Emerging methods and tools for environmental risk assessment, decision-making, and policy for nanomaterials: summary of NATO Advanced Research Workshop

Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint

Assessment and prediction for the ecotoxicity of engineered nanoparticles (ENPs) at the community or ecosystem levels represents a critical step toward a comprehensive understanding of the ecological risks of ENPs. Current studies on predicting the ecotoxicity of ENPs primarily focus on the cellular and individual levels, with limited exploration at the community or ecosystem levels. Herein, we present the first of the reports for the direct prediction of aquatic ecological risk for ENPs at the community level using machine learning (ML) approaches in the field of computational toxicology. Specifically, we extensively collected the threshold concentrations of twelve ENPs including metal- and carbon-based nanoparticles for aquatic species, i.e., hazardous concentrations at which 5% of species are harmed (HC5), established by a species sensitivity distribution. Afterwards, we used eight supervised ML methods including Adaboost, artificial neural network, C4.5 decision tree, K-nearest neighbor, logistic regression, Naive Bayes, random forest, and support vector machine to develop nine classification models and four regression models, respectively, for the qualitative and quantitative prediction of HC5. The evaluation of model performance yielded the internal validation accuracy of all classification models ranging from 71.4 to 100%, and the determination coefficient of regression models ranging from 0.702 to 0.999, indicating that the developed models showed good performance. By using a cross-validation method and an application domain characterization, the selected models were further validated to have powerful predictive ability. Furthermore, the incorporation of three nanostructural descriptors (metal oxide sublimation enthalpy, zeta potential, and specific surface area) linked to toxicity mechanisms (the release of metal ions, the stability of dispersions of particles in aqueous suspensions, and the surface properties of the material) effectively enhanced the prediction power and mechanistic interpretability of the selected models. These findings would not only be beneficial in the screening of ENPs with potential high ecological risks that need to be tested as a priority but also contribute to the development of environmental regulations and standards for ENPs.

QNAR modeling of cytotoxicity of mixing nano-TiO2 and heavy metals

The Quantitative Structure-Activity Relationship (QSAR) has been used to investigate organic mixtures but QSAR in the nanomaterial field (QNAR) is still new. Toxicity is a result of the interaction of many substances. QNAR research focuses on a single nanomaterial in the long-term. It is difficult to find an appropriate descriptor to build a model due to the complexity of the mixture. Here, we attempt to build a QNAR model to predict cell viability for HK-2 cells exposed to a mixture containing nano-TiO₂ and heavy metals. HK-2 cells were exposed to four groups of mixtures containing heavy-metals and nanomaterials and CCK8 was added to obtain the number of living cells. At the same time, ROS was investigated to study this mechanism. Each descriptor of the components and mixtures were obtained using the formula D_mix= [Formula: see text] respectively. We used the Multiple Partial Least Squares Regression (PLS) and Random Forest Regression (RF) to build a QNAR model. Both models reliably predict and assess viability of HK-2 cells exposed to the mixture. The RF model showed greater stability and higher precision in toxicity predictability and can be applied to environmental nano-toxicology.

Workshop Report: Governance of Emerging Nanotechnology Risks in the Semiconductor Industry

Assessment of risk in the field of nanotechnology requires an integrated multidisciplinary approach due to the complex and cross-disciplinary framework for materials and activities at the nanoscale. The present paper summarizes the workshop "Governance of emerging nano-risk in the semiconductor industry" held on April 26, 2018 in Brussels, Belgium. The event targeted representatives of stakeholder communities involved in the risk assessment and governance of the engineered nanomaterials. Nanoelectronics was selected as an impactful use case for risk assessment approaches and comparison to bottom-up nanofabrication. The workshop outlined key data gaps impeding successful assessment of risks associated with nanoparticle use in the industry, using the semiconductor industry as an example. The workshop outlined mitigation strategies informing future regulatory decisions and identified some directions for future efforts.

Guiding the development of sustainable nano-enabled products for the conservation of works of art: proposal for a framework implementing the Safe by Design concept

Nanotechnology provides innovative and promising solutions for the conservation of cultural heritage, but the development and application of new nano-enabled products pose concerns regarding their human health and environmental risks. To address these issues, we propose a sustainability framework implementing the Safe by Design concept to support product developers in the early steps of product development, with the aim to provide safer nano-formulations for conservation, while retaining their functionality. In addition, this framework can support the assessment of sustainability of new products and their comparison to their conventional chemical counterparts if any. The goal is to promote the selection and use of safer and more sustainable nano-based products in different conservation contexts. The application of the proposed framework is illustrated through a hypothetical case which provides a realistic example of the methodological steps to be followed, tailored and iterated along the decision-making process.

NECID – nano exposure and contextual information database

Nanotechnology is a rapidly evolving field allowing to design and obtain new, previously unknown nanostructured materials with unique properties and broad application. In addition to the wide range of potential benefits, the use of nanoobjects can also endanger human health. Due to the divergent results of published studies about impact of nanoobjects on health, different exposure measurement strategies and non-uniform and limited documentation the possibilities for comparing different measurements, and as well the use of research results to simulate and construct mathematical models are limited. In order to harmonize documentation, research results, exposure indicators and context for exposure measurement, the NECID (Nano Exposure and Contextual Information Database) database, a digital documentation platform for occupational exposure to nanoparticles, has been developed.

A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials

Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs). The extension of conventional (quantitative) structure-activity relationships ((Q)SARs) approach to nanotoxicology, i.e., nano-(Q)SARs, is a possible solution. The preliminary attempts of correlating ENMs' characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (Q)SARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(Q)SARs for metallic ENMs, on the aspects of data sources, reported nano-(Q)SARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.

Modeling adsorption of brominated, chlorinated and mixed bromo/chloro-dibenzo-p-dioxins on C60 fullerene using Nano-QSPR

Many technological implementations in the field of nanotechnology have involved carbon nanomaterials, including fullerenes such as the buckminsterfullerene, C60. The unprecedented properties of such organic nanomaterials (in particular their large surface area) gained extensive attention for their potential use as organic pollutant sorbents. Sorption interactions can be very hazardous and useful at the same time. This work investigates the influence of halogenation by bromine and/or chlorine in dibenzo-p-dioxins on their sorption ability on the C60 fullerene surface. Halogenated dibenzo-p-dioxins (PXDDs, where X = Br or Cl) are ever-present in the environment and accidently produced in many technological processes in only approximately known quantities. If all combinatorial Br and/or Cl dioxin substitution possibilities are present in the environment, the experimental characterization and investigation of sorbent effectiveness is more than difficult. In this work, we have developed a quantitative structure-property relationship (QSPR) model (R2 = 0.998), predicting the adsorption energy [kcal/mol] for 1,701 PXDDs adsorbed on C60 (PXDD@C60). Based on the QSPR model reported herein, we concluded that the lowest energy PXDD@C60 complexes are those that the World Health Organization (WHO) considers to be less dangerous with respect to the aryl hydrocarbon receptor (AhR) toxicity mechanism. Therefore, the effectiveness of fullerenes as sorbent agents may be underestimated as sorption could be less effective for toxic congeners than previously believed.

Multi-target QSTR modeling for simultaneous prediction of multiple toxicity endpoints of nano-metal oxides

The metal oxide nanoparticles (MeONPs) due to their unique physico-chemical properties have widely been used in different products. Current studies have established toxicity of some NPs to human and environment, hence, imply for their comprehensive safety assessment. Here, the potential of using a multi-target QSTR modeling for simultaneous prediction of multiple toxicity endpoints of various MeONPs has been investigated. A multi-target QSTR model has been established using four different experimental toxicity data sets of MeONPs. Diversity of the considered experimental toxicity data sets was tested using the Kruskal-Wallis (K-W) statistics. The optimal validated model yielded high correlations (R² between 0.828 and 0.956) between the experimental and simultaneously predicted endpoint toxicity values in test arrays for all the four systems. The structural features (oxygen percent, LogS, and Mulliken's electronegativity) identified by the QSTR model were mechanistically interpretable in view of the accepted toxicity mechanisms for NPs. Single target QSTR models were also established (R²_Test >0.882) for individual toxicity endpoint prediction of MeONPs. The performance of the multi-target QSTR model was closely comparable with individual models and with those reported earlier in the literature for toxicity prediction of NPs. The model reliably predicts the toxicity of all considered MeONPs, and the methodology is expected to provide guidance for the future design of safe NP-based products. The proposed multi-target QSTR can be successfully used for screening new, untested metal oxide NPs for their safety assessment within the defined applicability domain of the model.

Prediction of joint algal toxicity of nano-CeO2/nano-TiO2 and florfenicol: Independent action surpasses concentration addition

Co-exposure of aquatic organisms to engineered nanoparticles (ENPs) and antibiotics is likely to take place in the environment. However, the impacts of co-exposure on aquatic organisms are virtually unknown and understanding the joint toxicity of ENPs and antibiotics is a topic of importance. The independent action (IA) model and the concentration addition (CA) model are two of the most common approaches to mixture toxicity assessment. In this study, the joint toxicity of two ENPs (nCeO2 and nTiO2) and one antibiotic (florfenicol, FLO) to Chlorella pyrenoidosa was determined to compare the applicability of the IA and the CA model. Concentration-response analyses were performed for single toxicants and for binary mixtures containing FLO and one of the ENPs at two suspended particle concentrations. The effect concentrations and the observed effects of the binary mixtures were compared to the predictions of the joint toxicity. The observed toxicity associated with the nCeO2 or nTiO2 exposure was enhanced by the concomitant FLO exposure. The joint toxicity of nCeO2 and FLO was significantly higher than that of nTiO2 and FLO. Predictions based on the IA and CA models tend to underestimate the overall toxicity (in terms of median effect concentration) of the binary mixtures, but IA performs better than CA, irrespective of the effect level under consideration and the types of mixtures studied. This result underpins the need to consider the effects of mixtures of ENPs and organic chemicals on aquatic organisms, and the practicability of the IA and CA methods in toxicity assessment of ENPs.

An approach for environmental risk assessment of engineered nanomaterials using Analytical Hierarchy Process (AHP) and fuzzy inference rules

The usage of Engineered Nanoparticles (ENPs) in consumer products is relatively new and there is a need to conduct environmental risk assessment (ERA) to evaluate their impacts on the environment. However, alternative approaches are required for ERA of ENPs because of the huge gap in data and knowledge compared to conventional pollutants and their unique properties that make it difficult to apply existing approaches. This study aims to propose an ERA approach for ENPs by integrating Analytical Hierarchy Process (AHP) and fuzzy inference models which provide a systematic evaluation of risk factors and reducing uncertainty about the data and information, respectively. Risk is assumed to be the combination of occurrence likelihood, exposure potential and toxic effects in the environment. A hierarchy was established to evaluate the sub factors of these components. Evaluation was made with fuzzy numbers to reduce uncertainty and incorporate the expert judgements. Overall score of each component was combined with fuzzy inference rules by using expert judgements. Proposed approach reports the risk class and its membership degree such as Minor (0.7). Therefore, results are precise and helpful to determine the risk management strategies. Moreover, priority weights calculated by comparing the risk factors based on their importance for the risk enable users to understand which factor is effective on the risk. Proposed approach was applied for Ag (two nanoparticles with different coating) and TiO2 nanoparticles for different case studies. Results verified the proposed benefits of the approach.

Toxicity and inflammatory response in Swiss albino mice after intraperitoneal and oral administration of polyurethane nanoparticles

In this work in vivo experiments were conducted in order to characterize the biocompatibility of polyurethane nanoparticles (PU-NPs) after intraperitoneal (i.p.) and oral administration. Additionally, ex vivo assays were performed to assess human blood compatibility as well as in vitro assays to assess protein binding. Our results indicated that administration of three different concentrations of PU-NPs induced a significant increase in visceral fat accumulation after oral dosing. In addition, fat tissue of mice intraperitoneally treated with the highest concentration of nanoparticles showed diffuse mononuclear inflammatory infiltrate in the fat tissue. Histopathological assessment showed inflammatory infiltrate and hepatocyte vacuolization in the liver, inflammatory infiltration and vascular congestion in the lung and glomerular necrosis in the kidney. Hepatic enzymes related with liver function were significantly increased in both groups of mice treated with PU-NPs. The PU-NPs did not affect the human blood cells number as well as coagulation time but showed a susceptibility to bind in proteins commonly found in the blood stream. In addition, increased amounts of pro inflammatory cytokines in vivo, as well as ex vivo in human cells were observed. Further studies to establish the consequences of long-term exposure to PU-NPs are warranted.

Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms

Metal oxide nanomaterials are widely used in various areas; however, the divergent published toxicology data makes it difficult to determine whether there is a risk associated with exposure to metal oxide nanomaterials. The application of quantitative structure activity relationship (QSAR) modeling in metal oxide nanomaterials toxicity studies can reduce the need for time-consuming and resource-intensive nanotoxicity tests. The nanostructure and inorganic composition of metal oxide nanomaterials makes this approach different from classical QSAR study; this review lists and classifies some structural descriptors, such as size, cation charge, and band gap energy, in recent metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) studies and discusses the mechanism of metal oxide nanomaterials toxicity based on these descriptors and traditional nanotoxicity tests.

Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studies

The production of nanomaterials increases every year exponentially and therefore the probability these novel materials that they could cause adverse outcomes for human health and the environment also expands rapidly. We proposed two types of mechanisms of toxic action that are collectively applied in a nano-QSAR model, which provides governance over the toxicity of metal oxide nanoparticles to the human keratinocyte cell line (HaCaT). The combined experimental-theoretical studies allowed the development of an interpretative nano-QSAR model describing the toxicity of 18 nano-metal oxides to the HaCaT cell line, which is a common in vitro model for keratinocyte response during toxic dermal exposure. The comparison of the toxicity of metal oxide nanoparticles to bacteria Escherichia coli (prokaryotic system) and a human keratinocyte cell line (eukaryotic system), resulted in the hypothesis that different modes of toxic action occur between prokaryotic and eukaryotic systems.

Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver

The adaptive nature of the Forecasting the Impacts of Nanomaterials in the Environment (FINE) Bayesian network is explored. We create an updated FINE model (FINEAgNP-2) for predicting aquatic exposure concentrations of silver nanoparticles (AgNP) by combining the expert-based parameters from the baseline model established in previous work with literature data related to particle behavior, exposure, and nano-ecotoxicology via parameter learning. We validate the AgNP forecast from the updated model using mesocosm-scale field data and determine the sensitivity of several key variables to changes in environmental conditions, particle characteristics, and particle fate. Results show that the prediction accuracy of the FINEAgNP-2 model increased approximately 70% over the baseline model, with an error rate of only 20%, suggesting that FINE is a reliable tool to predict aquatic concentrations of nano-silver. Sensitivity analysis suggests that fractal dimension, particle diameter, conductivity, time, and particle fate have the most influence on aquatic exposure given the current knowledge; however, numerous knowledge gaps can be identified to suggest further research efforts that will reduce the uncertainty in subsequent exposure and risk forecasts.

Nano-quantitative structure-activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells

As experimental evaluation of the safety of nanoparticles (NPs) is expensive and time-consuming, computational approaches have been found to be an efficient alternative for predicting the potential toxicity of new NPs before mass production. In this background, we have developed here a regression-based nano quantitative structure-activity relationship (nano-QSAR) model to establish statistically significant relationships between the measured cellular uptakes of 109 magnetofluorescent NPs in pancreatic cancer cells with their physical, chemical, and structural properties encoded within easily computable, interpretable and reproducible descriptors. The developed model was rigorously validated internally as well as externally with the application of the principles of Organization for Economic Cooperation and Development (OECD). The test for domain of applicability was also carried out for checking reliability of the predictions. Important fragments contributing to higher/lower cellular uptake of NPs were identified through critical analysis and interpretation of the developed model. Considering all these identified structural attributes, one can choose or design safe, economical and suitable surface modifiers for NPs. The presented approach provides rich information in the context of virtual screening of relevant NP libraries.

Nanocatalysis: Academic Discipline and Industrial Realities

Nanotechnology plays a central role in both academic research and industrial applications. Nanoenabled products are not only found in consumer markets, but also importantly in business to business markets (B2B). One of the oldest application areas of nanotechnology is nanocatalysis—an excellent example for such a B2B market. Several existing reviews illustrate the scientific developments in the field of nanocatalysis. The goal of the present review is to provide an up-to-date picture of academic research and to extend this picture by an industrial and economic perspective. We therefore conducted an extensive search on several scientific databases and we further analyzed more than 1,500 nanocatalysis-related patents and numerous market studies. We found that scientists today are able to prepare nanocatalysts with superior characteristics regarding activity, selectivity, durability, and recoverability, which will contribute to solve current environmental, social, and industrial problems. In industry, the potential of nanocatalysis is recognized, clearly reflected by the increasing number of nanocatalysis-related patents and products on the market. The current nanocatalysis research in academic and industrial laboratories will therefore enable a wealth of future applications in the industry.

Nano-QSAR modeling for predicting biological activity of diverse nanomaterials

Case study-1 (diverse metal core NPs); case study-2 (similar metal core NPs); case study-3 (metal oxide NPs); case study-4 (surface modified multi-walled CNTs); case study-5 (fullerene derivatives).

Nanotoxicity comparison of four amphiphilic polymeric micelles with similar hydrophilic or hydrophobic structure

Background

Nanocarriers represent an attractive means of drug delivery, but their biosafety must be established before their use in clinical research.

Objectives

Four kinds of amphiphilic polymeric (PEG-PG-PCL, PEEP-PCL, PEG-PCL and PEG-DSPE) micelles with similar hydrophilic or hydrophobic structure were prepared and their in vitro and in vivo safety were evaluated and compared.

Methods

In vitro nanotoxicity evaluations included assessments of cell morphology, cell volume, inflammatory effects, cytotoxicity, apoptosis and membrane fluidity. An umbilical vein cell line (Eahy.926) and a kind of macrophages (J774.A1) were used as cell models considering that intravenous route is dominant for micelle delivery systems. In vivo analyses included complete blood count, lymphocyte subset analysis, detection of plasma inflammatory factors and histological observations of major organs after intravenous administration to KM mice.

Results

All the micelles enhanced inflammatory molecules in J774.A1 cells, likely resulting from the increased ROS levels. PEG-PG-PCL and PEEP-PCL micelles were found to increase the J774.A1 cell volume. This likely correlated with the size of PEG-PG-PCL micelles and the polyphosphoester structure in PEEP-PCL. PEG-DSPE micelles inhibited the growth of Eahy.926 cells via inducing apoptosis. This might relate to the structure of DSPE, which is a type of phospholipid and has good affinity with cell membrane. No evidence was found for cell membrane changes after treatment with these micelles for 24 h. In the in vivo study, during 8 days of 4 time injection, each of the four nanocarriers altered the hematic phase differently without changes in inflammatory factors or pathological changes in target organs.

Conclusions

These results demonstrate that the micelles investigated exhibit diverse nanotoxicity correlated with their structures, their biosafety is different in different cell model, and there is no in vitro and in vivo correlation found. We believe that this study will certainly provide more scientific understandings on the nanotoxicity of amphiphilic polymeric micelles.

In silico analysis of nanomaterials hazard and risk

Because a variety of human-related activities, engineer-ed nanoparticles (ENMs) may be released to various environmental media and may cross environmental boundaries, and thus will be found in most media. Therefore, the potential environmental impacts of ENMs must be assessed from a multimedia perspective and with an integrated risk management approach that considers rapid developments and increasing use of new nanomaterials. Accordingly, this Account presents a rational process for the integration of in silico ENM toxicity and fate and transport analyses for environmental impact assessment. This approach requires knowledge of ENM toxicity and environmental exposure concentrations. Considering the large number of current different types of ENMs and that those numbers are likely to increase, there is an urgent need to accelerate the evaluation of their toxicity and the assessment of their potential distribution in the environment. Developments in high throughput screening (HTS) are now enabling the rapid generation of large data sets for ENM toxicity assessment. However, these analyses require the establishment of reliable toxicity metrics, especially when HTS includes data from multiple assays, cell lines, or organisms. Establishing toxicity metrics with HTS data requires advanced data processing techniques in order to clearly identify significant biological effects associated with exposure to ENMs. HTS data can form the basis for developing and validating in silico toxicity models (e.g., quantitative structure-activity relationships) and for generating data-driven hypotheses to aid in establishing and/or validating possible toxicity mechanisms. To correlate the toxicity of ENMs with their physicochemical properties, researchers will need to develop quantitative structure-activity relationships for nanomaterials (i.e., nano-SARs). However, as nano-SARs are applied in regulatory applications, researchers must consider their applicability and the acceptance level of false positive relative to false negative predictions and the reliability of toxicity data. To establish the environmental impact of ENMs identified as toxic, researchers will need to estimate the potential level of environmental exposure concentration of ENMs in the various media such as air, water, soil, and vegetation. When environmental monitoring data are not available, models of ENMs fate and transport (at various levels of complexity) serve as alternative approaches for estimating exposure concentrations. Risk management decisions regarding the manufacturing, use, and environmental regulations of ENMs would clearly benefit from both the assessment of potential ENMs exposure concentrations and suitable toxicity metrics. The decision process should consider the totality of available information: quantitative and qualitative data and the analysis of nanomaterials toxicity, and fate and transport behavior in the environment. Effective decision-making to address the potential impacts of nanomaterials will require considerations of the relevant environmental, ecological, technological, economic, and sociopolitical factors affecting the complete lifecycle of nanomaterials, while accounting for data and modeling uncertainties. Accordingly, researchers will need to establish standardized data management and analysis tools through nanoinformatics as a basis for the development of rational decision tools.

Modeling approaches for characterizing and evaluating environmental exposure to engineered nanomaterials in support of risk-based decision making

As the use of engineered nanomaterials becomes more prevalent, the likelihood of unintended exposure to these materials also increases. Given the current scarcity of experimental data regarding fate, transport, and bioavailability, determining potential environmental exposure to these materials requires an in depth analysis of modeling techniques that can be used in both the near- and long-term. Here, we provide a critical review of traditional and emerging exposure modeling approaches to highlight the challenges that scientists and decision-makers face when developing environmental exposure and risk assessments for nanomaterials. We find that accounting for nanospecific properties, overcoming data gaps, realizing model limitations, and handling uncertainty are key to developing informative and reliable environmental exposure and risk assessments for engineered nanomaterials. We find methods suited to recognizing and addressing significant uncertainty to be most appropriate for near-term environmental exposure modeling, given the current state of information and the current insufficiency of established deterministic models to address environmental exposure to engineered nanomaterials.

Advancing risk assessment of engineered nanomaterials: application of computational approaches

Nanotechnology that develops novel materials at size of 100nm or less has become one of the most promising areas of human endeavor. Because of their intrinsic properties, nanoparticles are commonly employed in electronics, photovoltaic, catalysis, environmental and space engineering, cosmetic industry and - finally - in medicine and pharmacy. In that sense, nanotechnology creates great opportunities for the progress of modern medicine. However, recent studies have shown evident toxicity of some nanoparticles to living organisms (toxicity), and their potentially negative impact on environmental ecosystems (ecotoxicity). Lack of available data and low adequacy of experimental protocols prevent comprehensive risk assessment. The purpose of this review is to present the current state of knowledge related to the risks of the engineered nanoparticles and to assess the potential of efficient expansion and development of new approaches, which are offered by application of theoretical and computational methods, applicable for evaluation of nanomaterials.

Development of risk-based nanomaterial groups for occupational exposure control

Given the almost limitless variety of nanomaterials, it will be virtually impossible to assess the possible occupational health hazard of each nanomaterial individually. The development of science-based hazard and risk categories for nanomaterials is needed for decision-making about exposure control practices in the workplace. A possible strategy would be to select representative (benchmark) materials from various mode of action (MOA) classes, evaluate the hazard and develop risk estimates, and then apply a systematic comparison of new nanomaterials with the benchmark materials in the same MOA class. Poorly soluble particles are used here as an example to illustrate quantitative risk assessment methods for possible benchmark particles and occupational exposure control groups, given mode of action and relative toxicity. Linking such benchmark particles to specific exposure control bands would facilitate the translation of health hazard and quantitative risk information to the development of effective exposure control practices in the workplace. A key challenge is obtaining sufficient dose-response data, based on standard testing, to systematically evaluate the nanomaterials' physical-chemical factors influencing their biological activity. Categorization processes involve both science-based analyses and default assumptions in the absence of substance-specific information. Utilizing data and information from related materials may facilitate initial determinations of exposure control systems for nanomaterials.

Screening evaluation of the ecotoxicity and genotoxicity of soils contaminated with organic and inorganic nanoparticles: the role of ageing

This study aimed to evaluate the toxicity and genotoxicity of soils, and corresponding elutriates, contaminated with aqueous suspensions of two organic (vesicles of sodium dodecyl sulphate/didodecyl dimethylammonium bromide and of monoolein and sodium oleate) and five inorganic nanoparticles (NPs) (TiO(2), TiSiO(4), CdSe/ZnS quantum dots, Fe/Co magnetic fluid and gold nanorods) to Vibrio fischeri and Salmonella typhimurium (TA98 and TA100 strains). Soil samples were tested 2h and 30 days after contamination. Suspensions of NPs were characterized by Dynamic Light Scattering. Soils were highly toxic to V. fischeri, especially after 2h. After 30 days toxicity was maintained only for soils spiked with suspensions of more stable NPs (zeta potential>30 mV or <-30 mV). Elutriates were particularly toxic after 2h, except for soil spiked with Fe/Co magnetic fluid, suggesting that ageing may have contributed for degrading the organic shell of these NPs, increasing the mobility of core elements and the toxicity of elutriates. TA98 was the most sensitive strain to the mutagenic potential of soil elutriates. Only elutriates from soils spiked with gold nanorods, quantum dots (QDs) and TiSiO(4) induced mutations in both strains of S. typhimurium, suggesting more diversified mechanisms of genotoxicity.

Conceptual modeling for identification of worst case conditions in environmental risk assessment of nanomaterials using nZVI and C60 as case studies

Conducting environmental risk assessment of engineered nanomaterials has been an extremely challenging endeavor thus far. Moreover, recent findings from the nano-risk scientific community indicate that it is unlikely that many of these challenges will be easily resolved in the near future, especially given the vast variety and complexity of nanomaterials and their applications. As an approach to help optimize environmental risk assessments of nanomaterials, we apply the Worst-Case Definition (WCD) model to identify best estimates for worst-case conditions of environmental risks of two case studies which use engineered nanoparticles, namely nZVI in soil and groundwater remediation and C(60) in an engine oil lubricant. Results generated from this analysis may ultimately help prioritize research areas for environmental risk assessments of nZVI and C(60) in these applications as well as demonstrate the use of worst-case conditions to optimize future research efforts for other nanomaterials. Through the application of the WCD model, we find that the most probable worst-case conditions for both case studies include i) active uptake mechanisms, ii) accumulation in organisms, iii) ecotoxicological response mechanisms such as reactive oxygen species (ROS) production and cell membrane damage or disruption, iv) surface properties of nZVI and C(60), and v) acute exposure tolerance of organisms. Additional estimates of worst-case conditions for C(60) also include the physical location of C(60) in the environment from surface run-off, cellular exposure routes for heterotrophic organisms, and the presence of light to amplify adverse effects. Based on results of this analysis, we recommend the prioritization of research for the selected applications within the following areas: organism active uptake ability of nZVI and C(60) and ecotoxicological response end-points and response mechanisms including ROS production and cell membrane damage, full nanomaterial characterization taking into account detailed information on nanomaterial surface properties, and investigations of dose-response relationships for a variety of organisms.

Environmental risk analysis for nanomaterials: review and evaluation of frameworks

In response to the challenges of conducting traditional human health and ecological risk assessment for nanomaterials (NM), a number of alternative frameworks have been proposed for NM risk analysis. This paper evaluates various risk analysis frameworks proposed for NM based on a number of criteria. Among other results, most frameworks were found to be flexible for multiple NM, suitable for multiple decision contexts, included life cycle perspectives and precautionary aspects, transparent and able to include qualitative and quantitative data. Nevertheless, most frameworks were primarily applicable to occupational settings with minor environmental considerations, and most have not been thoroughly tested on a wide range of NM. Care should also be taken when selecting the most appropriate risk analysis strategy for a given risk context. Given this, we recommend a multi-faceted approach to assess the environmental risks of NM as well as increased applications and testing of the proposed frameworks for different NM.

Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles

It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.

Managing the "known unknowns": theranostic cancer nanomedicine and informed consent

The potential clinical applications and the economic benefits of theranostics represent a tremendous incentive to push research and development forward. However, we should also carefully examine the possible downsides. In this chapter, we address the issue of how theranostics might challenge our current concept of informed consent, especially the disclosure of information concerning diagnosis and treatment options to human subjects. We argue that our lack of data concerning long-term effects and risks of nanoparticles on human health and the environment could undermine the process when it comes to weighing the risks against the benefits. Our lack of an agreed upon framework for risk management in nanomedicine may require us to adopt an "upstream" approach that emphasizes communication and transparency among all relevant stakeholders to help them make informed choices that enable safety or progress.

An anticipatory governance approach to carbon nanotubes

Carbon nanotubes (CNTs) are novel materials with remarkable properties; possible beneficial applications include aircraft frames, hydrogen storage, environmental sensors, electrical transmission, and many more. At the same time, precise characterization of their potential toxicity remains elusive, in part because engineered nanostructures pose challenges to existing assays, predictive models, and dosimetry. While these obstacles are surmountable, their presence suggests that scientific uncertainty regarding the hazards of CNTs is likely to persist. Traditional U.S. policy approaches implicitly pose the question: "What level of evidence is necessary and sufficient to justify regulatory action?" In the case of CNTs, such a strategy of risk analysis is of limited immediate utility to both regulators essaying to carry out their mandates, and users of CNTs seeking to provide an appropriate level of protection to employees, customers, and other stakeholders. In contrast, the concept of anticipatory governance suggests an alternative research focus, that is: "Given the conflicted character of the data, how should relevant actors respond?" Adopting the latter theoretical framework, this article argues that currently available data support treating CNTs "as if" they are hazardous, while simultaneously highlighting some systemic uncertainties in many of the experiments carried out to date. Such a conclusion implies limiting exposure throughout product lifecycles, and also points to the possible applicability of various conceptual tools, such as life-cycle and multicriteria decision analysis approaches, in choosing appropriate courses of action in the face of prolonged uncertainty.

Organ distribution of quantum dots after intraperitoneal administration, with special reference to area-specific distribution in the brain

Quantum dots (QDs) are well known for their potential application in biosensing, ex vivo live-cell imaging and in vivo animal targeting. The brain is a challenging organ for drug delivery, because the blood brain barrier (BBB) functions as a gatekeeper guarding the body from exogenous substances. Here, we evaluated the distribution of bioconjugated QDs, i.e., captopril-conjugated QDs (QDs-cap) following intraperitoneal injection into male ICR mice as a model system for determining the tissue localization of QDs, employing ICP-MS and confocal microscopy coupled with spectrometric analysis. We have demonstrated that intraperitoneally administered QDs-cap were delivered via systemic blood circulation into liver, spleen, kidney and brain at 6 h after injection. QDs-cap were located predominantly inside the blood vessels in the liver, kidney and brain, but a few were distributed in the parenchyma, especially noteworthy in the brain. Careful studies on acute as well as chronic toxicity of QDs in the brain are required prior to clinical application to humans.

Physicochemical factors that affect metal and metal oxide nanoparticle passage across epithelial barriers

The diversity of nanomaterials in terms of size, shape, and surface chemistry poses a challenge to those who are trying to characterize the human health and environmental risks associated with incidental and unintentional exposures. There are numerous products that are already commercially available that contain solid metal and metal oxide nanoparticles, either embedded in a matrix or in solution. Exposure assessments for these products are often incomplete or difficult due to technological challenges associated with detection and quantitation of nanoparticles in gaseous or liquid carriers. The main focus of recent research has been on hazard identification. However, risk is a product of hazard and exposure, and one significant knowledge gap is that of the target organ dose following in vivo exposures. In order to reach target organs, nanoparticles must first breach the protective barriers of the respiratory tract, gastrointestinal tract, or skin. The fate of those nanoparticles that reach physiological barriers is in large part determined by the properties of the particles and the barriers themselves. This article reviews the physiological properties of the lung, gut, and skin epithelia, the physicochemical properties of metal and metal oxide nanoparticles that are likely to affect their ability to breach epithelial barriers, and what is known about their fate following in vivo exposures.

Redefining risk research priorities for nanomaterials

Chemical-based risk assessment underpins the current approach to responsible development of nanomaterials (NM). It is now recognised, however, that this process may take decades, leaving decision makers with little support in the near term. Despite this, current and near future research efforts are largely directed at establishing (eco)toxicological and exposure data for NM, and comparatively little research has been undertaken on tools or approaches that may facilitate near-term decisions, some of which we briefly outline in this analysis. We propose a reprioritisation of NM risk research efforts to redress this imbalance, including the development of more adaptive risk governance frameworks, alternative/complementary tools to risk assessment, and health and environment surveillance.