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New Tools and Approaches for Identifying Properties of Engineered Nanomaterials That Indicate Risks

This chapter articulates needs for tool development for exploring how properties of engineered nanomaterials (ENMs) influence critical biologic and environmental interactions (see Figure 2-1). The research needs are directed at the gaps in evidence presented in Chapter 3 and are based on the conceptual framework for assessing risks described in Chapter 2. The primary needs are access to nanomaterials for hypothesis-testing and for assessing exposure to and effects of ENMs; methods for characterizing materials, including methods for detecting, quantifying, and characterizing ENMs in environmental and biologic samples; exposure and toxicity-testing methods and reporting standards; exposure and effects modeling; and informatics for developing comprehensive predictive models of exposure, hazards, and risk. Informatics is defined here as the infrastructure and information science and technology needed to integrate data, information, and knowledge on the environmental, health, and safety (EHS) aspects of nanotechnology. An overall purpose of informatics in this context is to organize data so that they can be mined to determine how nanomaterial properties affect their exposure and hazard potential and to estimate overall risks to the environment and human health. (The research needs presented here are summarized according to categories of tools at the end of this chapter, Table 4-1.)

CHARACTERIZED NANOMATERIALS FOR NANOTECHNOLOGYRELATED ENVIRONMENTAL, HEALTH, AND SAFETY RESEARCH

Identifying ENM properties that influence biologic and environmental interactions will require well-characterized libraries of materials for hypothesis-testing and reference or standard test materials that may be used as benchmarks for comparison among studies, to validate protocols or measurements, or to test



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4 New Tools and Approaches for Identifying Properties of Engineered Nanomaterials That Indicate Risks This chapter articulates needs for tool development for exploring how properties of engineered nanomaterials (ENMs) influence critical biologic and environmental interactions (see Figure 2-1). The research needs are directed at the gaps in evidence presented in Chapter 3 and are based on the conceptual framework for assessing risks described in Chapter 2. The primary needs are access to nanomaterials for hypothesis-testing and for assessing exposure to and effects of ENMs; methods for characterizing materials, including methods for detecting, quantifying, and characterizing ENMs in environmental and biologic samples; exposure and toxicity-testing methods and reporting stan- dards; exposure and effects modeling; and informatics for developing compre- hensive predictive models of exposure, hazards, and risk. Informatics is de- fined here as the infrastructure and information science and technology needed to integrate data, information, and knowledge on the environmental, health, and safety (EHS) aspects of nanotechnology. An overall purpose of informat- ics in this context is to organize data so that they can be mined to determine how nanomaterial properties affect their exposure and hazard potential and to estimate overall risks to the environment and human health. (The research needs presented here are summarized according to categories of tools at the end of this chapter, Table 4-1.) CHARACTERIZED NANOMATERIALS FOR NANOTECHNOLOGY- RELATED ENVIRONMENTAL, HEALTH, AND SAFETY RESEARCH Identifying ENM properties that influence biologic and environmental in- teractions will require well-characterized libraries of materials for hypothesis- testing and reference or standard test materials that may be used as benchmarks for comparison among studies, to validate protocols or measurements, or to test 107

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108 Identifying Properties of Engineered Nanomaterials That Indicate Risks specific hypotheses related to material properties and specific outcomes (for example, mobility in the environment or toxic responses). The lack of wide- spread access to such materials and the lack of agreement as to which materials to consider as standards slows progress toward linking properties of ENMs with their effects, makes comparisons among studies difficult, and limits the utility of data collected for informatics (see section “Barriers to Informatics”). To characterize correlations between nanomaterial properties and the key interactions or end points in humans and the environment, several tools are needed, including adequately characterized materials that have different proper- ties, appropriate assays for examining interactions or end points, and experimen- tal data of sufficient breadth and depth for assessing correlations between nano- material properties and the behavior of the materials. Materials needed for developing those correlations are in four general categories, which are described below. Each type must be characterized sufficiently for test results to be repro- ducible and for correlations between observed effects and material structure and composition to be established and ultimately used to predict effects of new ma- terials on the basis of knowledge of their structure and composition. Research or Commercial Samples These samples may be available from R&D teams or from materials that are near commercialization or in commerce. Many EHS studies have been con- ducted with such materials because of their availability and because people or the environment may be exposed to these materials. The material definition and characterization metrics needed for nanomaterial research and commercial use are typically different from those needed to study material-effect correlations, and the former materials often do not have the definition, purity, or characteriza- tion needed for research purposes. It is important to study the biologic and eco- logic effects of the commercial materials, as such materials (and their impuri- ties) have the greatest potential compared to other types of materials to be released into the environment (Alvarez et al. 2009; Gottschalk and Nowack 2011). However, there are limitations to the use of commercial materials in the development of predictive models. The materials are generally insufficiently characterized; when they are studied in isolation, the polydispersity and lot-to- lot variation in their properties make them unsuitable for developing data that can be used for prediction. For greater utility in prediction, material characteri- zation that is specific to EHS research should be conducted in addition to that carried out by material researchers or producers (Bouwmeester et al. 2011). Reference Materials Reference materials are developed for hypothesis-driven research or for use as benchmarks to compare results among various tests or assays or among laboratories. They are designed and characterized so that material characteristics

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Environmental, Health, and Safety Aspects of Engineered Nanomaterials 109 can be linked to biologic-nanotechnologic or ecologic-nanotechnologic interac- tions or end points. Reference materials are often highly purified to reduce or eliminate the effects of impurities on responses (Oostingh et al. 2011). They may not attain the same level of scrutiny as standards (see discussion below), but they require a smaller investment of time and resources to develop. Sources of these materials include academic and government research laboratories (Na- tional Institute of Standards and Technology), commercial suppliers (for exam- ple, nanoComposix, Nanoprobes, Inc., and Strem Chemicals, Inc.), and interna- tional harmonization efforts (such as the Organisation for Economic Co- operation and Development and the International Alliance for NanoEHS Har- monization). Standard or reference materials can be used to compare test or measurement results among laboratories or to compare the results from different tests or measurements. However, because these materials typically represent specific, narrow structural types that are not easily manipulated to access a broad range of structural features, it is difficult to develop more general design rules from studies of these materials. Libraries Libraries are collections of reference materials in which structural or com- positional variables are systematically varied throughout a series of members of the library. For example, the nanoparticle core material and size might be kept constant while a surface coating varies in its external charge—positively, nega- tively, or not at all. Libraries allow the influence of nanomaterial structure and composition on biologic or ecologic effects to be explored so that quantitative structure-activity relationships can be determined. Libraries also facilitate explo- ration of hypotheses related to material-effect correlations. To serve that pur- pose, libraries should be appropriately defined and characterized as described above for reference materials. Ideally, the materials in libraries have sufficient range and granularity across the structural or compositional measures of interest. Given the importance of detailed characterization for establishing cause-effect correlations, characterization data on each sample lot need to be provided with each sample. Standards Standards are samples that have been thoroughly tested to support labora- tory comparisons or to calibrate and harmonize measurements conducted in dif- ferent laboratories. They typically are prepared and provided for by standard- setting organizations or agencies (for example, the National Institute of Stan- dards and Technology). The benefits of developing standard materials that meet the criteria for definition and characterization are clear; however, the time (years) and expense of developing such standards sometimes restrict their use in EHS studies.

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110 Identifying Properties of Engineered Nanomaterials That Indicate Risks Research Needs for Providing Well-Characterized Nanomaterials for Nanotechnology-Related Environmental, Health, and Safety Research  Development of characterized, reproducible, but not necessarily uni- form, “real-world” materials for testing.  Development of libraries of uniform, well-characterized reference ma- terials of varied size, shape, aspect ratio, surface charge, and surface function- ality.  Development of standard materials for calibrating various assays and measurement tools.  Development of new synthetic methods and postsynthesis separation and purification methods for accessing the different types of materials, reducing polydispersity, and decreasing lot-to-lot variability and for efficiently removing undesirable impurities from nanomaterials without causing their decomposition or agglomeration. TOOLS, STANDARDIZED CHARACTERIZATION METHODS, AND NOMENCLATURE OF ENGINEERED NANOMATERIALS Protocols for Measuring and Reporting a Minimum Set of Material Properties for Pristine Engineered Nanomaterials Used in Nanotechnology-Related Environmental, Health, and Safety Research With regard to characterization of research and commercial samples for EHS testing, there is a need for systematic approaches for adequately and sys- tematically defining the structure, composition (including surface chemistry), and purity of samples so that data reported through the nanotechnology-related EHS research community ultimately can be used to correlate structure and com- position of nanomaterials with their behaviors and effects. Most of the tools needed to accomplish that goal are available for pristine1 starting materials (Has- sellöv et al. 2008). One exception is the lack of tools for characterizing the de- tails of the surface chemistry of nanoparticles, including defects in surface lay- ers, mixtures of bound molecules, and conformation of the adsorbed layer of organic macromolecules of high molecular weight. That type of characterization should form the basis of a working definition (or nomenclature) for the material. For example, the intent would be to move from labeling a material as “gold nanoparticles” to the more specific designation of “mercaptopropionic acid sta- bilized 1.5  0.4 nm gold nanoparticles.” Each material lot needs to be charac- terized in that way (because of variations from batch to batch). Polydisperse and impure samples (for example, materials that have varied chemical composition 1 Pristine refers to the nanomaterial as manufactured, before any alterations in the environment.

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Environmental, Health, and Safety Aspects of Engineered Nanomaterials 111 or that contain endotoxins) are inherently more complicated to characterize be- cause they are mixtures. For commercial or research samples, a material should be characterized to assess purity and size distribution in the state in which it is provided to researchers. Reference materials and libraries may require extensive purification to remove impurities or to decrease polydispersity that complicates data interpretation and characterization. Despite concerted efforts to establish a minimum set of standard properties to define ENMs, there is still lack of agreement in the research community as to what constitutes this minimum set of properties. Yet there has been some pro- gress in demonstrating that there is overlap in their nanomaterial properties (MINChar Initiative 2009; Boverhof and David 2010). Without agreement on the properties and how they can be communicated, with full participation of the nanomaterial-EHS research community, it will not be possible to define the starting materials for nanomaterial-EHS research adequately or to create “classes” of ENMs that have similar surface chemistries and behaviors. There- fore efforts to compare results among studies with informatics or other ap- proaches will be hindered (see section “Barriers to Informatics”). Ultimately, a classification of ENMs will probably be needed for regulatory purposes, but the criteria for what constitutes a “class” have not been determined. Because of the complexity of nanomaterial structures and compositions, a wide array of techniques is typically needed to characterize each new nanomate- rial adequately. Each technique provides a partial definition of the material. For example, for a ligand-stabilized inorganic nanoparticle, transmission electron microscopy (TEM) and small-angle x-ray scattering can be used to define nanoparticle cores (von der Kammer et al. 2012); x-ray photoelectron spectros- copy and Fourier-transform infrared (FTIR) spectroscopy define surface chemis- try; atomic-force microscopy provides information about the overall dimension of the core plus shell; thermal gravimetric analysis provides the ratio of ligand mass to core mass; and solution methods, such as nuclear magnetic resonance spectrometry, can be used to detect small-molecule impurities. Because such exhaustive characterization of each nanomaterial sample is expensive and time- consuming, minimal characterization sets have been proposed (for example, Boverhof and David 2010). One approach is to make the same comprehensive or subset of measurements for every material; however, this approach can lead to unneeded measurements of some materials or insufficient characterization of others. Other approaches seek to determine the minimum material properties that need to be defined to describe materials used in nanotechnology-related EHS studies adequately and should address at least physical dimensions, com- position (including surface chemistry), and purity (MINChar Initiative 2009; Richman and Hutchison 2009). From these approaches key material descriptors should emerge that will facilitate attribution of material effects, data-sharing, and comparison of properties and effects between samples. In addition to assessment of pristine material samples and dry powders, analytic methods should include characterization of ENMs in various reference

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112 Identifying Properties of Engineered Nanomaterials That Indicate Risks suspension media that reflect real-world fluid suspension media and concentra- tions (for example, water, phosphate-buffered solution, lung fluid, and plasma) because ENM properties are determined in part by the dispersing fluid and ENM concentration (Oberdörster et al. 2005). Reactivity measurements are also needed and could include redox activity and reactive-oxygen species generation. Protocols and methods will need to be specific to a nanomaterial’s charac- teristics, including particle type, size, shape, coating type, and media type, be- cause not all methods will be applicable to all types of ENMs. There are some key issues that if left unaddressed lead to problems, including methods for dis- persing nanoparticles in media, protocols for reproducibly preparing samples for analysis and investigation, and approaches to using multiple instruments to cross-check and confirm results from techniques that may provide only partial answers. There is a need for widely accepted protocols for sample preparation and measurements; for example, see the National Cancer Institute Nanotechnol- ogy Characterization Laboratory’s effort to develop and publish assay cascade protocols (including NIST/NCL 2010). The sensitivity of the protocols to the array of variables that may affect their outcome (for example, solution pH and energy input for creating a dispersion) should be determined and reported as part of the protocols. Tools and methods are needed to characterize the surface properties of ENMs better in situ or in vivo. As discussed in Chapter 3, these properties will depend on the media in which they are dispersed so methods should be tailored to the exposure conditions. The surface properties of ENMs will determine their interactions with environmental and biologic media. Many tools are available to characterize size, elemental composition, and structure, but fewer are capable of characterizing only the surfaces of ENMs. Surface curvature, roughness, crystal faces, and defects may all affect the physical, chemical, and toxicologic proper- ties of an ENM; it is not possible to characterize those features adequately with existing microscopic and spectroscopic techniques (for example, electron spec- troscopy for chemical analysis, TEM, and FTIR). Surface functional groups— such as adsorbed or grafted surfactants, polymers, polyelectrolytes, proteins, and natural organic matter (NOM)—can prevent or enhance agglomeration and deposition (Phenrat et al. 2008; Saleh et al. 2008; Jarvie et al. 2009), toxicity (Gao et al. 2005; Nel et al. 2009; Phenrat et al. 2009), and bioavailability (Kreuter 1991). Despite the influence of bound coatings on ENM behavior, methods for readily measuring the distribution and, more important, the confor- mation of the bound species on the surface of ENMs are not widely available. Cryoelectron microscopy combined with computational methods can provide information on conformation of antibodies or other molecules, but these meth- ods are time-consuming, and results can be influenced by sample-preparation methods. Methods for measuring those features in vivo, in vitro, or in situ do not exist and their development is necessary to begin to correlate the in situ proper- ties of ENMs with their behavior and effects.

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Environmental, Health, and Safety Aspects of Engineered Nanomaterials 113 Research Needs for Developing Protocols for Measuring and Reporting a Minimum Set of Material Properties for Pristine Engineered Nanomaterials Used in Nanotechnology-Related Environmental, Health, and Safety Research  Identify agreed-on minimum characterization principles to develop standardized descriptors for ENMs related to the key physical characteristics of the materials that can be used to describe materials for data-reporting and in- formatics and for cross-referencing nomenclatures (that is, nanomaterial vo- cabularies and ontologies).  Determine best practices for characterizing groups of particle types (for example, by chemical composition or chemical-surface reactivity, for spe- cific size ranges, for specific coating types or structures, and in relevant suspen- sion media), including those to characterize reactive surface area, nanometer and subnanometer surface features of ENMs, and adsorbed molecules and mac- romolecules on ENMs.  Develop standard reactivity measures and protocols for ENMs, includ- ing a standardized approach for measuring the sensitivity of methods to impor- tant variables (for example, pH, ionic strength, organic matter, and biomacro- molecules). Detection and Characterization of Nanomaterials in Complex Biologic and Environmental Samples Chemical and physical information on ENMs in environmental and bio- logic matrices is needed. Many existing analytic techniques from material sci- ence and other disciplines are applicable to ENMs, but their use in measuring and characterizing low concentrations and heterogeneous matrices will require additional development or in some cases, development of completely new ap- proaches. A recent review by von der Kammer et al. (2012) summarizes many of the analytic tools and research needs for detecting and characterizing ENMs in environmental and biologic matrices. There are few analytic tools that can be used to quantify and characterize ENMs in situ (for example, in air, soil, or sediment samples), in vitro (for exam- ple, in cells or tissues), or in vivo at the low concentrations expected for most nanomaterials (in the low parts-per-billion to low parts-per-trillion range) (Has- sellöv et al. 2008; Gottschalk et al. 2009; Tiede et al. 2009; von der Kammer et al. 2012). Some examples include radiolabeled materials (Hong et al. 2009; Gib- son et al. 2011; Peterson et al. 2008); fluorescence (Schierz et al. 2010); mass spectrometry (MS) and single particle MS techniques (von der Kammer et al. 2012); spatially resolved X-ray analyses (von der Kammer 2012); and differen- tial mobility analysis (Morawska et al. 2009), a well developed technique used to quantify the number and size distribution of nanoparticles in air.

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114 Identifying Properties of Engineered Nanomaterials That Indicate Risks Because of the lack of analytic tools, relationships between properties of materials measured ex situ (for example, nanoparticle size by TEM) and their in situ or in vivo behaviors need to be inferred, and this limits our understanding of how ENMs may be affected by such processes as in situ and in vivo transforma- tions, biodistribution, and distribution in environmental samples. Tools for quantifying and characterizing ENMs in the environment or in organisms typically have either a broad or a narrow spectrum. Broad-spectrum tools are applicable to a variety of sample types but require relatively high con- centrations of materials (for example, non-spatially resolved synchrotron x-ray spectroscopy methods) and most often require removal from media to conditions that are not representative of in vivo or in situ environments (for example, mi- croscopy). Narrow-spectrum tools are highly specific to a material (for example, near-infrared detection of single-walled carbon nanotubes (Leeuw et al. 2007) that can be detected at low material concentrations and potentially under in situ or in vivo conditions, but modification of the material may limit sensitivity. These narrow-spectrum tools must be developed at great expense for each type of nanomaterial. The variety of ENMs that need to be studied makes use of nar- row-spectrum tools expensive and perhaps intractable. Detection in vivo or in situ can be difficult because of the low concentrations of materials released into an organism or the environment. Even if the material has not been transformed, detection is difficult; if it has been transformed, detection is even more difficult. Strategies and tools for detecting and tracking materials are needed. These strategies should include combinations of techniques to detect and characterize ENMs in complex matrices, and to differentiate between naturally occurring ENMs and naturally occurring nanomaterials (von der Kammer 2012). Fluorescence is a common strategy that is used to localize materials, but more general techniques are needed for materials that are not fluorescent or for situations in which incorporation of a fluorescent tag interferes with the proc- esses being investigated by modifying the material’s surface properties. Another approach that will benefit nanotechnology-related EHS research is to label (for example, radiolabels) and track surface functional groups (coatings) that are being used on ENMs; however, care must be taken to ensure that the functional groups are not readily removed from the ENM by chemical or biologic reac- tions. Labeling approaches will need to be coupled with sensitive high- resolution methods to characterize the interactions between ENMs and the me- dium at the site of distribution and localization. Tracking ENMs in vivo or in situ could advance research in the field considerably, but simply tracking the presence of ENMs in these systems is not sufficient to correlate their properties with their behaviors. Methods also are needed to characterize the surface proper- ties of ENMs in situ. Quantifying the number and distribution of particle sizes in air samples us- ing differential mobility analyzers (DMAs) is a well established technique (Ehara and Sakurai 2010). A DMA can quantify number concentration and size distributions, but used in isolation, it cannot determine chemical composition or surface area concentration. Further, it cannot distinguish between airborne

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Environmental, Health, and Safety Aspects of Engineered Nanomaterials 115 ENMs and naturally occurring or incidental nanomaterials. However, a DMA coupled with a single aerosol mass spectrometer can provide chemical speci- ation of airborne particles, and potentially can distinguish between ENMs and naturally occurring nanoparticles if the naturally occurring nanoparticles have a consistent chemical composition that is distinct from that of naturally occurring nanomaterials (Smith et al. 2010; Zhao et al. 2010). Because of the likelihood of human exposure to nanomaterials in manufacturing environments, further de- velopment of instrumentation that measures chemical composition, aggregation state, and distinguishes ENMs from naturally occurring nanomaterials in air samples is needed (for example, Zhao et al. 2010; Bzdek et al. 2011). As discussed in Chapter 3, ENMs will be transformed in the environment (for example, by aggregation, oxidation, sulfidation, or adsorption of macro- molecules). These transformations will affect distribution of ENMs in the envi- ronment or an organism. These modifications may also make their detection difficult (von der Kammer et al. 2012). Methods and tools are needed for assess- ing the transformation of ENMs in situ (for example, in soils, sediments, or treatment-plant effluent), in vitro (for example, in cells or tissue), and in vivo (for example, in rats). Research Needs for Detection and Characterization of Nanomaterials in Complex Biologic and Environmental Samples  Develop model ENMs that can be tracked without introduction of ex- perimental artifacts in exposure and toxicity studies.  Develop analytic tools and processes that can detect ENMs at low (relevant) concentrations in situ or in vivo, followed by tools to track and char- acterize ENM properties (for example, reactivity, reactive surface area, nano- meter and subnanometer surface features, aggregation-agglomeration, and ad- sorption of organic macromolecules) in situ or in vivo.  Develop tools and processes to assess the rate and degree of transfor- mation of ENMs in vivo or in situ, especially alteration of surface properties of ENMs due to adsorption of proteins and lipids (corona formation) and NOM. STANDARDIZED EXPERIMENTAL PROTOCOLS FOR NANOTECHNOLOGY-RELATED ENVIRONMENTAL, HEALTH, AND SAFETY RESEARCH Development of New Protocols or Modification of Existing Protocols for Toxicity Testing and Determination of Population and Ecosystem Effects A focused, coordinated research effort is needed to identify and validate existing or newly developed toxicity-testing protocols and best practices, such as dosimetrics (Teeguarden et al. 2007), for an agreed-on set of toxicity end points for ENMs (NRC 2007). The protocols would include rigorous physicochemical characterization of particle types, use of relevant cell types or cell systems (for

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116 Identifying Properties of Engineered Nanomaterials That Indicate Risks example, air-liquid interface) to simulate relevant in vivo exposures, relevant dose-response protocols, relevant time-course protocols, and assessments of biomarkers, such as inflammatory end points, that have relevance to in vivo pathway models (for example, sustained inflammation). For ecologic-health research, a set of sensitive species will need to be identified in the risk- characterization phase. Development of ecologic and human-health test methods should also include coordinated interlaboratory testing validation for existing toxicity tests and end points and appropriate doses and dosing protocols in the case of newly developed tests and end points. The appropriate end points for toxicity tests need to be determined. They should be determined from in vivo model pathways that are identified after inha- lation, ingestion, or dermal exposure to ENMs. The end points for measurement after pulmonary exposure could include the following: reactive oxidant species; inflammatory biomarkers; cytotoxicity end points; cell proliferation, fibrosis, and hyperplastic responses; and histopathology, particularly for time points after exposure. For environmental health, validation of standard measures of alterna- tive (non-acute) end points of exposure to ENMs (for example, growth, repro- duction, behavior, or stress) should be determined. Additional data are needed from assay systems that have sublethal outcomes and on more types of nanoma- terials to develop testing methods that are simple but have predictive value for hazard identification (Ankley et al. 2010). To develop simple non-in vivo assays that will eventually allow high- throughput testing and provide results that predict in vivo effects (hazard identi- fication), correlations need to be explored between the end points measured in vitro and the expected effects in vivo. That will require standardized and vali- dated in vitro methods (for example, standardized cell types and exposure proto- cols) that represent specific, realistic exposures (including the materials used and the exposure routes), and doses and validation against results of in vivo studies. This is a critical step in realizing the benefits of high-throughput screen- ing strategies proposed for ENMs. Development of appropriate in vitro assays that can predict in vivo re- sponses requires a detailed understanding of biodistribution of ENMs and the mechanistic pathways by which ENMs exert a toxic effect on a specific organ. Research is needed to elucidate those toxicity mechanisms for representative or- ganisms, considering appropriate dosimetry (see above) and well-characterized ENMs, so that ENM properties can be correlated with mechanisms of injury. Genomic tools may generate important hypotheses regarding toxicity mechanisms and may be useful for grouping nanomaterials by expected re- sponse on the basis of their properties, as has been observed in several studies with well-characterized chemicals (Bartosiewicz et al. 2001a,b; Hamadeh et al. 2002; Klaper and Thomas 2004; Dondero et al. 2011). However, in vivo data are needed to validate the genomic data with organism responses. Although genom- ics tools are available, research is needed to determine how much and what type of gene or protein expression changes will result in long-term effects of ENM

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Environmental, Health, and Safety Aspects of Engineered Nanomaterials 117 exposures. Gene and protein expression measured in vitro with these tools must be correlated with measured effects in vivo. The protocols for assessing ecotoxicity include those for assessing human toxicity but should also include protocols for predicting sensitive species and effects on communities and ecosystems if they are to be useful for risk assess- ment (Ankley et al. 2010). Those include effects on interactions among species, species community assemblages, biodiversity, and ecosystem function. There is no suite of standard tests for assessing community and ecosystem effects of chronic exposure to ENMs. That limitation is not peculiar to ENMs and presents a serious challenge to the modeling of ecologic effects. Research Needs for Development of New Protocols or Modification of Existing Protocols for Toxicity Testing  Develop new standard toxicity-testing protocols or modify existing pro- tocols for ENMs to include relevant cell types and organisms, appropriate do- simetrics, and appropriate toxicity end points (for example, chronic-toxicity end points) and validate those protocols.  Identify and validate toxicity-pathway models and mechanisms to cor- relate in vitro end points with in vivo responses.  Improve the interpretability of genomic tools by determining how gene expression and protein expression are related to ENM toxicity and mechanisms. Research Needs for Development of New Protocols or Modification of Existing Protocols for Determination of Population and Ecosystem Effects  Develop and validate a suite of standard tests that can indicate the po- tential for population or ecosystem effects of chronic ENM exposure on specific organisms.  Develop methods for understanding ecosystem effects (that is, effects on systems of systems) that result from indirect effects of nanomaterials, such as carbon and nitrogen cycling. Development of New Protocols or Modification of Existing Protocols for Exposure Assessment Exposure assessment and modeling (discussed later) will require informa- tion about sources, transport, transformations, persistence, and bioavailability of ENMs released into the environment (Johnston et al. 2010). Standard testing protocols need to be conducted to determine the properties that influence trans- port, transformation, persistence, and bioavailability. The protocols need to be assessed and validated with a variety of ENM types and classes and under an array of environmental conditions (for example, freshwater, seawater, terrestrial, and groundwater environments). Although it is desirable for the protocols to be

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132 Identifying Properties of Engineered Nanomaterials That Indicate Risks Informatics Needs for Model Development and Validation  An informatics portal similar to the wwPDB is needed to archive, or- ganize, curate, validate, and share structural models of nanomaterials and their surface coatings, both pristine and transformed, for collaborative use interna- tionally.  An extension of the database is needed to archive, organize, curate, validate, and share the predictive and probabilistic models and submodels to accelerate their development and use and to augment and complement experi- mental techniques.  New mechanisms are needed to aid in implementing the required col- laborative databases for structural models of ENMs and for development of predictive and probabilistic models. An implementation scenario for development of predictive and risk mod- els is discussed in Appendix B. Data-Sharing The preceding discussions highlighted the need for sharing data from spe- cific protocols; for example, for characterization of pristine and transformed ENMs in complex samples, for toxicity tests and ecosystem and population ef- fects, and for methods for exposure assessment and for characterizing trans- formed and weathered ENMs. The variety of protocols reinforces the need for data-sharing among diverse disciplines that use different techniques and prac- tices. It is important to provide data-sharing techniques that provide the scien- tific data requested and that describe both the data and the ENMs with sufficient detail to track which manufactured lot of a material the ENM samples were taken from to account for lot-to-lot variability. The informatics system should use specific nomenclatures and terms that have agreed-on definitions. The same requirements are appropriate for supporting model development and validation. Experience with today’s search engines, however, illustrates the lack of specificity that is achieved when only search terms are used—for example, the millions of “hits” that might have to be sifted through to extract the desired in- formation. Adding semantic content about the meanings and relationships of the search terms is the aim of the Semantic Web, sponsored by the World Wide Web Consortium of informatics systems providers and users (W3C Semantic Web 2011). Providing machine-usable logical relationships about and among search terms allows a search engine to reduce drastically the number of false “hits.” Ontologies improve the use of search terms by supplying definitions for each term and explicit logical relationships among the terms specified so that they can be interpreted and used by a computer. The effort to generate and main- tain an ontology is usually supported by a community that agrees on the concept definitions and logical relationships. Ontologies can be modified or extended to

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Environmental, Health, and Safety Aspects of Engineered Nanomaterials 133 accommodate different communities, and methods exist to map relationships between terms in different ontologies, allowing any (mapped) ontology to be used to mine data in a dataset organized by terms of a different ontology. Cur- rently, organizations involved in nanotechnology, both nationally and interna- tionally, support Web sites and portals to provide information and analyzed data. Very few portals offer raw data that may be stored locally in the laboratory gen- erating the data. Most of the portals use their own systems and offer their infor- mation through current search engines. Attempting to harmonize the search terms, data formats, curation levels, and security for these portals would entail a large enterprise that would be prone to failure because the databases may be linked to back-office applications that would be difficult to modify. Moving all the data to a central site has been tried and is usually very difficult because of issues involving data rights and security and the need to agree on common for- mats, procedures, and rules for governance. Informatics Needs for Data-Sharing  Use existing pilots to demonstrate the capability to federate the differ- ent sites through the use of semantic web technologies, including ontology de- velopment to enable data curation by experts in the data, and access control by the owners of the data. Using that method would allow international entities to come together on an equal footing to craft a short-term solution for collaborative protocol and model development. A modest effort would be needed to demonstrate current capability as a pilot project for use in an interoperable system to establish user requirements relevant to the entire community, and initial efforts are being un- dertaken within the nanotechnology informatics community (InterNano 2011).  Use and modify existing ontologies and semantic web applications, perhaps in collaboration with search-engine providers, to develop an automatic ontology “crawler” to update mappings among the ontologies used or adopted by the collaborating partners. Barriers to Informatics Successful implementation of the informatics strategy described in this chapter—including developing ontologies, data-sharing, and community model development—requires appropriate datasets as inputs. The emerging field of nanoinformatics, in contrast with the more fully developed field of bioinformat- ics, faces some specific challenges. Biopolymers are often discrete structures or sequences, whereas nanomaterials typically exhibit a dispersion of sizes, com- positions, and surface coatings. Such dispersions are difficult to define and re- duce to the precise code needed for informatics. Second, given the wide array of nanomaterial types, structural information for different classes of materials will

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134 Identifying Properties of Engineered Nanomaterials That Indicate Risks be based on different sets of analytic measurements that make direct compari- sons difficult. Because no one measurement can describe a nanomaterial com- pletely, an informatics approach will need to synthesize the information from multiple techniques to describe the material. Given the number of gaps in the data on the nanomaterials described in the literature, most materials are now incompletely described and will probably remain so unless incentives are devel- oped to characterize them. Finally, whereas biopolymers can be readily de- scribed by reference to their primary sequence and a series of letter codes or by a defined three-dimensional structure determined with x-ray crystallography, the different types of measurements (for example, images, histograms, optical spec- tra, and elemental composition) that are used to define nanomaterials are diffi- cult to reduce to code. Those complexities will result in barriers to the development of nanoin- formatics unless they are addressed through close interaction with the scientists who are producing and characterizing the new nanomaterials. One barrier is the relatively onerous process of data entry for nanomaterials. If the materials can- not be described as single structures or sequences, as is possible for biopoly- mers, describing their dispersity makes the process more time-consuming. In addition, uploading raw data that are in a wide array of nonstandard formats presents a barrier to those who might contribute to the database of materials. But it is important to have access to the raw data because producing a numerical descriptor from them often involves considerable interpretation. Who will generate the data for informatics, and what are the incentives for them to participate? From one perspective, the information used to populate the databases for nanoinformatics efforts will be developed by specialists using standard protocols and working with defined reference materials. That approach is relatively slow—working with one painstakingly produced and characterized material at a time. More rapid progress could be made if information on all ma- terials produced and characterized could be captured in the databases regardless of who produces the materials. The presence of such data would encourage bi- ologists and toxicologists to study the materials, but what is the incentive for the nanomaterials chemist to contribute this information? Recommendations for addressing barriers to informatics for nanomaterials: Provide incentives to nanomaterials innovators to characterize and report suffi- cient analytic data to define materials for comparison with other materials, in- cluding error, uncertainty and sensitivity data. For example,  Journals could require the data for publication.  Agencies could make collecting and sharing the data conditions for funding, perhaps through National Science Foundation data-management plans (see discussion in Chapter 6) or more specifically in nanotoxicology grants.

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Environmental, Health, and Safety Aspects of Engineered Nanomaterials 135 Recognize the challenges in comparing data. For example, size and size distri- butions from transmission electron microscopy are not all the same. They are not the raw data; the image is. Although it is necessary to obtain a representa- tive sample from a set of images, new standard methods for nanomaterials (for example, NIST/NCL 2010; ASTM 2011) reference best practices to control im- age selection bias (for example, Allen 1996; Jillavenkatesa et al. 2001), and evaluations of bias in instrumentation software for defining particle boundaries are being considered. It is difficult to develop structure-activity relationships when the structures are not concretely defined. Reduce barriers to nanomaterial innovators contributing to databases by engag- ing with them, understanding the complexities, and finding solutions that reduce the barriers. Provide incentives for companies to provide information on nanomaterials that they have pioneered. This will require finding creative ways to protect intellec- tual property. Work toward a model, such as the PDB of nanomaterials concept, but engage nanomaterial-synthesis experts at the beginning to identify and find solutions to obstacles. TABLE 4-1 Summary of Research Needs Identified in Chapter As Mapped to the Tools MATERIALS Well-characterized materials are needed, including: reference materials of varied size, shape, aspect ratio, surface charge, and surface functionality for testing; “real-world” materials for testing; “weathered” nanomaterials that are representative of those expected in vivo or in situ; materials that can be tracked (for example, for biodistribution or environmental partitioning studies) without introducing experimental artifacts in exposure and toxicity studies; and standard reference materials to use in calibrating assays and measurement tools. METHODS Develop and validate new or modify existing standard toxicity-testing protocols for ENMs, including relevant cell types and organisms, appropriate dosimetry and toxicity end points (for example, chronic effects), and gene and protein expression to identify and validate toxicity mechanisms, such as biodistribution of ENMs and toxicity-pathway models. Develop methods to extrapolate and predict long-term low-dose effects from short-term high-dose effects, and validate their accuracy through blinded test methods. Develop screening methods that can indicate the potential for bioavailability and potential for effects due to chronic ENM exposure or for indirect effects, that is, not direct toxicity from ENM exposures (for example, the effects of ENMs on carbon and nitrogen cycling). Develop and validate standard methods for measuring and reporting attachment affinities of ENMs to biologic and environmental surfaces to facilitate assigning values to parameters in exposure models. Develop methods to determine the reactivity and stability of ENMs in biologic and environmental samples, including standard measures for assessing and reporting reactivity (for example, generation of reactive oxygen species). (Continued)

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136 Identifying Properties of Engineered Nanomaterials That Indicate Risks TABLE 4-1 Continued Develop a standardized approach for measuring a method’s sensitivity to changes in important variables (for example, pH, ionic strength, organic matter, and biomacromolecules) and standard ways to report sensitivity. INSTRUMENTATION Develop new instrumentation and methods for existing instrumentation to isolate subpopulations of ENMs from polydisperse samples. Develop tools that can detect ENMs, especially at low (relevant) concentrations in situ or in vivo, followed by methods to track and characterize ENM properties (for example, reactivity, reactive surface area, nanometer and subnanometer surface features, aggregation, and adsorption of organic macromolecules). In the future, develop methods that operate unattended and monitor ENMs in the environment in different media, especially air and water. Develop tools to assess the rate and degree of transformation of ENMs in vivo or in situ, especially specific alteration of surface properties of ENMs due to adsorption of proteins and lipids (corona formation) and NOM. MODELS Develop models to estimate sources of ENMs released into the environment along a material’s life cycle and value chain. Modify traditional exposure models to include processes that affect ENM distribution in the environment and influence human exposure (for example, attachment to environmental and biologic surfaces, degradation rate, and dilution) and determine how to assign values to parameters in those models. Determine toxicity pathways for outcomes (for example, effects on survival and reproduction) that predict population effects of ENM exposure and formulate ecotoxicity models, using data on sublethal toxicity end points (including effects on growth, behavior, reproduction, development, and metabolism). Update inhalation models to include dependence on ENM shape, surface properties, and agglomeration on deposition efficiency, and the underlying mechanisms of deposition of inhaled ENMs in the respiratory tract. Identify pathways of elimination of ENMs after their biodistribution and accumulation in primary and secondary organs. Determine principle mechanisms of elimination as inputs into predictive bioinformatics modeling. Identify key uncertainties and sensitivities surrounding exposure assessment and effects models, estimate the ranges of the uncertainties and sensitivities, and incorporate the uncertainties into the models. INFORMATICS Identify minimum characterization principles to develop standardized descriptors (that is, metadata) for ENMs that are related to their key physical material characteristics for reporting and cross-referencing data on ENM properties and effects. Establish uniform metadata to describe ENM manufacturing and distribution processes and to correlate lot- to-lot variability of ENM properties with changes in synthesis and handling. Develop ontologies and data formats to allow relevant data on gene and protein expression to be correlated with ENM-toxicity mechanisms. Develop strategies for federating nanotechnology databases administered by different agencies, business entities, universities, and nongovernment organizations to allow seamless data exposure and data-sharing while protecting intellectual-property rights. Develop new mechanisms for digital archiving and annotating and updating of methods, data, tools, and models to spur rapid and efficient formation of new targeted national and international scientific collaborations. Develop and augment ontologies to support nanotechnology and nanoscience and in particular to develop an ontology “crawler” to aid in mapping relationships among ontologies.

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Environmental, Health, and Safety Aspects of Engineered Nanomaterials 137 REFERENCES Aitken, R.J., P. Borm, K. Donaldson, G. Ichihara, S. Loft, F. Marano, A.D. Maynard, G. Oberdörster, H. Stamm, V. Stone, L. Tran, and H. Wallin. 2009. Nanoparticles - one word: A multiplicity of different hazards. Nanotoxicology 3(4):263-264. Allen, T. 1996. Particle Size Measurement, Vol. 1. Powder Sampling and Particle Size Measurement, 5th Ed. London: Chapman & Hall. Alvarez, P.J., V. Colvin, J. Lead, and V. Stone. 2009. Research priorities to advance eco- responsible nanotechnology. ACS Nano. 3(7):1616-1619. Ankley, G.T., R.S. Bennett, R.J. Erickson, D.J. Hoff, M.W. Hornung, R.D. Johnson, D.R. Mount, J.W. Nichols, C.L. Russom, P.K. Schmieder, J.A. Serrrano, J.E. Tietge, and D.L. Villeneuve. 2010. Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environ. Toxicol. Chem. 29(3):730-741. ASTM. 2010. ASTM WK28974 - New Specification for a Standard File Format for the Submission and Exchange of Data on Nanomaterials and Characterizations. ASTM International, West Conshohocken, PA [online]. Available: http://www.astm.org/ DATABASE.CART/WORKITEMS/WK28974.htm [accessed June 8, 2011]. ASTM. 2011. ASTM WK29480 - New Guide for Size Measurement of Nanoparticles Using Atomic Force Microscopy (AFM). ASTM International, West Consho- hocken, PA [online]. Available: http://www.astm.org/DATABASE.CART/WORK ITEMS/WK29480.htm [accessed Nov. 23, 2011]. Bartosiewicz, M.J., D. Jenkins, S. Penn, J. Emery, and A. Buckpitt. 2001a. Unique gene expression patterns in liver and kidney associated with exposure to chemical toxicants. J. Pharmacol. Exp. Ther. 297(3):895-905. Bartosiewicz, M., S. Penn, and A. Buckpitt. 2001b. Applications of gene arrays in environmental toxicology: Fingerprints of gene regulation associated with cadmium chloride, benzo(a)pyrene, and trichloroethylene. Environ. Health Perspect. 109(1):71-74. Berman, H., K. Henrick, H. Nakamura, and J.L. Markley. 2007. The worldwide Protein Data Bank (wwPDB): Ensuring a single, uniform archive of PDB data. Nucleic Acids Res. 35 (suppl. 1):D301-D303. Bernhardt, E.S., B.P. Colman, M.F. Hochella, Jr., B.J. Cardinale, R.M. Nisbet, C.J. Richardson, and L. Yin. 2010. An ecological perspective on nanomaterial impacts in the environment. J. Environ. Qual. 39(6):1954-1965. Bottero, J.Y., and M.R. Wiesner. 2010. Considerations in evaluating the physicochemical properties and transformations of inorganic nanoparticles in water. Nanomedicine 5(6):1009-1014. Bouwmeester, H., I. Lynch, H.J. Marvin, K.A. Dawson, M. Berges, D. Braguer, H.J. Byrne, A. Casey, G. Chambers, M.J. Clift, G. Elia, T.F. Fernandes, L.B. Fjellsbø, P. Hatto, L. Juillerat, C. Klein, W.G. Kreyling, C. Nickel, M. Riediker, and V. Stone. 2011. Minimal analytical characterization of engineered nanomaterials needed for hazard assessment in biological matrices. Nanotoxicology 5(1):1-11. Boverhof, D.R., and R.M. David. 2010. Nanomaterial characterization: Considerations and needs for hazard assessment and safety evaluation. Anal. Bioanal. Chem. 396(3):953-961. Bzdek, B.R., C.A. Zordan, G.W. Luther, and M.V. Johnston. 2011. Nanoparticle chemi- cal composition during new particle formation. Aerosol Sci. Tecnol. 45(8):1041- 1048.

OCR for page 107
138 Identifying Properties of Engineered Nanomaterials That Indicate Risks Card, J.W., and B.A. Magnuson. 2009. Proposed minimum characterization parameters for studies on food and food-related nanomaterials. J. Food Sci. 74(8):vi-vii. Casals, E., T. Pfaller, A. Duschl, G.J. Oostingh, and V. Puntes. 2010. Time evolution of the nanoparticle protein corona. ACS Nano. 4(7):3623-3632. Cheng, Y.W., L.Y. Yin, S. Lin, M. Wiesner, E. Bernhardt, and J. Liu. 2011. Toxicity reduction of polymer-stabilized silver nanoparticles by sunlight. J. Phys. Chem. C 115(11):4425-4432. Creative Industries KTN (Knowledge Transfer Network). 2011. Beacon 10 IP & Open Source, Final report. Creative Industries Knowledge Transfer Network, University of Arts, London [online]. Available: https://connect.innovateuk.org/c/document_ library/get_file?p_l_id=1342553&folderId=1812583&name=DLFE-33289.pdf [accessed Nov. 10, 2011]. CTAC (Clinical Trials Advisory Committee). 2007. First Clinical Trials Advisory Com- mittee Meeting, January 10, 2007, Bethesda, MD. National Institutes of Health, National Cancer Institute [online]. Available: http://deainfo.nci.nih.gov/advisory/ ctac/0107/10jan07mins.pdf [accessed May 25, 2011]. Derry, J., L.M. Mangravite, C. Suver, M. Furia, D. Henderson, X. Schildwachter, J. Izant, S.K. Sieberts, M.R. Kellen, and S.H. Friend. 2011. Developing predictive molecular maps of human disease through community-based modeling. Nat. Prec. 713 (April 4, 2011): doi:10.1038/npre.2011.5883.1. Dondero, F., M. Banni, A. Negri, L. Boatti, A. Dagnino, and A. Viarengo. 2011. Interactions of a pesticide/heavy metal mixture in marine bivalves: A transcriptomic assessment. BMC Genomics 12(1):195. Ehara, K., and H. Sakurai. 2010. Metrology of airborne and liquid-borne nanoparticles: Current status and future needs. Metrologia 47(2):S83-S90. EPA (U.S. Environmental Protection Agency). 2010a. Exposure and Fate Assessment Screening Tool Version 2.0 (E-FAST V2.0) [online]. Available: http://www.epa. gov/opptintr/exposure/pubs/efast.htm [accessed May 8, 2011]. EPA (U.S. Environmental Protection Agency). 2010b. Interim Technical Guidance for Assessing Screening Level Environmental Fate and Transport of, and General Population, Consumer, and Environmental Exposure to Nanomaterials. U.S. Envi- ronmental Protection Agency. June 17, 2010 [online]. Available: http://www.epa. gov/opptintr/exposure/pubs/nanomaterial.pdf [accessed Apr. 23, 2011]. EPA (U.S. Environmental Protection Agency). 2010c. Total Risk Integrated Methodol- ogy (TRIM) – TRIM.FaTE [online]. Available: http://www.epa.gov/ttn/fera/trim_ fate.html [accessed May 8, 2011]. Gao, X.H., L.L. Yang, J.A. Petros, F.F. Marshall, J.W. Simons, and S. Nie. 2005. In vivo molecular and cellular imaging with quantum dots. Curr. Opin. Biotechnol. 16(1):63- 72. Gentil-Beccot, A., S. Mele, and T.C. Brooks. 2009. Citing and reading behaviours in high- energy physics. How a community stopped worrying about journals and learned to love repositories. SLAC Scientific Documents No. 13693 [online]. Available: http:// slac.stanford.edu/pubs/slacpubs/13500/slac-pub-13693.pdf [access Nov. 23, 2011]. Gibson, N., U. Holzwarth, K. Abbas, F. Simonelli, J. Kozempel, I. Cydzik, G. Cotogno, A. Bulgheroni, D. Gilliland, J. Ponti, F. Franchini, P. Marmorato, H. Stamm, W. Kreyling, A. Wenk, M. Semmler-Behnke, S. Buono, L. Maciocco, and N. Burgio. 2011. Radiolabelling of engineered nanoparticles for in vitro and in vivo tracing applications using cyclotron accelerators. Arch. Toxicol. 85(7):751-773. Goble, C.A., J. Bhagat, S. Aleksejevs, D. Cruickshank, D. Michaelides, D. Newman, M. Borkum, S. Bechhofer, M. Roos, P. Li, and D. De Roure. 2010. myExperiment: A

OCR for page 107
Environmental, Health, and Safety Aspects of Engineered Nanomaterials 139 repository and social network for the sharing of bioinformatics workflows. Nucl. Acids Res. 38:W677-W682. Gordon, N., and U. Sagman. 2003. Nanomedicine Taxonomy Briefing Paper. Canadian NanoBusiness Alliance, Toronto, Ontario. February 2003 [online]. Available: http://www.nanowerk.com/nanotechnology/reports/reportpdf/report31.pdf [accessed June 1, 2011]. Gottschalk, F., and B. Nowack. 2011. The release of engineered nanomaterials to the environment. J. Environ. Monit. 13(5):1145-1155. Gottschalk, F., T. Sonderer, R.W. Scholz, and B. Nowack. 2009. Modeled environmental concentrations of engineered nanomaterials (TiO2, ZnO, Ag, CNT, fullerenes) for different regions. Environ. Sci. Technol. 43(24):9216-9222. Hackley, V.A., M. Fritts, J.F. Kelly, A.K. Patri, and A.F. Rawle. 2009. Enabling stan- dards for nanomaterial characterization. Pp. 24-29 in INFOSM Informative Bulle- tin of the Interamerican Metrology System. August 13, 2009. Hamadeh, H.K., P.R. Bushel, S. Jayadev, O. DiSorbo, L. Bennett, L. Li, R. Tennant, R. Stoll, J.C. Barrett, R.S. Paules, K. Blanchard, and C.A. Afshari. 2002. Prediction of compound signature using high density gene expression profiling. Toxicol. Sci. 67(2):232-240. Hassellöv, M., J.W. Readman, J.F. Ranville, and K. Tiede. 2008. Nanoparticle analysis and characterization methodologies in environmental risk assessment of engi- neered nanoparticles. Ecotoxicology 17(5):344-361. Hong, H., Y. Zhang, J. Sun, and W. Cai. 2009. Molecular imaging and therapy of cancer with radiolabeled nanoparticles. Nano Today 4(5):399-413. IANH (International Alliance for NanoEHS Harmonization). 2011. International Alliance for NanoEHS Harmonization [online]. Available: http://www.nanoehsalliance.org/ sections/Home [accessed May 13, 2011]. InterNano. 2011. InterNano. Resources for Manufacturing. Nanoinformatics 2020 Road- map [online]. Available: http://eprints.internano.org/607/ [accessed Nov. 9, 2011]. Jarvie, H.P., H. Al-Obaidi, S.M. King, M.J. Bowes, M.J. Lawrence, A.F. Drake, M.A. Green, and P.J. Dobson. 2009. Fate of silica nanoparticles in simulated primary wastewater treatment. Environ. Sci. Technol. 43(22):8622-8628. Jeong, C.H., P.K. Hopke, D. Chalupa, and M. Utell. 2004. Characteristics of nucleation and growth events of ultrafine particles measured in Rochester, NY. Environ. Sci. Technol. 38(7):1933-1940. Jillavenkatesa, A., S.J. Dapkunas, and L.S.H. Lum. 2001. Particle Size Characterization. Special Publication 960-1. U.S. Department of Commerce, National Institute of Standards and Technology, Gaithersburg, MD [online]. Available: http://www.nist. gov/public_affairs/practiceguides/SP960-1.pdf [accessed Nov. 10, 2011]. Johnston, J.M., M. Lowry, S. Beaulieu, and E. Bowles. 2010. State-of-the-Science Report on Predictive Models and Modeling Approaches for Characterizing and Evaluating Exposure to Nanomaterials. EPA/600/R-10/129. U.S. Environmental Protection Agency, Washington, DC [online]. Available: http://www.epa.gov/athens/publica tions/reports/Johnston_EPA600R10129_State_of_Science_Predictive_Models.pdf [accessed Nov. 23, 2011]. Klaper, R., and M.A. Thomas. 2004. At the crossroads of genomics and ecology: The promise of a canary on a chip. BioScience 54(5):403-412. Klemm, J., N. Baker, D. Thomas, S. Harper, M.D. Hoover, M. Fritts, R. Cachau, S. Gaheen, S. Pan, G. Stafford, and D. Paik. 2010. nano-TAB: A Standard File Format for Data Submission and Exchange on Nanomaterials and Characterizations. Nanoinformatics

OCR for page 107
140 Identifying Properties of Engineered Nanomaterials That Indicate Risks Conference November 3-5, 2010, Arlington, VA [online]. Available: http://nanote chinformatics.org/posters [accessed Apr. 22, 2011]. Kozaki, K., Y. Kitamura, and R. Mizoguchi. 2011. Systematization of Nanotechnology Knowledge through Ontology Engineering: A Trial Development of Idea Creation Support System for Materials Design based on Functional Ontology. Poster Notes of the Second International Semantic Web Conference (ISWC 2003), October 20- 23, 2003, Sanibel Island, FL [online]. Available: http://www.ei.sanken.osaka- u.ac.jp/pub/kozaki/iswc2003pos_kozaki.pdf [accessed Apr. 22, 2011]. Kreuter, J. 1991. Nanoparticle-based drug delivery systems. J. Control. Rel. 16(1-2):169- 176. Leeuw, T.K., R.M. Reith, R.A. Simonette, M.F. Harden, P. Cherukuri, D.A. Tsyboulski, K.M. Beckingham, and R.B. Weisman. 2007. Single-walled carbon nanotubes in the intact organism: Near-IR imaging and biocompatibility studies in Drosophila. Nano Lett. 7(9):2650-2654. Lynch, I., T. Cedervall, M. Lundqvist, C. Cabaleiro-Lago, S. Linse, and K.A. Dawson. 2007. The nanoparticle - protein complex as a biological entity; A complex fluids and surface science challenge for the 21st century. Adv. Colloid Interface Sci. 134- 35:167-174. Metz, K.M., A.N. Mangham, M.J. Bierman, S. Jin, R.J. Hamers, and J.A. Pedersen. 2009. Engineered nanomaterial transformation under oxidative environmental condi- tions: Development of an in vitro biomimetic assay. Environ. Sci. Technol. 43(5):1598-1604. MINChar Initiative. 2009. Characterization Matters: Supporting Appropriate Material Characterization in Nanotoxicology Studies [online]. Available: http://characteriza tionmatters.org/ [accessed May 12, 2011]. Monopoli, M.P., D. Walczyk, A. Campbell, G. Elia, I. Lynch, F.B. Bombelli, and K.A. Dawson. 2011. Physical-chemical aspects of protein corona: Relevance to in vitro and in vivo biological impacts of nanoparticles. J. Am. Chem. Soc. 133(8):2525- 2534. Morawska, L., C. He, G. Johnson, H. Guo, E. Uhde, and G. Ayoko. 2009. Ultrafine parti- cles in indoor air of a school: Possible role of secondary organic aerosols. Environ. Sci. Technol. 43(24):9103-9109. Murashov, V., and J. Howard, eds. 2011. Pp. 196-197 in Nanotechnology Standards. Nanostructure Science and Technology Series. New York: Springer. Nel, A.E., L. Madler, D. Velegol, T. Xia, E.M. Hoek, P. Somasundaran, F. Klaessig, V. Castranova, and M. Thompson. 2009. Understanding biophysicochemical interac- tions at the nano-bio interface. Nat. Mater. 8(7):543-557. NIST/NCL (National Institute of Standards and Technology and Nanotechnology Char- acterization Laboratory). 2010. Measuring the Size of Nanoparticles Using Trans- mission Electron Microscopy (TEM). NIST-NCL Joint Assay Protocol, PCC-7. Version 1.1. U.S. Department of Commerce, National Institute of Standards and Technology, Gaithersburg, MD, and National Cancer Institute, Nanotechnology Characterization Laboratory, Frederick, MD [online]. Available: http://ncl.cancer.gov/NCL_Method_PCC-7.pdf [accessed May 12, 2011]. NRC (National Research Council). 2007. Toxicity Testing in the 21st Century: A Vision and a Strategy. Washington, DC: The National Academies Press. NRC (National Research Council). 2009. Science and Decisions: Advancing Risk As- sessment. Washington, DC: The National Academies Press. Oberdörster, G., A. Maynard, K. Donaldson, V. Castranova, J. Fitzpatrick, K. Ausman, J. Carter, B. Karn, W. Kreyling, D. Lai, S. Olin, N. Monteiro-Riviere, D. Warheit,

OCR for page 107
Environmental, Health, and Safety Aspects of Engineered Nanomaterials 141 and H. Yang. 2005. Principles for characterizing the potential human health effects from exposure to nanomaterials: Elements of a screening strategy. ILSI research foundation/Risk Science Institute Nanomaterial Toxicity Screening Working Group. Part. Fibre Toxicol. 2:8. OECD (Organisation for Economic Co-operation and Development). 2010. Guidance Manual for the Testing of Manufactured Nanomaterials OECD's Sponsorship Pro- gramme; First Revision. ENV/JM/MONO(2009)20/REV. Organization for Eco- nomic Co-operation and Development. June 2, 2010 [online]. Available: http://www.oecd.org/LongAbstract/0,3425,en_2649_34365_45409513_1_1_1_1,0 0.html [accessed Apr. 25, 2011]. Oostingh, G.J., E. Casals, P. Italiani, R. Colognato, R. Stritzinger, J. Ponti, T. Pfaller, Y. Kohl, D. Ooms, F. Favilli, H. Leppens, D. Lucchesi, F. Rossi, I. Nelissen, H. Thielecke, V.F. Puntes, A. Duschl, and D. Boraschi. 2011. Problems and chal- lenges in the development and validation of human cell-based assays to determine nanoparticle-induced immunomodulatory effects. Part. Fibre Toxicol. 8(1):8. OSI (OSI e-Infrastrustructure Working Group). 2007. Developing the UK’s e- Infrastructure for Science and Innovation, Report of the OSI e-Infrastructure Working Group, National e-Science Center. January 18, 2007 [online]. Available: http://immagic.com/eLibrary/ARCHIVES/GENERAL/NESC_UK/N070118O.pdf [accessed May 25, 2011]. Ostraat, M. 2011. The Nanomaterial Registry. Presentation at the Society for Toxicology Annual Meeting, March 6-10, 2011, Washington, DC [online]. Available: http://www.toxicology.org/isot/ss/nano/docs/Ostraat_guest_presentation.pdf [ac- cess May 12, 2011]. Petersen, E.J., Q.G. Huang, and W.J. Weber, Jr. 2008. Bioaccumulation of radio-labeled carbon nanotubes by Eisenia foetida. Environ. Sci. Technol. 42(8):3090-3095. Petosa, A.R., D.P. Jaisi, I.R. Quevedo, M. Elimelech, and N. Tufenkji. 2010. Aggregation and deposition of engineered nanomaterials in aquatic environments: Role of phys- icochemical interactions. Environ. Sci. Technol. 44(17):6532-6549. Phenrat, T., N. Saleh, K. Sirk, H.J. Kim, R.D. Tilton, and G.V. Lowry. 2008. Stabiliza- tion of aqueous nanoscale zerovalent iron dispersions by anionic polyelectrolytes: Adsorbed anionic polyelectrolyte layer properties and their effect on aggregation and sedimentation. J. Nanopart. Res. 10(5):795-814. Phenrat, T., T.C. Long, G.V. Lowry, and B. Veronesi. 2009. Partial oxidation ("aging") and surface modification decrease the toxicity of nanosized zerovalent iron. Environ. Sci. Technol. 43(1):195-200. Richman, E.K., and J.E. Hutchison. 2009. The nanomaterial characterization bottleneck. ACS Nano. 3(9):2441-2446. Saleh, N., H.J. Kim, T. Phenrat, K. Matyjaszewski, R.D. Tilton, and G.V. Lowry. 2008. Ionic strength and composition affect the mobility of surface-modified Fe0 nanoparticles in water-saturated sand columns. Environ. Sci. Technol. 42(9):3349- 3355. Schierz, P.A., A.N. Parks, and P.L. Ferguson. 2010. Characterization and Analysis of Single-walled Carbon Nanotubes in Complex Matrices by Asymmetric Flow FFF Coupled with NIRF Spectroscopy. Presentation at the 5th Annual Conference on Environmental Effects of Nanoparticles and Nanomaterials, August 2010, Clem- son, SC. Schmieder, P.K., G. Ankley, O. Mekenyan, J.D. Walker, and S. Bradbury. 2003. Quanti- tative structure-activity relationship models for prediction of estrogen receptor

OCR for page 107
142 Identifying Properties of Engineered Nanomaterials That Indicate Risks binding affinity of structurally diverse chemicals. Environ. Toxicol. Chem. 22(8):1844-1854. Smith, J.N., K.C. Barsanti, H.R. Friedli, M. Ehn, M. Kulmala, D.R. Collins, J.H. Scheckman, J. Williams, and P.H. McMurry. 2010. Observations of aminium salts in atmospheric nanoparticles and possible climatic implications. Proc. Natl. Acad. Sci. U.S.A. 107(15):6634-6639. Tan, W., R. Madduri, A. Nenadic, S. Soiland-Reyes, D. Sulakhe, I. Foster, and C. Goble. 2010. CaGrid Workflow Toolkit: A taverna based workflow tool for cancer grid. BMC Bionformatics 11:542. Teeguarden, J.G., P.M. Hinderliter, G. Orr, B.D. Thrall, and J.G. Pounds. 2007. Parti- cokinetics in vitro: Dosimetry considerations for in vitro nanoparticle toxicity as- sessments. Toxicol. Sci. 95(2):300-312. Thomas, D.G., R.V. Pappu, and N.A. Baker. 2011. NanoParticle Ontology for cancer nanotechnology research. J. Biomed. Inform. 44(1):59-74. Tiede, K., M. Hassellöv, E. Breitbarth, Q. Chaudhry, and A.B. Boxall. 2009. Considera- tions for environmental fate and ecotoxicity testing to support environmental risk assessments for engineered nanoparticles. J. Chromatogr. A. 1216(3):503-509. Vietti-Cook, A.L. 1999. Staff Requirements - SECY-98-144 - White Paper on Risk- Informed and Performance-Based Regulation. Memorandum to William D. Travers, Executive Director for Operations, from Annette L. Vietti-Cook, Secretary, U.S. Nu- clear Regulatory Commission. March 1, 1999 [online]. Available: http://pbadupws. nrc.gov/docs/ML0037/ML003753601.pdf [accessed Nov. 28, 2011]. von der Kammer, F., P.L. Ferguson, P.A. Holden, A. Masion, K.R. Rogers, S.J. Klaine, A.A. Koelmans, N. Horne, and J.M. Unrine. 2012. Analysis of engineered nano- materials in complex matrices (environment & biota): General considerations and conceptual case studies. Environ. Toxicol. Chem. 31(1):32-49. W3C Semantic Web. 2011. W3C Semantic Web Activity [online]. Available: http:// www.w3.org/2001/sw/ [accessed May 16, 2011]. Walczyk, D., F.B. Bombelli, M.P. Monopoli, I. Lynch, and K.A. Dawson. 2010. What the cell “sees” in bionanoscience. J. Am. Chem. Soc. 132(16):5761-5768. Wiesner, M.R., G.V. Lowry, P. Alvarez, D. Dionysiou, and P. Biswas. 2006. Assessing the risks of manufactured nanomaterials. Environ. Sci. Technol. 40(14):4336- 4345. Wiesner, M.R., G.V. Lowry, K.L. Jones, M.F. Hochella, Jr., R.T. Di Giulio, E. Casman, and E.S Bernhardt. 2009. Decreasing uncertainties in assessing environmental ex- posure, risk, and ecological implications of nanomaterials. Environ. Sci. Technol. 43(17):6458-6462. Zhao, J., F.L. Eisele, M. Titcombe, C.G. Kuang, and P.H. McMurry. 2010. Chemical ionization mass spectrometric measurements of atmospheric neutral clusters using the cluster-CIMS. J. Geophys. Res. 115:D08205.