Pathways of Toxicity

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

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.

Functional polymorphisms in Benzo(a)Pyrene-induced toxicity pathways associated with the risk on laryngeal squamous cell carcinoma

Benzo(a)Pyrene (BaP) is a well-known environmental carcinogen that poses a significant risk to human health. The pivotal genes and toxicity pathways have been identified as key events to construct the mode of action (MOA) of BaP. In this study, we focused on evaluating the association between genetic variants in BaP-disturbed toxicity pathways and the susceptibility of laryngeal squamous cell carcinoma (LSCC), based on the data of our previous genome-wide association analysis (GWAS). In addition, we investigated the biological roles of these significant polymorphisms by integrating bioinformatic annotation and experimental validation. Our findings revealed that 15 functional polymorphisms in AHR signaling, p53 signaling, NRF2 signaling, TGF-β signaling, STAT3 signaling, and IL-8 signaling pathways were significantly associated with susceptibility to LSCC. Our study provides a novel approach for identifying novel risk genetic loci utilizing GWAS data, and suggests potential targets for early detection of LSCC in the future.

G × E interactions as a basis for toxicological uncertainty

To transfer toxicological findings from model systems, e.g. animals, to humans, standardized safety factors are applied to account for intra-species and inter-species variabilities. An alternative approach would be to measure and model the actual compound-specific uncertainties. This biological concept assumes that all observed toxicities depend not only on the exposure situation (environment = E), but also on the genetic (G) background of the model (G × E). As a quantitative discipline, toxicology needs to move beyond merely qualitative G × E concepts. Research programs are required that determine the major biological variabilities affecting toxicity and categorize their relative weights and contributions. In a complementary approach, detailed case studies need to explore the role of genetic backgrounds in the adverse effects of defined chemicals. In addition, current understanding of the selection and propagation of adverse outcome pathways (AOP) in different biological environments is very limited. To improve understanding, a particular focus is required on modulatory and counter-regulatory steps. For quantitative approaches to address uncertainties, the concept of "genetic" influence needs a more precise definition. What is usually meant by this term in the context of G × E are the protein functions encoded by the genes. Besides the gene sequence, the regulation of the gene expression and function should also be accounted for. The widened concept of past and present "gene expression" influences is summarized here as Ge. Also, the concept of "environment" needs some re-consideration in situations where exposure timing (Et) is pivotal: prolonged or repeated exposure to the insult (chemical, physical, life style) affects Ge. This implies that it changes the model system. The interaction of Ge with Et might be denoted as Ge × Et. We provide here general explanations and specific examples for this concept and show how it could be applied in the context of New Approach Methodologies (NAM).

Development, scrutiny, and modulation of transient reporter gene assays of the xenobiotic metabolism pathway in zebrafish hepatocytes

The "toxicology in the twenty-first century" paradigm shift demands the development of alternative in vitro test systems. Especially in the field of ecotoxicology, coverage of aquatic species-specific assays is relatively scarce. Transient reporter gene assays could be a quick, economical, and reliable bridging technology. However, the user should be aware of potential pitfalls that are influenced by reporter vector geometry. Here, we report the development of an AhR-responsive transient reporter-gene assay in the permanent zebrafish hepatocytes cell line (ZFL). Additionally, we disclose how viral, constitutive promoters within reporter-gene assay cassettes induce squelching of the primary signal. To counter this, we designed a novel normalization vector, bearing an endogenous zebrafish-derived genomic promoter (zfEF1aPro), which rescues the squelching-delimited system, thus, giving new insights into the modulation of transient reporter systems under xenobiotic stress. Finally, we uncovered how the ubiquitously used ligand BNF promiscuously activates multiple toxicity pathways of the xenobiotic metabolism and cellular stress response in an orchestral manner, presumably leading to a concentration-related inhibition of the AhR/ARNT/XRE-toxicity pathway and non-monotonous concentration-response curves. We named such a multi-level inhibitory mechanism that might mask effects as "maisonette squelching." A transient reporter gene assay in zebrafish cell lines utilizing endogenous regulatory gene elements shows increased in vitro toxicity testing performance. Synthetic and constitutive promotors interfere with signal transduction ("squelching") and might increase cellular stress (cytotoxicity). The squelching phenomenon might occur on multiple levels (toxicity pathway crosstalk and normalization vector), leading to a complete silencing of the reporter signal.

The Toxicological Risk Assessment (TRA) of Total Chromium Impurities in Menthae piperitae tinctura (Mentha x piperita L., folium) Available in Polish Pharmacies Including Regulatory Approaches with Special Emphasis of Cr Speciation and Genotoxicity

Chromium is pharmacologically active and is not an essential element but is still very intriguing and demanding from the point of view of toxicological risk assessment. Especially as an elemental impurity in final pharmaceuticals. The aim of this article is toxicological risk assessment (TRA) of total Cr impurities in Menthae piperitae tinctura (Mentha x piperita L., folium) available in Polish pharmacies including triple approach. Obtained raw/basal results shows that impurities of total Cr impurities were present in all investigated pharmaceutical with Mentha x piperita L., folium. but at a relatively low level (in the range: 0.39-2.14 µg/L). The regulatory strategy based on the ICH Q3D (R1) elemental impurities guidelines confirms that all the requirements of the analyzed products meet the European Medicine Agency (EMA) requirements. Obtained results obtained show that the estimated maximum daily exposure to Cr (ng/day) are variable between the samples (0.521-3.792 ng/day), but at a relatively low level (< 4.0 ng/day).The final step confirms the safety of analyzed pharmaceuticals, because the comparison of the estimated results with the oral PDE value for Cr in final drugs suggested by the ICH Q3D guideline (10,700 µg/day) show that all the products are below this value. The approach based on the margin of exposure (MoE) for children and adults also confirms the safety of all the products with Mentha x piperita L., (in all cases MoE >  > 10 000). It can be concluded that all the samples analyzed should not represent any health hazard to patients due to Cr impurities. To our knowledge, this is the first study about application of comprehensive TRA of total Cr impurities in phytopharmaceuticals with Mentha x piperita L., folium available in European pharmacies. Additionally, we confirm the safety of Cr impurities by applying triple regulatory strategy without the application of an expansive and demanding HPLC-ICP-MS technique for Cr speciation.

LUHMES Dopaminergic Neurons Are Uniquely Susceptible to Ferroptosis

Ferroptosis is a necrotic cell death caused by lipid oxidation that may be responsible for neural degeneration in Parkinson's disease. We assessed whether three neuronal cell lines are sensitive to killing by ferroptosis. Ferroptosis inducer erastin killed LUHMES neurons at sub-micromolar concentrations, whereas neuronal cells derived from SH-SY5Y cells or neural stem cells were at least 50-fold less sensitive. LUHMES differentiated neurons were likewise sensitive to killing by RSL3 or ML210, inhibitors of the glutathione peroxidase 4 enzyme (GPX4) that consumes GSH to detoxify lipid peroxides. Additional assays showed that erastin, RSL3, and ML210 increased lipid peroxide levels, and that LUHMES neurons were protected from both peroxide accumulation and cell death by ferrostatin-1. A possible role of iron was assessed by evaluating the effects of five metal chelators on cytotoxicity of erastin and RSL3. LUHMES neurons were protected from RSL3 by three of the chelators, 2,3-dimercapto-1-propanesulfonic acid (DMPS), deferoxiprone (DFX), and deferiprone (DFP). Collectively, these results demonstrate the vulnerability of LUHMES neurons to ferroptosis by chemical treatments that disrupt glutathione synthesis, lipid peroxide detoxification, or iron metabolism. The same vulnerabilities may occur in CNS neurons, which reportedly generate low levels of GSH and metallothioneins, limiting their ability to neutralize oxidative stresses and toxic metals. These results suggest a rationale and methods to search for environmental toxicants that may exploit these vulnerabilities and promote neurodegenerative diseases.

Investigation of a derived adverse outcome pathway (AOP) network for endocrine-mediated perturbations

An adverse outcome pathway (AOP) is a compact representation of the available mechanistic information on observed adverse effects upon environmental exposure. Sharing of events across individual AOPs has led to the emergence of AOP networks. Since AOP networks are expected to be functional units of toxicity prediction, there is current interest in their development tailored to specific research question or regulatory problem. To this end, we have developed a detailed workflow to construct an endocrine-relevant AOP (ED-AOP) network based on the existing information available in AOP-Wiki. We propose a cumulative weight of evidence (WoE) score for each ED-AOP based on the WoE scores assigned to key event relationships (KERs) by AOP-Wiki, revealing gaps in AOP development. Connectivity analysis of the ED-AOP network comprising 48 AOPs reveals 7 connected components and 12 isolated AOPs. Subsequently, we apply standard network measures to perform an in-depth analysis of the two largest connected components of the ED-AOP network. Notably, the graph-theoretic analyses led to the identification of important events including points of convergence or divergence in the ED-AOP network. These findings can suggest potential adverse outcomes and facilitate the development of new endpoints or assays for chemical risk assessment. Detailed analysis of the largest component in the ED-AOP network gives insights on the systems-level perturbations caused by endocrine disruption, emergent paths, and stressor-event associations. In sum, the derived ED-AOP network can provide a broader view of the biological events disrupted by endocrine disruption, as well as facilitate better risk assessment of environmental chemicals.

Beyond pharmaceuticals: Fit-for-purpose new approach methodologies for environmental cardiotoxicity testing

Environmental factors play a substantial role in determining cardiovascular health, but data informing the risks presented by environmental toxicants is insufficient. In vitro new approach methodologies (NAMs) offer a promising approach with which to address the limitations of traditional in vivo and in vitro assays for assessing cardiotoxicity. Driven largely by the needs of pharmaceutical toxicity testing, considerable progress in developing NAMs for cardiotoxicity analysis has already been made. As the scientific and regulatory interest in NAMs for environmental chemicals continues to grow, a thorough understanding of the unique features of environmental cardiotoxicants and their associated cardiotoxicities is needed. Here, we review the key characteristics of as well as important regulatory and biological considerations for fit-for-purpose NAMs for environmental cardiotoxicity. By emphasizing the challenges and opportunities presented by NAMs for environmental cardiotoxicity we hope to accelerate their development, acceptance, and application.

A novel chemo-phenotypic method identifies mixtures of salpn, vitamin D3, and pesticides involved in the development of colorectal and pancreatic cancer

Environmental chemical (EC) exposures and our interactions with them has significantly increased in the recent decades. Toxicity associated biological characterization of these chemicals is challenging and inefficient, even with available high-throughput technologies. In this report, we describe a novel computational method for characterizing toxicity, associated biological perturbations and disease outcome, called the Chemo-Phenotypic Based Toxicity Measurement (CPTM). CPTM is used to quantify the EC "toxicity score" (Z_ts), which serves as a holistic metric of potential toxicity and disease outcome. CPTM quantitative toxicity is the measure of chemical features, biological phenotypic effects, and toxicokinetic properties of the ECs. For proof-of-concept, we subject ECs obtained from the Environmental Protection Agency's (EPA) database to the CPTM. We validated the CPTM toxicity predictions by correlating 'Z_ts' scores with known toxicity effects. We also confirmed the CPTM predictions with in-vitro, and in-vivo experiments. In in-vitro and zebrafish models, we showed that, mixtures of the motor oil and food additive 'Salpn' with endogenous nuclear receptor ligands such as Vitamin D3, dysregulated the nuclear receptors and key transcription pathways involved in Colorectal Cancer. Further, in a human patient derived cell organoid model, we found that a mixture of the widely used pesticides 'Tetramethrin' and 'Fenpropathrin' significantly impacts the population of patient derived pancreatic cancer cells and 3D organoid models to support rapid PDAC disease progression. The CPTM method is, to our knowledge, the first comprehensive toxico-physicochemical, and phenotypic bionetwork-based platform for efficient high-throughput screening of environmental chemical toxicity, mechanisms of action, and connection to disease outcomes.

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!

Trends in the Application of "Omics" to Ecotoxicology and Stress Ecology

Our ability to predict and assess how environmental changes such as pollution and climate change affect components of the Earth's biome is of paramount importance. This need positioned the fields of ecotoxicology and stress ecology at the center of environmental monitoring efforts. Advances in these interdisciplinary fields depend not only on conceptual leaps but also on technological advances and data integration. High-throughput "omics" technologies enabled the measurement of molecular changes at virtually all levels of an organism's biological organization and thus continue to influence how the impacts of stressors are understood. This bibliometric review describes literature trends (2000-2020) that indicate that more different stressors than species are studied each year but that only a few stressors have been studied in more than two phyla. At the same time, the molecular responses of a diverse set of non-model species have been investigated, but cross-species comparisons are still rare. While transcriptomics studies dominated until 2016, a shift towards proteomics and multiomics studies is apparent. There is now a wealth of data at functional omics levels from many phylogenetically diverse species. This review, therefore, addresses the question of how to integrate omics information across species.

The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology

Read-across (RAx) translates available information from well-characterized chemicals to a substance for which there is a toxicological data gap. The OECD is working on case studies to probe general applicability of RAx, and several regulations (e.g., EU-REACH) already allow this procedure to be used to waive new in vivo tests. The decision to prepare a review on the state of the art of RAx as a tool for risk assessment for regulatory purposes was taken during a workshop with international experts in Ranco, Italy in July 2018. Three major issues were identified that need optimization to allow a higher regulatory acceptance rate of the RAx procedure: (i) the definition of similarity of source and target, (ii) the translation of biological/toxicological activity of source to target in the RAx procedure, and (iii) how to deal with issues of ADME that may differ between source and target. The use of new approach methodologies (NAM) was discussed as one of the most important innovations to improve the acceptability of RAx. At present, NAM data may be used to confirm chemical and toxicological similarity. In the future, the use of NAM may be broadened to fully characterize the hazard and toxicokinetic properties of RAx compounds. Concerning available guidance, documents on Good Read-Across Practice (GRAP) and on best practices to perform and evaluate the RAx process were identified. Here, in particular, the RAx guidance, being worked out by the European Commission’s H2020 project EU-ToxRisk together with many external partners with regulatory experience, is given.

Use of LUCS (Light-Up Cell System) as an alternative live cell method to predict human acute oral toxicity

LUCS (Light-Up Cell System) is a new live cell test that allows assessment of a cell's homeostasis and its alteration by a toxic agent. To evaluate the effectiveness of LUCS as an alternative test method for acute oral toxicity, we compared EC₅₀s determined in HepG2 cells treated with 53 chemicals selected from the ACuteTox EU database with corresponding human blood LC₅₀s derived from human acute poisoning cases. Linear regression analysis showed that LUCS results predict human data to 69 %. Rodent oral LD₅₀s and LUCS EC₅₀s were then correlated to human LC₅₀s using shared data sets. Linear regression analyses comparing LUCS and animal data clearly showed that LUCS always predicts human toxicity better than animal data do. These successful prediction values prompted us to simplify the LUCS test, adapting it to regulatory and high throughput applications, resulting in a new protocol with consistent dose-response profiles and EC₅₀s. This study demonstrates that the LUCS test method could be relevant for assessing human acute oral toxicity with a simplified protocol adapted to commercially available fluorescence readers. We suggest that this new alternative method can be used for acute systemic toxicity testing in combination with other tests under European REACH and other regulations, wherever pertinent alternative methods are still lacking.

Connectivity Mapping: Methods and Applications

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

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.

Green Toxicology-Know Early About and Avoid Toxic Product Liabilities

Toxicology uniquely among the life sciences relies largely on methods which are more than 40-years old. Over the last 3 decades with more or less success some additions to and few replacements in this toolbox took place, mainly as alternatives to animal testing. The acceptance of such new approaches faces the needs of formal validation and the conservative attitude toward change in safety assessments. Only recently, there is growing awareness that the same alternative methods, especially in silico and in vitro tools can also much earlier and before validation inform decision-taking in the product life cycle. As similar thoughts developed in the context of Green Chemistry, the term of Green Toxicology was coined to describe this change in approach. Here, the current developments in the alternative field, especially computational and more organo-typic cell cultures are reviewed, as they lend themselves to front-loaded chemical safety assessments. The initiatives of the Center for Alternatives to Animal Testing Green Toxicology Collaboration are presented. They aim first of all for forming a community to promote this concept and then for a cultural change in companies with the necessary training of chemists, product stewards and later regulators.

Perspectives on In Vitro to In Vivo Extrapolations

Quantitative in vitro to in vivo extrapolation (QIVIVE) is broadly considered a prerequisite bridge from in vitro findings to a dose paradigm. Quality and relevance of cell systems are the first prerequisite for QIVIVE. Information-rich and mechanistic endpoints (biomarkers) improve extrapolations, but a sophisticated endpoint does not make a bad cell model a good one. The next need is reverse toxicokinetics (TK), which estimates the dose necessary to reach a tissue concentration that is active in vitro. The Johns Hopkins Center for Alternatives to Animal Testing (CAAT) has created a roadmap for animal-free systemic toxicity testing, in which the needs and opportunities for TK are elaborated, in the context of different systemic toxicities. The report was discussed at two stakeholder forums in Brussels in 2012 and in Washington in 2013; the key recommendations are summarized herein. Contrary to common belief and the Paracelsus paradigm of everything is toxic, the majority of industrial chemicals do not exhibit toxicity. Strengthening the credibility of negative results of alternative approaches for hazard identification, therefore, avoids the need for QIVIVE. Here, especially the combination of methods in integrated testing strategies is most promising. Two further but very different approaches aim to overcome the problem of modeling in vivo complexity: The human-on-a-chip movement aims to reproduce large parts of living organism's complexity via microphysiological systems, that is, organ equivalents combined by microfluidics. At the same time, the Toxicity Testing in the 21st Century (Tox-21c) movement aims for mechanistic approaches (adverse outcome pathways as promoted by Organisation for Economic Co-operation and Development (OECD) or pathways of toxicity in the Human Toxome Project) for high-throughput screening, biological phenotyping, and ultimately a systems toxicology approach through integration with computer modeling. These 21st century approaches also require 21st century validation, for example, by evidence-based toxicology. Ultimately, QIVIVE is a prerequisite for extrapolating Tox-21c such approaches to human risk assessment.

Weighted Gene Correlation Network Analysis (WGCNA) Reveals Novel Transcription Factors Associated With Bisphenol A Dose-Response

Despite Bisphenol-A (BPA) being subject to extensive study, a thorough understanding of molecular mechanism remains elusive. Here we show that using weighted gene correlation network analysis (WGCNA), which takes advantage of a graph theoretical approach to understanding correlations amongst genes and grouping genes into modules that typically have co-ordinated biological functions and regulatory mechanisms, that despite some commonality in altered genes, there is minimal overlap between BPA and estrogen in terms of network topology. We confirmed previous findings that ZNF217 and TFAP2C are involved in the estrogen pathway, and are implicated in BPA as well, although for BPA they appear to be active in the absence of canonical estrogen-receptor driven gene expression. Furthermore, our study suggested that PADI4 and RACK7/ZMYNDB8 may be involved in the overlap in gene expression between estradiol and BPA. Lastly, we demonstrated that even at low doses there are unique transcription factors that appear to be driving the biology of BPA, such as SREBF1. Overall, our data is consistent with other reports that BPA leads to subtle gene changes rather than profound aberrations of a conserved estrogen signaling (or other) pathways.

Transcriptome profiling of HepG2 cells exposed to the flame retardant 9,10-dihydro-9-oxa-10-phosphaphenanthrene 10-oxide (DOPO)

The flame retardant, 9,10-dihydro-9-oxa-10-phosphaphenanthrene 10-oxide (DOPO), has been receiving great interest given its superior fire protection properties, and its predicted low level of persistence, bioaccumulation, and toxicity. However, empirical toxicological data that are essential for a complete hazard assessment are severely lacking. In this study, we attempted to identify the potential toxicological modes of action by transcriptome (RNA-seq) profiling of the human liver hepatocellular carcinoma cell line, HepG2. Such insight may help in identifying compounds of concern and potential toxicological phenotypes. DOPO was found to have little cytotoxic potential, with lower effective concentrations compared to other flame retardants studied in the same cell line. Differentially expressed genes revealed a wide range of molecular effects including changes in protein, energy, DNA, and lipid metabolism, along with changes in cellular stress response pathways. In response to 250 μM DOPO, the most perturbed biological processes were fatty acid metabolism, androgen metabolism, glucose transport, and renal function and development, which is in agreement with other studies that observed similar effects of other flame retardants in other species. However, treatment with 2.5 μM DOPO resulted in very few differentially expressed genes and failed to indicate any potential effects on biology, despite such concentrations likely being orders of magnitude greater than would be encountered in the environment. This, together with the low levels of cytotoxicity, supports the potential replacement of the current flame retardants by DOPO, although further studies are needed to establish the nephrotoxicity and endocrine disruption of DOPO.

A Tox21 Approach to Altered Epigenetic Landscapes: Assessing Epigenetic Toxicity Pathways Leading to Altered Gene Expression and Oncogenic Transformation In Vitro

An emerging vision for toxicity testing in the 21st century foresees in vitro assays assuming the leading role in testing for chemical hazards, including testing for carcinogenicity. Toxicity will be determined by monitoring key steps in functionally validated molecular pathways, using tests designed to reveal chemically-induced perturbations that lead to adverse phenotypic endpoints in cultured human cells. Risk assessments would subsequently be derived from the causal in vitro endpoints and concentration vs. effect data extrapolated to human in vivo concentrations. Much direct experimental evidence now shows that disruption of epigenetic processes by chemicals is a carcinogenic mode of action that leads to altered gene functions playing causal roles in cancer initiation and progression. In assessing chemical safety, it would therefore be advantageous to consider an emerging class of carcinogens, the epigenotoxicants, with the ability to change chromatin and/or DNA marks by direct or indirect effects on the activities of enzymes (writers, erasers/editors, remodelers and readers) that convey the epigenetic information. Evidence is reviewed supporting a strategy for in vitro hazard identification of carcinogens that induce toxicity through disturbance of functional epigenetic pathways in human somatic cells, leading to inactivated tumour suppressor genes and carcinogenesis. In the context of human cell transformation models, these in vitro pathway measurements ensure high biological relevance to the apical endpoint of cancer. Four causal mechanisms participating in pathways to persistent epigenetic gene silencing were considered: covalent histone modification, nucleosome remodeling, non-coding RNA interaction and DNA methylation. Within these four interacting mechanisms, 25 epigenetic toxicity pathway components (SET1, MLL1, KDM5, G9A, SUV39H1, SETDB1, EZH2, JMJD3, CBX7, CBX8, BMI, SUZ12, HP1, MPP8, DNMT1, DNMT3A, DNMT3B, TET1, MeCP2, SETDB2, BAZ2A, UHRF1, CTCF, HOTAIR and ANRIL) were found to have experimental evidence showing that functional perturbations played “driver” roles in human cellular transformation. Measurement of epigenotoxicants presents challenges for short-term carcinogenicity testing, especially in the high-throughput modes emphasized in the Tox21 chemicals testing approach. There is need to develop and validate in vitro tests to detect both, locus-specific, and genome-wide, epigenetic alterations with causal links to oncogenic cellular phenotypes. Some recent examples of cell-based high throughput chemical screening assays are presented that have been applied or have shown potential for application to epigenetic endpoints.

Systems Toxicology: Real World Applications and Opportunities

Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized from empirical end points to describing modes of action as adverse outcome pathways and perturbed networks. Toward this aim, Systems Toxicology entails the integration of in vitro and in vivo toxicity data with computational modeling. This evolving approach depends critically on data reliability and relevance, which in turn depends on the quality of experimental models and bioanalysis techniques used to generate toxicological data. Systems Toxicology involves the use of large-scale data streams ("big data"), such as those derived from omics measurements that require computational means for obtaining informative results. Thus, integrative analysis of multiple molecular measurements, particularly acquired by omics strategies, is a key approach in Systems Toxicology. In recent years, there have been significant advances centered on in vitro test systems and bioanalytical strategies, yet a frontier challenge concerns linking observed network perturbations to phenotypes, which will require understanding pathways and networks that give rise to adverse responses. This summary perspective from a 2016 Systems Toxicology meeting, an international conference held in the Alps of Switzerland, describes the limitations and opportunities of selected emerging applications in this rapidly advancing field. Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized, from empirical end points to pathways of toxicity. This requires the integration of in vitro and in vivo data with computational modeling. Test systems and bioanalytical technologies have made significant advances, but ensuring data reliability and relevance is an ongoing concern. The major challenge facing the new pathway approach is determining how to link observed network perturbations to phenotypic toxicity.

Green Toxicology: a strategy for sustainable chemical and material development

Green Toxicology refers to the application of predictive toxicology in the sustainable development and production of new less harmful materials and chemicals, subsequently reducing waste and exposure. Built upon the foundation of "Green Chemistry" and "Green Engineering", "Green Toxicology" aims to shape future manufacturing processes and safe synthesis of chemicals in terms of environmental and human health impacts. Being an integral part of Green Chemistry, the principles of Green Toxicology amplify the role of health-related aspects for the benefit of consumers and the environment, in addition to being economical for manufacturing companies. Due to the costly development and preparation of new materials and chemicals for market entry, it is no longer practical to ignore the safety and environmental status of new products during product development stages. However, this is only possible if toxicologists and chemists work together early on in the development of materials and chemicals to utilize safe design strategies and innovative in vitro and in silico tools. This paper discusses some of the most relevant aspects, advances and limitations of the emergence of Green Toxicology from the perspective of different industry and research groups. The integration of new testing methods and strategies in product development, testing and regulation stages are presented with examples of the application of in silico, omics and in vitro methods. Other tools for Green Toxicology, including the reduction of animal testing, alternative test methods, and read-across approaches are also discussed.

Sex-Specific Associations between Particulate Matter Exposure and Gene Expression in Independent Discovery and Validation Cohorts of Middle-Aged Men and Women

BACKGROUND

Particulate matter (PM) exposure leads to premature death, mainly due to respiratory and cardiovascular diseases.

OBJECTIVES

Identification of transcriptomic biomarkers of air pollution exposure and effect in a healthy adult population.

METHODS

Microarray analyses were performed in 98 healthy volunteers (48 men, 50 women). The expression of eight sex-specific candidate biomarker genes (significantly associated with PM₁₀ in the discovery cohort and with a reported link to air pollution-related disease) was measured with qPCR in an independent validation cohort (75 men, 94 women). Pathway analysis was performed using Gene Set Enrichment Analysis. Average daily PM_2.5 and PM₁₀ exposures over 2-years were estimated for each participant's residential address using spatiotemporal interpolation in combination with a dispersion model.

RESULTS

Average long-term PM₁₀ was 25.9 (± 5.4) and 23.7 (± 2.3) μg/m³ in the discovery and validation cohorts, respectively. In discovery analysis, associations between PM₁₀ and the expression of individual genes differed by sex. In the validation cohort, long-term PM₁₀ was associated with the expression of DNAJB5 and EAPP in men and ARHGAP4 (p = 0.053) in women. AKAP6 and LIMK1 were significantly associated with PM₁₀ in women, although associations differed in direction between the discovery and validation cohorts. Expression of the eight candidate genes in the discovery cohort differentiated between validation cohort participants with high versus low PM₁₀ exposure (area under the receiver operating curve = 0.92; 95% CI: 0.85, 1.00; p = 0.0002 in men, 0.86; 95% CI: 0.76, 0.96; p = 0.004 in women).

CONCLUSIONS

Expression of the sex-specific candidate genes identified in the discovery population predicted PM₁₀ exposure in an independent cohort of adults from the same area. Confirmation in other populations may further support this as a new approach for exposure assessment, and may contribute to the discovery of molecular mechanisms for PM-induced health effects.

From the exposome to mechanistic understanding of chemical-induced adverse effects

The exposome encompasses an individual's exposure to exogenous chemicals, as well as endogenous chemicals that are produced or altered in response to external stressors. While the exposome concept has been established for human health, its principles can be extended to include broader ecological issues. The assessment of exposure is tightly interlinked with hazard assessment. Here, we explore if mechanistic understanding of the causal links between exposure and adverse effects on human health and the environment can be improved by integrating the exposome approach with the adverse outcome pathway (AOP) concept that structures and organizes the sequence of biological events from an initial molecular interaction of a chemical with a biological target to an adverse outcome. Complementing exposome research with the AOP concept may facilitate a mechanistic understanding of stress-induced adverse effects, examine the relative contributions from various components of the exposome, determine the primary risk drivers in complex mixtures, and promote an integrative assessment of chemical risks for both human and environmental health.

Evaluation of in silico tools to predict the skin sensitization potential of chemicals

Public domain and commercial in silico tools were compared for their performance in predicting the skin sensitization potential of chemicals. The packages were either statistical based (Vega, CASE Ultra) or rule based (OECD Toolbox, Toxtree, Derek Nexus). In practice, several of these in silico tools are used in gap filling and read-across, but here their use was limited to make predictions based on presence/absence of structural features associated to sensitization. The top 400 ranking substances of the ATSDR 2011 Priority List of Hazardous Substances were selected as a starting point. Experimental information was identified for 160 chemically diverse substances (82 positive and 78 negative). The prediction for skin sensitization potential was compared with the experimental data. Rule-based tools perform slightly better, with accuracies ranging from 0.6 (OECD Toolbox) to 0.78 (Derek Nexus), compared with statistical tools that had accuracies ranging from 0.48 (Vega) to 0.73 (CASE Ultra - LLNA weak model). Combining models increased the performance, with positive and negative predictive values up to 80% and 84%, respectively. However, the number of substances that were predicted positive or negative for skin sensitization in both models was low. Adding more substances to the dataset will increase the confidence in the conclusions reached. The insights obtained in this evaluation are incorporated in a web database www.asopus.weebly.com that provides a potential end user context for the scope and performance of different in silico tools with respect to a common dataset of curated skin sensitization data.

21st Century Cell Culture for 21st Century Toxicology

There is no good science in bad models. Cell culture is especially prone to artifacts. A number of novel cell culture technologies have become more broadly available in the 21st century, which allow overcoming limitations of traditional culture and are more physiologically relevant. These include the use of stem-cell derived human cells, cocultures of different cell types, scaffolds and extracellular matrices, perfusion platforms (such as microfluidics), 3D culture, organ-on-chip technologies, tissue architecture, and organ functionality. The physiological relevance of such models is further enhanced by the measurement of biomarkers (e.g., key events of pathways), organ specific functionality, and more comprehensive assessment cell responses by high-content methods. These approaches are still rarely combined to create microphysiological systems. The complexity of the combination of these technologies can generate results closer to the in vivo situation but increases the number of parameters to control, bringing some new challenges. In fact, we do not argue that all cell culture needs to be that sophisticated. The efforts taken are determined by the purpose of our experiments and tests. If only a very specific molecular target to cell response is of interest, a very simple model, which reflects this, might be much more suited to allow standardization and high-throughput. However, the less defined the end point of interest and cellular response are, the better we should approximate organ- or tissue-like culture conditions to make physiological responses more probable. Besides these technologic advances, important progress in the quality assurance and reporting on cell cultures as well as the validation of cellular test systems brings the utility of cell cultures to a new level. The advancement and broader implementation of Good Cell Culture Practice (GCCP) is key here. In toxicology, this is a major prerequisite for meaningful and reliable results, ultimately supporting risk assessment and product development decisions.

FutureTox III: Bridges for Translation

Future Tox III, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in November 2015. Building upon Future Tox I and II, Future Tox III was focused on developing the high throughput risk assessment paradigm and taking the science of in vitro data and in silico models forward to explore the question-what progress is being made to address challenges in implementing the emerging big-data toolbox for risk assessment and regulatory decision-making. This article reports on the outcome of the workshop including 2 examples of where advancements in predictive toxicology approaches are being applied within Federal agencies, where opportunities remain within the exposome and AOP domains, and how collectively the toxicology community across multiple sectors can continue to bridge the translation from historical approaches to Tox21 implementation relative to risk assessment and regulatory decision-making.

Information-dependent enrichment analysis reveals time-dependent transcriptional regulation of the estrogen pathway of toxicity

The twenty-first century vision for toxicology involves a transition away from high-dose animal studies to in vitro and computational models (NRC in Toxicity testing in the 21st century: a vision and a strategy, The National Academies Press, Washington, DC, 2007). This transition requires mapping pathways of toxicity by understanding how in vitro systems respond to chemical perturbation. Uncovering transcription factors/signaling networks responsible for gene expression patterns is essential for defining pathways of toxicity, and ultimately, for determining the chemical modes of action through which a toxicant acts. Traditionally, transcription factor identification is achieved via chromatin immunoprecipitation studies and summarized by calculating which transcription factors are statistically associated with up- and downregulated genes. These lists are commonly determined via statistical or fold-change cutoffs, a procedure that is sensitive to statistical power and may not be as useful for determining transcription factor associations. To move away from an arbitrary statistical or fold-change-based cutoff, we developed, in the context of the Mapping the Human Toxome project, an enrichment paradigm called information-dependent enrichment analysis (IDEA) to guide identification of the transcription factor network. We used a test case of activation in MCF-7 cells by 17β estradiol (E2). Using this new approach, we established a time course for transcriptional and functional responses to E2. ERα and ERβ were associated with short-term transcriptional changes in response to E2. Sustained exposure led to recruitment of additional transcription factors and alteration of cell cycle machinery. TFAP2C and SOX2 were the transcription factors most highly correlated with dose. E2F7, E2F1, and Foxm1, which are involved in cell proliferation, were enriched only at 24 h. IDEA should be useful for identifying candidate pathways of toxicity. IDEA outperforms gene set enrichment analysis (GSEA) and provides similar results to weighted gene correlation network analysis, a platform that helps to identify genes not annotated to pathways.

Adverse Outcome Pathways for Regulatory Applications: Examination of Four Case Studies With Different Degrees of Completeness and Scientific Confidence

Adverse outcome pathways (AOPs) offer a pathway-based toxicological framework to support hazard assessment and regulatory decision-making. However, little has been discussed about the scientific confidence needed, or how complete a pathway should be, before use in a specific regulatory application. Here we review four case studies to explore the degree of scientific confidence and extent of completeness (in terms of causal events) that is required for an AOP to be useful for a specific purpose in a regulatory application: (i) Membrane disruption (Narcosis) leading to respiratory failure (low confidence), (ii) Hepatocellular proliferation leading to cancer (partial pathway, moderate confidence), (iii) Covalent binding to proteins leading to skin sensitization (high confidence), and (iv) Aromatase inhibition leading to reproductive dysfunction in fish (high confidence). Partially complete AOPs with unknown molecular initiating events, such as 'Hepatocellular proliferation leading to cancer', were found to be valuable. We demonstrate that scientific confidence in these pathways can be increased though the use of unconventional information (eg, computational identification of potential initiators). AOPs at all levels of confidence can contribute to specific uses. A significant statistical or quantitative relationship between events and/or the adverse outcome relationships is a common characteristic of AOPs, both incomplete and complete, that have specific regulatory uses. For AOPs to be useful in a regulatory context they must be at least as useful as the tools that regulators currently possess, or the techniques currently employed by regulators.

Reference compounds for alternative test methods to indicate developmental neurotoxicity (DNT) potential of chemicals: example lists and criteria for their selection and use

There is a paucity of information concerning the developmental neurotoxicity (DNT) hazard posed by industrial and environmental chemicals. New testing approaches will most likely be based on batteries of alternative and complementary (non-animal) tests. As DNT is assumed to result from the modulation of fundamental neurodevelopmental processes (such as neuronal differentiation, precursor cell migration or neuronal network formation) by chemicals, the first generation of alternative DNT tests target these processes. The advantage of such types of assays is that they capture toxicants with multiple targets and modes-of-action. Moreover, the processes modelled by the assays can be linked to toxicity endophenotypes, i.e. alterations in neural connectivity that form the basis for neurofunctional deficits in man. The authors of this review convened in a workshop to define criteria for the selection of positive/negative controls, to prepare recommendations on their use, and to initiate the setup of a directory of reference chemicals. For initial technical optimization of tests, a set of >50 endpoint-specific control compounds was identified. For further test development, an additional “test” set of 33 chemicals considered to act directly as bona fide DNT toxicants is proposed, and each chemical is annotated to the extent it fulfills these criteria. A tabular compilation of the original literature used to select the test set chemicals provides information on statistical procedures, and toxic/non-toxic doses (both for pups and dams). Suggestions are provided on how to use the >100 compounds (including negative controls) compiled here to address specificity, adversity and use of alternative test systems.

Quality assurance of metabolomics

Metabolomics promises a holistic phenotypic characterization of biological responses to toxicants. This technology is based on advanced chemical analytical tools with reasonable throughput, including mass-spectroscopy and NMR. Quality assurance, however – from experimental design, sample preparation, metabolite identification, to bioinformatics data-mining – is urgently needed to assure both quality of metabolomics data and reproducibility of biological models. In contrast to microarray-based transcriptomics, where consensus on quality assurance and reporting standards has been fostered over the last two decades, quality assurance of metabolomics is only now emerging. Regulatory use in safety sciences, and even proper scientific use of these technologies, demand quality assurance. In an effort to promote this discussion, an expert workshop discussed the quality assurance needs of metabolomics.

The goals for this workshop were 1) to consider the challenges associated with metabolomics as an emerging science, with an emphasis on its application in toxicology and 2) to identify the key issues to be addressed in order to establish and implement quality assurance procedures in metabolomics-based toxicology. Consensus has still to be achieved regarding best practices to make sure sound, useful, and relevant information is derived from these new tools.

Molecular profiles to biology and pathways: a systems biology approach

Interpreting molecular profiles in a biological context requires specialized analysis strategies. Initially, lists of relevant genes were screened to identify enriched concepts associated with pathways or specific molecular processes. However, the shortcoming of interpreting gene lists by using predefined sets of genes has resulted in the development of novel methods that heavily rely on network-based concepts. These algorithms have the advantage that they allow a more holistic view of the signaling properties of the condition under study as well as that they are suitable for integrating different data types like gene expression, gene mutation, and even histological parameters.

Metabolomic network analysis of estrogen-stimulated MCF-7 cells: a comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis

In the context of the Human Toxome project, mass spectroscopy-based metabolomics characterization of estrogen-stimulated MCF-7 cells was studied in order to support the untargeted deduction of pathways of toxicity. A targeted and untargeted approach using overrepresentation analysis (ORA), quantitative enrichment analysis (QEA) and pathway analysis (PA) and a metabolite network approach were compared. Any untargeted approach necessarily has some noise in the data owing to artifacts, outliers and misidentified metabolites. Depending on the chemical analytical choices (sample extraction, chromatography, instrument and settings, etc.), only a partial representation of all metabolites will be achieved, biased by both the analytical methods and the database used to identify the metabolites. Here, we show on the one hand that using a data analysis approach based exclusively on pathway annotations has the potential to miss much that is of interest and, in the case of misidentified metabolites, can produce perturbed pathways that are statistically significant yet uninformative for the biological sample at hand. On the other hand, a targeted approach, by narrowing its focus and minimizing (but not eliminating) misidentifications, renders the likelihood of a spurious pathway much smaller, but the limited number of metabolites also makes statistical significance harder to achieve. To avoid an analysis dependent on pathways, we built a de novo network using all metabolites that were different at 24 h with and without estrogen with a p value <0.01 (53) in the STITCH database, which links metabolites based on known reactions in the main metabolic network pathways but also based on experimental evidence and text mining. The resulting network contained a "connected component" of 43 metabolites and helped identify non-endogenous metabolites as well as pathways not visible by annotation-based approaches. Moreover, the most highly connected metabolites (energy metabolites such as pyruvate and alpha-ketoglutarate, as well as amino acids) showed only a modest change between proliferation with and without estrogen. Here, we demonstrate that estrogen has subtle but potentially phenotypically important alterations in the acyl-carnitine fatty acids, acetyl-putrescine and succinoadenosine, in addition to likely subtle changes in key energy metabolites that, however, could not be verified consistently given the technical limitations of this approach. Finally, we show that a network-based approach combined with text mining identifies pathways that would otherwise neither be considered statistically significant on their own nor be identified via ORA, QEA, or PA.

In silico toxicology: computational methods for the prediction of chemical toxicity

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147-172. doi: 10.1002/wcms.1240 For further resources related to this article, please visit the WIREs website.

The Human Toxome Collaboratorium: A Shared Environment for Multi-Omic Computational Collaboration within a Consortium

The Human Toxome Project is part of a long-term vision to modernize toxicity testing for the 21st century. In the initial phase of the project, a consortium of six academic, commercial, and government organizations has partnered to map pathways of toxicity, using endocrine disruption as a model hazard. Experimental data is generated at multiple sites, and analyzed using a range of computational tools. While effectively gathering, managing, and analyzing the data for high-content experiments is a challenge in its own right, doing so for a growing number of -omics technologies, with larger data sets, across multiple institutions complicates the process. Interestingly, one of the most difficult, ongoing challenges has been the computational collaboration between the geographically separate institutions. Existing solutions cannot handle the growing heterogeneous data, provide a computational environment for consistent analysis, accommodate different workflows, and adapt to the constantly evolving methods and goals of a research project. To meet the needs of the project, we have created and managed The Human Toxome Collaboratorium, a shared computational environment hosted on third-party cloud services. The Collaboratorium provides a familiar virtual desktop, with a mix of commercial, open-source, and custom-built applications. It shares some of the challenges of traditional information technology, but with unique and unexpected constraints that emerge from the cloud. Here we describe the problems we faced, the current architecture of the solution, an example of its use, the major lessons we learned, and the future potential of the concept. In particular, the Collaboratorium represents a novel distribution method that could increase the reproducibility and reusability of results from similar large, multi-omic studies.

Supporting read-across using biological data

Read-across, i.e. filling toxicological data gaps by relating to similar chemicals, for which test data are available, is usually done based on chemical similarity. Besides structure and physico-chemical properties, however, biological similarity based on biological data adds extra strength to this process. In the context of developing Good Read-Across Practice guidance, a number of case studies were evaluated to demonstrate the use of biological data to enrich read-across. In the simplest case, chemically similar substances also show similar test results in relevant in vitro assays. This is a well-established method for the read-across of e.g. genotoxicity assays. Larger datasets of biological and toxicological properties of hundreds and thousands of substances become increasingly available enabling big data approaches in read-across studies. Several case studies using various big data sources are described in this paper. An example is given for the US EPA's ToxCast dataset allowing read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example for REACH registration data enhancing read-across for acute toxicity studies is given. A different approach is taken using omics data to establish biological similarity: Examples are given for stem cell models in vitro and short-term repeated dose studies in rats in vivo to support read-across and category formation. These preliminary biological data-driven read-across studies highlight the road to the new generation of read-across approaches that can be applied in chemical safety assessment.

Toxicity testing in the 21st century beyond environmental chemicals

After the publication of the report titled Toxicity Testing in the 21^st Century – A Vision and a Strategy, many initiatives started to foster a major paradigm shift for toxicity testing – from apical endpoints in animal-based tests to mechanistic endpoints through delineation of pathways of toxicity (PoT) in human cell based systems. The US EPA has funded an important project to develop new high throughput technologies based on human cell based in vitro technologies. These methods are currently being incorporated into the chemical risk assessment process. In the pharmaceutical industry, the efficacy and toxicity of new drugs are evaluated during preclinical investigations that include drug metabolism, pharmacokinetics, pharmacodynamics and safety toxicology studies. The results of these studies are analyzed and extrapolated to predict efficacy and potential adverse effects in humans. However, due to the high failure rate of drugs during the clinical phases, a new approach for a more predictive assessment of drugs both in terms of efficacy and adverse effects is getting urgent. The food industry faces the challenge of assessing novel foods and food ingredients for the general population, while using animal safety testing for extrapolation purposes is often of limited relevance. The question is whether the latest paradigm shift proposed by the Tox21c report for chemicals may provide a useful tool to improve the risk assessment approach also for drugs and food ingredients.

2014 Lush Science Prize

The Lush Prize supports animal-free testing by rewarding the most effective projects and individuals who have been working toward the goal of replacing animals in product or ingredient safety testing. A Background Paper is prepared each year, prior to the judging process, to provide the panel with a brief overview of current developments in the field of Replacement alternatives, particularly those relevant to the concept of toxicity pathways. This Background Paper includes information on recent work by the relevant scientific institutions and projects in this area, including AXLR8, OECD, CAAT, The Hamner Institutes, the Human Toxome Project, EURL ECVAM, ICCVAM, the US Tox21 Programme, the ToxCast programme, and the Human Toxicology Project Consortium. Recent developments in toxicity pathway research are also assessed by reviewing the relevant literature, with a view to presenting the two papers receiving the highest score to the judges for consideration.

A shift in paradigm towards human biology-based systems for cholestatic-liver diseases

Cholestatic-liver diseases (CLDs) arise from diverse causes ranging from genetic factors to drug-induced cholestasis. The so-called diseases of civilization (obesity, diabetes, metabolic disorders, non-alcoholic liver disease, cardiovascular diseases, etc.) are intricately implicated in liver and gall bladder diseases. Although CLDs have been extensively studied, there seem to be important gaps in the understanding of human disease. Despite the fact that many animal models exist and substantial clinical data are available, translation of this knowledge towards therapy has been disappointingly limited. Recent advances in liver cell culture such as in vivo-like 3D cultivation of human primary hepatic cells, human induced pluripotent stem cell-derived hepatocytes; and cutting-edge analytical techniques such as 'omics' technologies and high-content screenings could play a decisive role in deeper mechanistic understanding of CLDs. This Topical Review proposes a roadmap to human biology-based research using omics technologies providing quantitative information on mechanisms in an adverse outcome/disease pathway framework. With modern sensitive tools, a shift in paradigm in human disease research seems timely and even inevitable to overcome species barriers in translation.

Human-based systems in drug and chemical safety testing--toward replacement, the 'single R'

The Three Rs was a concept originally conceived as a means of reducing the suffering of laboratory animals that are used largely in identifying any potential safety issues with chemicals to which humans may be exposed. However, with growing evidence of the shortcomings of laboratory animal testing to reliably predict human responsiveness to such chemicals, questions are now being asked as to whether it is appropriate to use animals as human surrogates at all. This raises the question of whether, of the original Three Rs, two--Reduction and Refinement--are potentially redundant, and whether, instead, we should concentrate on the third R: Replacement. And if this is the best way forward, it is inevitable that this R should be based firmly on human biology. The present review outlines the current state-of-the-art regarding our access to human biology through in vitro, in silico and in vivo technologies, identifying strengths, weaknesses and opportunities, and goes on to address the prospect of achieving a single R, with some suggestions as to how to progress toward this goal.

MPTP's pathway of toxicity indicates central role of transcription factor SP1

Deriving a Pathway of Toxicity from transcriptomic data remains a challenging task. We explore the use of weighted gene correlation network analysis (WGCNA) to extract an initial network from a small microarray study of MPTP toxicity in mice. Five modules were statistically significant; each module was analyzed for gene signatures in the Chemical and Genetic Perturbation subset of the Molecular Signatures Database as well as for over-represented transcription factor binding sites and WGCNA clustered probes by function and captured pathways relevant to neurodegenerative disorders. The resulting network was analyzed for transcription factor candidates, which were narrowed down via text-mining for relevance to the disease model, and then combined with the large-scale interaction FANTOM4 database to generate a genetic regulatory network. Modules were enriched for transcription factors relevant to Parkinson's disease. Transcription factors significantly improved the number of genes that could be connected in a given component. For each module, the transcription factor that had, by far, the highest number of interactions was SP1, and it also had substantial experimental evidence of interactions. This analysis both captures much of the known biology of MPTP toxicity and suggests several candidates for further study. Furthermore, the analysis strongly suggests that SP1 plays a central role in coordinating the cellular response to MPTP toxicity.

Systems Toxicology: The Future of Risk Assessment

Risk assessment, in the context of public health, is the process of quantifying the probability of a harmful effect to individuals or populations from human activities. With increasing public health concern regarding the potential risks associated with chemical exposure, there is a need for more predictive and accurate approaches to risk assessment. Developing such an approach requires a mechanistic understanding of the process by which xenobiotic substances perturb biological systems and lead to toxicity. Supplementing the shortfalls of traditional risk assessment with mechanistic biological data has been widely discussed but not routinely implemented in the evaluation of chemical exposure. These mechanistic approaches to risk assessment have been generally referred to as systems toxicology. This Symposium Overview article summarizes 4 talks presented at the 35th Annual Meeting of the American College of Toxicology.

The human toxome project

The Human Toxome Project, funded as an NIH Transformative Research grant 2011-2016, is focused on developing the concepts and the means for deducing, validating and sharing molecular pathways of toxicity (PoT). Using the test case of estrogenic endocrine disruption, the responses of MCF-7 human breast cancer cells are being phenotyped by transcriptomics and mass-spectroscopy-based metabolomics. The bioinformatics tools for PoT deduction represent a core deliverable. A number of challenges for quality and standardization of cell systems, omics technologies and bioinformatics are being addressed. In parallel, concepts for annotation, validation and sharing of PoT information, as well as their link to adverse outcomes, are being developed. A reasonably comprehensive public database of PoT, the Human Toxome Knowledge-base, could become a point of reference for toxicological research and regulatory test strategies.

International STakeholder NETwork (ISTNET): creating a developmental neurotoxicity (DNT) testing road map for regulatory purposes

A major problem in developmental neurotoxicity (DNT) risk assessment is the lack of toxicological hazard information for most compounds. Therefore, new approaches are being considered to provide adequate experimental data that allow regulatory decisions. This process requires a matching of regulatory needs on the one hand and the opportunities provided by new test systems and methods on the other hand. Alignment of academically and industrially driven assay development with regulatory needs in the field of DNT is a core mission of the International STakeholder NETwork (ISTNET) in DNT testing. The first meeting of ISTNET was held in Zurich on 23-24 January 2014 in order to explore the concept of adverse outcome pathway (AOP) to practical DNT testing. AOPs were considered promising tools to promote test systems development according to regulatory needs. Moreover, the AOP concept was identified as an important guiding principle to assemble predictive integrated testing strategies (ITSs) for DNT. The recommendations on a road map towards AOP-based DNT testing is considered a stepwise approach, operating initially with incomplete AOPs for compound grouping, and focussing on key events of neurodevelopment. Next steps to be considered in follow-up activities are the use of case studies to further apply the AOP concept in regulatory DNT testing, making use of AOP intersections (common key events) for economic development of screening assays, and addressing the transition from qualitative descriptions to quantitative network modelling.