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.

toxFlow: A Web-Based Application for Read-Across Toxicity Prediction Using Omics and Physicochemical Data.

We present toxFlow, a web application developed for enrichment analysis of omics data coupled with read-across toxicity prediction. A sequential analysis workflow is suggested where users can filter omics data using enrichment scores and incorporate their findings into a correlation-based read-across technique for predicting the toxicity of a substance based on its analogs. Either embedded or in-house gene signature libraries can be used for enrichment analysis. The suggested approach can be used for toxicity prediction of diverse chemical entities; however, this article focuses on the multiperspective characterization of nanoparticles and selects their neighbors based on both physicochemical and biological similarity criteria. In addition, visualization options are offered to interactively explore correlation patterns in the data, whereas results can be exported for further analysis. toxFlow is accessible at http://147.102.86.129:3838/toxflow .

Consumption of Red/Processed Meat and Colorectal Carcinoma: Possible Mechanisms Underlying the Significant Association.

Epidemiology and experimental studies provide an overwhelming support of the notion that diets high in red or processed meat accompany an elevated risk of developing pre-neoplastic colorectal adenoma and frank colorectal carcinoma (CRC). The underlying mechanisms are disputed; thus several hypotheses have been proposed. A large body of reports converges, however, on haem and nitrosyl haem as major contributors to the CRC development, presumably acting through various mechanisms. Apart from a potentially higher intestinal mutagenic load among consumers on a diet rich in red/processed meat, other mechanisms involving subtle interference with colorectal stem/progenitor cell survival or maturation are likewise at play. From an overarching perspective, suggested candidate mechanisms for red/processed meat-induced CRC appear as three partly overlapping tenets: (i) increased N-nitrosation/oxidative load leading to DNA adducts and lipid peroxidation in the intestinal epithelium, (ii) proliferative stimulation of the epithelium through haem or food-derived metabolites that either act directly or subsequent to conversion, and (iii) higher inflammatory response, which may trigger a wide cascade of pro-malignant processes. In this review, we summarize and discuss major findings of the area in the context of potentially pertinent mechanisms underlying the above-mentioned association between consumption of red/processed meat and increased risk of developing CRC.

Arsenic alters global histone modifications in lymphocytes in vitro and in vivo.

Arsenic, an established carcinogen and toxicant, occurs in drinking water and food and affects millions of people worldwide. Arsenic appears to interfere with gene expression through epigenetic processes, such as DNA methylation and post-translational histone modifications. We investigated the effects of arsenic on histone residues in vivo as well as in vitro. Analysis of H3K9Ac and H3K9me3 in CD4+ and CD8+ sorted blood cells from individuals exposed to arsenic through drinking water in the Argentinean Andes showed a significant decrease in global H3K9me3 in CD4+ cells, but not CD8+ cells, with increasing arsenic exposure. In vitro studies of inorganic arsenic-treated T lymphocytes (Jurkat and CCRF-CEM, 0.1, 1, and 100 µg/L) showed arsenic-related modifications of H3K9Ac and changes in the levels of the histone deacetylating enzyme HDAC2 at very low arsenic concentrations. Further, in vitro exposure of kidney HEK293 cells to arsenic (1 and 5 µM) altered the protein levels of PCNA and DNMT1, parts of a gene expression repressor complex, as well as MAML1. MAML1 co-localized and interacted with components of this complex in HEK293 cells, and in silico studies indicated that MAML1 expression correlate with HDAC2 and DNMT1 expression in kidney cells. In conclusion, our data suggest that arsenic exposure may lead to changes in the global levels of H3K9me3 and H3K9Ac in lymphocytes. Also, we show that arsenic exposure affects the expression of PCNA and DNMT1-proteins that are part of a gene expression silencing complex.

Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of 'Omics' Data from Human Cell Cultures.

This paper outlines the work for which Roland Grafström and Pekka Kohonen were awarded the 2014 Lush Science Prize. The research activities of the Grafström laboratory have, for many years, covered cancer biology studies, as well as the development and application of toxicity-predictive in vitro models to determine chemical safety. Through the integration of in silico analyses of diverse types of genomics data (transcriptomic and proteomic), their efforts have proved to fit well into the recently-developed Adverse Outcome Pathway paradigm. Genomics analysis within state-of-the-art cancer biology research and Toxicology in the 21st Century concepts share many technological tools. A key category within the Three Rs paradigm is the Replacement of animals in toxicity testing with alternative methods, such as bioinformatics-driven analyses of data obtained from human cell cultures exposed to diverse toxicants. This work was recently expanded within the pan-European SEURAT-1 project (Safety Evaluation Ultimately Replacing Animal Testing), to replace repeat-dose toxicity testing with data-rich analyses of sophisticated cell culture models. The aims and objectives of the SEURAT project have been to guide the application, analysis, interpretation and storage of 'omics' technology-derived data within the service-oriented sub-project, ToxBank. Particularly addressing the Lush Science Prize focus on the relevance of toxicity pathways, a 'data warehouse' that is under continuous expansion, coupled with the development of novel data storage and management methods for toxicology, serve to address data integration across multiple 'omics' technologies. The prize winners' guiding principles and concepts for modern knowledge management of toxicological data are summarised. The translation of basic discovery results ranged from chemical-testing and material-testing data, to information relevant to human health and environmental safety.

Meta-Analysis of Integrated Proteomic and Transcriptomic Data Discerns Structure-Activity Relationship of Carbon Materials with Different Morphologies.

The Fiber Pathogenicity Paradigm (FPP) establishes connections between fiber structure, durability, and disease-causing potential observed in materials like asbestos and synthetic fibers. While emerging nanofibers are anticipated to exhibit pathogenic traits according to the FPP, their nanoscale diameter limits rigidity, leading to tangling and loss of fiber characteristics. The absence of validated rigidity measurement methods complicates nanofiber toxicity assessment. By comprehensively analyzing 89 transcriptomics and 37 proteomics studies, this study aims to enhance carbon material toxicity understanding and proposes an alternative strategy to assess morphology-driven toxicity. Carbon materials are categorized as non-fibrous, high aspect ratio with shorter lengths, tangled, and rigid fibers. Mitsui-7 serves as a benchmark for pathogenic fibers. The meta-analysis reveals distinct cellular changes for each category, effectively distinguishing rigid fibers from other carbon materials. Subsequently, a robust random forest model is developed to predict morphology, unveiling the pathogenicity of previously deemed non-pathogenic NM-400 due to its secondary structures. This study fills a crucial gap in nanosafety by linking toxicological effects to material morphology, in particular regarding fibers. It demonstrates the significant impact of morphology on toxicological behavior and the necessity of integrating morphological considerations into regulatory frameworks.

Requirement of apoptotic protease-activating factor-1 for bortezomib-induced apoptosis but not for Fas-mediated apoptosis in human leukemic cells.

Bortezomib is a highly selective inhibitor of the 26S proteasome and has been approved for clinical use in the treatment of relapsing and refractory multiple myeloma and mantle cell lymphoma. Clinical trials are also underway to assess the role of bortezomib in several other human malignancies, including leukemia. However, the mechanism(s) by which bortezomib acts remain to be fully understood. Here, we studied the molecular requirements of bortezomib-induced apoptosis using the human T-cell leukemic Jurkat cells stably transfected with or without shRNA against apoptotic protease-activating factor-1 (Apaf-1). The Apaf-1-deficient Jurkat T cells were resistant to bortezomib-induced apoptosis, as assessed by caspase-3 activity, poly(ADP-ribose) polymerase cleavage, phosphatidylserine externalization, and hypodiploid DNA content. In contrast, Apaf-1-deficient cells were sensitive to Fas-induced apoptosis. Bortezomib induced an upregulation of the pro-apoptotic protein Noxa, loss of mitochondrial transmembrane potential, and release of cytochrome c in cells expressing or not expressing Apaf-1. Transient silencing of Apaf-1 expression in RPMI 8402 T-cell leukemic cells also diminished bortezomib-induced apoptosis. Fas-associated death domain (FADD)-deficient Jurkat cells were resistant to Fas-mediated apoptosis yet remained sensitive to bortezomib. Our results show that bortezomib induces apoptosis by regulating pathways that are mechanistically different from those activated upon death receptor ligation. Furthermore, in silico analyses of public transcriptomics databases indicated elevated Apaf-1 expression in several hematologic malignancies, including acute lymphoblastic and myeloid leukemia. We also noted variable Apaf-1 expression in a panel of samples from patients with acute lymphoblastic leukemia. Our results suggest that the expression of Apaf-1 may be predictive of the response to proteasome inhibition.

Differentiation-promoting culture of competent and noncompetent keratinocytes identifies biomarkers for head and neck cancer.

Aberrant contact-inhibited proliferation and differentiation induction couple with tumor severity, albeit with an imprecise association with prognosis. Assessment of contact inhibition and differentiation-promoting culture in this study of normal and immortalized oral keratinocytes (NOK and SVpgC2a, respectively) demonstrated elevated cloning ability and saturation density in the immortalized versus normal state, including consistent absence of differentiated morphological features. Transcriptomic analysis implicated 48 gene ontology categories, 8 molecular networks, and 10 key regulator genes in confluency-induced differentiation of NOK, all of which remained nonregulated in SVpgC2a. The SVpgC2a versus NOK transcriptome enriched 52 gene ontology categories altogether, 18 molecular networks, and 39 key regulator genes, several of which were associated with epithelial-mesenchymal transition. Assessment of the previously described gene sets relative to training data sets of head and neck squamous cell carcinoma samples, one including data on tumor differentiation and patient outcome and one present in the Human Gene Expression Map, identified four genes with association to poor survival (COX7A1, MFAP5, MPDU1, and POLD1). This gene set predicted poor outcome in an independent data set of 71 head and neck squamous cell carcinomas. The present study defines, for the first time to our knowledge, the broad gene spectrum that couples to induction, and loss, of oral keratinocyte differentiation. Bioinformatics assessments of the results relative to clinical data generated novel differentiation-related tumor biomarkers relevant to patient outcome.

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

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

Biomarkers of nanomaterials hazard from multi-layer data.

There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.

Combining factors on protein and gene level to predict radioresponse in head and neck cancer cell lines.

BACKGROUND: Radiotherapy is the main therapy for head and neck squamous cell carcinoma (HNSCC); however, treatment resistance and local recurrence are significant problems, highlighting the need for predictive markers. In this study, we evaluated selected proteins, mutations, and single nucleotide polymorphisms (SNPs) involved in apoptosis, cell proliferation, and DNA repair alone or combined as predictive markers for radioresponse in 42 HNSCC cell lines. METHODS: The expression of epidermal growth factor receptor, survivin, Bax, Bcl-2, Bcl-X(L) , cyclooxygenase-2 (COX-2), and heat shock protein 70 was analyzed by ELISA. Furthermore, mutations and SNPs in the p53 gene as well as SNPs in the MDM2, XRCC1, and XRCC3 genes were analyzed for their relation to radioresponse. To enable the evaluation of the predictive value of several factors combined, each cell line was allocated points based on the number of negative points (NNP) system, and the NNP sum was correlated with radioresponse. RESULTS: Survivin was the only factor that alone was significantly correlated with the intrinsic radiosensitivity (IR; r = 0.36, P = 0.02). The combination of survivin, Bax, Bcl-2, Bcl-X(L) , COX-2, and the p53 Arg72Pro polymorphism was found to most strongly correlate with radioresponse (r = 0.553, P < 0.001). CONCLUSION: These data indicate that the IR of 42 HNSCC cell lines can be predicted by a panel of factors on both the protein and gene levels. Moreover, among the investigated factors, survivin was the most promising biomarker of radioresponse.

Reliable Surface Analysis Data of Nanomaterials in Support of Risk Assessment Based on Minimum Information Requirements.

The minimum information requirements needed to guarantee high-quality surface analysis data of nanomaterials are described with the aim to provide reliable and traceable information about size, shape, elemental composition and surface chemistry for risk assessment approaches. The widespread surface analysis methods electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) were considered. The complete analysis sequence from sample preparation, over measurements, to data analysis and data format for reporting and archiving is outlined. All selected methods are used in surface analysis since many years so that many aspects of the analysis (including (meta)data formats) are already standardized. As a practical analysis use case, two coated TiO2 reference nanoparticulate samples, which are available on the Joint Research Centre (JRC) repository, were selected. The added value of the complementary analysis is highlighted based on the minimum information requirements, which are well-defined for the analysis methods selected. The present paper is supposed to serve primarily as a source of understanding of the high standardization level already available for the high-quality data in surface analysis of nanomaterials as reliable input for the nanosafety community.

Towards FAIR nanosafety data.

Nanotechnology is a key enabling technology with billions of euros in global investment from public funding, which include large collaborative projects that have investigated environmental and health safety aspects of nanomaterials, but the reuse of accumulated data is clearly lagging behind. Here we summarize challenges and provide recommendations for the efficient reuse of nanosafety data, in line with the recently established FAIR (findable, accessible, interoperable and reusable) guiding principles. We describe the FAIR-aligned Nanosafety Data Interface, with an aggregated findability, accessibility and interoperability across physicochemical, bio-nano interaction, human toxicity, omics, ecotoxicological and exposure data. Overall, we illustrate a much-needed path towards standards for the optimized use of existing data, which avoids duplication of efforts, and provides a multitude of options to promote safe and sustainable nanotechnology.

ELIXIR and Toxicology: a community in development.

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects.

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data.

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment.

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

The ontogeny, distribution, and regulation of alcohol dehydrogenase 3: implications for pulmonary physiology.

Class III alcohol dehydrogenase (ADH3), also termed formaldehyde dehydrogenase or S-nitrosoglutathione reductase, plays a critical role in the enzymatic oxidation of formaldehyde and reduction of nitrosothiols that regulate bronchial tone. Considering reported associations between formaldehyde vapor exposure and childhood asthma risk, and thus potential involvement of ADH3, we reviewed the ontogeny, distribution, and regulation of mammalian ADH3. Recent studies indicate that multiple biological and chemical stimuli influence expression and activity of ADH3, including the feedback regulation of nitrosothiol metabolism. The levels of ADH3 correlate with, and potentially influence, bronchial tone; however, data gaps remain with respect to the expression of ADH3 during postnatal and early childhood development. Consideration of ADH3 function relative to the respiratory effects of formaldehyde, as well as to other chemical and biological exposures that might act in an additive or synergistic manner with formaldehyde, might be critical to gain better insight into the association between formaldehyde exposure and childhood asthma.

Comparative hazard characterization in food toxicology.

Historically, different approaches have been adopted for comparing and characterizing hazards that can be found in the very complex mixture of substances present in food. In this review a variety of prominent risk assessment models are evaluated in the context of food safety. In their current state of refinement, though, they show limited applicability for comparative hazard characterization and impact magnitude scoring of adverse effects of substances in food. Nonetheless, some existing models hold building blocks and modelling concepts that appear promising for further development and integration. Thus, a new, dedicated, and generally accepted model is needed that is capable of generating relevant "Impact Magnitude Score" (IMS) values for comparing potentially toxic substances in food. A brief outline of requirements for a model (Guided Toxicology-assessment of Health Impact; GTHI) is presented that considers "severity" (S), "duration" (D), and "proportion of population affected" (P). An important demand on such a model is to provide significantly improved food safety evaluation amenable to regulatory agencies and consumers. This review is based on a project entitled "Promoting food safety through a new integrated risk analysis approach for foods" (acronym: "SAFE FOODS") that is under the subsidy of the European Commission.