In vivo effects: Methodologies and biokinetics of inhaled nanomaterials

NanoImpact 10 (2018) 38–60

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Review article

In vivo effects: Methodologies and biokinetics of inhaled nanomaterials a,⁎

Günter Oberdörster , Thomas A.J. Kuhlbusch

T

b

a

University of Rochester, Department of Environmental Medicine, Rochester, NY 14642, USA Federal Institute of Occupational Safety and Health (BAuA), Friedrich-Henkel-Weg 1 – 25, 44149 Dortmund, Germany and Center for Nanointegration (CENIDE), University of Duisburg-Essen, Carl-Benz-Straße 199, 47057 Duisburg, Germany b

A R T I C L E I N F O

A B S T R A C T

Keywords: Exposure-dose-response Risk assessment Predictive toxicology Biodissolution Extrapolation modelling

Inhalation is the prevailing route of inadvertent exposure for manufactured nanomaterials (MNs). For assessing potential adverse effects, indepth knowledge about Exposure-Dose-Response relationships is required to define a risk as a function of hazard and relevant exposure. Intrinsic (physico-chemical) and extrinsic (functional) MN properties determine the biological/toxicological properties (effects) of MNs. Predictive testing strategies are useful for comparative hazard and risk characterization against toxicologically well-defined positive and negative benchmark materials involving studies in rodents, cells, and cell-free (abiotic) assays. Inhalation studies can be used for hazard identification as well as for hazard and risk characterization of inhaled MNs. A design to provide dose-response data is ideal, but less so if only exposure-response data are available. Information should also be provided for biokinetics and for identifying secondary targets. Bolus-type dosing (intratracheal instillation; oropharyngeal aspiration) can be useful for hazard identification and characterization, but not for risk characterization. Combining results from bolus dosing or in vitro tests with results of a subchronic inhalation study of the same group of MNs can be a suitable predictive bridging approach. In vitro cellular assays designed to determine in vivo effects and underlying mechanisms present additional challenges. Cellular dose equivalency to in vivo is difficult to achieve because of static, mostly acute in vitro systems with no MN clearance. The dose dependency of mechanisms has to be considered as well. Still, in vitro tests are suitable for toxicity ranking against well-characterized benchmarks (Hazard ID). Regarding abiotic assays, predictive toxicity ranking using the metric of specific MN surface reactivity (ROS assays) is a promising screening tool, but requires further validation and standardization. Dynamic abiotic dissolution assays are also a promising tool for predicting in vivo dissolution rates but require standardization. Information about MN dissolution using static (equilibrium solubility, μg/L) and dynamic (dissolution rate, ng/cm2/day) abiotic in vitro assays provide different information about the solubilization of MNs reflecting either static in vitro or dynamic in vivo conditions. Results of both assays may be useful for categorization if performed in physiologically relevant fluids. Because the in vivo dissolution rates of MNs can differ widely, it is too simplistic to group MNs just into soluble and poorly soluble materials. Static (equilibrium solubility) and dynamic (dissolution rate) abiotic assays are based on different concepts. Results from dynamic dissolution in relevant physiological fluids - rather than just water - add valuable information about the extrinsic functional characteristics of MNs, which may be considered as a grouping tool into high, moderate, low and insoluble MNs. Systemic biodistribution of MNs depends on the point-of-entry. For example, MNs deposited by inhalation or instillation in the respiratory tract distribute differently than intravenously administered MNs; thus, biokinetic models based on data from intravenous MN administration should not be used to model biodistribution following inhalation. The significance of biodissolution for biokinetics, effects and underlying mechanisms has to be assessed in separate in vivo studies, involving biopersistence/biodurability and ultra-high resolution imaging for analysing bioprocessing and biotransformations at a sub-cellular level. With respect to grouping, several strategies are necessary to cover all classes of MNs of different compositions and for different exposure routes, all of which are to be considered in regulatory decision-making. The suggested grouping and extrapolation framework presented in this paper could be pivotal in leveraging subchronic inhalation data with data from alternative test methods, thus leading to more efficient, cost-effective, and – in the long run – animal and cost saving methods to obtain needed input data for regulatory use.

⁎

Corresponding author. E-mail address: [email protected] (G. Oberdörster).

https://doi.org/10.1016/j.impact.2017.10.007 Received 21 April 2017; Received in revised form 10 October 2017; Accepted 24 October 2017 Available online 21 November 2017 2452-0748/ © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

NanoImpact 10 (2018) 38–60

G. Oberdörster, T.A.J. Kuhlbusch

1. Introduction

doses. For example, it is not appropriate and scientifically not justifiable to perform an acute short-term study, in vivo or in vitro, with a single dose suggesting that this is realistic because it is equivalent to the predicted total dose, accumulated over 45 years of inhalation exposure at a workplace (Gangwal et al., 2011). This is highly misleading and results cannot be considered as relevant (Oberdörster, 2012). High, irrelevant experimental doses not only “make the poison”, but also determine the mechanism, as pointed out by Slikker et al. (2004). Validation and scientific acceptance of toxicological results is essential for regulatory acceptance. An understanding of dosimetry and extrapolation modelling is essential for translating results of inhalation tests with MNs to be applied for regulatory purposes. Fig. 2 shows the complexity of respiratory tract dosimetry to emphasize the importance of expressing and analysing data in the form of Exposure-Dose-Response relationships. Most often, results are only reported as Exposure-Response correlations. This is insufficient, it does not consider the fundamental importance of Dose in toxicology, making it difficult to use results of an inhalation study as input for extrapolation modelling where the deposited and long-term retained doses are needed to characterize effects. While this document focuses on regulatory processes using established guidelines (e.g., OECD, 2016c, d) and presents suggestions for expansion and new to be established testing protocols, it should not be forgotten that a wide spectrum of scientific data is used for setting limits for inhalation exposure, including e.g., classification as carcinogens, irritants, allergens. Importantly, clinical and epidemiological data should be of highest preference, these are dealing with the ultimate species of interest, humans.

The topic of in vivo effects and biokinetics of inhaled nanomaterials, as well as the other topics in this special issue of NanoImpact, is specifically focusing on regulatory needs for nanomaterials in the context of answering regulatory risk assessment questions. Sayre et al. (2017) provided detailed background information to introduce this series of papers including well-thought-through regulatory questions for each topic. The following critical review and discussions are in response to the questions for the topic in vivo effects and may serve as input for the regulatory decision process directed at the human health protection from potential adverse effects in inhaled MNs. The three common exposure routes for manufactured nanomaterials (MNs) are inhalation, oral and dermal exposure. The latter exposure route is most often viewed as the one of least concern due to the efficient barrier function of healthy skin (Prow et al., 2011). To our knowledge, no quantitative comparable exposure assessments for inhalational and oral exposure have been conducted and published. Exposure of the respiratory tract as point-of-entry includes also exposure via the gastro-intestinal (GI) tract with those MNs that are cleared by mucociliary clearance towards the oro-pharynx, to be swallowed into the GI-tract. Overall, most publications deal with exposure of the respiratory tract and its cells and view this as the exposure route of highest concern due to direct interactions in the lung and because of subsequent significant transport into the human body. Whereas inhalation of MNs is the only physiological mode of exposure for the respiratory tract, bolus-type intratracheal inhalation or oro-pharyngeal aspiration as less expensive and easy to execute dosing methods are used as alternatives to evaluate effects of MNs in the respiratory tract. However, it has been shown that a dose administered to the respiratory tract as bolus within less than 1 s will induce significantly greater lung inflammation compared to the same dose administered over several hours or days. This is due to the huge difference in the dose rate which has to be considered (Baisch et al., 2014). Nonetheless, bolus-type delivery for establishing dose-response relationships is still useful for hazard identification and ranking as shown by Warheit et al. (2005), but the results cannot be used for risk characterization (Driscoll et al., 2000). The National Academy of Sciences suggested four steps in the risk assessment paradigm (NAS, 1983): Hazard Identification; Hazard Characterization; Exposure Assessment; and Risk Characterization. Fig. 1 shows the inter-relationships between these steps specifically adapted for manufactured nanoparticles (NPs), and with the added step of Risk Management. Other important determinants for regulatory decision-making include knowledge about the correlation between physico-chemical MN properties and biological/toxicological properties, including also results of functional assays determined using in vivo as well as cellular and non-cellular in vitro1 studies such as dissolution and inherent ROS-inducing capacity of MNs (see Gao and Lowry, 2017). Such results are valuable for an initial categorization of MNs to inform regulators or manufacturers about a potential hazard so as to guide decisions regarding either additional testing requirements or no further action for additional testing. The difficulties associated with establishing a perfect grouping were summarized by participants of a multi-disciplinary workshop on nanomaterial risk potential and regulatory decisions: “Although no single categorization strategy is likely to work for all classes of engineered nanomaterials (ENMs) in all regulatory situations, it may be possible to develop a general framework that can be adapted and customized for specific ENM compositions and specific regulatory contexts” (Godwin et al., 2015). Of great consequence for the design of any study assessing the toxicity of inhaled MNs, in vitro or in vivo, is the selection of relevant

1.1. Inhalation protocols for risk and hazard characterization The OECD recommends recording toxicity and ecotoxicity relative to at least three metrics, mass, particle number and surface area (OECD, 2012). However, for mammalian toxicity, solubility (dissolution rates) and specific surface reactivity, should also be assessed as functional endpoints. Necessary information before conducting a test, e.g., 90-day inhalation study, include MMAD, GSD, mass concentration, agglomeration/aggregation state, shape, chemistry, density, and several others as shown in Table 1 (Oberdörster et al., 2015). Although Table 1 is based on a paper with focus on CNT/CNF, it serves generally as guidance for other MNs as well. The authors discuss objectives for acute/subacute, subchronic and chronic inhalation studies, concluding that acute inhalation studies will give important information for the design of subsequent subchronic 90day studies. However, chronic effect studies are often more of a priority for evaluating toxicity because - if appropriately designed - a full risk assessment including risk characterization for regulatory actions to prevent long-term effects can be performed. Acute studies, though, should be designed with the same attention to detail. However, subchronic data are currently thought to reflect better the potential responses in workers producing or handling MNs who are exposed to low concentrations of aerosolized nanomaterials over long periods of time. The question as to whether data from different animal models allow reliable interspecies extrapolations needs to recognize that differences between animal species and subsequent extrapolation to humans will not be straightforward. For example, it is well-known that the overloadinduced lung tumours in rats resulting from chronic inhalation exposure of poorly soluble particles of low toxicity (PSLT particles) are not even extrapolatable to mice or hamsters, which makes it very questionable as to whether overload-induced lung tumours in rats caused by inhaled PSLT particles can be extrapolated to humans. Thus, the concept for deriving a “safe” human exposure level should be based on the retained lung burden that did not induce inflammation or fibrosis in a long-term rat inhalation study with PSLT particles, since these effects are preconditions for overload induced tumours in rats. Currently, when lacking chronic 2-year inhalation studies, a

1 The term “in vitro” is used in this article in its literal sense to include both cellular and non-cellular (cell-free; acellular; abiotic) assays (see also Fig. 1, Hazard Characterization).

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G. Oberdörster, T.A.J. Kuhlbusch

Fig. 1. Risk assessment and risk management paradigm for manufactured nanoparticles (MNs) (modified from Oberdörster et al., 2005). Both Hazard Characterization and Risk Characterization are two key components of risk assessment, the former providing information for grouping or ranking, the latter quantifying the degree of a risk, e.g., establishing exposure limits. Whereas bolus-type dosing studies, appropriately performed (multi-dose), are useful for ranking/grouping purposes, results of inhalation studies are required for a full assessment (both hazard and risk characterization). Fig. 2. Factors involved in respiratory tract dosimetry.

that there is an appropriate analytical method available. This and also the inclusion of mandatory lung lavage analyses are important additions for extrapolation modelling with the aim of risk characterization by establishing NOAELs or LOAELs rather than only NOAECs or LOAECs.2 Knowing the retained lung burden during and at different timepoints post-exposure, the following can be determined: MN lung clearance and retention kinetics, critical dosimetric comparisons to clearance and retention data of well established benchmark materials (allowing comparative Dose - Response relationships to be analyzed), and the dosimetric integration of in vivo and in vitro responses. The

generally accepted alternative approach for assessing the inhalation toxicity of MNs for regulatory use are results from a subchronic inhalation study. Examples of 90-day inhalation studies include: three multi-walled carbon nanotubes, one with carbon nanofibers, three carbon black studies, two TiO2 studies and a nanosilver study (Kasai et al., 2015; Pauluhn, 2010; Ma-Hock et al., 2009; DeLorme et al., 2012; Bermudez et al., 2004; Creutzenberg, 2013; Sung et al., 2009). The draft of the new revised OECD TGs 413 and 412 (OECD, 2016 c and d) provides information for all endpoints that are required for regulatory use of subchronic and subacute inhalation study findings for chemicals, including nanomaterials. These endpoints include mandatory measurement of the retained lung burden – if technically feasible – in order to establish Exposure-Dose-Response relationships. These mandatory lung burden measurements apply when there is indication of pulmonary retention of the inhaled nanomaterial and with the provision

2 NOAEL (LOAEL) = No (Low) Observed Adverse Effect Level; NOAEC (LOAEC) = No (Low) Observed Adverse Effect Concentration.

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used. This raises the problem of altering/changing the surface characteristics of the initially pristine MNs which affects their interaction with cells. Hence the relevancy of such results for regulatory risk assessment purposes must be questioned. Longer-term protocols for assessing chronic and carcinogenic effects of nanomaterials have not yet been addressed by the OECD, although there are OECD test guidelines (i.e., TG 452, OECD, 2009) for conventional chemicals that may apply to assessment of chronic MN effects. The question about the need for having nanomaterial-specific test guidelines for chronic exposures can for now be answered that the same nanomaterial specific requirements detailed in the subchronic guideline can readily be included in longer-term test guidelines for inhalation testing. However, there will be a need to establish nanomaterial-specific guidelines directed at grouping/categorization of nanomaterials (ENV, 2016). These should include the testing for solubility/dissolution as well as for assays determining the specific inherent ROS-inducing capacity of MNs (see Fig. 4).

Table 1 Recommendation for exposure characterization of sampled and airborne CNT/CNF. (Modified from Oberdörster et al., 2005.) Characterization type

Size distribution (primary particles) Agglomeration/aggregation state Density Shape Surface area Concentration, mass and number Composition Surface chemistry Surface contamination Surface charge – suspension/ solution Surface charge – powder Crystal structure Particle physicochemical structure Solubility Porosity Method of production Preparation process Heterogeneity Prior storage of material

Human exposure

Rodent exposure

Aerosol

Supplied material

Aerosol

Filter samples

E

D

E

D

E

D

E

Da

D E O E

E E E –

D E D E

N E Da E

E O D O

E E E O

E D D D

E D E E

D D D

O E E

D D D

E E E

1.2. Short-term vs. longer-term Inhalation for toxicity ranking and risk assessment

N N E E D E

E E E E E E

N N E E E E

E N E E E E

Although not yet addressed by OECD guidelines, future working groups may discuss the possibility of deriving a NOAEL or LOAEL from shorter 5-day inhalation studies (STIS) in lieu of longer-term studies has been considered by Landsiedel et al. (2014) for purposes of hazard characterization. Replacing longer-term studies by STIS, though, requires validation of their reliability for obtaining sufficient information regarding chronic toxicity in the primary organ of entry as well as in secondary remote organs. Keller et al. (2014), when comparing 5-day short-term inhalation of ceria NPs to subacute 4-week inhalation, found that there was a fast return of the initial inflammatory response after the end of the 5-day exposure whereas the inflammatory response after 4 weeks of exposure (at the same lung burden) showed a much longer and persistent inflammatory state. Thus, although 5-day inhalation may not be predictive for longer-term effects, it still provides information for hazard ranking and grouping. Keller et al. (2014) also found that the metric “surface area” correlated best with the results rather than mass or volume of the NPs. While this applies to NPs with low biosolubility, it has to be determined whether for rapidly dissolving NPs their mass may be an appropriate metric. A 5-day STIS study in rats, with different carbon-based MNs (MWCNT; graphene, graphite nano-platelets, carbon black) at 3 concentrations with 3-week post-exposure observation time, was designed by Ma-Hock et al. (2013) to assess comparative hazard ranking by using different dosemetrics. Carbon black was the least biologically active material, yet attempts to rank the different carbon-based MNs by either retained volume, surface area or mass did not identify a preferred metric, possibly due to the difficulty of reliably determining the retained lung burden of the carbonaceous MN. Gosens et al. (2016) reported results of a multi-dose STIS (5-day) inhalation study in rats based on the CxT (exposure concentration x exposure time) concept: Using a single concentration of CuO NPs of 13.2 mg/m3, they exposed 4 groups for different time periods each day resulting in four exposure groups ranging from 0.6–13.2 mg/m3 for 5 days. The authors established exposure-response correlations based on calculated 24-h equivalent exposure concentrations, and determined retained lung burdens at 24-h post-exposure. They found the inhaled CuO NPs to be highly biosoluble, inducing significant but short-lived pulmonary inflammation. Risk characterization and dose-response analysis were not performed and lung clearance rates could not be determined. This study confirms that STIS are suitable for hazard identification and grouping for acute exposures, but may not be predictive for long-term effects. Landsiedel et al. (2014) when using short-term inhalation studies with 3-week post-exposure observation for 13 metal-oxide MNs

E: These characterizations are considered to be essential and to be available for each characterization at least once. D: These characterizations are considered to provide valuable information, but are not recommended as essential due to constraints associated with complexity, cost and method availability. O: These characterizations are considered to provide valuable but non-essential information N: These characterizations are not considered to be essential for all studies. a The CNT/CNF property is assumed to be the same as in the supplied material and therefore no need to repeat measurement. If the CNT/CNF material has been altered from that supplied (e.g., micronization), then the new properties as used in the rodent assay have to be determined.

availability of lung burden data from subchronic inhalation tests is of critical importance for a comparison of doses administered in in vitro and shorter-term inhalation/bolus-type studies normalized by the metric “per cell surface area” or “per cell”. Not all of the subchronic studies cited above included the most recent suggested/adopted additions of OECD TG 413 such as retained lung burden measurement and lung lavage analysis, which would be necessary for an appropriate determination of an NOAEL or LOAEL. The 412/413 OECD test guidelines for inhalation also address the need for preparing aerosols that are rodent-respirable when designing and executing subchronic inhalation studies. For example, this may require micronization of particle agglomerates that are respirable for humans in order to make them rodent respirable resulting in proper dosing of the lung. For example, large airborne sizes of agglomerated particles may readily be respirable by humans, but not by mice and rats. This difference must be considered when designing animal inhalation studies. With regard to the need to have more explicit inhalation guidance for different nanomaterials (regarding sample preparation, dosing and dosimetry as suggested by Oberdörster et al., 2015), no further guidance for sample preparation for inhalation studies is needed at this time: the focus for any MN is that they be prepared and administered as rodent-respirable aerosols, and that all related data on size, size distribution, concentration, etc., of the administered material must be reported. For other modes of exposure, such as non-physiological bolus-type delivery, for in vivo and generally in vitro studies, dispersants are often

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diverse chemical compositions can be grouped according to their inflammatory potential which can be quantified at non-cytotoxic concentrations. The authors also expressed the IC50 values by the metric cell surface area, concluding that their applied doses are indeed high compared to in vivo studies or considering occupational exposure limits (OEL). While this study confirms in general the usefulness of in vitro assays for hazard ranking and grouping, there are several limitations, including the use of exposure-response rather than dose-response relationships (no attempts to incorporate in vitro dosimetry, i.e., information about actual dose to target cells) and lack of in vivo validation. The limitation of STIS and acute in vitro studies with respect to risk characterization and evaluation of long-term effects are overcome by performing subchronic and chronic inhalation studies. However, the use of animals and very high costs present both ethical and economic restrictions. In contrast to STIS, subacute (28 day) studies may be a preferable alternative for predicting long-term effects by extrapolation models. Many of the needs related to modification of this protocol for nanomaterials have already been addressed under the OECD Draft TG 412 for nanomaterials (as noted above; the draft TG also recommends an extended recovery period of 90 days). Further, the 28-day test may more accurately reflect longer-term effects due to improved data obtained on retained lung burdens, inflammatory responses, and biokinetics. Table 2 outlines objectives and designs of subacute to chronic inhalation studies. This table also includes a suggestion to limit the highest exposure concentrations in subchronic and shorter studies to avoid unrealistic exposure scenarios, which would not be useful for regulatory decisions, in addition to unnecessary and wasteful animal usage. The proposed highest concentration for a subchronic inhalation study of 50 mg/m3 in Table 2 is in stark contrast to the draft OECD Guidelines 412 and 413. The limit concentration of 5 g/m3 (100 times higher!) in these guidelines has questionable regulatory and no practical or scientific relevance, given what is known about nanomaterials. An intratracheal instillation or oropharyngeal aspiration study cannot replace the need for subchronic or chronic inhalation testing as discussed in the introductory section. Dose rates are very different, in addition to the non-physiological mode of dosing the respiratory tract with a bolus all of which is irrelevant for real-world conditions. Bolustype delivery represents extremely high dose rates, with the associated effects of altering mechanisms underlying the induced effects. Although still useful for hazard ranking, it is scientifically not acceptable to use results for risk characterization (setting exposure limits). For example, to illustrate the difference between bolus-type and inhalation exposure, a recent editorial by Shatkin and Oberdorster (2016) clarified that a bolus-type intratracheal delivery of 3.3 μg nano-cellulose per mouse would be equivalent to a one-minute inhalation of an exposure concentration of 1.73 g/m3, which is completely unrealistic and meaningless.

concluded that STIS allowed ranking of the MNs regarding their potency by establishing a No Observed Adverse Exposure Concentration (NOAEC) as threshold for dividing MNs into active and passive materials. They further concluded that STIS results provide information on biokinetics and pulmonary and extra-pulmonary effects that is essential for designing subsequent longer-term studies. In a recent follow-up study, Wiemann et al. (2016) expanded on these STIS results by comparing them with results of an in vitro alveolar macrophage assay for predicting the short-term inhalation toxicity of MNs (hazard identification: see also Drasler et al., 2017). They used 18 inorganic MNs to expose a rat macrophage cell line at 4 concentrations under protein-free culture conditions with 16 h incubation time. This study includes several important highly desirable design features that should be considered prototypical for guidelines to be developed as standard for this kind of short-term assay: (i) separation of active (defined when NOAEC < 10 mg/m3 in STIS assay) from passive (defined when NOAEC > 10 mg/m3 in STIS assay) MNs by establishing an in vitro threshold based on the dosemetric particle surface area (specific surface area might be preferable); (ii) requirement that at least two of four toxicity endpoints show significant effects below the threshold for a nanomaterial to be categorized as active (endpoints are LDH; β-glucuronidase; TNFα; H2O2 release by macrophages); (iii) inclusion of a positive (crystalline silica) and negative (corundum) benchmark particle against which the MNs to be tested can be compared (more basic data for corundum have to be established, e.g., lung retention kinetics, to validate its use as negative benchmark); and (iv) comparison of results against those generated using the STIS protocol performed with the same materials. The outcome showed that this macrophage assay was highly predictive for the STIS results and also allows grouping the MNs by biological activity. Further validation is still necessary before this assay can be generally accepted as a predictive tool for regulatory grouping based on hazard ranking and thereby reducing animal usage. One shortcoming, though, is that this assay establishes Exposure-Response data rather than Dose-Response relationships, both for the in vitro and the STIS data. As an example, cellular uptake of the different MNs within the 16-h incubation in vitro was not uniformly the same, which affects the comparability of cellular responses; incorporation of in vitro dosimetry (e.g., DeLoid et al., 2017) should eliminate this shortcoming. Similarly, expressing STIS results as dose-response rather than exposure-response will facilitate the in vitro – in vivo comparison. While Wiemann et al. (2016) compared results of the macrophage in vitro assay with results from STIS, an earlier study using multiple cell types (alveolar and bronchial epithelial cells, monocytic/macrophage cells, human peripheral blood mononuclear cells, rat alveolar macrophages and human red blood cells) determined haemolytic, pro-inflammatory cytokine, and cytotoxic responses of 9 different metal oxide NPs at 24 h and compared results to the acute inflammatory response in vivo induced by intratracheal instillation in rats (Cho et al., 2013). The authors concluded that with respect to hazard identification the predictivity of in vitro assays was good for NPs that act via soluble ions, but was limited for NPs acting via surface reactivity. Limitations of this study are that only one concentration – expressed as particle surface area/mL (exposure - response) – was used without considering actual multiple dose-response relationships. A most recent study by Bhattacharya et al. (2017) employed a human macrophage THP.1 cell line to determine the hazard potential (ranking and grouping) of 19 MNs (carbon nanotubes, metal-oxides) by calculating the IC50, expressed as administered mass/mL cell culture medium at 24 and 48 h of exposure. The authors differentiated between cytotoxic responses and activation of inflammatory responses; they concluded that MNs of

1.3. Generation of aerosols Aerosol generation including the mode of exposure of animals are of special importance for inhalation studies. A constant, reproducible method delivering the requested particle concentrations and size distributions is a prerequisite for long term studies. Basically, three different aerosol generation methods can be differentiated, dry, wet and in-situ generation. Examples for all three types of generation are given for nano-TiO2 aerosol generation: dry and wet generation (Noël et al., 2013), wet generation (Gomez et al., 2013) and in-situ (Jang and Kim, 2001). Briefly described, dry aerosol generation can be done by brush or fluidized bed generation (Noël et al., 2013) or Vortex shaker using mechanical forces to bring powders into a moving gas phase to which the animals are exposed. Wet aerosol generation is done by producing suspensions of the nano-objects of interest, and subsequent nebulization by forcing the suspension through nozzles followed by aerosol drying. 42

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Table 2 Acute to chronic inhalation exposures of rodents to CNT/CNF for toxicity testing (rat as preferred species, 4–6 h/day; 5 days/week; whole body). Modified from Oberdörster et al., 2015. Acute/Subacute (14–28 days) hazard ID and ranking (pos/neg control?) • ToMayobtain preceded by i.t. instillation or 1 day inhalation • with berange of doses to estimate inhaled concentration with MPPD model

and test rat respirable aerosol with range of • Assure concentr. use workplace or consumer exposure data • Ifto available inform aerosol generation determine concentration for 90-day; (range• Tofinding) collect biokinetic data for portal of entry, and • Topossibly identification of secondary target organs, incl. pleura, and fetus

guidance for dose levels for mechanistic in • Tovitroprovide testing, incl. secondary organs observation period desirable • Post-exposure (~ 2 months)

Subchronic (90 days)

Chronic (2 years)

NOAEL • ToUsederive minimum 3 conc, including known or • expected human exposure levels; both sexes optional

If no effect at 50 mg/m rodent respirable • aerosol, then no need to do chronic study identify hazard: total resp.tract; pleura, • Tocardiovascular, CNS, bone marrow target organs • Identify select conc. for chronic study • ToDetailed biokinetics: retention, clearance, • organ accumulation, long-term effects? • Predicting perform risk assessment by extrapolation to • Tohuman via dosimetric extrapolation observation period for longer• Post-exposure term effects to assess progression-regression 3

a

determine long latency effects (cancer); life shortening; • Toextrapulmonary target organs conc; based on 90-day or range-finding study results; • 3include human exposure level; high dose: MTD; low dose: no significant effect

total respiratory tract, pleura and systemic effects, • Tonoseassess to alveoli; cardiovascular, CNS, bone marrow, others (reproductive?)

determine detailed biokinetics: resp.tract retention, • Toclearance, organ accumulation perform extrapolation to human for risk assessment • ToPost-exposure period up to a total study • duration of 30observation months (if survival of > 20%)

(~3 months) a This suggestion is based on the fact that such high concentrations will never be reached in a repeat exposure scenario, and it would be a waste of animals (ethical) and monetary resources, (Bernstein et al., 2005) to design a chronic workplace study at higher concentrations. It should even be considered to move this suggestion to the acute/subacute category and not follow up with a subchronic study if an appropriate post-exposure (~60 days) observation period is included. Even shorter-term (5 days) inhalation studies with CNT/CNF < 10 mg/ m3 or a single 6-h. inhalation at 30 mg/m3 have induced significant effects (Ma-Hock et al., 2009; Ryman-Rasmussen et al., 2009; Stapleton et al., 2012; Shvedova et al., 2008). Thus, a cutoff at 50 mg/m3 in a subchronic study is very conservative.

aerosol generation. In this case, no in situ production method is available, and wet aerosol generation has the problem that no appropriate CNT suspension can be achieved. Instead, different dry aerosol generation methods are seen as the best suitable. Beside aerosol generation the delivery of the aerosol to the port of the inhalation system also has to be considered and carefully designed, as well as the aerosol well characterized: agglomeration processes and losses to walls, for example, can significantly alter the aerosol from the point of generation to the nose of the animals (Oberdörster et al., 2015). The different methods to generate aerosols outlined above could be expanded by adding more examples and details. However, it is beyond the scope of this document trying to discuss more specifics. Inhalation toxicologists have to be inventive to achieve aerosolization of new MNs by adapting/revising/combining existing generation methods but also have to consider possible cross sensitivities when evaluating their study results. One general principle of aerosol generation to consider for toxicity testing in rodents is: If information on aerosol characteristics is available from human workplaces then the aim would be to reproduce them for the rodent study, keeping in mind that the aerosols need to be respirable for the rodent species. To evaluate this, the MPPD model (Miller et al., 2016; ARA, 2017 [www.ara.com]) can be used to determine which region in the human upper (naso-pharyngeal) or lower (trachea-bronchial, alveolar) respiratory tract are targeted at the workplace, and then the particle size distribution (PSD) can be adjusted accordingly for the rodent species. This PSD selection procedure should also take into account aerosol aggregation and agglomeration state. Without any information from human exposures, observing the principle of rodent respirability is essential, as spelled out in the revised OECD TG 412/413 guidelines. MNs may sometimes agglomerate to large MMADs when aerosolized, changing deposition efficiency throughout the respiratory tract.

Nozzles can vary significantly in size, which, together with the nanoobject concentration in the suspension, determines the state of agglomeration of the aerosol. Aerosolization of aqueous MN suspension by an ultrasonic nebulizer has also been used. In situ production of nano-object aerosols is done by fresh generation of the nano-objects e.g. in flames or by spray pyrolysis. In this case feed rates of the precursor as well as the cooling and dilution rate after nano-object formation determine particle concentration and size distribution. Whereas the methods listed above refer to aerosol generation technology for dispersing MNs that are available commercially or prepared by specific chemical processes, there is an increasing use of an electric spark-generated de novo synthesis of nano-aerosols of diverse metals and their oxides and of elemental carbon (Meuller et al., 2012). This method is very useful for generating very pure nanoparticles of essentially any metal, as well as their oxides by changing the carrier gas composition of normally inert nature (e.g., argon). The principle of aerosol generation is an evaporation/condensation process caused by high voltage spark between two opposing electrodes of the specific material selected for a specific aerosol. This method provides a continuous aerosol of defined nanoparticle size and size distribution with low contamination probability compared to the other methods mentioned above. Each of the above methods has advantages and disadvantages. Dry aerosol generation does not alter the chemical surface composition while wet generation often leads to changes due to impurities in the suspension. On the other hand, aerosols with lower mean particle diameters are often achieved by using wet aerosol generation compared to dry generation since higher dispersion energy levels can be applied in the liquid phase. The purest way of producing aerosols for exposure studies is the in situ production, which in the case of flame reaction may contain some compounds stemming from the precursors of the nanoobject production. The influence of side products or precursors influencing the exposure can be minimized using spark-generated de novo synthesis methods where applicable. However, it should be noted that for large scale market production only the first two methods, dry and wet generation, are applicable. It is obvious from this brief summary that for each nanomaterial the aerosol generation method has to be carefully chosen. While for TiO2 one appropriate way of aerosol generation is by flame pyrolysis, this is not an option for Carbon Nanotubes (CNT). Oberdörster et al. (2015) evaluate diverse examples of CNT

1.4. Integrating subchronic inhalation with simpler approaches to facilitate risk characterization Due to ethical and cost reasons, it is prohibitive to design new subchronic inhalation studies for every nanomaterial. For such untested nanomaterials that are very similar (categorization) in terms of the physical and likely toxicological characteristics to a previously tested MN, it may be possible to leverage the results of a subchronic inhalation 43

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Fig. 4. Examples of cell-free (acellular) assays determining ROS activity.

reactivity (per unit dosemetric) of the particle, an extrinsic particle property. The term reactivity describes the very general concept of the capability of particles to undergo chemical reactions, e.g., generating or cleaving chemical bonds or act as electron donor or acceptor. Methods of determining reactivity are the measurement of redox potentials, the ability to act as a catalyst, or the potential of a particle to produce reactive oxygen species (ROS). The latter reaction is considered especially important since ROS are also generated in cells during e.g. inflammation. Acellular measurement methods have been developed to determine the potential of particles to produce ROS. ROS refers to a number of reactive molecules and free radicals derived from molecular oxygen. Consequently, different measurement methods and assays are available for detection of the ROS formation potential. Recent overviews on acellular and cellular detection methods have been published and a tiered assessment approach suggested (Riebeling et al., 2016; Hellack et al., 2017). The tiered approach in Riebeling et al. (2016) suggests to use first acellular test methods (Fig. 4), for example the ferric-reducing ability of serum (FRAS) assay and the electron spin resonance (ESR) spectroscopy with different spin traps (He et al., 2014). In a second step a cellular assay like DCF can be considered, accompanied by an assessment of protein carbonylation (see Gao and Lowry, 2017). An explicit indication which acellular or combination of acellular assays should be used is given by Riebeling et al. (2016) and outlined in Gao and Lowry (2017). Studies like those from Pal et al. (2014) and projects like nanOxiMet (www.nanOxiMet.eu) are currently needed to determine which combination of acellular assays best to use. Combinations currently considered are e.g. the ESR method with two different spin traps (DMPO and CPH) since they reflect two independent reactivity mechanisms. One of the outcomes envisaged for nanOxiMet is the recommendation of a set of test methods and further elaborations of a tiered approach to be used for the assessment of oxidative stress. Thus, reliance on only one ROS assay is discouraged, and results of several tests should be evaluated. Cell-free assays to determine MN-bound ROS activity have been used as a screening tool to categorize NPs in cellular or cell-free assays for hazard ranking (e.g. Bello et al., 2009; Rushton et al., 2010; Fig. 4). Results of these assays are expressed as H2O2 equivalent of the released ROS. ROS-inducing capacity (H2O2 equivalents) can be expressed by different metrics, based on mass, surface area or number of NPs. Fig. 5a and b show results for different types of carbon NPs using the DCFH-DA assay. ROS activity, expressed as H2O2 equivalents, are normalized by particle mass or particle surface area, revealing that surface area is a better fitting metric, allowing grouping into different activity categories. There is also a good correlation between the cell-free ROS activity (DCFH-DA assay) and acute pulmonary inflammation, and ROS activity ranking induced by intratracheal instillation of NPs in rats (Fig. 6) when results of both the in vitro and in vivo effects are normalized per unit surface area (Rushton et al., 2010). This normalization per cm2 surface (specific surface reactivity) is essential for purposes of ranking unknown MNs against well-established

Fig. 3. Estimation of chronic NOAEC from subchronic rodent study using MPPD modelling of experimentally determined subchronic NOAEC and associated NOAEL. (Modified from Oberdörster, 2002.)

study that has already been completed with results from alternative toxicity test methods (STIS or in vitro) of both the untested and previously tested MN. This will provide relative ranking data for the similar nanomaterial to satisfy the needs of enabling regulatory human health risk assessments (see more below). The question as to whether it is acceptable to replace a subchronic inhalation study with a 5- or 28-day inhalation study to characterize a risk still needs to be thoroughly discussed. As discussed before, results of presently available 5-day inhalation studies (STIS) alone are not satisfactory for this purpose (Keller et al., 2014); for a subacute 28-day study with appropriate post-exposure observation it still needs to be validated as to whether it will be sufficient for predicting longer-term effects and associated risks. Suggestions of how to extrapolate results of a shorter-term or a subchronic inhalation study to predict chronic outcomes have been made, relying heavily on dosimetric extrapolation using available predictive particle deposition models for different animal species as well as humans (Fig. 3). As pointed out above, bridging acute studies with subchronic studies may be considered if a new MN is to be tested, and a reliable subchronic study of a related MN has already been done. This bridging concept could be integrated into a more complete risk assessment and grouping framework to address pulmonary effects of MNs. The bridging approach to arrive at a Human Equivalent Concentration (HEC) value (without performing a new subchronic study on the new MN) could involve the following steps, based on the hypothesis that HECs of different MNs are ranked in the same order as their hazards: Determine the difference in hazard ranking derived from an appropriately designed short-term study (in vitro; instillation; 5-day STIS: slopes of the doseresponses) between the untested MN and the MN with available subchronic results and apply the difference (x-fold lower or higher dose for same response) to the HEC derived for the MN in the subchronic study. Although a reasonable hypothesis, it is very speculative at this point and requires new studies and validation for this kind of extrapolated risk characterization. With respect to hazard characterization only, bridging in vitro or other short-term tests (instillation, STIS) with subchronic studies can be considered useful as a basis for grouping. Nevertheless, studies characterizing hazard and comparing results from different short-term tests should be done to confirm the same outcome in terms of ranking order from the different tests. 1.5. Functional MN properties for ranking 1.5.1. ROS-inducing/generating capacity Poorly soluble particles react with their tissue/cell surroundings via the surface area. Whether and how they react depends on the environmental conditions and especially the reactivity or specific 44

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Fig. 5. a/b: Cell-free assays with carbon based NPs: H2O2 generation based on a) particle mass correlation, and b) particle surface area correlation. (From Oberdörster et al., 2017.)

species of oxygen radicals generated by specific MNs. For example, the DCFH-DA assay seems to be very sensitive towards detecting nanoparticle size-dependent surface area defects (Jiang et al., 2008) (Fig. 7). Thus, the default assumption that the specific ROS surface reactivity for a specific MN is independent across the nano-size range (1–100 nm) needs to be critically assessed. Still, measuring specific surface reactivity – abiotically, or in vitro with cells – seems to be a promising tool for predicting in vivo reactivity, but further validation though using additional MNs is necessary. For MNs that are soluble at different dissolution rates in vivo, − discussed in next section - surface area normalization may be difficult because of changing surface area with decreasing particle size. 1.6. Biodissolution and biodurability Physiochemical and functional properties that affect the toxicity of MNs are shown in Fig. 8. Among these, dissolution is of specific interest since it represents an extrinsic property affecting MN biopersistence and biokinetics (Fig. 9). This includes the overall retention, because the pulmonary retention half-time (T1/2) of MNs is not only determined by alveolar macrophage-mediated clearance and translocation processes, but for biosoluble MNs is strongly influenced by intracellular and extracellular dissolution of retained particles in the lung. Knowing the in vivo (bio)dissolution rate will allow prediction of the overall clearance rate and associated overall T1/2 of particles of different solubilities, provided the dissolved fraction (ions) are cleared from the lung within a short period of time (see Gao and Lowry, 2017). Utembe et al. (2015) discussed the importance of dissolution and biodurability of MNs, emphasizing the need for determining dissolution rates and dissolution rate constants for risk assessment. In vitro cell-free assays to determine particle dissolution or solubility involve static (solubility) and dynamic (dissolution rate) systems using artificial fluids simulating either extracellular conditions (pH 7.4) or intracellular (phagolysosomal, pH 4.5) conditions (Fig. 10) (Potter and Mattson, 1991). The extracellular fluid determines dissolution of inhaled particles in the epithelial lining fluid, and the intracellular fluid measures dissolution in the phagolysosome of alveolar macrophages following phagocytosis of the deposited particles (Guldberg et al., 1995). Common to all static systems - also referred to as bio-elution (Henderson et al., 2014) - is establishing the equilibrium solubility. Thus, the static system reflects conditions that are present in in vitro cell cultures exposed to MNs (μg/L at equilibrium). The dynamic system establishes a dissolution rate resembling in vivo conditions (ng/cm2/day dissolved) which is more relevant and applicable to in vivo dissolution behaviour. Phagolysosomal fluids (pH 4.5) generally lead to faster

Fig. 6. Correlation of in vivo pulmonary inflammation and cell-free in vitro ROS activity expressed per unit particle surface. (From Rushton et al., 2010.)

Fig. 7. ROS/cm2 response of anatase TiO2 in cell free assay as function of particle size showing that the surface reactivity can be size-dependent. (From Oberdörster, 2010; Jiang et al., 2008.)

benchmark materials. As indicated before, it is advisable to use two or three different assays available for measuring ROS activity (see Fig. 4 above) because each performs slightly differently with respect to the 45

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Fig. 8. Physicochemical and functional NP properties of relevance for toxicology.

transformation questions have to be raised (i) as to whether effects induced by ZnO and CuO2 should still be considered NP specific – probably not – and importantly, (ii) is there a threshold in terms of the in vivo dissolution rate above which a MN should no longer be considered a nano-object toxicologically? Wohlleben et al. (2013) tested a 28-day static abiotic solubility assay using a phagolysosomal fluid simulant, water and HCl, in addition to several other assays, when comparing different physico-chemical properties of 15 MNs with respect to identifying assays for grouping. Although the authors noted some correlation between known in vivo inflammatory potency and reactivity and ion release, they pointed out the need for more detailed investigations of such correlations. Of interest is their observation of morphological changes of MNs consistent with Ostwald ripening and their recrystallization in acidic fluids. This finding raises immediate questions about the dependence of the recrystallization phenomenon seen in the abiotic solubility assay on the MN concentration used, and also as to whether such Ostwald ripening may also occur under realistic in vivo conditions? Refinements and standardization of abiotic solubility/dissolution assays, e.g., the choice of MN concentration, need to be explored in order to obtain and validate in vitro results that are predictive for in vivo MN dissolution and associated bioprocessing. The relevance and appropriateness of the composition of simulated lung fluids has to be considered as well. Stefaniak et al. (2005) reviewed the impact of variations in phagolysosomal fluid simulant composition that can influence dissolution rates. They emphasized the

dissolution, which will shorten the overall lung clearance rate of MN and their respective retention half-times considerably, because generally the total pulmonary clearance rate is the sum of the rates of mechanical and dissolution clearance (Fig. 9). However, the actual in vivo retention characteristics of dissolved metal MNs (metal ions) will depend also on the subsequent fate of the metal ions. Will they bind to specific proteins, e.g., metallothionein or cysteine (Cd, Zn), SOD (copper, Zn) or undergo chemical interactions at subcellular compartments with P or S, including formation of new, possibly persistent compounds. Such “bioprocessing” can be detected by ultra-high resolution TEM and STEM and EELS analysis, as was recently reported for inhaled SiO2 nanoparticles at the 2016 SOT Annual Meeting (Graham et al., 2016). Thus, even if the dynamic dissolution assay may predict accurately the in vivo dissolution rate (bdiss) in the lung – which needs to be verified – it cannot be assumed that the overall lung clearance rate (btot) can be estimated from the results of such assays because the statement of overall clearance rate in Fig. 9 is strictly applicable only for soluble particles whose ions are rapidly cleared from the lung. Without knowledge of the fate of their ions, btot estimates for soluble metal particles derived from in vitro bdiss require confirmation by in vivo studies. In this context, a recent in vitro study with a human T-lymphocyte cell-line demonstrated complete and rapid transformation of ZnO and CuO NPs due to biodissolution into cysteine complexes, almost identical to the results seen when the cells were dosed with the soluble ZnSO4 and CuSO4 salts (Ivask et al., 2017). Thus, in the case of rapid

Fig. 9. Determinants of pulmonary biopersistence of inhaled particles.

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Fig. 10. Acellular in vitro solubility/dissolution systems using simulated lung fluids. The upper panel shows the static solubility assay determining equilibrium solubility (μg/L). The lower panel shows 2 different flow-through dynamic dissolution assays using either a “push through” (ultrafiltration) or “double chamber” (dialysis membrane) design measuring full dissolution rate (ng/ cm2/day).

importance of a fluid simulant to mimic the chemical properties of the fluid in the biological environment in which they are present. Guldberg et al. (1998) suggested a serum ultrafiltrate as acidic fluid simulant that has been accepted as a standard in the European Certification Board for Mineral Wool Products (EURIMA) test guidelines (Sebastian et al., 2002). It is not known as to whether the lack in simulant fluids of specific proteins and other components, existing in vivo, cause significant differences between in vitro and in vivo results. There are no data to compare dissolution of the same metal compounds when using differing fluid compositions. Studies comparing the importance of the individual constituents of fluid simulants need to be designed. Even with that knowledge though, it remains to be determined as to whether and how specific metal ions interact with respiratory tract tissues. More studies/ data are needed to accept static or dynamic acellular in vitro solubility/ dissolution assays as standards. An existing OECD draft report on biodurability of MNs is still in a very preliminary stage (OECD, 2016a). Lacking is a critical discussion about the uncertainties associated with the assumption that dissolution rates determined in cell-free assays are equivalent to in vivo dissolution rates. The finding of a wide range of dissolution rates among the different MNs needs to be emphasized, rather than simply assuming there are only two classes - soluble and poorly soluble - particles. In addition, the intra- and extra-cellular dissolution of MNs in vivo needs to be considered. Cellular and cell-free in vitro assays for predicting intra- and extra-cellular in vivo dissoluaiton of MNs need to be developed and validated for standardization. As mentioned above, advanced high resolution TEM/STEM studies can shed light on the subcellular fate, chemical transformations and

subcellular localization (nucleus; mitochondria) of metals and ions and thereby assist in our efforts to characterize and model the biokinetics of inhaled metal MNs which contributes to the understanding of mechanisms. Cell-free dynamic dissolution assays are promising tools for predicting in vivo dissolution rates but require validation and standardization. Ranking results against well-characterized positive and negative Benchmark materials should be considered and be made a rule. Validation via comparison with in vivo dissolution rates and information about the interaction of metal particles and their released ions with lung tissue components (bioprocessing) is desirable. At the 2016 Nanotoxicology Conference in Boston a case study with inhaled SiO2 nanoparticles was presented for obtaining in vivo dissolution rates of inhaled particles of unknown in vivo solubility with the goal to use the result as input into a dosimetric extrapolation model for deriving a Human Equivalent Concentration (Fig. 11) (Oberdörster et al., 2016). The obvious advantage of determining dissolution from an in vivo inhalation study is that the result is the “effective” in vivo dissolution, including interactions with potential targets in the lung as described later. The conceptual approach starts with an important initial step involving characterization of the generated SiO2 aerosol to determine the effective aerosol density, ρeff, “in vivo”. Particles generated for inhalation studies or at workplaces exist generally as agglomerates or aggregates; this reduces the actual effective airborne density of the agglomerate aerosol because of the void spaces between the agglomerated particles. Fig. 12 outlines the approach for deriving ρeff in vivo using a 4-h rat inhalation study with aerosols of agglomerated nanoparticles. In the case of nano-SiO2, the resulting ρeff of 0.165 g/cm3 is 16 times less than the material density of 47

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Fig. 11. Dosimetric extrapolation of inhaled particle exposure concentrations and doses in the respiratory tract from rats to humans. (Modified from Oberdörster, 1989.)

very different and can be very misleading. In addition, solubility in this paper is determined using the static aqueous solubility method which is different from the dynamic in vivo dissolution rate. Apart from cell-free dynamic and static in vitro assays, results of cellular in vitro assays may be more closely resembling in vivo dissolution when using alveolar macrophages (AM) as most relevant cell type for phagocytizing and dissolving inhaled particulate materials. Indeed, pioneering studies in the late 80′s and early 90′s starting with measuring the phagolysosomal pH in AM in vitro and in vivo followed by dissolution of micro-sized cobalt-oxide and Mn-oxide particles established the methodology and reported first findings comparing different animal species and humans (Lundborg et al., 1985, 1995; Nyberg and Camner, 1989; Nyberg et al., 1992; Kreyling et al., 1990, 1991). Results indicated that in general the phagolysosomal pH of AM from experimental animals (rabbit, rat, dog) and humans was similar (4.5–4.9) and that in vitro AM cultures had slightly higher pH (+ ~0.5). Dissolution of particles in human and rabbit AM determined after 1–3 days in culture showed significant differences between highly soluble (Mnoxide, 44% at 72 h.) and less soluble (Co-oxide) microparticles, but the amount dissolved was similar between the different mammalian species. However, an estimated dissolution T1/2 of 4 days could not explain the difference to retention T1/2 reported for inhaled Mn-oxide particles in humans and guinea pigs (Lundborg et al., 1985). As discussed by Kreyling et al. (1990) cell-free dissolution studies in lung fluid simulants with different micro-sized metal compounds (Co, Be, Mn, As) showed lower dissolution rates compared to higher rates in AM. They concluded that the AM in vitro assay may be more appropriate for predicting in vivo dissolution of inhaled particles than cell-free in vitro assays. In their innovative studies with 4 sizes of monodisperse porous radioactive 57Co3O4 particles (0.3, 0.7, 0.8, 1.7 μm), Kreyling et al. (1990) optimized the AM in vitro dissolution assay and determined dissolution over a two-week incubation period, using AM lavaged from 6 dogs and from 2 humans. Over the two-week incubation period, frequent samples of media of the wells were vacuum filtered to separate dissolved from particulate Co; AM cell layers were lysed and lysates also filtered to separate dissolved from particulate Co. The authors differentiated between leached 57Co (initially dissolved within hours),

Fig. 12. Approach for deriving ρeff in vivo using a 4-h rat inhalation study with aerosols of agglomerated/aggregated nanoparticles.

SiO2. The more realistic value of ρeff should be applied for interpreting results of a subsequent longer-term exposure-dose-response study in rats to determine the “effective” in vivo dissolution rate. With respect to grouping, it is not sufficient to use only one characteristic – solubility/ dissolution - for grouping or read across, for reasons mentioned before (interaction/binding of dissolved ions with cellular components). Even the dynamic abiotic dissolution rate does not necessarily predict the in vivo rate. When considering the use of dissolution as a grouping tool, the question has to be asked - what is the correlation with toxicity ranking? As an example, consider ZnO and CuO, both of which are very soluble in vivo, but with very different in vivo toxicity. Using only one physico-chemical characteristic alone for grouping of metal MNs for regulatory purposes is not sufficient and can be misleading, but using solubility/dissolution in combination with other physico-chemical properties makes good sense. For example, Arts et al. (2014) discuss several endpoints, including solubility in their recommendations for groupings; however, they suggest water as medium for determining solubility in their paper, but water solubility as a metric is likely to be

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dissolution in culture medium (very low, ranging from 0.2–4.5%, greater for smaller particles) and dissolution in AM. They established a dissolution model to calculate a dissolution rate constant based on particle mass, assuming a constant ratio of surface (inner and outer surface of porous Co-oxide) to mass. The phagolysosomal dissolution rate in AM was much faster for smaller than for larger particles, resulting in 50, 5, 3 and 2% of initial particle mass dissolved for 0.3, 0.7, 0.8 and 1.7 μm particle sizes. Even the mean dissolution rate constant (per cm2) was 5-fold greater for 0.3 μm particles compared to the larger particles, which, the authors speculated, was likely due to differences in physical and chemical properties. The in vitro Co-dissolution rates and reported in vivo Co translocation rates into blood from 2-week dog and human inhalation and lung clearance studies were very similar for dogs, but also similar for humans, although with greater variability given that there were data of only two human subjects. This in vitro two-week dissolution assay with AM of humans and experimental animals established by Kreyling et al. (1990) could be a promising tool to simulate and predict human in vivo dissolution of inhaled MNs. This assay could also be standardized for grouping MNs according to their in vivo dissolution rate. More studies are required to validate this assay, including comparisons to cell-free in vitro dissolution studies with diverse MNs of different solubilities, specifically including also materials dissolving in extracellular lining fluid at a more neutral pH of 7.4, which can be significant (Gillespie et al., 2010; Shi et al., 2017). Aspects of binding of dissolved ions to proteins, lipoproteins or subcellular components as well as bioprocessing should also be investigated since they also play a role, as discussed before. Kreyling et al. (1990) did not discuss the possibility of a direct translocation of the Co-oxide microparticles to extrapulmonary organs via the blood compartment when they compared the in vitro dissolution rates to earlier studies of in vivo translocation rates determined in earlier inhalation studies in dogs and humans. While such adjustment is not necessary since mechanical transport into blood and secondary organs does not play a role for micro-sized particles at low non-inflammatory lung burdens, extrapulmonary particle translocation needs to be taken into account when results of nanoparticle dissolution – assessed in the AM in vitro assay – are compared to in vivo clearance data. As recently described by a NCRP report focusing on dosimetry of radioactive NPs (NCRP, 2017), the present Human Respiratory Tract Model on dosimetry of inhaled airborne radionuclides (ICRP 66, 1994) should be updated to include separate mechanical and dissolution clearance rates for the translocation pathway of inhaled NPs from lung to blood. Underlying mechanisms, however, were not discussed in the NCRP publication. Dissolution rates determined by the AM bioassay in vitro for a specific NP could be used to its in vivo estimate clearance kinetic based on the relationship that total lung clearance is the sum of AM-mediated mechanical clearance plus its dissolution clearance. This correlation is valid only when dissolved ions are quickly cleared from the lung and will not bind to proteins or other tissue components which would affect this relationship. As an example, the dissolution rate of 0.3 μm Co-oxide particles in dog AM was determined as 4.8% per day (0.048/day fractional rate) (Kreyling et al., 1990), and earlier studies with inhaled insoluble alumino-silicate particles in dogs showed a long-term clearance rate of 0.003–0.0039/day (Snipes et al., 1983; Kreyling, 1990). Combining the two rates shows that the overall (total) lung clearance rate for 0.3 μm Co-oxide in dogs would be very fast with an overall T1/ 2 of about 13–14 days. This may apply only to the first phase (~ 2 weeks) of lung clearance, which is the duration of the AM dissolution assay. The validity of this application of the AM dissolution assay needs to be tested in further studies. As well, the same concept could be applied to results of cell-free dissolution studies and its validity tested. The concept of using differences in the pulmonary clearance/retention kinetics between inhaled poorly soluble and soluble NPs to determine the aforementioned “effective” in vivo dissolution rate was recently applied in a 4-week rat inhalation study (Oberdörster et al.,

Fig. 13. Concept of estimating in vivo dissolution of NPs: Accumulation in rat lungs of inhaled SiO2 NPs established in 4-week inhalation study; (4 h/day; 5 days/week; 4 weeks; conc 4.6 mg/m3; MMAD = 0.49 μm; GSD = 1.91); comparison to modelled accumulation of inhaled PSP with same aerosol and exposure characteristics. The difference in clearance rates between SiO2 and PSP is due to dissolution of SiO2 NPs.

2016). An example of this approach, depicted in Fig. 13, involves inhalation exposure (4 h/day, 5 days/week, 4 weeks) of rats to amorphous SiO2 (20–40 nm); measurement of retained lung burden on day 1 and day 28 for constructing the build-up curve, assuming monoexponential clearance3 and estimating overall pulmonary clearance rate (btot) using the MPPD model (Version 3.04). This is done by iteratively changing the input for btot until the retained lung burden fits the experimentally measured amount at the end of exposure. The difference of this clearance rate to the clearance rate of a Poorly Soluble Particle of low toxicity (PSP, shown as red curve in Fig. 13, with normal rat particle clearance rate of 0.01, [Morrow, 1992]) is then the in vivo dissolution rate of the inhaled particle. For the SiO2 NP inhalation described here, the in vivo dissolution rate turned out to be 0.0195/day, equivalent to an in vivo dissolution halftime of 35.5 days and an overall T1/2 of 23.5 days (from combined mechanical and dissolution clearance). This T1/2 is consistent with retention data collected in a short 14-day post-inhalation exposure observation period of the 4-week rat study. Examination of lung tissue of rats with inhaled SiO2 nanoparticles confirmed significant bioprocessing and dissolution post-exposure, consistent with the in vivo dissolution rate determined in the inhalation study (Graham et al., 2017). This concept to determine in vivo dissolution of MNs has to be validated by additional studies. Some of several comments/questions to be answered are: Are pulmonary clearance rates of nano- vs. micro- PSP particles comparable? The derived overall clearance rate incorporates dissolution best if dissolved ions are rapidly cleared; prolonged retention (due to protein binding, recrystallization, bioprocessing) underestimates the true dissolution rate; it does, however, reflect the “effective” clearance rate governed by dissolution. Differences to results of in vitro dissolution rates (cell-free; or AM assay) performed additionally could indicate secondary mechanisms of prolonged retention. A longer post-exposure time with more lung burden data will allow to more precisely define the overall T1/2, to be considered in future studies. Older studies reporting the in vivo dissolution of vitreous fibres (Potter and Mattson, 1991) seem to show a very good agreement between dynamic acellular dissolution rates and measured in vivo dissolution (Eastes et al., 2000). However, there are no well-designed studies with nanomaterials showing similarly good agreement between results of cell-free in vitro and in vivo measurements. A significant problem – as discussed before – is the binding of dissolved ions to 3

NOTE:

At =

a 5 × b 7

(l − e−bt)

At = lung burden at time t; a = daily deposited dose; b = clearance rate; t = days of exposure

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specific proteins/lipids during in vivo dissolution of metallic MNs which makes it difficult to use in vitro data for deriving and predicting overall retention characteristics in the respiratory tract and secondary organs. Regarding in vitro assays, number of questions still need to be answered: How well can dynamic cell-free or cellular in vitro dissolution serve as a surrogate for in vivo dissolution? What is the difference of results of dynamic compared to results from static solubility assays? What is the importance of individual constituents and the lack thereof in simulated fluids in in vitro assays? Key is to first validate the appropriateness of the composition of the simulant fluids so that results of cell-free in vitro dissolution rates may reflect the true in vivo dissolution in the lungs. Then, secondly - the pulmonary retention kinetics of the dissolved metals has to be determined. A goal could be to establish a library of in vitro extracellular and intracellular dissolution rates for different metal MNs in order to assess their contribution to grouping. This includes the importance for understanding biokinetics and other effects because the fate of metal compounds depends on their interactions with cellular and tissue proteins in vivo which can be investigated in advanced in vivo studies of bioprocessing. Clearly, more data are needed to understand in vitro (acellular and cellular) – in vivo correlations and to optimize, validate and eventually standardize assays.

et al., 2013). Ideally, a material may serve as both benchmark and reference material. Benchmarks could be a material that has been identified in a previous, appropriately designed subchronic study. Its inclusion will save both costs and animals required for a new study. Wiemann et al. (2016) used the same principle in their in vitro macrophage assay and selected crystalline silica and corundum as positive and negative controls (benchmarks). (ii) Quantitative endpoints (e.g., lung lavage parameters), and measured retained lung burdens (recently introduced as mandatory – if technically feasible – in the new OECD draft guidelines for subchronic inhalation) both have to be included. Without lung burden analyses – as discussed before - the following cannot be determined: MN lung clearance and retention kinetics, critical dosimetry comparisons to well established benchmark materials (allowing comparative Dose - Response relationships to be analyzed), and the dosimetric integration of in vivo and in vitro responses. Incorporating measurement of retained lung burden in the study design allows a coupling of subchronic and subacute inhalation data with other appropriate test data (from shorter-term inhalation, and cellular and acellular assays) which can facilitate grouping and may result in saving animals.

1.7. Comparative risk assessment

(iii) Exposure-Dose-Response relationships should be established, requiring a minimum of three exposure concentrations, with the lowest ideally showing no effect (NOAEC) or minimal effect (LOAEC) and the highest concentration not exceeding a Maximum Tolerated Dose (MTD, Oberdörster et al., 2015). Expressing the Dose-Response by different dose metrics will help in the analysis of specific mechanisms. (iv) The hazard should be assessed by analysing Dose-Response slopes and comparing and ranking them against benchmarks; and risks be characterized against benchmarks by using the NOAEL (or if LOAEL only is available - using a Benchmark Dose (BMD) estimation (EPA, 2016: http://epa.gov/ncea/bmds/dwnldu.html; updated 2016) to determine a “safe” subchronic exposure level for the rat. (v) A chronic “safe” level for rats should be estimated by dosimetry-

Fig. 14 illustrates several concepts involved in the design and execution of subchronic inhalation studies, which also includes a response to the question of replacing or bridging available subchronic studies with shorter duration studies when new MNs are to be tested. The suggested approach is based on comparative hazard and risk characterization incorporating several concepts when designing a three-month subchronic inhalation study: (i) A positive and/or negative benchmark material should be selected, against which the test material can be compared. Participants of a multi-stakeholder workshop defined such benchmark materials as being toxicologically well-characterized, to be contrasted with reference materials which are metrologically well-characterized (Nel

Fig. 14. Approach for comparative hazard and risk characterization of inhaled nanoparticles based on subchronic (3 months) rat inhalation studies. NOAEL: no observed adverse effect level; BMD: benchmark dose; OEL: occupational exposure level; ATS: alternative testing strategy.

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considered. Taking oral exposure first, the issue is again that of doses, and delivery as bolus (gavage) vs. mixing with food and/or drinking water. Additionally, the time of day is important given that rodents are nocturnally active animals, so that gavage delivery during daytime is non-physiological and may cause differences in GI tract effects and associated translocation to secondary organs. Several studies have applied oral gavages of diverse MNs to rats, including three 28-day gavage studies, and reported very little systemic uptake and no treatment related adverse responses (Buesen et al., 2014; Hendrickson et al., 2016; van der Zande et al., 2012; Heringa et al., 2016). For example, Heringa et al. (2016), developed a biokinetic model for oral intake of pigmentary TiO2 applied as food additive (microTiO2), and reported maximally 0.02% uptake for nano-TiO2 on day 6 after dosing, although with uncertain TiO2 detection sensitivity. For developing a biokinetic model following oral intake, data after intravenous injection of nano-TiO2 was used. However, the recent publications by Kreyling et al. (2017a, 2017b, 2017c) clearly demonstrated that the biodistribution of nano-TiO2 following intravenous injection is significantly different than after oral and respiratory tract administration. These authors had delivered radio-labeled TiO2 particles (12 nm) to rats by gavage (30–80 μg/kg BW), intratracheally (40–240 μg/kg BW) and intravenously (40 to 400 μg/kg BW) and performed a complete mass balance of biodistribution to organs and associated retention kinetics up to 28 days post-exposure. Although administered bolus doses were different (highest for i.v.), these unique studies show convincingly that biodistribution data after i.v. injection cannot be used to predict biokinetics following oral or respiratory tract exposure. Results from dosing the respiratory tract and administration of MNs intravenously also show distinct differences in terms of accumulation in secondary organs (Kreyling et al., 2014; Hirn et al., 2011; Schleh et al., 2013). Konduru et al. (2015) also observed that the relative distribution of Ceria NPs to secondary organs was different between intratracheally, orally and intravenously administered Ceria NPs; additional silica coating and ex vivo incubation with lung lavage fluid and plasma altered biodistribution of Ceria, suggesting to the authors that protein corona formation affects biodistribution. Furthermore, the assumption that the biodistribution of micro- and nanoparticles is the same could not be verified by Hendrickson et al. (2016), who found that biodistribution of a daily very high dose (250 mg/kg rat) of micro- and nano-TiO2 for 28 days was indeed different, but very low. No indications of toxicity were observed. Thus, at present there are no data showing any risk from oral intake of MN at realistic exposure scenarios. With respect to dermal exposure, studies have shown that healthy skin is generally protective against translocation of dermally administered nanoparticles. However, damage caused by sunburn or other injury has been shown to result in minimal translocation of administered nanomaterials as was shown by Mortensen et al. (2008) with quantum dots (see also Kuhlbusch et al., 2017, discussing oral and dermal exposure).

based intraspecies extrapolation (Fig. 3) and the Human Equivalent Concentration (HEC) determined via interspecies dosimetry extrapolation (Fig. 11). A further calculation of an Occupational Exposure Level (OEL) requires the application of uncertainty or assessment factors for different endpoints (e.g., between species; subchronic to chronic; inter-individual sensitivity; LOAEL to NOAEL) as part of the regulatory process. (vi) Consider comparison with - and possibly expansion to - in vitro studies indicated in the lower left corner of Fig. 14. This involves a comparison of hazards from in vitro studies with those derived from the subchronic study and a dosimetry comparison between in vivo and in vitro data, based on dose-response (rather than exposureresponse) subchronic inhalation data. A recent publication by Drew et al. (2017) extends the concept of comparative hazard and risk characterization by describing a quantitative framework for grouping nanoscale and microscale particles. A database of 25 in vivo studies was selected, mostly bolus-type singledose instillation/aspiration and four subchronic inhalation, in rats and mice. Deposited lung doses were normalized as retained mass per gram of lung, estimated assuming to be as delivered in bolus studies, or predicted using the MPPD model for the few inhalation studies. It appears though that clearance was not considered in the repeat inhalation studies. The critical endpoint was pulmonary neutrophilic inflammation. Micro-TiO2 and crystalline SiO2 were considered negative and positive benchmark materials, respectively, they were in the lowest (TiO2) and highest (SiO2) of four potency groups. Application of this framework to 6 additionally selected MNs grouped 5 correctly into the highest potency group. The authors acknowledged several limitations of their study, including the lack of more comprehensive data, the inability to use particle surface area as dosemetric, no information of in vivo solubility of the particles, combining studies using unrealistic highdose rates (bolus administration) and low-dose rate (inhalation) in the same analysis, inconsistently available physico-chemical characteristics, prevalence of single-dose studies, considering endpoints other than acute pulmonary inflammation, including other endpoints. Additional more comprehensive data are needed for validation of this framework before it can be used to develop categorical OELs for MNs. As indicated in Fig. 14, in vitro – in vivo dosimetric comparisons should be included whenever data are available in order to expand our knowledge base for eventually establishing validated in vitro assays that allow to move beyond hazard ranking to include risk characterization from results of in vitro studies. Such in vitro - in vivo dosimetric exposure comparison is shown in Fig. 15. The challenge is how in vitro and in vivo doses can be aligned. One proposal is to express the dose per cell surface area or per cell number by several dosemetrics, such as MN mass, surface area, volume, or specific surface area reactivity. Both the deposited dose (external) and the uptake dose (internal) need to be considered in in vitro assays when establishing Exposure-Dose-Response relationships. While at first glance the dose equivalence based on cell surface (or cell number) sounds reasonable, one has to consider significant differences in terms of a static in vitro cell cultures (no clearance) vs. a dynamic in vivo system, and the short-term acute nature of in vitro studies vs. longer-duration in vivo studies. Thus, a dosimetric comparison of in vitro/in vivo hazard ranking may be most meaningful if compared to short duration in vivo studies (bolus dosing, single 2–6 h. inhalation, or even 5-day STIS). Such acute hazard may – or may not be similar to a chronic hazard, which needs to be determined in order to further validate in vitro/in vivo extrapolation (further in vitro dosimetry see Drasler et al., 2017).

1.9. Other endpoints Based on available peer-reviewed literature, adverse cardiovascular effects of nano-sized particles have been observed which could be as important as those in the respiratory tract, specifically in susceptible parts of the population as shown in epidemiological studies involving ambient ultrafine particles (Pekkanen et al., 2002; Zareba et al., 2009) as well as in animal studies with MNs (Nurkiewicz et al., 2009). Other endpoints, such as neurological or immunological ones, need to be considered as well. There are a number of studies demonstrating such effects following exposure via the respiratory tract (CalderonGarciduenas et al., 2004; Elder et al., 2006; Oberdörster, 2010; Stone et al., 2017). Associations with neurodegenerative effects have been suggested and studies are ongoing to confirm translocation of inhaled MNs via olfactory and trigeminal neuronal pathways to the central

1.8. Other exposure pathways Other exposure pathways include oral and dermal exposure, and in addition, if medical applications are considered as well, intravenous, ocular, and potentially parenteral injections might have to be 51

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Fig. 15. In vitro – in vivo dosimetric extrapolation in nanotoxicology for respiratory tract exposures (modified from Oberdörster et al., 2015). VCM: Volumetric Centrifugation Method (DeLoid et al., 2014). ISDD: In vitro Sedimentation, Diffusion, Dosimetry: (Hinderliter et al., 2010), DG: Distorted Grid (soluble): (DeLoid et al., 2015), MPPD: Multiple Path Particle Dosimetry: (Miller et al., 2016).

that in addition to lung tumours, there was also mesothelioma induction. (Suzui et al., 2016). Questions and problems associated with different modes of administration, such as bolus-type instillation vs. physiological inhalation, should be addressed in the respective guidelines for long-term exposure studies of the respiratory tract, because long-term inhalation is the only way to determine induction of malignant tumours in animal studies using realistic exposure modes. For clarification, a thorough discussion of pros and cons regarding dosing to determine carcinogenicity of nanomaterials introduced into the respiratory tract would be necessary and is highly recommended. Convening an international scientific workshop with members from academia, industry, and regulatory agencies would be extremely beneficial to discuss this issue in order to get some clarification about MN study design and interpretation, and understanding the distinction between hazard identification and risk characterization and between scientific acceptance and regulatory needs. Regarding the adequacy of testing the mutagenic potential of nanomaterials, a key issue with MN mutagenicity assays again is the appropriateness of administered doses since most of them are done as acute in vitro studies; this is not addressed in a workshop report of OECD mostly dealing with chemicals (OECD, 2014). In vitro mutagenicity tests need to be validated by in vivo tests. However, in vivo acute or short-term mutagenicity studies are generally also using very high unrealistic doses, requiring critical review.

nervous system. Such neuronal delivery of inhaled MNs to the CNS bypasses the very tight blood brain barrier which blood-borne MNs would have to overcome. Retrograde neuronal transport to the hypoglossus and facial nuclei of the CNS of nano-sized particles (ferritin, iron-dextran) following injection into the tongue and vibrissal muscles of mice has been reported (Malmgren et al., 1978; Olsson and Kristensson, 1978, 1981). Within the CNS, retrograde neuronal transport of iron-dextran from the corpus striatum to the substantia nigra was reported by Nguyen-Legros et al. (1981). Most recently a study involving human subjects by Maher et al. (2016) indeed observed the appearance of nano-magnetite particles originating from combustion processes that had translocated to the brain of Alzheimer's disease patients, suggesting a possible causal relationship. In general, suggested threshold concentrations for inhaled MNs for avoiding pulmonary effects also seem to be protective for secondary organs. However, as was recently shown, inhalation of AgNPs in subchronic rat studies induced greater effects in the liver than in the lung (Weldon et al., 2016). A specific question relates to induction of carcinogenicity by fibrous nanomaterials such as CNTs. Although several studies allege that CNTs, due to their fibrous nature, may be acting like asbestos, this association in terms of asbestos-like underlying mechanisms (based on the Dose, Dimension, Durability paradigm, DDD) should be viewed with caution: Multi-walled and single-walled carbon nanotubes and carbon nano-fibres do not conform to the WHO definition of a fibre (longer than 5 μm).Thus, observed carcinogenic activity in rodent studies may be due to a non-fibre mechanism similar to that which is known from high dose studies of TiO2 and carbon nanoparticles involving the particle lung overload phenomenon. However, certain MWCNT may be more reactive. A recent IARC classification of a specific MWCNT (MITSUI-7) has confirmed this to be an animal carcinogen and was classified as a possible human carcinogen (Group 2B) (IARC et al., 2014), based on mesothelioma induction following intraperitoneal bolus injection into the pleural and abdominal cavities of rodents. A most recent chronic multi-dose inhalation study by Kasai et al. (2016) involving two years exposure of rats to MITSUI-7 multi-walled carbon nanotubes showed for two higher concentrations the induction of lung tumours, but no mesothelioma. On the other hand, a published Japanese study of different multi-walled carbon nanotubes using an intratracheal administration of only one single extraordinarily high dose of a MWCNT in rats reported

1.10. Dosemetrics/Modes of Action (MOA) The fact that physicochemical properties of MNs affect their biological/toxicological responses raises the question as to the most appropriate or most useful dosemetric. For example, MN surface area has been suggested as a proper metric (Duffin et al., 2007; Wiemann et al., 2016; Braakhuis et al., 2016; Rushton et al., 2010), whereas this could not be confirmed in other studies (Gosens et al., 2016, 2014; Warheit et al., 2006). Surface reactivity, or ROS-inducing potential, seems to be a valuable predictor for in vivo toxicity, since it combines a physical property (surface area) and a functional endpoint, the MN capacity to induce ROS (MN-bound ROS). The biodistribution of MNs delivered to a point-of-entry (e.g. respiratory tract) and changing their surfaces due to protein corona formation upon deposition will be different compared to 52

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Table 3 Differences of inhaled nano/micro-particles: findings from in vivo and in vitro studies. Modified from Oberdörster, 2010.

Nanoparticles (<100 nm) General characteristics: Ratio: number/surface area/volume Agglomeration in air, liquids Deposition in respiratory tract Protein/lipid adsorption in vitro Translocation to secondary target organs: Clearance — mucociliary — alv. Macrophages (uptake) — epithelial cells — lymphatic — blood circulation — sensory neurons (uptake + transport) Proteinl/lipid adsorption in vivo Cell entry/uptake — mitochondria — nucleus Effects (caveat: dose!) : at secondary target organs at portal of entry (resp. tract) — inflammation — oxidative stress — activation of signaling pathways — genotoxicity, carcinogenicity

high likely (dependent on medium; surface) diffusion; throughout resp. tract

Larger Particles (>500 nm) low less likely for larger particles sedimentation, impaction, interception; throughout resp. tract less effective

very effective and important for biokinetics and effects yes

generally not (to liver under “overload”)

probably yes poor (inhaled as singlets) yes yes yes yes yes

efficient efficient mainly under overload yes (humans; rodents under overload) under overload no some

yes (caveolae; clathrin; lip. rafts; diffusion) yes yes (<40 nm)

yes (primarily phagocytic cells) no no

yes (direct and via mediators) yes yes yes yes some

yes (via mediators) yes yes yes yes some

nano- and micro-particles affects the activation of defense mechanisms requires further studies. The most prominent mechanism is based on ROS-induction, which in turn is due to the aforementioned inherent ROS-inducing capacity of nanomaterials as well as specific molecular activation pathways resulting in ROS-production in the cells. Comparative studies, micro vs. nano, would be needed to define a nanospecific MOA, one of which would be related to bioprocessing or biotransformation effects seen in in vivo exposure studies (Graham et al., 2014, 2017). For example, generation of secondary nanoparticles in tissues due to dissolution and bioprocessing will modify the retention kinetics of nanomaterials in specific organs which could serve as another endpoint for categorization of nanomaterials. In addition to determining a “most meaningful” metric, one needs to consider also the most practical metric for exposure surveillance, which generally is the airborne mass concentration (see T. Kuhlbusch et al., 2017). Again, characterizing physico-chemical properties of a given nanomaterial will allow to define OELs and other limit values based on its usefulness of an exposure metric for practical applications. Among the most appropriate dosemetrics, functional measures, such as specific surface reactivity (ROS activity per unit MN surface area) and biopersistence (solubility and dissolution rate in vivo) should be highlighted as influencing both effects and biokinetics (see Figs. 4 and 9). Is it helpful to establish MOAs/AOPs for nanomaterials or groups of them? Probably yes, but it may not be as useful for ranking: For example, it is likely that there are the same or similar MOAs underlying fibrotic effects of crystalline silica and TiO2 with obvious different rankings of toxicity between the two. This implies that MOAs/AOPs have to be critically analyzed with respect to the dose inducing a specific mechanism. Here again, specific surface reactivity could be the decisive metric.

delivery via a different point-of-entry which may have to be assessed using different dosemetrics. However, as long as physicochemical properties of nanomaterials have been characterized appropriately, the main metrics of mass, surface area, volume, and number counts can be expressed relative to each other in order to determine the appropriate metric for a given exposure route. Although MN surface area, as intrinsic property, is widely considered as the appropriate metric, it is questionable whether specific surface area (cm2/g) is indeed a universal metric. While this metric is useful for characterizing different sizes of the same MN or between different MNs of low (cyto)toxicity, specific surface area per se is a crude indicator for more reactive MNs. In this case, as discussed earlier, specific surface reactivity, as functional or extrinsic property, should be considered as a meaningful metric to express a response per cm2 MN. However, additional information involving dissolution (in vivo) and biopersistence have to be taken into account. Another question should also be considered: Are there nano-specific MOAs? Some differences of biological/toxicological properties between micro- and nano-particles are summarized in Table 3. Because there is no sharp boundary between micro and nano – 100 nm is often suggested but biologically not supported – this table leaves open a grey zone between 100 nm and 500 nm size in the listing of particle properties. Because there is no generally accepted justifiable exact size limit for nano-objects, the grey zone might as well be between 100 nm and 200 nm, to be defined by future data. Major differences include less efficient phagocytosis of nanoparticles by alveolar macrophages resulting in increased accumulation in the interstitium in rodent studies. For microparticles, this is seen only under particle overload conditions; however, there is a major difference in humans compared to rodents in that microparticles deposited in the alveolar region are to a significant degree cleared to the interstitium, which contrasts with the microparticle kinetics in rats (Gregoratto et al., 2010). Whether the differences in interstitial access and cell entry between 53

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rates, surface characteristics, but also inflammatory conditions and disease states in specific primary or secondary target organs. Organs that should be examined as a priority in studies that focus on biokinetics following inhalation or oral exposure include liver, spleen, kidney, bone marrow, CNS, and local and systemic lymph nodes. Depending on the study objective, other tissue may be included, e.g., heart, reproductive organs, bone, aortic tissue, CSF (cerebrospinal fluid) and any other potential target tissue. As indicated earlier, the choice of target tissues may depend on the outcome of preliminary studies: guidance may have to be developed as to when measuring accumulated burden at specific target sites should be required; for example, based on accumulation and retention kinetics in certain target tissues, or adverse effects observed there. Being able to correlate effects with the retained dose is essential for dosimetric risk extrapolation. Regarding the importance of protein corona formation in primary and secondary organs, there are no published studies with adequate in vivo data to validate results of numerous in vitro studies showing the impact of protein or lipoprotein corona on particle cell interactions and biodistribution. Until more data are available, in vitro results are to be viewed critically as to their in vivo relevance and usefulness for extrapolation. The statement that systemic availability of MN after subchronic inhalation exposures is low and, therefore, unlikely to contribute to significant doses of nanomaterials in distal organs is not necessarily correct: Indeed, present studies of nanomaterial exposure report very low translocation rates, but the accumulated retained dose depends on the retention kinetics in a specific tissue; for example, it is well known that cadmium binds to metallothionein which accumulates in the long term in both liver and kidneys in humans and animals, and – after exceeding a critical concentration – may cause adverse effects (Nordberg and Nordberg, 1988; Oberdörster, 1992) Even though low doses may accumulate in a target tissue, it depends on the sensitivity of a target organ as to whether effects are induced. (Is it the critical organ? See Weldon et al., 2016 discussed below, where not the lung, but the liver was found to be the more sensitive organ for inhaled Ag NPs). A more recent finding in a two-year rat inhalation study at a high concentration of nano-BaSO4 (50 mg/m3) was that in the early exposure phase hardly any Ba retention in the respiratory tract was seen, but high accumulation of barium was found in bone. However after several months of exposure, Ba retention in the lung strongly increased, eventually causing inflammatory effects very similar to those seen with any other nanoparticle repeatedly administered at high doses (Landsiedel et al., 2016). Thus, apparent high initial dissolution rates of BaSO4 in the lung were seemingly followed later by lower dissolution associated with increased retention in the lung after continued inhalation exposure, possibly as result of saturation processes in the lower respiratory tract. In line with this finding, an earlier intratracheal nano-BaSO4 rat instillation study by Konduru et al. (2014) reported rapid pulmonary clearance, suggestive of dissolution (pulmonary retention halftime 9.6 days) of intratracheally-instilled BaSO4. The authors also observed high translocation to the bone and low pulmonary inflammatory responses following a 4- and 13-week inhalation exposure at 50 mg/m3. In a parallel gavage study, nearly 100% of gavaged BaSO4 was excreted via faeces. Another study by Konduru et al. (2015) using intratracheal, intravenous and gavage exposure of rats to CeO2 nanoparticles, with or without silica coating, revealed a significant influence of coating on pulmonary retention post-instillation. In vitro incubation of these particles with alveolar lung lining fluid showed significant differences in protein adsorption, and the authors concluded that protein corona formation after intratracheal and intravenous administration contributed to the different retention behaviour. However, no direct in vivo verification of the formation of corona was provided. Most nanoparticle aerosols consist of agglomerated particles, which may affect biodistribution following deposition in the respiratory tract. Does de-agglomeration occur in the lung – possibly aided by surfactant

1.11. Biokinetics and extrapolation modelling An important aim of inhalation studies, short-term or long-term, relates to obtaining information on respiratory tract kinetics and quantitation of systemic biokinetics identifying potential target tissues. An OECD workshop on toxicokinetics (OECD, 2016b) noted that OECD TG 417 addresses chemicals and should be extended to include nanoparticles. Accordingly, the workshop report contains several points which should be considered in a revision or supplement to TG 417 dealing with the toxicokinetics of nanoparticles after inhalation and oral exposure. This workshop report, however, does not emphasize the difference between solubility and dissolution rate in its question and answer (Q&A) section. For example, the question: “If highly soluble, would a nano-sized material no longer have to be considered as NP?”, could be rephrased more directly: “If the in vivo dissolution rate is high (e.g., > 2.8 day− 1, equivalent to a dissolution T1/2 of < 0.25 days [ < 6 h, actual value requires discussion]), would a nano-sized material no longer have to be defined as an MN?” The relevance of this question was previously introduced in Biodissolution and Biodurability by citing the most recent publication by Ivask et al. (2017): This article showed the remarkable similarity of effects and biotransformations when either rapidly dissolving metal NPs or their soluble salts were administered to cells. Some text in the OECD Workshop Report should be corrected, such as considering exposure being an “external dose”. Exposure is not equivalent to a dose. Moreover, there is no emphasis/discussion about the impact of dose and dose-rate on biokinetics in the OECD Workshop Report, a significant omission. Importantly, though, the workshop participants acknowledged the need for more in vivo data, including developing new methods. Other new draft guidelines for inhalation testing (OECD TGs 412/ 413) describe toxicokinetic endpoints as optional. The OECD Workshop report supports the inclusion of toxicokinetic parameters in the subacute inhalation test TG 412. Serial sacrifices during and post inhalation exposure with sampling of secondary organs should be part of the study design as should be the availability of sensitive analytical tools. The decision as to whether to include respective endpoints in the study design should be made based on preliminary or range finding studies for identifying sensitive target tissues, e.g. the CNS as critical organ, the cardiovascular system, or reproductive organs. As stated before, the biopersistence of inhaled manufactured nanomaterials is significantly influenced by dissolution in epithelial lung lining fluid and intracellularly in phagolysosomes. Particles depositing on the epithelium of the respiratory tract are subjected to dissolution in the epithelial lining fluid (pH ~ 7.4), and – following phagocytosis by alveolar macrophages – dissolution in the acidic (pH ~ 4.5) phagolysosomal fluid, all of which contribute to the overall pulmonary clearance of particles (Guldberg et al., 1995 [see discussion in Biodissolution and biodurability section]); Fig. 8 shows the different components of biopersistence as a function of physiological clearance and biodurability. The term “biodurability” as used in the OECD (2016a) document should not be used for cell-free solubility/cell-free solubility or dissolution studies. These determine just solubility or dissolution, depending on the system used; as to whether they mimic in vivo or inside cell (biological systems) conditions remains to be verified. Parameters governing nanomaterial biodistribution can differ from those associated with conventional chemicals, because of differences in nanomaterial sizes as solids and, most importantly, there can be large differences in in vivo dissolution rates. These can differ significantly between different nanomaterials, yet the biodistribution and fate of dissolved MN ions are likely to be the same or similar to those of the same chemical solute. This includes distribution systemically, but also binding locally to cell/tissue components affecting pulmonary retention times when combined with mechanical particle clearance. Specific factors controlling barriers to biodistribution of nanomaterials include particle size and size distribution, in vivo dissolution 54

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contributing to uncertainty - when extrapolating from rats to humans, and when modelling silver biodistribution involving both soluble and particulate forms. Likewise, a recent draft of a NIOSH Current Intelligence Bulletin for silver nanoparticle exposure (NIOSH, 2016) relied heavily on nano-Ag biodistribution data established by Bachler et al. (2013). In this biokinetic model, silver biokinetics were modelled based on many unproven assumptions about Ag clearance behaviour following different exposure routes. Clearly, if inhalation is the mode of exposure, then biokinetic data should be based on inhalation studies rather than intravenous data when deriving and modelling human exposure limits. There will always be significant uncertainties when extrapolating from animals to humans with respect to species-specific sensitivity, for causing effects, raising questions about the applicability of data regarding biodistribution. In general though, deriving a human equivalent concentration (HEC) for inhaled nanomaterials using dosimetric extrapolation modelling (Fig. 9) is relatively straightforward and well established using available validated particle deposition models (MPPD). However, incorporation of uncertainty or assessment factors to derive an OEL from a HEC involves large uncertainties which has to be recognized, as done by the use of assessment/uncertainty factors (Fig. 14). Changing biokinetics of NPs due to a compromised or diseased state will likely occur, because of altered plasma composition that may modulate NP-protein interactions, or because of altered blood distribution (Oberdörster et al., 2005). It could be argued, therefore, that the critical organ where adverse effects occur, will change in situations of altered physiological conditions or in a diseased organism. Indeed, the recent paper by Campagnolo et al. (2017) reported that in pregnant mice inhaled Ag NPs not only translocated to placenta and foetuses but also caused inflammatory conditions and increased resorbtion of foetuses. The availability of particle dosimetry models (MPPD, ICRP, NCRP) for inhalation is essential for deriving exposure limits as indicated above. The question as to whether such models also exist for inhalation of MN resembling fibres has to consider the following points: It is a common misunderstanding thinking that fibrous nanomaterials, like multi-walled carbon nanotubes or single-walled nanotubes or carbon nanofibers, require the use of a fibre-specific deposition model. When aerosolized at the workplace, these materials occur in agglomerates without a specific fibrous structure; individual fibres, straight and curved, are highly entangled forming low-density agglomerates (Tsai et al., 2009; Methner et al., 2010) implying that the aerodynamic

action and dependent on physico-chemical properties of agglomerated NPs? A study with TiO2 and carbon black agglomerates showed increased pulmonary interstitial access only for TiO2 (Oberdörster et al., 1992). One explanation given by these authors was that TiO2 deagglomerated more easily in the alveolar region than the carbon black agglomerates. A more recent study (Balasubramanian et al., 2013) reported increased distribution to secondary organs of rats after inhalation of two types of 45 nm agglomerated gold NPs when the agglomerates had a primary size of 7 nm compared to 20 nm. A convincing explanation for this finding is that de-agglomeration of the agglomerates deposited on the alveolar surface resulted in small (7 nm) and larger (20 nm) particles, favoring greater translocation of the smaller ones (Kreyling et al., 2014). Information about the state of agglomeration (weak van der Waals forces) vs. aggregation (strong chemical bonds) as well as primary particle size of inhaled MNs, therefore, is important for potential targeting of secondary organs during inhalation exposure (Jiang et al., 2009). The biokinetics of nanomaterials following intravenous exposure is different from the systemic biokinetics from uptake of nanomaterials administered to the respiratory tract as described earlier (Kreyling et al., 2014, 2017a, 2017b, 2017c; Hirn et al., 2011; Schleh et al., 2013; Rinderknecht et al., 2007). Fig. 16 shows the significant differences in dose rates and point-of-entry between these two routes of exposure, which significantly affects nanomaterial biodistribution to secondary organs, and which additionally may be governed by the formation of a changing protein corona. Thus, the results of intravenous bolus administration of high doses and associated very high dose rate! - should not be assumed to reflect systemic distribution from acute or long-term dosing via the respiratory tract. High doses may not be relevant in a regulatory context. The study by Geraets et al. (2014) used intravenous doses in rats ranging from 2 to ~ 10 mg TiO2 nanoparticles per animal, which would be equivalent to over 600 mg up to over 3 g intravenously given to humans. The relevance of these doses can be questioned. Detailed data on biokinetics following inhalation exposure are extremely valuable for purposes of risk assessment when deriving exposure limit values, as has been exemplified in a study by Weldon et al. (2016). They derived occupational limit values for inhaled silver nanoparticles based on results of subchronic rat inhalation studies. It turned out that the most sensitive (critical) organ following subchronic AgNP inhalation was the liver, requiring data to establish a biokinetic model. The authors of this study still had to make several assumptions –

Fig. 16. Intratracheal and intravenous delivery of NPs: Impact on biokinetics. Intra-arterial indicates input into Vena pulmonalis which carries arterial blood. (Modified from Oberdörster, 2010.)

intratracheal

Intra-arterial

Lung intravenous

Design

DOSE AND DOSE RATE

Liver, other organs

i.v.

i.tr. inh.

Dose hi hi lo/hi Dose rate hi hi lo Sec. coat. plasma ELF Entry Circ. venous arterial Entry rate hi lo 55

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properties are not determined by the individual fibre shape. Rather, determining MMAD (Mass Median Aerodynamic Diameter) and GSD (Geometric Standard Deviation) using impactors is - in lieu of better data - appropriate when applying present particle dosimetry models to aerosol agglomerates generated from tangled fibrous nanomaterial in animal studies (Asgharian, personal communication). It is important though, to determine the effective density of the airborne agglomerates, which is obviously much lower than the density of the material forming the agglomerates. Several papers suggesting methods to determine effective density have been published (Charvet et al., 2014, 2015; Maricq and Xu, 2004; Spencer et al., 2007; Miller et al., 2013; Park et al., 2003; Hering and Stolzenburg, 1995; Wang et al., 2015). However, what matters is knowing the actual density present during the inhalation study as emphasized before (see discussion in Biodissolution and Biodurability, Fig. 12).

2. Conclusions

targets with sufficient post-exposure observation. For subchronic and chronic inhalation, dosimetric extrapolations are accepted methods to derive HECs (risk characterization) and eventually OELs when appropriately designed as Exposure-DoseResponse with sufficient post-exposure observation. Subacute 28-day studies with post-exposure time may be of sufficient duration as well, which requires further validation though. A most critical item which has now been accepted in revised OECD inhalation TGs is the measurement of retained lung burdens of MNs. Knowledge of lung burden data allows to determine MN lung clearance and retention kinetics, to perform critical dosimetric comparisons to well established benchmark materials (allowing comparative Dose-Response relationships to be analyzed), and to establish the dosimetric integration of in vivo and in vitro responses. Bolus-type dosing (intratracheal instillation; oropharyngeal aspiration) can be useful for hazard characterization, but not for risk characterization. Identifying secondary targets beyond the port of entry (biokinetics) has to consider the extraordinary high and unrealistic dose rate of bolus dosing. One question to consider is whether results of short-term tests (bolus dosing; STIS, in vitro) of benchmark MN X and untested MN Y (assuming both are considered to belong to same “group”) can be used to predict the outcome of subchronic inhalation for MN Y from the results available for subchronic inhalation of MN X? This could be accomplished by applying the ratio of slopes of short-term dose-responses of both MNs to the dose-response slope of the subchronic study for MN X. Again, as suggested in Fig. 14, the choice of different dosemetrics should be considered.

2.1. Risk assessment and physicochemical properties

2.4. Cellular in vitro assays

Among the three most common exposure routes for manufactured nanomaterials (MNs), inhalation is the most important, followed by oral and dermal exposure. Assessing risk as a function of hazard and exposure requires specific information about physico-chemical properties of MNs to be combined with the identification and characterization of a hazard and the assessment of exposure. These initial steps in the risk assessment process serve as inputs for the final step of quantitative risk characterization. Intrinsic (physico-chemical) and extrinsic (functional) MN properties have to be considered as determinants of biological/toxicological properties of MNs, the latter providing important information about functional characteristics, such as ROS-inducing capacity and dissolution behaviour in vivo.

These present many challenges. Cellular dose equivalency to in vivo (see Fig. 15) is difficult to achieve because of static, mostly acute in vitro systems with no clearance. Are they predictive for chronic effects? The dose dependency of mechanisms (the dose makes the mechanism) has to be considered as well. In vitro tests are suitable for toxicity ranking against well-characterized benchmarks (Hazard ID). However, one caveat is the impact of solubility in the static (no clearance) in vitro systems (e.g., ZnO NPs) affecting the outcome in contrast to the dynamic in vivo conditions.

1.12. Beyond mammalian model testing A question was raised regarding key triggers that indicate that biological monitoring or health surveillance of exposed individuals is needed? The short answer is: Obvious triggers are high exposure concentrations (by mass and number concentrations); particle size distribution data in the thermodynamic and aerodynamic range of respirability; worker complaints, and, of course, red flags raised by toxicity data from appropriate animal studies as well as information from toxicity data bases of related compounds in question.

2.5. Abiotic (acellular) assays Predictive toxicity ranking using the metric of specific MN surface reactivity (ROS assays) is a promising screening tool, but requires further validation and standardization (i.e., comparing abiotic test ranking vs. in vivo vs. in vitro cellular is needed). Dynamic dissolution assays are also a promising tool for predicting in vivo dissolution rates but require standardization. A caveat to this is that we need to take account of interaction of dissolved ions with subcellular structures and proteins in vivo (binding, retention), which does not occur in cell-free in vitro assays.

2.2. Predictive MN testing/risk assessment/modelling: benchmarking Comparative hazard and risk characterization done against positive and negative benchmarks such as crystalline nano-silica or corundum, is a useful approach to categorize new MNs. Benchmark materials need to be toxicologically well-characterized and validated, ideally they should also be certified as reference materials (metrology). A goal could be to establish and validate in vitro assays with incorporation of welldefined negative and positive benchmark materials for hazard identification and ranking focusing on similarities of physico-chemical properties and specific effects. Using the hazard data for risk characterization needs further development of in vitro – in vivo dosimetric extrapolation and comparison to quantitative risks that may have been established for the benchmarks.

2.6. Biokinetics; biodissolution 2.6.1. Route of exposure Systemic biodistribution of MNs depends on the point-of-entry. For example, MNs deposited by inhalation or instillation in the respiratory tract distribute differently than intravenously administered MNs; thus, biokinetic models based on data from intravenous MN administration should not be used to model biodistribution following inhalation.

2.3. In vivo assays Inhalation studies in general (short-term to chronic) can be used for hazard identification and characterization and risk characterization of inhaled MNs, best when designed to provide dose-response data, but limited when only exposure-response data are available. Information should also be provided for biokinetics and for identifying secondary

2.7. Biodissolution/biodurability Static (equilibrium solubility, μg/L) and dynamic (dissolution rate, ng/cm2/day) abiotic solubility assays provide different information 56

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about the solubilization of MNs reflecting either the static conditions in in vitro cell assays or the dynamic in vivo conditions. Results of both assays may be useful for categorization if performed in physiologically relevant fluids. Cellular in vitro assay to determine the pulmonary dissolution rate may be a promising tool when AM as the most relevant cell-type are used. Results from such in vitro assays with microparticles show good agreement with in vivo kinetics; yet further studies using MNs of different solubilities are required to validate this in vitro AM assay for nanomaterials. The AM dissolution assay could be standardized for grouping of MNs regarding their in vivo solubility. A new concept to determine in vivo the dissolution rates of MNs from a subacute rodent inhalation study needs further confirmation with different MNs. The in vivo solubilization behaviour of MNs can differ widely. It is too simplistic to group MNs just into soluble and poorly soluble materials. Static (equilibrium solubility) and dynamic (dissolution rate) abiotic assays are based on different concepts, which have to be kept in mind. The static system reflects the in vitro situation, the dynamic system the in vivo behaviour. However, even with the latter system longer retention times may occur in vivo than predicted by abiotic in vitro dissolution results because of possible binding of dissolved ions to subcellular structures or proteins in vivo. Still, results from dynamic dissolution in relevant physiological fluids - rather than just water - add valuable information to the extrinsic functional characteristics of MNs. Expressing the dissolution rate per cm2 MN surface per unit time could be considered as a grouping tool into high, moderate, low and insoluble MNs. The significance of biodissolution for biokinetics, effects and underlying mechanisms has to be assessed in separate studies, involving determining biopersistence/biodurability and ultra-high resolution imaging for analysing bioprocessing and biotransformations at a subcellular level.

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Glossary AM: Alveolar Macrophage ARA: Applied Research Associates, Inc. ATS: Alternative Testing Strategy bdiss: Lung dissolution rate of deposited/retained particles BMD: Benchmark Dose btot: Total lung clearance rate (mechan. plus dissol. of depos/ret. particles) BW: Bodyweight CNS: Central Nervous System CNT/CNF: Carbon Nanotubes/Carbon Nanofibers CPH: Spin-trapping reagent CSF: Cerebrospinal fluid DCF: DichloroFluorescein DCFH-DA: 2′,7′-dichlorodihydrofluorescein-diacetate DDD: Dose, Dimension, Durability

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