!

Constructing a developmental and reproductive toxicity database of chemicals (DART NIHS DB) for integrated approaches to testing and assessment

Developmental and reproductive toxicity (DART) is an important endpoint, and databases (DBs) are essential for evaluating the risk of untested substances using alternative methods. We have constructed a reliable and transparent DART DB, which we named DART NIHS DB, using the publicly available datasets of DART studies of industrial chemicals conducted by Japanese government ministries in accordance with the corresponding OECD test guidelines (OECD TG421 and TG422). This DB is unique because its dataset chemicals have little overlap with those of ToxRefDB, which compiles large-scale DART data, and it is reliable because the included datasets were created after reviewing the individual study reports. In DART NIHS DB, 171 of 404 substances exhibited signs of DART, which occurred during fertility and early embryonic development (49 substances), organogenesis (59 substances), and the perinatal period (161 substances). When the lowest-observed-adverse-effect level (LOAEL) of DART was compared with that of repeated-dose toxicity (RDT), 15 substances (12%) had a lower LOAEL for DART than for RDT. Of these, five substances displayed significant DART at doses of ≤ 50 mg/kg bw/day. The chemical and toxicity information in this DB will be useful for the development of stage-specific adverse outcome pathways (AOPs) via integration with mechanistic information. The whole datasets of the DB can be implemented in read-across support tools such as the OECD QSAR Toolbox, which will further lead to future integrated approaches to testing and assessment based on AOPs.

Retinaldehyde Dehydrogenase Inhibition-Related Adverse Outcome Pathway: Potential Risk of Retinoic Acid Synthesis Inhibition during Embryogenesis

Retinoic acid (RA) is one of the factors crucial for cell growth, differentiation, and embryogenesis; it interacts with the retinoic acid receptor and retinoic acid X receptor to eventually regulate target gene expression in chordates. RA is transformed from retinaldehyde via oxidization by retinaldehyde dehydrogenase (RALDH), which belongs to the family of oxidoreductases. Several chemicals, including disulphiram, diethylaminobenzaldehyde, and SB-210661, can effectively inhibit RALDH activity, potentially causing reproductive and developmental toxicity. The modes of action can be sequentially explained based on the molecular initiating event toward key events, and finally the adverse outcomes. Adverse outcome pathway (AOP) is a conceptual and theoretical framework that describes the sequential chain of casually liked events at different biological levels from molecular events to adverse effects. In the present review, we discussed a recently registered AOP (AOP₂₉₇; inhibition of retinaldehyde dehydrogenase leads to population decline) to explain and support the weight of evidence for RALDH inhibition-related developmental toxicity using the existing knowledge.

Daphnia magna Gut-Specific Transcriptomic Responses to Feeding Inhibiting Chemicals and Food Limitation

Transcriptomic responses combined with apical adverse ecologically relevant outcomes have proven to be useful to unravel and anchor molecular mechanisms of action to adverse outcomes. This is the case for feeding inhibition responses in the model ecotoxicological species Daphnia magna. The aim of the present study was to assess the transcriptomic responses in guts dissected from D. magna individuals exposed to concentrations of selected compounds that inhibit feeding and compare them with the responses associated to 2 levels of food restriction (low food and starvation). Chemical treatments included cadmium, copper, fluoranthene, λ-cyhalothrin, and the cyanotoxin anatoxin-a. Although the initial hypothesis was that exposure to chemical feeding inhibitors should elicit similar molecular responses as food limitation, the corresponding gut transcriptomic responses differed significantly. In moderate food limitation conditions, D. magna individuals increased protein and carbohydrate catabolism, likely to be used as energetic sources, whereas under severe starving conditions most metabolism-related pathways appeared down-regulated. Treatment with chemical feeding inhibitors promoted cell turnover-related signaling pathways in the gut, probably to renew tissue damage caused by the reported oxidative stress effects of these compounds, and inhibited the transcription of gut digestive gene enzymes and energetic metabolic pathways. We conclude that chemical feeding inhibitors, rather than mimicking the physiological response to low- or no-food conditions, cause specific toxic effects, preventing Daphnia both from feeding and from adjusting its metabolism to the resulting low energy intake. Environ Toxicol Chem 2021;40:2510-2520. © 2021 SETAC.

An overview of current practices for regulatory risk assessment with lessons learnt from cosmetics in the European Union

Risk assessments of various types of chemical compounds are carried out in the European Union (EU) foremost to comply with legislation and to support regulatory decision-making with respect to their safety. Historically, risk assessment has relied heavily on animal experiments. However, the EU is committed to reduce animal experimentation and has implemented several legislative changes, which have triggered a paradigm shift towards human-relevant animal-free testing in the field of toxicology, in particular for risk assessment. For some specific endpoints, such as skin corrosion and irritation, validated alternatives are available whilst for other endpoints, including repeated dose systemic toxicity, the use of animal data is still central to meet the information requirements stipulated in the different legislations. The present review aims to provide an overview of established and more recently introduced methods for hazard assessment and risk characterisation for human health, in particular in the context of the EU Cosmetics Regulation (EC No 1223/2009) as well as the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation (EC 1907/2006).

Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach

Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal and organize public high-throughput screening data for compounds with nuclear estrogen receptor α and β (ERα and ERβ) binding potentials. The target activity was rodent uterotrophic bioactivity driven by ERα/ERβ activations. After training, the resultant network successfully inferred critical relationships among ERα/ERβ target bioassays, shown as weights of 6521 edges between 1071 neurons. The resultant network uses an adverse outcome pathway (AOP) framework to mimic the signaling pathway initiated by ERα and identify compounds that mimic endogenous estrogens (i.e., estrogen mimetics). The k-DNN can predict estrogen mimetics by activating neurons representing several events in the ERα/ERβ signaling pathway. Therefore, this virtual pathway model, starting from a compound's chemistry initiating ERα activation and ending with rodent uterotrophic bioactivity, can efficiently and accurately prioritize new estrogen mimetics (AUC = 0.864-0.927). This k-DNN method is a potential universal computational toxicology strategy to utilize public high-throughput screening data to characterize hazards and prioritize potentially toxic compounds.

Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity

Introduction: Genotoxicity is an imperative component of the human health safety assessment of chemicals. Its secure forecast is of the utmost importance for all health prevention strategies and regulations.Areas covered: We surveyed several types of alternative, animal-free approaches ((quantitative) structure-activity relationship (Q)SAR, read-across, Adverse Outcome Pathway, Integrated Approaches to Testing and Assessment) for genotoxicity prediction within the needs of regulatory frameworks, putting special emphasis on data quality and uncertainties issues.Expert opinion: (Q)SAR models and read-across approaches for in vitro bacterial mutagenicity have sufficient reliability for use in prioritization processes, and as support in regulatory decisions in combination with other types of evidence. (Q)SARs and read-across methodologies for other genotoxicity endpoints need further improvements and should be applied with caution. It appears that there is still large room for improvement of genotoxicity prediction methods. Availability of well-curated high-quality databases, covering a broader chemical space, is one of the most important needs. Integration of in silico predictions with expert knowledge, weight-of-evidence-based assessment, and mechanistic understanding of genotoxicity pathways are other key points to be addressed for the generation of more accurate and trustable results.

Concurrent Evaluation of Mortality and Behavioral Responses: A Fast and Efficient Testing Approach for High-Throughput Chemical Hazard Identification

The continual introduction of new chemicals into the market necessitates fast, efficient testing strategies for evaluating their toxicity. Ideally, these high-throughput screening (HTS) methods should capture the entirety of biological complexity while minimizing reliance on expensive resources that are required to assess diverse phenotypic endpoints. In recent years, the zebrafish (Danio rerio) has become a preferred vertebrate model to conduct rapid in vivo toxicity tests. Previously, using HTS data on 1060 chemicals tested as part of the ToxCast program, we showed that early, 24 h post-fertilization (hpf), behavioral responses of zebrafish embryos are predictive of later, 120 h post-fertilization, adverse developmental endpoints-indicating that embryonic behavior is a useful endpoint related to observable morphological effects. Here, our goal was to assess the contributions (i.e., information gain) from multiple phenotypic data streams and propose a framework for efficient identification of chemical hazards. We systematically swept through analysis parameters for data on 24 hpf behavior, 120 hpf behavior, and 120 hpf morphology to optimize settings for each of these assays. We evaluated the concordance of data from behavioral assays with that from morphology. We found that combining information from behavioral and mortality assessments captures early signals of potential chemical hazards, obviating the need to evaluate a comprehensive suite of morphological endpoints in initial screens for toxicity. We have demonstrated that such a screening strategy is useful for detecting compounds that elicit adverse morphological responses, in addition to identifying hazardous compounds that do not disrupt the underlying morphology. The application of this design for rapid preliminary toxicity screening will accelerate chemical testing and aid in prioritizing chemicals for risk assessment.

Mechanistic Computational Model for Extrapolating In vitro Thyroid Peroxidase (TPO) Inhibition Data to Predict Serum Thyroid Hormone Levels in Rats

High throughput (HTP) in vitro assays are developed to screen chemicals for their potential to inhibit thyroid hormones (THs) synthesis. Some of these experiments, such as the thyroid peroxidase (TPO) inhibition assay, are based on thyroid microsomal extracts. However, the regulation of thyroid disruption chemicals (TDCs) is based on THs in vivo serum levels. This necessitates the estimation of TDCs in vivo tissue levels in the thyroid where THs synthesis inhibition by TPO takes place. The in vivo tissue levels of chemicals are controlled by pharmacokinetic determinants such as absorption, distribution, metabolism and excretion (ADME), and can be described quantitatively in physiologically based pharmacokinetic (PBPK) models. An integrative computational model including chemical specific PBPK and TH kinetics models provides a mechanistic quantitative approach to translate thyroidal HTP in vitro assays to in vivo measures of circulating THs serum levels. This computational framework is developed to quantitatively establish the linkage between applied dose, chemical thyroid tissue levels, thyroid TPO inhibition potential, and in vivo TH serum levels. Once this link is established quantitively, the overall model is used to calibrate the TH kinetics parameters using experimental data for THs levels in thyroid tissue and serum for the two drugs Propylthiouracil (PTU) and Methimazole (MMI). The calibrated quantitative framework is then evaluated against literature data for the environmental chemical ethylenethiourea (ETU). The linkage of PBPK and TH kinetics models illustrates a computational framework that can be extrapolated to humans to screen chemicals based on their exposure levels and potential to disrupt serum THs levels in vivo.

In Vitro Liver Toxicity Testing of Chemicals: A Pragmatic Approach

The liver is among the most frequently targeted organs by noxious chemicals of diverse nature. Liver toxicity testing using laboratory animals not only raises serious ethical questions, but is also rather poorly predictive of human safety towards chemicals. Increasing attention is, therefore, being paid to the development of non-animal and human-based testing schemes, which rely to a great extent on in vitro methodology. The present paper proposes a rationalized tiered in vitro testing strategy to detect liver toxicity triggered by chemicals, in which the first tier is focused on assessing general cytotoxicity, while the second tier is aimed at identifying liver-specific toxicity as such. A state-of-the-art overview is provided of the most commonly used in vitro assays that can be used in both tiers. Advantages and disadvantages of each assay as well as overall practical considerations are discussed.

Adverse Outcome Pathway Development for Assessment of Lung Carcinogenicity by Nanoparticles

Lung cancer, one of the most common and deadly forms of cancer, is in some cases associated with exposure to certain types of particles. With the rise of nanotechnology, there is concern that some engineered nanoparticles may be among such particles. In the absence of epidemiological evidence, assessment of nanoparticle carcinogenicity is currently performed on a time-consuming case-by-case basis, relying mainly on animal experiments. Non-animal alternatives exist, including a few validated cell-based methods accepted for regulatory risk assessment of nanoparticles. Furthermore, new approach methodologies (NAMs), focused on carcinogenic mechanisms and capable of handling the increasing numbers of nanoparticles, have been developed. However, such alternative methods are mainly applied as weight-of-evidence linked to generally required animal data, since challenges remain regarding interpretation of the results. These challenges may be more easily overcome by the novel Adverse Outcome Pathway (AOP) framework, which provides a basis for validation and uptake of alternative mechanism-focused methods in risk assessment. Here, we propose an AOP for lung cancer induced by nanosized foreign matter, anchored to a selection of 18 standardized methods and NAMs for in silico- and in vitro-based integrated assessment of lung carcinogenicity. The potential for further refinement of the AOP and its components is discussed in relation to available nanosafety knowledge and data. Overall, this perspective provides a basis for development of AOP-aligned alternative methods-based integrated testing strategies for assessment of nanoparticle-induced lung cancer.

A comprehensive review of mechanistic insights into formaldehyde-induced nasal cavity carcinogenicity

According to the International Agency for Research on Cancer classification, formaldehyde is a human carcinogen that targets the nasal cavity. In humans and rats, inhaled formaldehyde is primarily deposited in the nasal cavity mucosa, metabolized to the less toxic formic acid, and finally excreted into the urine or exhaled. Thus, formaldehyde-induced nasal carcinogenicity may be a direct effect of formaldehyde itself, although the underlying mechanisms remain unclear. With regard to cytotoxicity, degeneration and necrosis of nasal respiratory cells occur in rats after short exposure to formaldehyde. Cell proliferation is increased in the damaged cells, suggesting its critical roles both in the early stages and throughout the entire process of nasal carcinogenicity. Hyperplasia, squamous metaplasia, and dysplasia of the damaged epithelium frequently appear as morphological precursor lesions. With regard to genotoxicity, in addition to DNA-protein crosslinks, oxidative DNA damage also occurs in the exposed nasal mucosal cells. Sustained exposure to formaldehyde may cause nasal carcinogenicity through cytotoxicity and auxiliary genotoxicity. In this review, we discuss adverse outcome pathways through which cytotoxicity can lead to carcinogenicity and the development of integrated approaches for testing and assessment for nongenotoxic carcinogens.

FutureTox IV Workshop Summary: Predictive Toxicology for Healthy Children

FutureTox IV, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in November 2018. Building upon FutureTox I, II, and III, this conference focused on the latest science and technology for in vitro profiling and in silico modeling as it relates to predictive developmental and reproductive toxicity (DART). Publicly available high-throughput screening data sets are now available for broad in vitro profiling of bioactivities across large inventories of chemicals. Coupling this vast amount of mechanistic data with a deeper understanding of molecular embryology and post-natal development lays the groundwork for using new approach methodologies (NAMs) to evaluate chemical toxicity, drug efficacy, and safety assessment for embryo-fetal development. NAM is a term recently adopted in reference to any technology, methodology, approach, or combination thereof that can be used to provide information on chemical hazard and risk assessment to avoid the use of intact animals (U.S. Environmental Protection Agency [EPA], Strategic plan to promote the development and implementation of alternative test methods within the tsca program, 2018, https://www.epa.gov/sites/production/files/2018-06/documents/epa_alt_strat_plan_6-20-18_clean_final.pdf). There are challenges to implementing NAMs to evaluate chemicals for developmental toxicity compared with adult toxicity. This forum article reviews the 2018 workshop activities, highlighting challenges and opportunities for applying NAMs for adverse pregnancy outcomes (eg, preterm labor, malformations, low birth weight) as well as disorders manifesting postnatally (eg, neurodevelopmental impairment, breast cancer, cardiovascular disease, fertility). DART is an important concern for different regulatory statutes and test guidelines. Leveraging advancements in such approaches and the accompanying efficiencies to detecting potential hazards to human development are the unifying concepts toward implementing NAMs in DART testing. Although use of NAMs for higher level regulatory decision making is still on the horizon, the conference highlighted novel testing platforms and computational models that cover multiple levels of biological organization, with the unique temporal dynamics of embryonic development, and novel approaches for estimating toxicokinetic parameters essential in supporting in vitro to in vivo extrapolation.

Computational prediction of photosensitizers’ toxicity

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, and to assess potential mutagenic impurities in development and degradants as part of life-cycle management. In this study, for the first time, in silico approaches were used to analyze the possible dark toxicity of photosensitive systems based on chlorin e6 and assessed possible toxicity of these compositions. By applying quantitative structure-activity relationship models (QSARs) and modeling adverse outcome pathways (AOPs), a potential toxic effect of water-soluble (chlorin e6 and chlorin e6 aminoamid) and hydrophobic (tetraphenylporphyrin) photosensitizers (PS) was predicted. Particularly, PSs’ protein binding ability, reactivity to form peptide adducts, glutathione conjugation, activity in dendritic cells, and gene expression activity in keratinocytes were explored. Using a metabolism simulator, possible PS metabolites were predicted and their potential toxicity was assessed as well. It was shown that all tested porphyrin PS and their predicted metabolites possess low activity in the mentioned processes and therefore are unable to cause significant adverse toxic effects under dark conditions.

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.

Requirements for Animal Experiments: Problems and Challenges

In vivo models remain a principle screening tool in the drug discovery pipeline. Here, the challenges associated with the need for animal experiments, as well as their impact on research, individual/societal, and economic contexts are discussed. A number of alternatives that, with further development, optimization, and investment, may replace animal experiments are also revised.

Non-Animal Strategies for Toxicity Assessment of Nanoscale Materials: Role of Adverse Outcome Pathways in the Selection of Endpoints

Faster, cheaper, sensitive, and mechanisms-based animal alternatives are needed to address the safety assessment needs of the growing number of nanomaterials (NM) and their sophisticated property variants. Specifically, strategies that help identify and prioritize alternative schemes involving individual test models, toxicity endpoints, and assays for the assessment of adverse outcomes, as well as strategies that enable validation and refinement of these schemes for the regulatory acceptance are needed. In this review, two strategies 1) the current nanotoxicology literature review and 2) the adverse outcome pathways (AOPs) framework, a systematic process that allows the assembly of available mechanistic information concerning a toxicological response in a simple modular format, are presented. The review highlights 1) the most frequently assessed and reported ad hoc in vivo and in vitro toxicity measurements in the literature, 2) various AOPs of relevance to inhalation toxicity of NM that are presently under development, and 3) their applicability in identifying key events of toxicity for targeted in vitro assay development. Finally, using an existing AOP for lung fibrosis, the specific combinations of cell types, exposure and test systems, and assays that are experimentally supported and thus, can be used for assessing NM-induced lung fibrosis, are proposed.

Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples

Mucus hypersecretion contributes to lung function impairment observed in COPD (chronic obstructive pulmonary disease), a tobacco smoking-related disease. A detailed mucus hypersecretion adverse outcome pathway (AOP) has been constructed from literature reviews, experimental and clinical data, mapping key events (KEs) across biological organisational hierarchy leading to an adverse outcome. AOPs can guide the development of biomarkers that are potentially predictive of diseases and support the assessment frameworks of nicotine products including electronic cigarettes. Here, we describe a method employing manual literature curation supported by a focused automated text mining approach to identify genes involved in 5 KEs contributing to decreased lung function observed in tobacco-related COPD. KE genesets were subsequently confirmed by unsupervised clustering against 3 different transcriptomic datasets including (1) in vitro acute cigarette smoke and e-cigarette aerosol exposure, (2) in vitro repeated incubation with IL-13, and (3) lung biopsies from COPD and healthy patients. The 5 KE genesets were demonstrated to be predictive of cigarette smoke exposure and mucus hypersecretion in vitro, and less conclusively predict the COPD status of lung biopsies. In conclusion, using a focused automated text mining and curation approach with experimental and clinical data supports the development of risk assessment strategies utilising AOPs.

Integrated approaches to testing and assessment as a tool for the hazard assessment and risk characterization of cosmetic preservatives

The safety assessment of cosmetic products is based on the safety of the ingredients, which requires information on chemical structures, toxicological profiles, and exposure data. Approximately 6% of the population is sensitized to cosmetic ingredients, especially preservatives and fragrances. In this context, the aim of this study was to perform a hazard assessment and risk characterization of benzalkonium chloride (BAC), benzyl alcohol (BA), caprylyl glycol (CG), ethylhexylglycerin (EG), chlorphenesin (CP), dehydroacetic acid (DHA), sodium dehydroacetate (SDH), iodopropynyl butylcarbamate (IPBC), methylchloroisothiazolinone and methylisothiazolinone (MCI/MIT), methylisothiazolinone (MIT), phenoxyethanol (PE), potassium sorbate (PS), and sodium benzoate (SB). Considering the integrated approaches to testing and assessment (IATA) and weight of evidence (WoE) as a decision tree, based on published safety reports. The hazard assessment was composed of a toxicological matrix correlating the toxicity level, defined as low (L), moderate (M), or high (H) and local or systemic exposure, considering the endpoints of skin sensitization, skin irritation, eye irritation, phototoxicity, acute oral toxicity, carcinogenicity, mutagenicity/genotoxicity, and endocrine activity. In a risk assessment approach, most preservatives had a margin of safety (MoS) above 100, except for DHA, SDH, and EG, considering the worst-case scenario (100% dermal absorption). However, isolated data do not set up a safety assessment. It is necessary to carry out a rational risk characterization considering hazard and exposure assessment to estimate the level of risk of an adverse health outcome, based on the concentration in a product, frequency of use, type of product, route of exposure, body surface location, and target population.

High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures

The ToxCast in vitro screening program has provided concentration-response bioactivity data across more than a thousand assay endpoints for thousands of chemicals found in our environment and commerce. However, most ToxCast screening assays have evaluated individual biological targets in cancer cell lines lacking integrated physiological functionality (such as receptor signaling, metabolism). We evaluated differentiated HepaRGTM cells, a human liver-derived cell model understood to effectively model physiologically relevant hepatic signaling. Expression of 93 gene transcripts was measured by quantitative polymerase chain reaction using Fluidigm 96.96 dynamic arrays in response to 1060 chemicals tested in eight-point concentration-response. A Bayesian framework quantitatively modeled chemical-induced changes in gene expression via six transcription factors including: aryl hydrocarbon receptor, constitutive androstane receptor, pregnane X receptor, farnesoid X receptor, androgen receptor, and peroxisome proliferator-activated receptor alpha. For these chemicals the network model translates transcriptomic data into Bayesian inferences about molecular targets known to activate toxicological adverse outcome pathways. These data also provide new insights into the molecular signaling network of HepaRGTM cell cultures.

Immunocompetent Human Intestinal Models in Preclinical Drug Development

The intestinal epithelial barrier, together with the microbiome and local immune system, is a critical component that maintains intestinal homeostasis. Dysfunction may lead to chronic inflammation, as observed in inflammatory bowel diseases. Animal models have historically been used in preclinical research to identify and validate new drug targets in intestinal inflammatory diseases. Yet, limitations about their biological relevance to humans and advances in tissue engineering have forced the development of more complex three-dimensional reconstructed intestinal epithelium. By introducing immune and commensal microbial cells, these models more accurately mimic the gut's physiology and the pathophysiological changes occurring in vivo in the inflamed intestine. Specific advantages and limitations of two-dimensional (2D) and three-dimensional (3D) intestinal models such as coculture systems, organoids, and microfluidic devices to study inflammatory and immune-related responses are highlighted. While current cell culture models lack the cellular and molecular complexity observed in vivo, the emphasis is put on how these models can be used to improve preclinical drug development for inflammatory diseases of the intestine.

Generating adverse outcome pathway (AOP) of inorganic arsenic-induced adult male reproductive impairment via integration of phenotypic analysis in comparative toxicogenomics database (CTD) and AOP wiki

BACKGROUND

Inorganic arsenic (iAs) is a worldwide environmental pollutant which exerts complicated and various toxic effects in organisms. Increasingly epidemic studies have revealed the association between iAs exposure and adult male reproductive impairment. Consistent with the proposal for toxicity testing in the 21st century (TT21C), the adverse outcome pathway (AOP) framework may help unravel the iAs-caused molecular and functional changes leading to male reproductive impairment.

METHOD

Combining CTD's phenotype-disease inference data, iAs-phenotypes were anchored to five male reproductive diseases induced by iAs, and local network topological algorithm was applied in prioritizing their interference significance. Through integrating analysis in AOP Wiki knowledge base, filtered phenotypes were linked to key events consisting of AOPs and assembled together based on evidentially upstream and downstream relationships.

RESULTS

A subset of 655 phenotypes were filtered from CTD as potential key events and showed a significant coherence in five reproductive diseases wherein 39 significant phenotypes showed a good clustering features involving cell cycle, ROS and mitochondria function. Two AOP subnetworks were enriched in AOP Wiki where testosterone reduction and apoptosis of sperm served as focus events respectively. Besides, a candidates list of molecular initialing events was provided of which glucocorticoid receptor activation was overall assessed as an example.

CONCLUSION

This study applied computational and bioinformatics methods in generating AOPs for arsenic reproductive toxicity, which identified the imperative roles of testosterone reduction, response to ROS, spermatogenesis and provided a global view about their internal association. Furthermore, this study helped address the existing knowledge gaps for future experimental verification.

A direct peptide reactivity assay using a high-throughput mass spectrometry screening platform for detection of skin sensitizers

Chemical-peptide conjugation is the molecular initiating event in skin sensitization. The OECD test guideline uses a high-performance liquid chromatography/ultraviolet (HPLC/UV) detection method to quantify chemical-peptide conjugation in a direct peptide reactivity assay (DPRA), which measures the depletion of two synthetic peptides containing lysine or cysteine residues. To improve assay throughput, sensitivity and accuracy, an automated 384-well plate-based RapidFire solid-phase extraction (SPE) system coupled with tandem mass spectrometry (MS/MS) DPRA was developed and validated in the presence of a newly designed internal standard. Compared to the HPLC/UV-based DPRA, the automated SPE-MS/MS-based DPRA improved throughput from 16 min to 10 s per sample, and substrate peptides usage was reduced from 100 mM to 5 μM. When implementing the SPE-MS/MS-based DPRA into a high-throughput platform, we found 10 compounds that depleted lysine peptide and 24 compounds that depleted cysteine peptide (including 7 unreported chemicals from 55 compounds we tested) in a concentration-response manner. The adduct formation between cysteine and cinnamic aldehyde and ethylene glycol dimethacrylate were further analyzed using high-performance liquid chromatography time-of-flight mass spectrometry (HPLC-TOF-MS) to confirm the conjugation. Overall, the automated SPE-MS/MS-based platform is an efficient, economic, and accurate way to detect skin sensitizers.

Computational Biology and in silico Toxicodynamics

New approach methodologies (NAMs) refer to any non-animal technology, methodology, approach, or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals. A spectrum of in silico models is needed for the integrated analysis of various domains in toxicology to improve predictivity and reduce animal testing. This review focuses on in silico approaches, computer models, and computational intelligence for developmental and reproductive toxicity (predictive DART), providing a means to measure toxicodynamics in simulated systems for quantitative prediction of adverse outcomes phenotypes.

Environmental effects of offshore produced water discharges: A review focused on the Norwegian continental shelf

Produced water (PW), a large byproduct of offshore oil and gas extraction, is reinjected to formations or discharged to the sea after treatment. The discharges contain dispersed crude oil, polycyclic aromatic hydrocarbons (PAHs), alkylphenols (APs), metals, and many other constituents of environmental relevance. Risk-based regulation, greener offshore chemicals and improved cleaning systems have reduced environmental risks of PW discharges, but PW is still the largest operational source of oil pollution to the sea from the offshore petroleum industry. Monitoring surveys find detectable exposures in caged mussel and fish several km downstream from PW outfalls, but biomarkers indicate only mild acute effects in these sentinels. On the other hand, increased concentrations of DNA adducts are found repeatedly in benthic fish populations, especially in haddock. It is uncertain whether increased adducts could be a long-term effect of sediment contamination due to ongoing PW discharges, or earlier discharges of oil-containing drilling waste. Another concern is uncertainty regarding the possible effect of PW discharges in the sub-Arctic Southern Barents Sea. So far, research suggests that sub-arctic species are largely comparable to temperate species in their sensitivity to PW exposure. Larval deformities and cardiac toxicity in fish early life stages are among the biomarkers and adverse outcome pathways that currently receive much attention in PW effect research. Herein, we summarize the accumulated ecotoxicological knowledge of offshore PW discharges and highlight some key remaining knowledge needs.

De Novo Development of a Quantitative Adverse Outcome Pathway (qAOP) Network for Ultraviolet B (UVB) Radiation Using Targeted Laboratory Tests and Automated Data Mining

Ultraviolet B (UVB) radiation is a natural nonchemical stressor posing potential hazards to organisms such as planktonic crustaceans. The present study was conducted to revisit the lethal effects of UVB on crustaceans, generate new experimental evidence to fill in knowledge gaps, and develop novel quantitative adverse outcome pathways (qAOPs) for UVB. A combination of laboratory and computational approaches was deployed to achieve the goals. For targeted laboratory tests, Daphnia magna was used as a prototype and exposed to a gradient of artificial UVB. Targeted bioassays were used to quantify the effects of UVB at multiple levels of biological organization. A toxicity pathway network was assembled based on the new experimental evidence and previously published data extracted using a novel computational tool, the NIVA Risk Assessment Database (NIVA RAdb). A network of AOPs was developed, and weight of evidence was assessed based on a combination of the current and existing data. In addition, quantitative key event relationships in the AOPs were developed by fitting the D. magna data to predefined models. A complete workflow for assembly and evaluation of qAOPs has been presented, which may serve as a good example for future de novo qAOP development for chemical and nonchemical stressors.

Employment of cytology for in vitro skin irritation test using a reconstructed human epidermis model, Keraskin™

Skin irritation tests using reconstructed human epidermis (RhE) employ viability as an endpoint, but color interference or borderline results are often problematic. We examined whether the cytology of cells from treated RhE could determine skin irritancy. Six chemicals (three irritants; DnP, 1-B, PH, three non-irritants; DP, APA, HS) were evaluated in a RhE, Keraskin™. DP, HS, and PH were clearly classified with viability, but DnP, 1-B, and APA were often falsely determined, due to borderline values falling near the cutoff, 50%. In histology, the tissues treated with DnP, 1-B, and PH showed erosion of the stratum corneum, vacuolization, and necrosis in the basal layer. DP- and HS-treated tissues showed relatively normal morphology but APA induced necrosis similar to irritants. Cytology revealed that DnP, 1-B or PH depleted cells and induced irregular and abnormal cell shapes. In contrast, relatively regular and normal shapes and clear distinction between the nucleus and cytoplasm was observed for DP, APA and HS. To further confirm it, additional 10 substances, including false positives from OECD TG 439, were tested. Overall (16 substances in total), cytology: total area predicted the skin irritancy of test chemicals with the highest accuracy (87.5%) followed by cytology: cell count (81.3%), histology (75%) and viability (68.8%), confirming the utility of cytology as an alternative endpoint in the skin irritation test using RhE.

Spatiotemporal variation in occurrence and co-occurrence of pesticides, hormones, and other organic contaminants in rivers in the Chesapeake Bay Watershed, United States

Investigating the spatiotemporal dynamics of contaminants in surface water is crucial to better understand how introduced chemicals are interacting with and potentially influencing aquatic organisms and environments. Within the Chesapeake Bay Watershed, United States, there are concerns about the potential role of contaminant exposure on fish health. Evidence suggests that exposure to contaminants in surface water is causing immunosuppression and intersex in freshwater fish species. Despite these concerns, there is a paucity of information regarding the complex dynamics of contaminant occurrence and co-occurrence in surface water across both space and time. To address these concerns, we applied a Bayesian hierarchical joint-contaminant model to describe the occurrence and co-occurrence patterns of 28 contaminants and total estrogenicity across six river sites and over three years. We found that seasonal occurrence patterns varied by contaminant, with the highest occurrence probabilities during the spring and summer months. Additionally, we found that the proportion of agricultural landcover in the immediate catchment, as well as stream discharge, did not have a significant effect on the occurrence probabilities of most compounds. Four pesticides (atrazine, metolachlor, fipronil and simazine) co-occurred across sites after accounting for environmental covariates. These results provide baseline information on the contaminant occurrence patterns of several classes of compounds within the Chesapeake Bay Watershed. Understanding the spatiotemporal dynamics of contaminants in surface water is the first step in investigating the effects of contaminant exposure on fisheries and aquatic environments.

In Silico Identification of Chemicals Capable of Binding to the Ecdysone Receptor

The process of molting, known alternatively as ecdysis, is a feature integral in the life cycles of species across the arthropod phylum. Regulation occurs as a function of the interaction of ecdysteroid hormones with the arthropod nuclear ecdysone receptor-a process preceding the triggering of a series of downstream events constituting an endocrine signaling pathway highly conserved throughout environmentally prevalent insect, crustacean, and myriapod organisms. Inappropriate ecdysone receptor binding and activation forms the essential molecular initiating event within possible adverse outcome pathways relating abnormal molting to mortality in arthropods. Definition of the characteristics of chemicals liable to stimulate such activity has the potential to be of great utility in mitigation of hazards posed toward vulnerable species. Thus the aim of the present study was to develop a series of rule-sets, derived from the key structural and physicochemical features associated with identified ecdysone receptor ligands, enabling construction of Konstanz Information Miner (KNIME) workflows permitting the flagging of compounds predisposed to binding at the site. Data describing the activities of 555 distinct chemicals were recovered from a variety of assays across 10 insect species, allowing for formulation of KNIME screens for potential binding activity at the molecular initiating event and adverse outcome level of biological organization. Environ Toxicol Chem 2020;39:1438-1450. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays

Molecular initiating events (MIEs) are key events in adverse outcome pathways that link molecular chemistry to target biology. As they are based on chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modeling. In this work, we aim to link ligand chemical structures to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

Adverse outcome pathways as a tool for the design of testing strategies to support the safety assessment of emerging advanced materials at the nanoscale

Toxicity testing and regulation of advanced materials at the nanoscale, i.e. nanosafety, is challenged by the growing number of nanomaterials and their property variants requiring assessment for potential human health impacts. The existing animal-reliant toxicity testing tools are onerous in terms of time and resources and are less and less in line with the international effort to reduce animal experiments. Thus, there is a need for faster, cheaper, sensitive and effective animal alternatives that are supported by mechanistic evidence. More importantly, there is an urgency for developing alternative testing strategies that help justify the strategic prioritization of testing or targeting the most apparent adverse outcomes, selection of specific endpoints and assays and identifying nanomaterials of high concern. The Adverse Outcome Pathway (AOP) framework is a systematic process that uses the available mechanistic information concerning a toxicological response and describes causal or mechanistic linkages between a molecular initiating event, a series of intermediate key events and the adverse outcome. The AOP framework provides pragmatic insights to promote the development of alternative testing strategies. This review will detail a brief overview of the AOP framework and its application to nanotoxicology, tools for developing AOPs and the role of toxicogenomics, and summarize various AOPs of relevance to inhalation toxicity of nanomaterials that are currently under various stages of development. The review also presents a network of AOPs derived from connecting all AOPs, which shows that several adverse outcomes induced by nanomaterials originate from a molecular initiating event that describes the interaction of nanomaterials with lung cells and involve similar intermediate key events. Finally, using the example of an established AOP for lung fibrosis, the review will discuss various in vitro tests available for assessing lung fibrosis and how the information can be used to support a tiered testing strategy for lung fibrosis. The AOPs and AOP network enable deeper understanding of mechanisms involved in inhalation toxicity of nanomaterials and provide a strategy for the development of alternative test methods for hazard and risk assessment of nanomaterials.

Computational approach for collection and prediction of molecular initiating events in developmental toxicity

Developmental toxicity is defined as the occurrence of adverse effects on the developing organism as a result from exposure to a toxic agent. These alterations can have long-term acute effects. Current in vitro models present important limitations and the evaluation of toxicity is not entirely objective. In silico methods have also shown limited success, in part due to complex and varied mechanisms of action that mediate developmental toxicity, which are sometimes poorly understood. In this article, we compiled a dataset of compounds with developmental toxicity categories and annotated mechanisms of action for both toxic and non-toxic compounds (DVTOX). With it, we selected a panel of protein targets that might be part of putative Molecular Initiating Events (MIEs) of Adverse Outcome Pathways of developmental toxicity. The validity of this list of candidate MIEs was studied through the evaluation of new drug-target relationships that include such proteins, but were not part of the original database. Finally, an orthology analysis of this protein panel was conducted to select an appropriate animal model to assess developmental toxicity. We tested our approach using the zebrafish embryo toxicity test, finding positive results.

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.

Critical Review of Read-Across Potential in Testing for Endocrine-Related Effects in Vertebrate Ecological Receptors

Recent regulatory testing programs have been designed to evaluate whether a chemical has the potential to interact with the endocrine system and could cause adverse effects. Some endocrine pathways are highly conserved among vertebrates, providing a potential to extrapolate data generated for one vertebrate taxonomic group to others (i.e., biological read-across). To assess the potential for biological read-across, we reviewed tools and approaches that support species extrapolation for fish, amphibians, birds, and reptiles. For each of the estrogen, androgen, thyroid, and steroidogenesis (EATS) pathways, we considered the pathway conservation across species and the responses of endocrine-sensitive endpoints. The available data show a high degree of confidence in the conservation of the hypothalamus-pituitary-gonadal axis between fish and mammals and the hypothalamus-pituitary-thyroid axis between amphibians and mammals. Comparatively, there is less empirical evidence for the conservation of other EATS pathways between other taxonomic groups, but this may be due to limited data. Although more information on sensitive pathways and endpoints would be useful, current developments in the use of molecular target sequencing similarity tools and thoughtful application of the adverse outcome pathway concept show promise for further advancement of read-across approaches for testing EATS pathways in vertebrate ecological receptors. Environ Toxicol Chem 2020;39:739-753. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Toxicity testing is evolving!

The efficient management of the continuously increasing number of chemical substances used in today's society is assuming greater importance than ever before. Toxicity testing plays a key role in the regulatory decisions of agencies and governments that aim to protect the public and the environment from the potentially harmful or adverse effects of these multitudinous chemicals. Therefore, there is a critical need for reliable toxicity-testing methods to identify, assess and interpret the hazardous properties of any substance. Traditionally, toxicity-testing approaches have been based on studies in experimental animals. However, in the last 20 years, there has been increasing concern regarding the sustainability of these methodologies. This has created a real need for the development of new approach methodologies (NAMs) that satisfy the regulatory requirements and are acceptable and affordable to society. Numerous initiatives have been launched worldwide in attempts to address this critical need. However, although the science to support this is now available, the legislation and the pace of NAMs acceptance is lagging behind. This review will consider some of the various initiatives in Europe to identify NAMs to replace or refine the current toxicity-testing methods for pharmaceuticals. This paper also presents a novel systematic approach to support the desired toxicity-testing methodologies that the 21st century deserves.

Adverse outcome pathways for chemical toxicity and their applications to workers' health: a literature review

OBJECTIVE AND METHODS

Various papers related to the application of adverse outcome pathways (AOPs) for the prevention of occupational disease were reviewed. The Internet was used as the primary tool to search for the necessary research data and information, using such online resources as Google Scholar, ScienceDirect, Scopus, NDSL, and PubMed. The key search terms were "adverse outcome pathway," "toxicology," "risk assessment," "human," "worker," "occupational safety and health," and so on.

RESULTS AND CONCLUSION

The aim of this paper is to explain the use of AOP for the understanding of chemical toxicity as a conceptual means and to predict the toxic mechanism. The tools of AOP have emerged as a forward-looking alternative to the existing chemical risk assessment paradigm. AOP is being applied to the assessment of acute toxicity and to chronic toxic chemicals in the workplace. Not only can it lead to breakthroughs in occupational and environmental cancer prevention, it is also widely used in chemical risk assessment and has led to breakthroughs in the prevention of occupational disease in the workplace.

OECD approaches and considerations for regulatory evaluation of endocrine disruptors

Identifying the potential endocrine disruptor hazard of environmental chemicals is a regulatory mandate for many countries. However, due to the adaptive nature of the endocrine system, absence of a single method capable of identifying endocrine disruption, and the latency between exposure to endocrine disrupting chemical during sensitive life stages and the manifestation of adverse responses, satisfying the regulatory requirement needed to identify a chemical as an endocrine disruptor is a challenge. There are now a variety of validated regulatory tests that can be used in combination to provide evidence that a chemical affects the oestrogen, androgen, thyroid, and steroidogenic pathways of vertebrates, but most rely (at least to some extent) on animal testing and require considerable cost and time to produce the necessary data. Emerging research methods are able to evaluate other endocrine pathways, incorporate more sensitive endpoints, and combine multiple alternative methods to predict in vivo outcomes. Some research approaches may also bridge gaps that have been identified in current endocrine regulatory testing. For the near term, considering new endpoints in a regulatory context may require adding them to existing test methods in order to establish relationships between the traditional and the innovative. From the outset, endocrine testing has always required integration of multiple methods that provide data on different levels of biological organisation, thus, the area of endocrine disruption is particularly adaptable to adverse outcome pathway (AOP) frameworks and integrated test methods built around AOPs. Herein, we provide a review of the status of endocrine disruptors in the OECD context, examples where innovation from research is needed to improve or bridge gaps in endocrine testing, and suggestions for regulators and researchers to facilitate uptake of innovate methods for endocrine disruptor regulatory testing. The increase in several human complex human disorders that include an endocrine component and the alarming decrease in wildlife biodiversity are commanding directives to include the best, most informative, innovative approaches to accelerate the rate and throughput of chemical evaluation for endocrine disruption.

Potential frameworks to support evaluation of mechanistic data for developmental neurotoxicity outcomes: A symposium report

A key challenge in systematically incorporating mechanistic data into human health assessments is that, compared to studies of apical health endpoints, these data are both more abundant (mechanistic studies routinely outnumber other studies by several orders of magnitude) and more heterogeneous (e.g. different species, test system, tissue, cell type, exposure paradigm, or specific assays performed). A structured decision-making process for organizing, integrating, and weighing mechanistic DNT data for use in human health risk assessments will improve the consistency and efficiency of such evaluations. At the Developmental Neurotoxicology Society (DNTS) 2016 annual meeting, a symposium was held to address the application of existing organizing principles and frameworks for evaluation of mechanistic data relevant to interpreting neurotoxicology data. Speakers identified considerations with potential to advance the use of mechanistic DNT data in risk assessment, including considering the context of each exposure, since epigenetics, tissue type, sex, stress, nutrition and other factors can modify toxicity responses in organisms. It was also suggested that, because behavior is a manifestation of complex nervous system function, the presence and absence of behavioral change itself could be used to organize the interpretation of multiple complex simultaneous mechanistic changes. Several challenges were identified with frameworks and their implementation, and ongoing research to develop these approaches represents an early step toward full evaluation of mechanistic DNT data for assessments.

Integrative assessment of low-dose gamma radiation effects on Daphnia magna reproduction: Toxicity pathway assembly and AOP development

High energy gamma radiation is potentially hazardous to organisms, including aquatic invertebrates. Although extensively studied in a number of invertebrate species, knowledge on effects induced by gamma radiation is to a large extent limited to the induction of oxidative stress and DNA damage at the molecular/cellular level, or survival, growth and reproduction at the organismal level. As the knowledge of causal relationships between effects occurring at different levels of biological organization is scarce, the ability to provide mechanistic explanation for observed adverse effects is limited, and thus development of Adverse Outcome Pathways (AOPs) and larger scale implementation into next generation hazard and risk predictions is restricted. The present study was therefore conducted to assess the effects of high-energy gamma radiation from cobalt-60 across multiple levels of biological organization (i.e., molecular, cellular, tissue, organ and individual) and characterize the major toxicity pathways leading to impaired reproduction in the model freshwater crustacean Daphnia magna (water flea). Following gamma exposure, a number of bioassays were integrated to measure relevant toxicological endpoints such as gene expression, reactive oxygen species (ROS), lipid peroxidation (LPO), neutral lipid storage, adenosine triphosphate (ATP) content, apoptosis, ovary histology and reproduction. A non-monotonic pattern was consistently observed across the levels of biological organization, albeit with some variation at the lower end of the dose-rate scale, indicating a complex response to radiation doses. By integrating results from different bioassays, a novel pathway network describing the key toxicity pathways involved in the reproductive effects of gamma radiation were proposed, such as DNA damage-oocyte apoptosis pathway, LPO-ATP depletion pathway, calcium influx-endocrine disruption pathway and DNA hypermethylation pathway. Three novel AOPs were proposed for oxidative stressor-mediated excessive ROS formation leading to reproductive effect, and thus introducing the world's first AOPs for non-chemical stressors in aquatic invertebrates.

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.

Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices

Advanced material development, including at the nanoscale, comprises costly and complex challenges coupled to ensuring human and environmental safety. Governmental agencies regulating safety have announced interest toward acceptance of safety data generated under the collective term New Approach Methodologies (NAMs), as such technologies/approaches offer marked potential to progress the integration of safety testing measures during innovation from idea to product launch of nanomaterials. Divided in overall eight main categories, searchable databases for grouping and read across purposes, exposure assessment and modeling, in silico modeling of physicochemical structure and hazard data, in vitro high-throughput and high-content screening assays, dose-response assessments and modeling, analyses of biological processes and toxicity pathways, kinetics and dose extrapolation, consideration of relevant exposure levels and biomarker endpoints typify such useful NAMs. Their application generally agrees with articulated stakeholder needs for improvement of safety testing procedures. They further fit for inclusion and add value in nanomaterials risk assessment tools. Overall 37 of 50 evaluated NAMs and tiered workflows applying NAMs are recommended for considering safer-by-design innovation, including guidance to the selection of specific NAMs in the eight categories. An innovation funnel enriched with safety methods is ultimately proposed under the central aim of promoting rigorous nanomaterials innovation.

Twenty years of transcriptomics, 17alpha-ethinylestradiol, and fish

In aquatic toxicology, perhaps no pharmaceutical has been investigated more intensely than 17alpha-ethinylestradiol (EE2), the active ingredient of the birth control pill. At the turn of the century, the fields of comparative endocrinology and endocrine disruption research witnessed the emergence of omics technologies, which were rapidly adapted to characterize potential hazards associated with exposures to environmental estrogens, such as EE2. Since then, significant advances have been made by the scientific community, and as a result, much has been learned about estrogen receptor signaling in fish from environmental xenoestrogens. Vitellogenin, the egg yolk precursor protein, was identified as a major estrogen-responsive gene, establishing itself as the premier biomarker for estrogenic exposures. Omics studies have identified a plethora of estrogen responsive genes, contributing to a wealth of knowledge on estrogen-mediated regulatory networks in teleosts. There have been ~40 studies that report on transcriptome responses to EE2 in a variety of fish species (e.g., zebrafish, fathead minnows, rainbow trout, pipefish, mummichog, stickleback, cod, and others). Data on the liver and testis transcriptomes dominate in the literature and have been the subject of many EE2 studies, yet there remain knowledge gaps for other tissues, such as the spleen, kidney, and pituitary. Inter-laboratory genomics studies have revealed transcriptional networks altered by EE2 treatment in the liver; networks related to amino acid activation and protein folding are increased by EE2 while those related to xenobiotic metabolism, immune system, circulation, and triglyceride storage are suppressed. EE2-responsive networks in other tissues are not as comprehensively defined which is a knowledge gap as regulated networks are expected to be tissue-specific. On the horizon, omics studies for estrogen-mediated effects in fish include: (1) Establishing conceptual frameworks for incorporating estrogen-responsive networks into environmental monitoring programs; (2) Leveraging in vitro and computational toxicology approaches to identify chemicals associated with estrogen receptor-mediated effects in fish (e.g., male vitellogenin production); (3) Discovering new tissue-specific estrogen receptor signaling pathways in fish; and (4) Developing quantitative adverse outcome pathway predictive models for estrogen signaling. As we look ahead, research into EE2 over the past several decades can serve as a template for the array of hormones and endocrine active substances yet to be fully characterized or discovered.

Deciphering Adverse Outcome Pathway Network Linked to Bisphenol F Using Text Mining and Systems Toxicology Approaches

Bisphenol F (BPF) is one of several Bisphenol A (BPA) substituents that is increasingly used in manufacturing industry leading to detectable human exposure. Whereas a large number of studies have been devoted to decipher BPA effects, much less is known about its substituents. To support decision making on BPF's safety, we have developed a new computational approach to rapidly explore the available data on its toxicological effects, combining text mining and integrative systems biology, and aiming at connecting BPF to adverse outcome pathways (AOPs). We first extracted from different databases BPF-protein associations that were expanded to protein complexes using protein-protein interaction datasets. Over-representation analysis of the protein complexes allowed to identify the most relevant biological pathways putatively targeted by BPF. Then, automatic screening of scientific abstracts from literature using the text mining tool, AOP-helpFinder, combined with data integration from various sources (AOP-wiki, CompTox, etc.) and manual curation allowed us to link BPF to AOP events. Finally, we combined all the information gathered through those analyses and built a comprehensive complex framework linking BPF to an AOP network including, as adverse outcomes, various types of cancers such as breast and thyroid malignancies. These results which integrate different types of data can support regulatory assessment of the BPA substituent, BPF, and trigger new epidemiological and experimental studies.