Toxicity testing in the 21st century: implications for human health risk assessment

Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity

Developing mechanistic non-animal testing methods based on the adverse outcome pathway (AOP) framework must incorporate molecular and cellular key events associated with target toxicity. Using data from an in vitro assay and chemical structures, we aimed to create a hybrid model to predict hepatotoxicants. We first curated a reference dataset of 869 compounds for hepatotoxicity modeling. Then, we profiled them against PubChem for existing in vitro toxicity data. Of the 2560 resulting assays, we selected the mitochondrial membrane potential (MMP) assay, a high-throughput screening (HTS) tool that can test chemical disruptors for mitochondrial function. Machine learning was applied to develop quantitative structure-activity relationship (QSAR) models with 2536 compounds tested in the MMP assay for screening new compounds. The MMP assay results, including QSAR model outputs, yielded hepatotoxicity predictions for reference set compounds with a Correct Classification Ratio (CCR) of 0.59. The predictivity improved by including 37 structural alerts (CCR = 0.8). We validated our model by testing 37 reference set compounds in human HepG2 hepatoma cells, and reliably predicting them for hepatotoxicity (CCR = 0.79). This study introduces a novel AOP modeling strategy that combines public HTS data, computational modeling, and experimental testing to predict chemical hepatotoxicity.

Toxicity screening of bisphenol A replacement compounds: cytotoxicity and mRNA expression in LMH 3D spheroids

Previously, we showed that the chicken LMH cell line cultured as 3D spheroids may be a suitable animal free alternative to primary chicken embryonic hepatocytes (CEH) for avian in vitro chemical screening. In this study, cytotoxicity and mRNA expression were determined in LMH 3D spheroids following exposure to bisphenol A (BPA), five BPA replacement compounds (BPF, TGSH, DD-70, BPAF, BPSIP), and 17β estradiol (E2). Results were compared to an earlier study that evaluated the same endpoints for these chemicals in CEH. BPA and the replacement compounds had LC50 values ranging from 16.6 to 81.8 μM; DD-70 and BPAF were the most cytotoxic replacements (LC50 = 17.23 ± 4.51 and 16.6 ± 4.78 μM). TGSH and DD-70 modulated the greatest number of genes, although fewer than observed in CEH. Based on the expression of apovitellenin and vitellogenin, BPAF was the most estrogenic compound followed by BPF, BPSIP, and BPA. More estrogen-responsive genes were modulated in LMH spheroids compared to CEH. Concentration-dependent gene expression revealed that DD-70 and BPAF altered genes related to lipid and bile acid regulation. Overall, cytotoxicity and clustering of replacements based on gene expression profiles were similar between LMH spheroids and CEH. In addition to generating novel gene expression data for five BPA replacement compounds in an in vitro avian model, this research demonstrates that LMH spheroids may represent a useful animal free alternative for avian toxicity testing.

Providing Adverse Outcome Pathways from the AOP-Wiki in a Semantic Web Format to Increase Usability and Accessibility of the Content

Introduction:

The AOP-Wiki is the main platform for the development and storage of adverse outcome pathways (AOPs). These AOPs describe mechanistic information about toxicodynamic processes and can be used to develop effective risk assessment strategies. However, it is challenging to automatically and systematically parse, filter, and use its contents. We explored solutions to better structure the AOP-Wiki content, and to link it with chemical and biological resources. Together, this allows more detailed exploration, which can be automated.

Materials and Methods:

We converted the complete AOP-Wiki content into resource description framework (RDF) triples. We used >20 ontologies for the semantic annotation of property–object relations, including the Chemical Information Ontology, Dublin Core, and the AOP Ontology.

Results:

The resulting RDF contains >122,000 triples describing 158 unique properties of >15,000 unique subjects. Furthermore, >3500 link-outs were added to 12 chemical databases, and >7500 link-outs to 4 gene and protein databases. The AOP-Wiki RDF has been made available at https://aopwiki.rdf.bigcat-bioinformatics.org

Discussion:

SPARQL queries can be used to answer biological and toxicological questions, such as listing measurement methods for all Key Events leading to an Adverse Outcome of interest. The full power that the use of this new resource provides becomes apparent when combining the content with external databases using federated queries.

Conclusion:

Overall, the AOP-Wiki RDF allows new ways to explore the rapidly growing AOP knowledge and makes the integration of this database in automated workflows possible, making the AOP-Wiki more FAIR.

Classification of advanced methods for evaluating neurotoxicity

Purpose of review

As fields such as neurotoxicity evaluation and neuro-related drug research are increasing in popularity, there is a demand for the expansion of neurotoxicity research. Currently, neurotoxicity is assessed by measuring changes in weight and behavior. However, measurement of such changes does not allow the detection of subtle and inconspicuous neurotoxicity. In this review, methods for advancing neurotoxicity research are divided into molecule-, cell-, circuit-, and animal model-based methods, and the results of previous studies assessing neurotoxicity are provided and discussed.

Recent findings

In coming decades, cooperation between universities, national research institutes, industrial research institutes, governments, and the private sector will become necessary when identifying alternative methods for neurotoxicity evaluation, which is a current goal related to improving neurotoxicity assessment and an appropriate approach to neurotoxicity prediction. Many methods for measuring neurotoxicity in the field of neuroscience have recently been reported. This paper classifies the supplementary and complementary experimental measures for evaluating neurotoxicity.

Advanced preclinical models for evaluation of drug-induced liver injury - consensus statement by the European Drug-Induced Liver Injury Network [PRO-EURO-DILI-NET]

Drug-induced liver injury (DILI) is a major cause of acute liver failure (ALF) and one of the leading indications for liver transplantation in Western societies. Given the wide use of both prescribed and over the counter drugs, DILI has become a major health issue for which there is a pressing need to find novel and effective therapies. Although significant progress has been made in understanding the molecular mechanisms underlying DILI, our incomplete knowledge of its pathogenesis and inability to predict DILI is largely due to both discordance between human and animal DILI in preclinical drug development and a lack of models that faithfully recapitulate complex pathophysiological features of human DILI. This is exemplified by the hepatotoxicity of acetaminophen (APAP) overdose, a major cause of ALF because of its extensive worldwide use as an analgesic. Despite intensive efforts utilising current animal and in vitro models, the mechanisms involved in the hepatotoxicity of APAP are still not fully understood. In this expert Consensus Statement, which is endorsed by the European Drug-Induced Liver Injury Network, we aim to facilitate and outline clinically impactful discoveries by detailing the requirements for more realistic human-based systems to assess hepatotoxicity and guide future drug safety testing. We present novel insights and discuss major players in APAP pathophysiology, and describe emerging in vitro and in vivo pre-clinical models, as well as advanced imaging and in silico technologies, which may improve prediction of clinical outcomes of DILI.

The effects of gene × environment interactions on silver nanoparticle toxicity in the respiratory system: An adverse outcome pathway

The Adverse Outcome Pathway (AOP) framework is serving as a basis to integrate new data streams in order to enhance the power of predictive toxicology. AOP development for engineered nanomaterials (ENM), including silver nanoparticles (AgNP), is currently lagging behind other chemicals of regulatory interest due to our limited understanding of the mechanism by which underlying genetics or diseases directly modify host response to AgNP exposures. This also highlights the importance of considering the Aggregate Exposure Pathway (AEP) framework, which precedes the AOP framework and outlines source to target site exposure. The AEP and AOP frameworks interface at the target site, where a molecular initiating event (MIE) occurs and is followed by key events (KE) for adverse cellular and organ responses along a biological pathway and ends with the adverse organism response. The primary goal of this study is to use AgNP to interrogate the AEP-AOP framework by organizing and integrating in vitro dose-response data and in vivo exposure-response data from previous studies to evaluate the effects of interactions between host genetic and acquired factors, or gene × environment interactions (G × E), on AgNP toxicity in the respiratory system. Using this framework will help us to identify plausible key event relationships (KER) between MIE and adverse organism responses when KE are not measured using the same assay in order to derive future predictive models, guide research, and support development of tools for making risk-based, regulatory decisions on ENM. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.

Profiling the Tox21 Chemical Collection for Acetylcholinesterase Inhibition

BACKGROUND

Inhibition of acetylcholinesterase (AChE), a biomarker of organophosphorous and carbamate exposure in environmental and occupational human health, has been commonly used to identify potential safety liabilities. So far, many environmental chemicals, including drug candidates, food additives, and industrial chemicals, have not been thoroughly evaluated for their inhibitory effects on AChE activity. AChE inhibitors can have therapeutic applications (e.g., tacrine and donepezil) or neurotoxic consequences (e.g., insecticides and nerve agents).

OBJECTIVES

The objective of the current study was to identify environmental chemicals that inhibit AChE activity using in vitro and in silico models.

METHODS

To identify AChE inhibitors rapidly and efficiently, we have screened the Toxicology in the 21st Century (Tox21) 10K compound library in a quantitative high-throughput screening (qHTS) platform by using the homogenous cell-based AChE inhibition assay and enzyme-based AChE inhibition assays (with or without microsomes). AChE inhibitors identified from the primary screening were further tested in monolayer or spheroid formed by SH-SY5Y and neural stem cell models. The inhibition and binding modes of these identified compounds were studied with time-dependent enzyme-based AChE inhibition assay and molecular docking, respectively.

RESULTS

A group of known AChE inhibitors, such as donepezil, ambenonium dichloride, and tacrine hydrochloride, as well as many previously unreported AChE inhibitors, such as chelerythrine chloride and cilostazol, were identified in this study. Many of these compounds, such as pyrazophos, phosalone, and triazophos, needed metabolic activation. This study identified both reversible (e.g., donepezil and tacrine) and irreversible inhibitors (e.g., chlorpyrifos and bromophos-ethyl). Molecular docking analyses were performed to explain the relative inhibitory potency of selected compounds.

CONCLUSIONS

Our tiered qHTS approach allowed us to generate a robust and reliable data set to evaluate large sets of environmental compounds for their AChE inhibitory activity. https://doi.org/10.1289/EHP6993.

A Critical Perspective on 3D Liver Models for Drug Metabolism and Toxicology Studies

The poor predictability of human liver toxicity is still causing high attrition rates of drug candidates in the pharmaceutical industry at the non-clinical, clinical, and post-marketing authorization stages. This is in part caused by animal models that fail to predict various human adverse drug reactions (ADRs), resulting in undetected hepatotoxicity at the non-clinical phase of drug development. In an effort to increase the prediction of human hepatotoxicity, different approaches to enhance the physiological relevance of hepatic in vitro systems are being pursued. Three-dimensional (3D) or microfluidic technologies allow to better recapitulate hepatocyte organization and cell-matrix contacts, to include additional cell types, to incorporate fluid flow and to create gradients of oxygen and nutrients, which have led to improved differentiated cell phenotype and functionality. This comprehensive review addresses the drug-induced hepatotoxicity mechanisms and the currently available 3D liver in vitro models, their characteristics, as well as their advantages and limitations for human hepatotoxicity assessment. In addition, since toxic responses are greatly dependent on the culture model, a comparative analysis of the toxicity studies performed using two-dimensional (2D) and 3D in vitro strategies with recognized hepatotoxic compounds, such as paracetamol, diclofenac, and troglitazone is performed, further highlighting the need for harmonization of the respective characterization methods. Finally, taking a step forward, we propose a roadmap for the assessment of drugs hepatotoxicity based on fully characterized fit-for-purpose in vitro models, taking advantage of the best of each model, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields.

Rapid Zebrafish Behavioral Profiling Assay Accelerates the Identification of Environmental Neurodevelopmental Toxicants

Rapid and cost-effective in vivo assays to screen potential environmental neurodevelopmental toxicants are necessary to address the limitations of in vitro platforms, such as the inability to fully recapitulate the developmental and physiological processes of whole organisms. In the present study, a rapid zebrafish behavioral profiling assay was developed to characterize the neurodevelopmental effects of environmental substances by quantitatively evaluating multiple spontaneous movement features of zebrafish embryos. This video analysis-based assay automatically segmented every embryo and thus was able to accurately quantify spontaneous movement features, including frequency, duration, intensity, interval, and the number of continuous movements. When tested with eight environmental substances known to be neurodevelopmental toxicants, such as chlorpyrifos and bisphenol A, the assay successfully captured frequency alterations that were well-documented in previous studies while also providing additional information. Using an optimized procedure, we further assessed 132 potential neurotoxins that spanned a wide range of molecular targets, many of which were previously detected in environmental waterbodies. The distinct altered behavioral barcodes indicated that the spontaneous movement was impacted by diverse neuroactive substances, and the effects could be effectively evaluated with the developed assay. The web-based tool, named EMAnalysis, is further provided at http://www.envh.sjtu.edu.cn/zebrafish_contraction.jsp. Thus, this assay provides an efficient platform to accelerate the pace of neurotoxic environmental contaminant discoveries.

Drivers of and Obstacles to the Adoption of Toxicogenomics for Chemical Risk Assessment: Insights from Social Science Perspectives

BACKGROUND

Some 20 y ago, scientific and regulatory communities identified the potential of omics sciences (genomics, transcriptomics, proteomics, metabolomics) to improve chemical risk assessment through development of toxicogenomics. Recognizing that regulators adopt new scientific methods cautiously given accountability to diverse stakeholders, the scope and pace of adoption of toxicogenomics tools and data have nonetheless not met the ambitious, early expectations of omics proponents.

OBJECTIVE

Our objective was, therefore, to inventory, investigate, and derive insights into drivers of and obstacles to adoption of toxicogenomics in chemical risk assessment. By invoking established social science frameworks conceptualizing innovation adoption, we also aimed to develop recommendations for proponents of toxicogenomics and other new approach methodologies (NAMs).

METHODS

We report findings from an analysis of 56 scientific and regulatory publications from 1998 through 2017 that address the adoption of toxicogenomics for chemical risk assessment. From this purposeful sample of toxicogenomics discourse, we identified major categories of drivers of and obstacles to adoption of toxicogenomics tools and data sets. We then mapped these categories onto social science frameworks for conceptualizing innovation adoption to generate actionable insights for proponents of toxicogenomics.

DISCUSSION

We identify the most salient drivers and obstacles. From 1998 through 2017, adoption of toxicogenomics was understood to be helped by drivers such as those we labeled Superior scientific understanding, New applications, and Reduced cost & increased efficiency but hindered by obstacles such as those we labeled Insufficient validation, Complexity of interpretation, and Lack of standardization. Leveraging social science frameworks, we find that arguments for adoption that draw on the most salient drivers, which emphasize superior and novel functionality of omics as rationales, overlook potential adopters' key concerns: simplicity of use and compatibility with existing practices. We also identify two perspectives-innovation-centric and adopter-centric-on omics adoption and explain how overreliance on the former may be undermining efforts to promote toxicogenomics. https://doi.org/10.1289/EHP6500.

The effects of genotype × phenotype interactions on silver nanoparticle toxicity in organotypic cultures of murine tracheal epithelial cells

Silver nanoparticles (AgNP) are used in multiple applications but primarily in the manufacturing of antimicrobial products. Previous studies have identified AgNP toxicity in airway epithelial cells, but no in vitro studies to date have used organotypic cultures as a high-content in vitro model of the conducting airway to characterize the effects of interactions between host genetic and acquired factors, or gene × phenotype interactions (G × P), on AgNP toxicity. In the present study, we derived organotypic cultures from primary murine tracheal epithelial cells (MTEC) to characterize nominal and dosimetric dose-response relationships for AgNPs with a gold core on barrier dysfunction, glutathione (GSH) depletion, reactive oxygen species (ROS) production, lipid peroxidation, and cytotoxicity across two genotypes (A/J and C57BL/6J mice), two phenotypes ('Normal' and 'Type 2 [T2]-Skewed'), and two exposures (an acute exposure of 24 h and a subacute exposure of 4 h, every other day, over 5 days [5 × 4 h]). We characterized the 'T2-Skewed' phenotype as an in vitro model of chronic respiratory diseases, which was marked by increased sensitivity to AgNP-induced barrier dysfunction, GSH depletion, ROS production, lipid peroxidation, and cytotoxicity, suggesting that asthmatics are a sensitive population to AgNP exposures in occupational settings. This also suggests that exposure limits, which should be based upon the most sensitive population, should be derived using in vitro and in vivo models of chronic respiratory diseases. This study highlights the importance of considering dosimetry as well as G × P effects when screening and prioritizing potential respiratory toxicants. Such in vitro studies can be used to inform regulatory policy aimed at special protections for all populations.

Standards of evidence and causality in regulatory science: Risk and benefit assessment

In this paper we analyze the Russo-Williamson Thesis (RWT) as a standard of evidence in regulatory science, in risk as well as benefit assessment. In our analysis we take account of the recent controversies that have taken place in regulation with respect to the evidentiary requirements necessary for regulatory decision making. RWT's main point is that not only probabilistic but also mechanistic evidence is necessary for being able to infer the existence of causal links. We ask in which way RWT could have an impact upon current decision making about subjecting to regulation (or, to the contrary, leaving them unregulated) certain chemical substances, food stuffs, health claims, and other typical objects of regulation. We show that the application of RWT resolves some of the problems posed by current standards of evidence. RWT makes it possible to determine with higher accuracy if a particular substance should be subjected to regulation or not, even though under certain circumstances RWT itself may turn into a source of regulatory error. The adequacy of RWT as a standard of evidence depends on the precise manner of its application to regulation (particularly the consideration of mechanistic evidence as a complementary or necessary requirement), as well as the assessment of its non-epistemic consequences.

Today's Challenges to De-Risk and Predict Drug Safety in Human "Mind-the-Gap"

Current gaps in drug safety sciences can result from the inability (1) to identify hazard across multiple target organs, (2) to predict and risk assess with certainty against drug safety liabilities for the major target organs, (3) to optimally manage and mitigate against drug safety liabilities, and (4) to apply principles of governance on the generation, integration, and use of experimental data. Translational safety assessment to evaluate several target-organ drug toxicities can only be partially achieved by use of current in vitro and in vivo test systems. What remains to be tackled necessitates the deployment of in vitro-human-relevant test systems to address human specific or selective forms of toxicities. Nevertheless, such models may only address in part some of the requirements in today's armament of biomedical tools essential for improving the discovery of drug candidates. Refinement of in silico tools, Target Safety Assessment and a greater understanding of mechanistic insights of toxicities might provide future opportunities to better identify drug safety liabilities. The increasing diversity of drug modalities present further challenges for nonclinical and clinical development requiring further research to develop suitable test systems and technologies. Our ability to optimally manage and mitigate safety risk will come from the greater refinement of safety margin estimates, provision and use of human-relevant safety biomarkers, and understanding of the translation from in silico, in vitro, and in vivo studies to human. An improvement of governance frameworks and standards at all levels within organizations, national, and international, can only help facilitate drug discovery and development programs.

The Effects of Genotype × Phenotype Interactions on Transcriptional Response to Silver Nanoparticle Toxicity in Organotypic Cultures of Murine Tracheal Epithelial Cells

The airway epithelium is critical for maintaining innate and adaptive immune responses, and occupational exposures that disrupt its immune homeostasis may initiate and amplify airway inflammation. In our previous study, we demonstrated that silver nanoparticles (AgNP), which are engineered nanomaterials used in multiple applications but primarily in the manufacturing of many antimicrobial products, induce toxicity in organotypic cultures derived from murine tracheal epithelial cells (MTEC), and those differentiated toward a "Type 2 [T2]-Skewed" phenotype experienced an increased sensitivity to AgNP toxicity, suggesting that asthmatics could be a sensitive population to AgNP exposures in occupational settings. However, the mechanistic basis for this genotype × phenotype (G × P) interaction has yet to be defined. In this study, we conducted transcriptional profiling using RNA-sequencing to predict the enrichment of specific canonical pathways and upstream transcriptional regulators to assist in defining a mechanistic basis for G × P effects on AgNP toxicity. Organotypic cultures were derived from MTEC across 2 genetically inbred mouse strains (A/J and C57BL/6J mice), 2 phenotypes ("Normal" and "T2-Skewed"), and 1 AgNP exposure (an acute 24 h exposure) to characterize G × P effects on transcriptional response to AgNP toxicity. The "T2-Skewed" phenotype was marked by increased pro-inflammatory T17 responses to AgNP toxicity, which are significant predictors of neutrophilic/difficult-to-control asthma and suggests that asthmatics could be a sensitive population to AgNP exposures in occupational settings. This study highlights the importance of considering G × P effects when identifying these sensitive populations, whose underlying genetics or diseases could directly modify their response to AgNP exposures.

CaCO3 as an Environmentally Friendly Renewable Material for Drug Delivery Systems: Uptake of HSA-CaCO3 Nanocrystals Conjugates in Cancer Cell Lines

Chemical and biochemical functionalization of nanoparticles (NPs) can lead to an active cellular uptake enhancing their efficacy thanks to the targeted localization in tumors. In the present study calcium carbonate nano-crystals (CCNs), stabilized by an alcohol dehydration method, were successfully modified by grafting human serum albumin (HSA) on the surface to obtain a pure protein corona. Two types of CCNs were used: naked CaCO3 and the (3-aminopropyl)triethoxysilane (APTES) modified CaCO3-NH2. The HSA conjugation with naked CCN and amino-functionalized CCN (CCN-NH2) was established through the investigation of modification in size, zeta potential, and morphology by Transmission Electron Microscopy (TEM). The amount of HSA coating on the CCNs surface was assessed by spectrophotometry. Thermogravimetric analysis (TGA) and Differential scanning calorimetry (DSC) confirmed the grafting of APTES to the surface and successive adsorption of HSA. Furthermore, to evaluate the effect of protein complexation of CCNs on cellular behavior, bioavailability, and biological responses, three human model cancer cell lines, breast cancer (MCF7), cervical cancer (HeLa), and colon carcinoma (Caco-2) were selected to characterize the internalization kinetics, localization, and bio-interaction of the protein-enclosed CCNs. To monitor internalization of the various conjugates, chemical modification with fluorescein-isothiocyanate (FITC) was performed, and their stability over time was measured. Confocal microscopy was used to probe the uptake and confirm localization in the perinuclear region of the cancer cells. Flow cytometry assays confirmed that the bio-functionalization influence cellular uptake and the CCNs behavior depends on both cell line and surface features.

Applicability of a human cell co-culture model to evaluate antioxidant responses triggered by chemical mixtures in fish and oyster homogenates

The accumulation of chemical compounds in fish tissue represents significant health concerns for seafood consumers, but little is known about the risks to human health associated with such substances. The identification of adverse biological responses upon exposure to contaminants has been facilitated by the development of in vitro systems resembling the human dietary pathway. The present study explores the applicability of an organotypic co-culture system, using intestinal (Caco-2) and hepatic (HepaRG) cell lines, to provide insight into the toxicity of chemical mixtures found in commercially available seafood. Chemical extractions were conducted utilizing fish and oyster standard reference material (SRM) from the U.S. National Institute of Standards and Technology (NIST). Cells were seeded in monoculture and co-culture systems and exposed to SRM extracts before measurements of cytotoxicity and antioxidant responses. Exposure to oyster extracts led to significant cell mortality in monocultures. HepaRG cells in monoculture expressed lower levels of glutathione peroxidase and superoxide dismutase than HepaRG cells in co-culture, upon exposure to both oyster and fish extracts. These observations illustrate the importance of organotypic co-culture models to explore biological responses that could be otherwise difficult to evaluate in monocultures, and the adverse effects associated with the consumption of contaminated seafood.

Multi-omics analysis: Repeated exposure of a 3D bronchial tissue culture to whole-cigarette smoke

Cigarette smoke (CS) is a major risk factor in the development of chronic inflammatory lung diseases such as chronic obstructive pulmonary disease. A comprehensive investigation of the biological impacts of chronic CS exposure on lung tissue is therefore important for understanding the pathogenesis of lung disease. We used three-dimensional (3D) organotypic human bronchial tissue cultures and metabolomics, transcriptomics, and proteomics to investigate changes in biological processes affected by repeated whole-CS exposure. We found that CS perturbed central carbon metabolism in relation with oxidative stress responses. Epidermal growth factor receptor, which is involved in the early-stage pathogenesis of airway diseases, was identified as a key regulator of the perturbed processes. Proteomic analysis of proteins in the apical surface liquid of the 3D bronchial tissue cultures indicated that repeated whole-CS exposure induced alterations in the secretion of several known biomarkers of airway diseases, including mucins and matrix metalloproteinases. These findings are consistent with observations from lung disease patients. Overall, our results suggest that 3D bronchial tissue cultures can provide valuable information on tissue-specific alterations in biological processes induced by chronic exposure to CS.

Statistical relationship between metabolic decomposition and chemical uptake predicts bioconcentration factor data for diverse chemical exposures

BACKGROUND

A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of organisms exposed to chemicals in the environment from in vitro exposure assay data. Although toxicokinetic modeling approaches promise to bridge in vitro screening data with in vivo effects, they are often encumbered by a need for redesign or re-parameterization when applied to different tissues or chemicals.

RESULTS

We demonstrate a parameterization of reverse toxicokinetic (rTK) models developed for the adult zebrafish (Danio rerio) based upon particle swarm optimizations (PSO) of the chemical uptake and degradation rates that predict bioconcentration factors (BCF) for a broad range of chemicals. PSO reveals a relationship between chemical uptake and decomposition parameter values that predicts chemical-specific BCF values with moderate statistical agreement to a limited yet diverse chemical dataset, and all without a need to retrain the model to new data.

CONCLUSIONS

The presented model requires only the octanol-water partitioning ratio to predict BCFs to a fidelity consistent with existing QSAR models. This success begs re-evaluation of the modeling assumptions; specifically, it suggests that chemical uptake into arterial blood may be limited by transport across gill membranes (diffusion) rather than by counter-current flow between gill lamellae (convection). Therefore, more detailed molecular modeling of aquatic respiration may further improve predictive accuracy of the rTK approach.

A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening

The new paradigm of toxicity testing approaches involves rapid screening of thousands of chemicals across hundreds of biological targets through use of in vitro assays. Such assays may lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system are unable to be replicated in an in vitro environment. In the current study, a workflow is presented for complementing in vitro testing results with in silico and in vitro techniques to identify inactive parents that may produce active metabolites. A case study applying this workflow involved investigating the influence of metabolism for over 1,400 chemicals considered inactive across18 in vitro assays related to the estrogen receptor (ER) pathway. Over 7,500 first-generation and second-generation metabolites were generated for these in vitro inactive chemicals using an in silico software program. Next, a consensus model comprised of four individual quantitative structure activity relationship (QSAR) models was used to predict ER-binding activity for each of the metabolites. Binding activity was predicted for ~8-10% of metabolites in each generation, with these metabolites linked to 259 in vitro inactive parent chemicals. Metabolites were enriched in substructures consisting of alcohol, aromatic, and phenol bonds relative to their inactive parent chemicals, suggesting these features are potentially favorable for ER-binding. The workflow presented here can be used to identify parent chemicals that can be potentially bioactive, to aid confidence in high throughput risk screening.

Comparison of Points of Departure for Health Risk Assessment Based on High-Throughput Screening Data

BACKGROUND

The National Research Council's vision for toxicity testing in the 21st century anticipates that points of departure (PODs) for establishing human exposure guidelines in future risk assessments will increasingly be based on in vitro high-throughput screening (HTS) data.

OBJECTIVES

The aim of this study was to compare different PODs for HTS data. Specifically, benchmark doses (BMDs) were compared to the signal-to-noise crossover dose (SNCD), which has been suggested as the lowest dose applicable as a POD.

METHODS

Hill models were fit to > 10,000 in vitro concentration-response curves, obtained for > 1,400 chemicals tested as part of the U.S. Tox21 Phase I effort. BMDs and lower confidence limits on the BMDs (BMDLs) corresponding to extra effects (i.e., changes in response relative to the maximum response) of 5%, 10%, 20%, 30%, and 40% were estimated for > 8,000 curves, along with BMDs and BMDLs corresponding to additional effects (i.e., absolute changes in response) of 5%, 10%, 15%, 20%, and 25%. The SNCD, defined as the dose where the ratio between the additional effect and the difference between the upper and lower bounds of the two-sided 90% confidence interval on absolute effect was 1, 0.67, and 0.5, respectively, was also calculated and compared with the BMDLs.

RESULTS

The BMDL₄₀, BMDL₂₅, and BMDL₁₈, defined in terms of extra effect, corresponded to the SNCD_1.0, SNCD_0.67, and SNCD_0.5, respectively, at the median. Similarly, the BMDL₂₅, BMDL₁₇, and BMDL₁₃, defined in terms of additional effect, corresponded to the SNCD_1.0, SNCD_0.67, and SNCD_0.5, respectively, at the median.

CONCLUSIONS

The SNCD may serve as a reference level that guides the determination of standardized BMDs for risk assessment based on HTS concentration-response data. The SNCD may also have application as a POD for low-dose extrapolation.

Physiological fidelity or model parsimony? The relative performance of reverse-toxicokinetic modeling approaches

Background

Physiologically-based toxicokinetic (PBTK) models are often developed to facilitate in vitro to in vivo extrapolation (IVIVE) using a top-down, compartmental approach, favoring architectural simplicity over physiological fidelity despite the lack of general guidelines relating model design to dynamical predictions. Here we explore the impact of design choice (high vs. low fidelity) on chemical distribution throughout an animal’s organ system.

Results

We contrast transient dynamics and steady states of three previously proposed PBTK models of varying complexity in response to chemical exposure. The steady states for each model were determined analytically to predict exposure conditions from tissue measurements. Steady state whole-body concentrations differ between models, despite identical environmental conditions, which originates from varying levels of physiological fidelity captured by the models. These differences affect the relative predictive accuracy of the inverted models used in exposure reconstruction to link effects-based exposure data with whole-organism response thresholds obtained from in vitro assay measurements.

Conclusions

Our results demonstrate how disregarding physiological fideltiy in favor of simpler models affects the internal dynamics and steady state estimates for chemical accumulation within tissues, which, in turn, poses significant challenges for the exposure reconstruction efforts that underlie many IVIVE methods. Developing standardized systems-level models for ecological organisms would not only ensure predictive consistency among future modeling studies, but also ensure pragmatic extrapolation of in vivo effects from in vitro data or modeling exposure-response relationships.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0407-3) contains supplementary material, which is available to authorized users.

Carbon Nanotubes Induced Fibrogenesis on Nanostructured Substrates

While the rapidly evolving nanotechnology has shown promise in electronics, energy, healthcare and many other fields, there is an increasing concern about the adverse health consequences of engineered nanomaterials. To accurately evaluate the toxicity of nanomaterials, in vitro models incorporated with in vivo microenvironment characteristics are desirable. This study aims to delineate the influence of nanotopography on fibrogenic response of normal human lung fibroblasts to multi-walled carbon nanotubes (MWCNTs). Nanoscale gratings and pillars of various heights were fabricated on polydimethylsiloxane substrates. Cell spreading and biomechanics were measured, and fibrogenic responses including proliferation, collagen production and reactive oxygen species generation of the fibroblasts grown on the nanostructured substrates in response to MWCNTs were assessed. It was observed that the cells could be largely stretched on shallow nanogratings, leading to stiffer cytoskeleton and nucleus, enhanced cell proliferation and collagen production, and consequently, toxic response sensitivity of the fibroblasts was undermined. In contrast, the cell spreading and stiffness could be reduced using tall, isotropic nanopillars, which significantly improved the cell toxic sensitivity to the MWCNTs. In addition to highlighting the significant influence of cell-nanotopography interactions on cell sensing CNTs, this study contributed to development of physiologically relevant in vitro models for nanotoxicology study.

Increase of cytotoxicity during wastewater chlorination: Impact factors and surrogates

Toxic and harmful disinfection byproducts (DBPs) were formed during wastewater chlorination. It was recently suggested that cytotoxicity to mammalian cells reflects risks posed by chlorinated wastewater. Here, ATP assays were performed to evaluate the cytotoxicity to mammalian cells. Chlorination significantly increased cytotoxicity of treated wastewater. Factors affecting cytotoxicity formation during wastewater chlorination were investigated. Quenching with sodium thiosulfate and ascorbic acid decreased the formed cytotoxicity, while ammonium kept the cytotoxicity stable. The chlorine dose required for the maximum cytotoxicity increase was dramatically affected by DOC and ammonia concentrations. The maximum cytotoxicity increase, defined as the cytotoxicity formation potential (CtFP), occurred when wastewater was treated for 48h with a chlorine dose of 2·DOC+11·NH₃N+10 (mg-Cl₂/L). During chlorination, the amounts of AOX formation was found to be significantly correlated with cytotoxicity formation when no DBPs were destroyed. AOX formation could be used as a surrogate to estimate cytotoxicity increase during wastewater chlorination. Besides, the CtFP of 14 treated wastewater samples was assessed ranged from 5.4-20.4mg-phenol/L. The CtFP could be estimated from UV₂₅₄ of treated wastewater because CtFP and UV₂₅₄ were strongly correlated.

Safety assessment of nanomaterials using an advanced decision-making framework, the DF4nanoGrouping

As presented at the 2016 TechConnect World Innovation Conference on 22-25 May 2016 in Washington DC, USA, the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) 'Nano Task Force' proposes a Decision-making framework for the grouping and testing of nanomaterials (DF4nanoGrouping) consisting of three tiers to assign nanomaterials to four main groups with possible further subgrouping to refine specific information needs. The DF4nanoGrouping covers all relevant aspects of a nanomaterial's life cycle and biological pathways: intrinsic material properties and system-dependent properties (that depend upon the nanomaterial's respective surroundings), biopersistence, uptake and biodistribution, and cellular and apical toxic effects. Use, release, and exposure route may be applied as 'qualifiers' to determine if, e.g., nanomaterials cannot be released from products, which may justify waiving of testing. The four main groups encompass (1) soluble, (2) biopersistent high aspect ratio, (3) passive, and (4) active nanomaterials. The DF4nanoGrouping foresees a stepwise evaluation of nanomaterial properties and effects with increasing biological complexity. In case studies covering carbonaceous nanomaterials, metal oxide, and metal sulfate nanomaterials, amorphous silica and organic pigments (all nanomaterials having primary particle sizes below 100 nm), the usefulness of the DF4nanoGrouping for nanomaterial hazard assessment was confirmed. The DF4nanoGrouping facilitates grouping and targeted testing of nanomaterials. It ensures that sufficient data for the risk assessment of a nanomaterial are available, and it fosters the use of non-animal methods. No studies are performed that do not provide crucial data. Thereby, the DF4nanoGrouping serves to save both animals and resources.

The C. elegans model in toxicity testing

Caenorhabditis elegans is a small nematode that can be maintained at low cost and handled using standard in vitro techniques. Unlike toxicity testing using cell cultures, C. elegans toxicity assays provide data from a whole animal with intact and metabolically active digestive, reproductive, endocrine, sensory and neuromuscular systems. Toxicity ranking screens in C. elegans have repeatedly been shown to be as predictive of rat LD50 ranking as mouse LD50 ranking. Additionally, many instances of conservation of mode of toxic action have been noted between C. elegans and mammals. These consistent correlations make the case for inclusion of C. elegans assays in early safety testing and as one component in tiered or integrated toxicity testing strategies, but do not indicate that nematodes alone can replace data from mammals for hazard evaluation. As with cell cultures, good C. elegans culture practice (GCeCP) is essential for reliable results. This article reviews C. elegans use in various toxicity assays, the C. elegans model's strengths and limitations for use in predictive toxicology, and GCeCP. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Journal of Applied Toxicology published by John Wiley & Sons Ltd.

The social costs of nitrogen

Despite growing recognition of the negative externalities associated with reactive nitrogen (N), the damage costs of N to air, water, and climate remain largely unquantified. We propose a comprehensive approach for estimating the social cost of nitrogen (SCN), defined as the present value of the monetary damages caused by an incremental increase in N. This framework advances N accounting by considering how each form of N causes damages at specific locations as it cascades through the environment. We apply the approach to an empirical example that estimates the SCN for N applied as fertilizer. We track impacts of N through its transformation into atmospheric and aquatic pools and estimate the distribution of associated costs to affected populations. Our results confirm that there is no uniform SCN. Instead, changes in N management will result in different N-related costs depending on where N moves and the location, vulnerability, and preferences of populations affected by N. For example, we found that the SCN per kilogram of N fertilizer applied in Minnesota ranges over several orders of magnitude, from less than $0.001/kg N to greater than $10/kg N, illustrating the importance of considering the site, the form of N, and end points of interest rather than assuming a uniform cost for damages. Our approach for estimating the SCN demonstrates the potential of integrated biophysical and economic models to illuminate the costs and benefits of N and inform more strategic and efficient N management.

Evaluating the Impact of Uncertainties in Clearance and Exposure When Prioritizing Chemicals Screened in High-Throughput Assays

The toxicity-testing paradigm has evolved to include high-throughput (HT) methods for addressing the increasing need to screen hundreds to thousands of chemicals rapidly. Approaches that involve in vitro screening assays, in silico predictions of exposure concentrations, and pharmacokinetic (PK) characteristics provide the foundation for HT risk prioritization. Underlying uncertainties in predicted exposure concentrations or PK behaviors can significantly influence the prioritization of chemicals, though the impact of such influences is unclear. In the current study, a framework was developed to incorporate absorbed doses, PK properties, and in vitro dose-response data into a PK/pharmacodynamic (PD) model to allow for placement of chemicals into discrete priority bins. Literature-reported or predicted values for clearance rates and absorbed doses were used in the PK/PD model to evaluate the impact of their uncertainties on chemical prioritization. Scenarios using predicted absorbed doses resulted in a larger number of bin misassignments than those scenarios using predicted clearance rates, when comparing to bin placement using literature-reported values. Sensitivity of parameters on the model output of toxicological activity was examined across possible ranges for those parameters to provide insight into how uncertainty in their predicted values might impact uncertainty in activity.

Improving the prediction of organism-level toxicity through integration of chemical, protein target and cytotoxicity qHTS data

Prediction of compound toxicity is essential because covering the vast chemical space requiring safety assessment using traditional experimentally-based, resource-intensive techniques is impossible. However, such prediction is nontrivial due to the complex causal relationship between compound structure and in vivo harm. Protein target annotations and in vitro experimental outcomes encode relevant bioactivity information complementary to chemicals' structures. This work tests the hypothesis that utilizing three complementary types of data will afford predictive models that outperform traditional models built using fewer data types. A tripartite, heterogeneous descriptor set for 367 compounds was comprised of (a) chemical descriptors, (b) protein target descriptors generated using an algorithm trained on 190 000 ligand-protein interactions from ChEMBL, and (c) descriptors derived from in vitro cell cytotoxicity dose-response data from a panel of human cell lines. 100 random forests classification models for predicting rat LD50 were built using every combination of descriptors. Successive integration of data types improved predictive performance; models built using the full dataset had an average external correct classification rate of 0.82, compared to 0.73-0.80 for models built using two data types and 0.67-0.78 for models built using one. Pairwise comparisons of models trained on the same data showed that including a third data domain on top of chemistry improved average correct classification rate by 1.4-2.4 points, with p-values <0.01. Additionally, the approach enhanced the models' applicability domains and proved useful for generating novel mechanism hypotheses. The use of tripartite heterogeneous bioactivity datasets is a useful technique for improving toxicity prediction. Both protein target descriptors - which have the practical value of being derived in silico - and cytotoxicity descriptors derived from experiment are suitable contributors to such datasets.

Mind the Gap! A Journey towards Computational Toxicology

Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale. However, the predictive potential of these models presents strengths and weaknesses. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. On the other, the unconscious use of in silico methods can mislead end-users with elusive results. The focus of this review is on the basic scientific and regulatory recommendations in the derivation and application of computational models. Attention is paid to examine the interplay between computational toxicology and drug discovery and development. Avoiding the easy temptation of an overoptimistic future, we report our view on what can, or cannot, realistically be done. Indeed, studies of safety/toxicity represent a key element of chemical prioritization programs carried out by chemical industries, and primarily by pharmaceutical companies.

The MARINA Risk Assessment Strategy: A Flexible Strategy for Efficient Information Collection and Risk Assessment of Nanomaterials

An engineered nanomaterial (ENM) may actually consist of a population of primary particles, aggregates and agglomerates of various sizes. Furthermore, their physico-chemical characteristics may change during the various life-cycle stages. It will probably not be feasible to test all varieties of all ENMs for possible health and environmental risks. There is therefore a need to further develop the approaches for risk assessment of ENMs. Within the EU FP7 project Managing Risks of Nanoparticles (MARINA) a two-phase risk assessment strategy has been developed. In Phase 1 (Problem framing) a base set of information is considered, relevant exposure scenarios (RESs) are identified and the scope for Phase 2 (Risk assessment) is established. The relevance of an RES is indicated by information on exposure, fate/kinetics and/or hazard; these three domains are included as separate pillars that contain specific tools. Phase 2 consists of an iterative process of risk characterization, identification of data needs and integrated collection and evaluation of data on the three domains, until sufficient information is obtained to conclude on possible risks in a RES. Only data are generated that are considered to be needed for the purpose of risk assessment. A fourth pillar, risk characterization, is defined and it contains risk assessment tools. This strategy describes a flexible and efficient approach for data collection and risk assessment which is essential to ensure safety of ENMs. Further developments are needed to provide guidance and make the MARINA Risk Assessment Strategy operational. Case studies will be needed to refine the strategy.

Toxicity of aerosols of nicotine and pyruvic acid (separate and combined) in Sprague-Dawley rats in a 28-day OECD 412 inhalation study and assessment of systems toxicology

Toxicity of nebulized nicotine (Nic) and nicotine/pyruvic acid mixtures (Nic/Pyr) was characterized in a 28-day Organization for Economic Co-operation and Development 412 inhalation study with additional transcriptomic and lipidomic analyses. Sprague-Dawley rats were nose-only exposed, 6 h/day, 5 days/week to filtered air, saline, nicotine (50 µg/l), sodium pyruvate (NaPyr, 33.9 µg/l) or equimolar Nic/Pyr mixtures (18, 25 and 50 µg nicotine/l). Saline and NaPyr caused no health effects, but rats exposed to nicotine-containing aerosols had decreased body weight gains and concentration-dependent increases in liver weight. Blood neutrophil counts were increased and lymphocyte counts decreased in rats exposed to nicotine; activities of alkaline phosphatase and alanine aminotransferase were increased, and levels of cholesterol and glucose decreased. The only histopathologic finding in non-respiratory tract organs was increased liver vacuolation and glycogen content. Respiratory tract findings upon nicotine exposure (but also some phosphate-buffered saline aerosol effects) were observed only in the larynx and were limited to adaptive changes. Gene expression changes in the lung and liver were very weak. Nic and Nic/Pyr caused few significant changes (including Cyp1a1 gene upregulation). Changes were predominantly related to energy metabolism and fatty acid metabolism but did not indicate an obvious toxicity-related response. Nicotine exposure lowered plasma lipids, including cholesteryl ester (CE) and free cholesterol and, in the liver, phospholipids and sphingolipids. Nic, NaPyr and Nic/Pyr decreased hepatic triacylglycerol and CE. In the lung, Nic and Nic/Pyr increased CE levels. These data suggest that only minor biologic effects related to inhalation of Nic or Nic/Pyr aerosols were observed in this 28-day study.

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.

Functional genomic screening approaches in mechanistic toxicology and potential future applications of CRISPR-Cas9

Characterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment. Chemical toxicants act by many different mechanisms, however, and the genes involved in adverse outcome pathways (AOPs) and AOP networks are not yet characterized. Functional genomic approaches can reveal both toxicity pathways and susceptibility genes, through knockdown or knockout of all non-essential genes in a cell of interest, and identification of genes associated with a toxicity phenotype following toxicant exposure. Screening approaches in yeast and human near-haploid leukemic KBM7 cells have identified roles for genes and pathways involved in response to many toxicants but are limited by partial homology among yeast and human genes and limited relevance to normal diploid cells. RNA interference (RNAi) suppresses mRNA expression level but is limited by off-target effects (OTEs) and incomplete knockdown. The recently developed gene editing approach called clustered regularly interspaced short palindrome repeats-associated nuclease (CRISPR)-Cas9, can precisely knock-out most regions of the genome at the DNA level with fewer OTEs than RNAi, in multiple human cell types, thus overcoming the limitations of the other approaches. It has been used to identify genes involved in the response to chemical and microbial toxicants in several human cell types and could readily be extended to the systematic screening of large numbers of environmental chemicals. CRISPR-Cas9 can also repress and activate gene expression, including that of non-coding RNA, with near-saturation, thus offering the potential to more fully characterize AOPs and AOP networks. Finally, CRISPR-Cas9 can generate complex animal models in which to conduct preclinical toxicity testing at the level of individual genotypes or haplotypes. Therefore, CRISPR-Cas9 is a powerful and flexible functional genomic screening approach that can be harnessed to provide unprecedented mechanistic insight in the field of modern toxicology.

Interpreting in vitro developmental toxicity test battery results: The consideration of toxicokinetics

In the EU collaborative project ChemScreen an alternative, in vitro assay-based test strategy was developed to screen compounds for reproductive toxicity. A toxicokinetic modeling approach was used to allow quantitative comparison between effective concentrations in the in vitro test battery and observations of developmental toxicity in vivo. This modeling strategy is based on (1) the definition of relevant observations of toxicity in vivo, (2) simulation of the corresponding systemic concentrations in vivo by toxicokinetic modeling, and (3) correction for differences in protein binding and lipid partitioning between plasma and in vitro test media. The test results of a feasibility study with a number of known reproductive toxicants has been described previously (Piersma et al. [15]). In the present paper, we take a more detailed look at the toxicokinetics of these compounds, and add the analysis of some compounds from subsequent studies. We discuss how the consideration of toxicokinetics allowed comparison between test systems with differing test medium composition, has helped to interpret the in vitro findings in light of in vivo observations, and to gain confidence in the predictive value of the test battery outcomes. The same toxicokinetic modeling strategy, in reverse order, can now be used for risk assessment purposes to predict toxic doses in vivo from effective concentrations in vitro.

Environmental side effects of pharmaceutical cocktails: what we know and what we should know

Cocktails of pharmaceuticals are released in the environment after human consumption and due to the incomplete removal at the wastewater treatment plants. Pharmaceuticals are considered as contaminants of emerging concern and, a plethora of journal articles addressing their possible adverse effects have been published during the past 20 years. The emphasis during the early years of research within this field, was on the assessment of acute effects of pharmaceuticals applied singly, leading to results regarding their environmental risk, potentially not realistic or relevant to the actual environmental conditions. Only recently has the focus been shifted to chronic exposure and to the assessment of cocktail effects. To this end, this review provides an up-to-date compilation of 57 environmental and human toxicology studies published during 2000-2014 dealing with the adverse effects of pharmaceutical mixtures. The main challenges regarding the design of experiments and the analysis of the results regarding the effects of pharmaceutical mixtures to different biological systems are presented and discussed herein. The gaps of knowledge are critically reviewed highlighting specific future research needs and perspectives.

Assessing vanadium and arsenic exposure of people living near a petrochemical complex with two-stage dispersion models

The goal of this study is to demonstrate that it is possible to construct a two-stage dispersion model empirically for the purpose of estimating air pollution levels in the vicinity of petrochemical plants. We studied oil refineries and coal-fired power plants in the No. 6 Naphtha Cracking Complex, an area of 2,603-ha situated on the central west coast of Taiwan. The pollutants targeted were vanadium (V) from oil refineries and arsenic (As) from coal-fired power plants. We applied a backward fitting method to determine emission rates of V and As, with 192 PM10 filters originally collected between 2009 and 2012. Our first-stage model estimated emission rates of V and As (median and 95% confidence intervals at 0.0202 (0.0040-0.1063) and 0.1368 (0.0398-0.4782) g/s, respectively. In our second stage model, the predicted zone-average concentrations showed a strong correlation with V, but a poor correlation with As. Our findings show that two-stage dispersion models are relatively precise for estimating V levels at residents' addresses near the petrochemical complex, but they did not work as well for As levels. In conclusion, our model-based approach can be widely used for modeling exposure to air pollution from industrial areas in countries with limited resources.

Structure-activity relationship models for rat carcinogenesis and assessing the role mutagens play in model predictivity

We previously demonstrated that fragment based cat-SAR carcinogenesis models consisting solely of mutagenic or non-mutagenic carcinogens varied greatly in terms of their predictive accuracy. This led us to investigate how well the rat cancer cat-SAR model predicted mutagens and non-mutagens in their learning set. Four rat cancer cat-SAR models were developed: Complete Rat, Transgender Rat, Male Rat and Female Rat, with leave-one-out (LOO) validation concordance values of 69%, 74%, 67% and 73%, respectively. The mutagenic carcinogens produced concordance values in the range 69-76% compared with only 47-53% for non-mutagenic carcinogens. As a surrogate for mutagenicity, comparisons between single site and multiple site carcinogen SAR models were analysed. The LOO concordance values for models consisting of 1-site, 2-site and 4+-site carcinogens were 66%, 71% and 79%, respectively. As expected, the proportion of mutagens to non-mutagens also increased, rising from 54% for 1-site to 80% for 4+-site carcinogens. This study demonstrates that mutagenic chemicals, in both SAR learning sets and test sets, are influential in assessing model accuracy. This suggests that SAR models for carcinogens may require a two-step process in which mutagenicity is first determined before carcinogenicity can be accurately predicted.

ITS-NANO--prioritising nanosafety research to develop a stakeholder driven intelligent testing strategy

Background

To assess the risk of all nanomaterials (NMs) on a case-by-case basis is challenging in terms of financial, ethical and time resources. Instead a more intelligent approach to knowledge gain and risk assessment is required.

Methods

A framework of future research priorities was developed from the accorded opinion of experts covering all major stake holder groups (government, industry, academia, funders and NGOs). It recognises and stresses the major topics of physicochemical characterisation, exposure identification, hazard identification and modelling approaches as key components of the current and future risk assessment of NMs.

Results

The framework for future research has been developed from the opinions of over 80 stakeholders, that describes the research priorities for effective development of an intelligent testing strategy (ITS) to allow risk evaluation of NMs. In this context, an ITS is a process that allows the risks of NMs to be assessed accurately, effectively and efficiently, thereby reducing the need to test NMs on a case-by-case basis.

For each of the major topics of physicochemical characterisation, exposure identification, hazard identification and modelling, key-priority research areas are described via a series of stepping stones, or hexagon diagrams structured into a time perspective. Importantly, this framework is flexible, allowing individual stakeholders to identify where their own activities and expertise are positioned within the prioritisation pathway and furthermore to identify how they can effectively contribute and structure their work accordingly. In other words, the prioritisation hexagon diagrams provide a tool that individual stakeholders can adapt to meet their own particular needs and to deliver an ITS for NMs risk assessment. Such an approach would, over time, reduce the need for testing by increasing the reliability and sophistication of in silico approaches.

The manuscript includes an appraisal of how this framework relates to the current risk assessment approaches and how future risk assessment could adapt to accommodate these new approaches. A full report is available in electronic format (pdf) at http://www.nano.hw.ac.uk/research-projects/itsnano.html.

Conclusion

ITS-NANO has delivered a detailed, stakeholder driven and flexible research prioritisation (or strategy) tool, which identifies specific research needs, suggests connections between areas, and frames this in a time-perspective.