A critical review of in vitro dosimetry for engineered nanomaterials.

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A critical review of in vitro dosimetry for engineered nanomaterials

A major obstacle in the development of accurate cellular models for investigating nanobio interactions in vitro is determination of physiologically relevant measures of dose. Comparison of biological responses to nanoparticle exposure typically relies on administered dose metrics such as mass concentration of suspended particles, rather than the effective dose of particles that actually comes in contact with the cells over the time of exposure. Adoption of recently developed dosimetric methodologies will facilitate determination of effective dose delivered to cells in vitro, thereby improving the accuracy and reliability of in vitro screening data, validation of in vitro with in vivo data, and comparison across multiple datasets for the large variety of nanomaterials currently in the market.

Joel M Cohen1, Glen M DeLoid1 & Philip Demokritou*,1 Center for Nanotechnology & Nanotoxicology, Department of Environmental Health, Harvard School of Public Health, 655 Huntington Ave Boston, MA 02115, USA *Author for correspondence: Tel.: +1 617 432 3481 pdemokri@ hsph.harvard.edu

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Keywords: dosimetry • fate and transport modeling • hazard ranking • in vitro • nanosafety • nanotoxicology

The growing number of engineered nanomaterials (ENMs) utilized in a wide variety of consumer products and biomedical applications has led to increasing concerns of environmental and occupational exposures [1–7] . The unique physicochemical properties that distinguish ENMs from their micron-sized counterparts (reactive surface area, surface energy, mobility, quantum size effects, etc.) endow them with exceptional performance in consumer products, though may also be responsible for unique biological effects that can render them unsafe for humans and the environment [8–10] . Recent reports suggest exposure to ENMs may cause a variety of pulmonary and cardiovascular effects [3,11–14] , although the underlying toxicity mechanisms are not currently well understood [8,9,15] . Due to the high cost and laborious nature of in vivo toxicity studies, and the large and rapidly growing number and variety of ENMs entering the consumer market [16,17] , most nanotoxicology efforts have focused on the development of high-throughput in vitro screening methods for hazard ranking purposes [15–21] . Several groups have endeavored

10.2217/nnm.15.129 © 2015 Future Medicine Ltd

to develop efficient and inexpensive screening tools to correlate cellular toxicity with ENM characteristics such as size, shape and surface area [18,19,22,23] . High-throughput in vitro toxicity assays are utilized to assess multiple toxicity endpoints, in multiple cell lines, of libraries of ENMs over a range of exposure times and concentrations. However, results of in vitro assays to date have too often conflicted across published studies, as well as with data from animal studies [3,18,24,25] , and therefore have not earned widespread acceptance as powerful and reliable screening tools. Such discrepancies can be partially explained by the lack of proper characterization of ENMs in both the raw material form and in liquid suspension, as well as the lack of standardized ENM dispersion protocols [26,27] . Another major obstacle to the development of cost-effective and reliable in vitro toxicological screening methods is the need for accurate and physiologically relevant dosimetry [18,24,25,28,29] . Paracelsus, the ‘father’ of toxicology, recognized the significance of dose over 500 years ago, communicating the message in his famous dictum ‘Dosis Sola

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Review Cohen, DeLoid & Demokritou Facit Venenum’, or ‘the dose makes the poison’. This concept is considered the ‘holy grail’ of modern toxicology, though it has not yet been fully adapted to and integrated into the field of nanotoxicology [30] . Based on a recent PubMed search, it is clear that the number of publications concerning nanomaterial toxicity is increasing exponentially, with 7597 papers being published on the subject since 2000 (Figure 1) . In stark contrast, the number of articles that address nanomaterial dosimetry in some capacity is significantly less over the same time period (900 papers) (Figure 1) . The number of articles addressing in vitro nanomaterial dosimetry is even smaller (131 papers), and the number of articles actually estimating the effective particle dose delivered to cells in culture is smaller still. It is important to note that dosimetry considerations hold implications for any investigation of in vitro nano-biointeractions, including studies of cellular uptake, particle translocation across cellular monolayers (alveolar epithelium, gastrointestinal epithelium, blood–brain barrier, etc.), as well as ecotoxicity and nanomedicine assays [31] . Furthermore, selection of exposure concentrations for in vitro study often lacks scientific justification, with doses often chosen to be very high and well above levels commensurate with realistic potential occupational or environmental exposures [32] . In order for in vitro data to be validated with in vivo animal studies, the effective target doses investigated should be equivalent, 2000

or at least comparable [3,28,33] . Yet in some instances, nanotoxicologists have used particle concentrations for in vitro investigations for which corresponding in vivo doses cause ‘overload’, in which pulmonary clearance became severely impaired [3,28,33–35] . In this review, we summarize published work pertaining to dispersion preparation, characterization and dose computation, the essential methodological components of a robust nanodosimetry system or workflow. We further demonstrate that careful selection of relevant exposures for in vitro studies requires determination of equivalent doses between various experimental systems. Specifically, airway particle deposition inhalation models, such as the multiple-path particle dosimetry (MPPD) model [36] , enable calculation of deposited doses in the lung, which may be suitable for direct comparison with equivalent delivered dose values in an in vitro system [3,37–38] . Comparing in vivo and in vitro biological responses at equivalent doses is of great importance for refining and validating in vitro screening assays. Finally, the future outlook for nanomaterial dosimetry and its implications for toxicology are discussed. Current methodologies for in vitro dosimetry ENM dispersion preparation & characterization

For most in vitro systems wherein cells in culture are exposed to nanoparticles dispersed in culture media

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(i.e., as opposed to air–liquid interface systems), a systematic methodology for in vitro dosimetry begins with careful preparation and characterization of ENM agglomerates in suspension. ENMs suspended in complex physiological media may flocculate, agglomerate, dissolve and interact with serum components [27,39– 41] in a manner largely dependent on the dispersion preparation protocol, particle characteristics such as primary particle size, shape, chemical composition and surface chemistry [42–45] , as well as media properties such as ionic strength, pH and protein content [42,43,46] . In order to optimize ENM dispersibility in suspension, ultrasonic energy is used in a process known as sonication, wherein sound waves are propagated in liquid media, leading to alternating highpressure (compression) and low-pressure (rarefaction) cycles [26] . Sonication has proven more energy-efficient and can achieve a higher degree of de-agglomeration, at constant specific energy, than other conventional dispersion techniques [26] . A number of factors impact the ability of sonication to reduce agglomeration and improve dispersion stability over time, such as sonication energy (measured in watts), the duration of sonication and the volume of the dispersion undergoing sonication [27] . Improved reliability and reproducibility of ENM dispersion characteristics across nanotoxicology labs therefore requires standardized dispersion protocols, such as those recently developed by the National Institute of Standards and Technology (NIST) [47] , Organisation for Economic Cooperation and Development (OECD) [48] , European Framework 7 project Risk Assessment of Engineered Nanoparticles (ENPRA) [49] , the NANOGENOTOX team of the French agency for Food, Environmental and Occupational Safety [50] , the National Institute of Environmental Health Sciences (NIEHS) Nano Go Consortium [51,52] and other academic groups [53,54] . Key elements of proposed standardized dispersion protocols are summarized in Table 1. In reviewing the proposed standardized protocols, it becomes clear that elements necessary for achieving stable, relatively monodisperse suspensions suitable for in vitro testing include the following: identification of the materialspecific critical sonication energy required to achieve the smallest possible agglomerates that are stable over time; pre-dispersion sonication of ENM powder in sterile deionized water and dilution of water dispersions in relevant test media at desired concentrations. In order to ensure that data are reproducible, and to facilitate cooperative work across institutions and labs, reporting of dispersion protocol details, including dispersion volume, media and buffer formulations, pH, sonication times or energy input (in J/l) as well as application details, including elapsed time


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between sonication, dilution and use of the dispersion, cell culture format, volume of dispersion applied to cells and final dispersion pH, ionic strength and dissolved organic matter is essential. Finally, in order to enable subsequent modeling and dose computation, it is imperative that characterization of critical parameters that drive particle transport in liquid suspension (agglomerate diameter and effective density) be performed and reported. It is worth noting that characterization of effective density, a major determinant of particle fate and transport, is rarely measured and taken into consideration. Among all published standardized protocols for suspension preparation and characterization, only the one presented by Cohen et al. [53] included measurement of effective density. Adoption of best practices proposed in the referenced protocols will enable nanotoxicologists to achieve fairly monodisperse suspensions of the smallest possible ENM agglomerates in solution that are stable over time. Agglomeration reduces the total number of free particles in suspension, thereby altering (reducing) the total surface area available for interaction with cells in vitro, and also influences the delivery of particles to cells. Nanoparticles suspended in physiological media can exist as stabilized and nonagglomerating units such as spheres or rods, or can form fractal and porous agglomerates comprising multiple primary particles with trapped suspension fluid and associated proteins (see Figure 2A). Agglomerate size distributions can be measured by a variety of methods, including analytical ultracentrifugation (AUC), dynamic light scattering, hydrodynamic chromatography, nanoparticle tracking analysis, laser diffraction spectrometry, x-ray disc centrifugation, tunable resistive pulse sensing, etc. [37,55] . Measurement of agglomerate effective density has presented a greater challenge, and is often overlooked by nanotoxicologists in spite of the large implications it holds for particle delivery to cells and dosimetry. The effective density of nanomaterial agglomerates can differ from the density of the raw material by as much as several fold, primarily because of the irregular and fractal quality of nanomaterial primary particles that leads to intraparticle trapping of culture media [56] and the corona of adsorbed protein that coats the surface of the primary particles [44,45,57] . Because effective density is directly proportional to the rate of agglomerate sedimentation, its accurate measurement is key to estimating delivered dose over time in vitro. Estimates for effective density can be calculated based upon a theoretical fractal-based model for agglomeration [58] . Alternatively, the sedimentation coefficient of a suspended ENM can be measured

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Table 1. Proposed dispersion protocols for nanomaterial suspensions. Publication/protocol

Key element of approach

Notes

NIST/CEINT [47]

• Predispersion sonication of ENM powder in deionized and sterilized water

• Provided method is specific for nano-TiO2 particles

• Addition of proteins (bovine serum albumin, etc.) as an agglomerate stabilizing agent

• Includes calorimetric calibration of delivered sonication power for specific sonicator settings

• Preparation of dispersions in relevant test media

OECD [48]

• General guidance similar to NIST protocol for TiO2

• Applicable for wide range of nanomaterial compositions

• Requires detailed reporting of dispersion volume, media, pH, use of buffers, sonication times or energy input (in J/l), details on how long after sonication before test dispersion was applied to cells, total volume of dispersion applied to cells, details on pH, ionic strength and dissolved organic matter present in final test dispersion

• Requires comprehensive characterization of nanomaterial powder prior to dispersion preparation (primary particle size, size distribution, surface area, surface chemistry, surface charge, crystal structure, interfacial tension, dustiness, crystallite size, photocatalytic activity, pore density, porosity, indication of aspect ratio)

ENPRA [49]

• Generic protocol applicable for all nanomaterials

• Dispersion media comprises 2% (w/v) serum-water (fetal calf serum) solution

• Hydrophobic materials require prewetting step in ethanol

• Test material is mixed in medium at a concentration of 2.56 mg/ml, and particles are dispersed using a 300 W probe-sonicator set at low amplitude (10%) for 16 min

• To prevent excessive heating, the dispersion is cooled in an ice-water bath during sonication

NANOGENOTOX [50]

• Generic protocol similar to ENRPA

• Dispersion media comprises 0.05% (w/v) bovine serum albumin

• All samples (hydrophobic and hydrophilic) undergo prewetting step in ethanol

NIEHS Nano Go Consortium [51,52]

• ENM stock solutions prepared in sterile water at 5 mg/ml using a water bath sonicator or cup horn sonicator

• TiO2-NB particles were stirred rather than sonicated to prevent mechanical shear from causing axial fractures

• Stock solutions then diluted in cell culture media

Cohen et al. (2014) [53] • Identification of material-specific critical sonication • General protocol applicable for wide range energy required to achieve smallest possible of low aspect ratio materials agglomerates that are stable over time

• Predispersion sonication of ENM powder in deionized and sterilized water

• Includes extra step of identifying and reporting material-specific critical sonication energy

• Dilution of dispersions in relevant test media

• Highlights importance of dosimetry by requiring characterization of critical parameters that drive particle delivery to cells in vitro (agglomerate diameter and effective density)

• Characterization of critical parameters that drive transport (agglomerate diameter and effective density)

ENM: Engineered nanomaterial.

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directly by AUC [43,55,59] . Although AUC results can provide accurate measurements of ENM effective density, this method requires expensive equipment and is relatively limited in terms of throughput. Multiple samples can be run by the LUMiFuge, (LUM, CO, USA), but this instrument has only rudimentary optics and is therefore not ideal for particle sizing, which is required for accurate density determination. The more popular analytical centrifuges from CPS and Brookhaven, though more modestly priced, can run only one sample at a time. Importantly, most nano-

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toxicology labs do not currently have ready access to an analytical ultracentrifuge. The recently developed volumetric centrifugation method (VCM) can be used to measure effective density with high-throughput using a standard benchtop centrifuge (such as the Beckman Allegra series in which up to 148 samples can be run simultaneously), and relatively inexpensive packed cell volume (PCV) tubes [56] . Detailed protocols for the VCM approach, along with results demonstrating validation with AUC data have been reported in the literature [3,53,56,60] .

Intra-agglomerate media: media and proteins trapped within pores of ENM agglomerate

Protein corona

ENM aggregate

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ENM transport by diffusion and sedimentation

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Delivered dose Nanomedicine © Future Science Group (2015)

Figure 2. Agglomeration and particle delivery to cells in vitro. (A) ENMs suspended in cell culture media can form porous fractal agglomerates comprising multiple primary particles with media trapped between primary particles. (B) ENM liquid suspensions applied to cells settle over time as a result of mass transport (sedimentation and diffusion). The initially administered dose is the concentration of ENM in the initially homogeneous suspension. As transport progresses, agglomerates are concentrated near or deposited onto the cells. The mass of ENM deposited per area is the delivered dose. ENM: Engineered nanomaterial.



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Review Cohen, DeLoid & Demokritou As shown by DeLoid et al., the effective densities of metal and metal oxide agglomerates in suspension can vary significantly from those of the corresponding raw ENMs. For example, CeO2 nanoparticles, with a raw material density of 7.65 g/cm3, were determined by VCM to have an effective density of just 2.37 g/cm3. It should be noted that the effective density estimated by the Sterling equation was 5.06 g/cm3, although VCM and Sterling equation estimates of effective density for most ENMs studied were generally in better agreement. It must be noted that for partially soluble materials, dynamic changes to effective density due to mass loss associated with dissolution over time must be accounted for by time-resolved dissolution analysis in the relevant cell culture media [56] . Integrated in vitro dosimetry platforms

Variability in the settling times of various low aspect ratio nanoparticles presents a major challenge for determining comparable dose metrics by which to compare and rank hazard potential for large panels of materials. Cohen et al. recently reported a wide range of predicted settling times for various low aspect ratio ENMs based on differences in agglomerate diameter and effective density (see Figure 3) [53] . These results highlight the importance of deriving accurate measures of dose for interpreting nanobio interactions in vitro. It is worth emphasizing that in vitro nanotoxicology is comparative by nature. ENMs are usually ranked both among themselves and in comparison with well-characterized positive and negative control particles. It is essential that such comparisons be made on the basis of the same delivered dose. Direct measurement of cellular uptake may be one way to accurately report the effective dose. Cellular uptake of ENMs can be monitored using particles bearing various detectable and quantifiable markers. Examples include fluorescently labeled polystyrene beads [61,62] , superparamagnetic iron oxide tracer particles [61,63] , and neutron activated tracer particles [60] . Alternatively, uptake can be measured by direct quantification of the ENM material using inductively coupled plasma mass spectrometry (ICP-MS) [64–66] . Additionally, the sedimentation and cellular uptake of agglomerated particles can be recorded and quantified by video microscopy [67] . Despite the availability of these various methods, the task of measuring uptake is not trivial when screening large batches of ENMs with variable properties and varying degrees of suitability for detection by these various methods (fluorescence, ICP-MS, gamma spectroscopy, etc.). Moreover, although the extent of uptake may be a reasonable predictor of some potential biological effects, it is by no means the only way ENM exposure can influence

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a cell. By merely contacting cell surface molecules, which include myriad receptors, channels and other proteins, as well as a variety of lipid components, even transiently, the external ENM could conceivably initiate or modulate signaling pathways, alter the function of ion channels, interfere with paracellular and intercellular signaling and so on. As an alternative approach, hybrid integrated in vitro dosimetry platforms have been recently developed to calculate the dose delivered to cells as a function of exposure time. These methodologies comprise a two-step process. The first step involves careful preparation and characterization of ENM suspensions for agglomerate size, effective density, etc., as described in the previous section. The second step involves using the data collected from the first step as inputs to advanced computational models designed to estimate fate and transport of particles and particle delivery to cells over time. In a typical temperature and humidity-controlled in vitro nanotoxicity experiment, delivery of ENMs to cells in culture is determined by two fundamental transport mechanisms – diffusion and sedimentation [53,56,61] . Rates of diffusional transport are a function of particle size, media viscosity and temperature, and can be estimated from the diffusion coefficient, D, defined by the Stokes-Einstein equation as: k T D= B 6rhr in which k B is the Boltzmann constant (kg m2 s-2 K-1), T is the absolute temperature (K), η is the media dynamic viscosity (kg m-1 s-1) and r is the particle radius (m) in suspension, and Fick’s first law, which defines the quantity of solute transported per unit cross-sectional area per unit time, or the flux, J, as dC J = -D dz dC where dz is the concentration gradient (kg m-4) along the direction of the flux. The time required for a particle to diffuse a given distance in one dimension can be calculated from the equation

^ 2h t = xr 2D where the numerator is defined as the root mean squared distance, or the distance that the average particle of a given size will travel by diffusion [68,69] . On the other hand, a particle sediments at a rate determined by the balance of opposing forces acting upon the particle: the acceleration force (e.g., gravitational or centrifugal), the counter buoyant force caused



• Transport of polydisperse suspensions (which, to a greater or lesser degree, is the case with most ENM suspensions) can be accurately modeled by assuming a single average hydrodynamic diameter for the agglomerates;

CeO2-C CeO2-A CeO2-B Fe 2O3 nanohorns SiO2

A-TiO 2

• Agglomerate density can be accurately calculated, using the Sterling equation, from estimated theoretical fractal dimensions of the agglomerated material [58,61] ;

SiO2

where vs is the settling velocity (m s-1), g is acceleration due to gravity (m s-2), rE is the effective density (kg m-3), rmedia is the media density (kg m-3). Early contributions in terms of numerical models to estimate the fate and transport of particles in suspension include the in vitro sedimentation, diffusion and dosimetry model (ISDD), which allows, for a given ENM media combination, estimation of the fraction of administered particles that would deposit on cells as a function of time [61] . The ISDD model software is readily available from the authors, easy to use, has been validated for various classes of agglomerating and nonagglomerating nanomaterials [56,60–62] , and facilitates consideration of accurate dosimetry in vitro. However, the ISDD model is based on the simplifying assumptions that:

• The size, number and effective density of agglomerates remain constant over time (i.e., ENM dissolution and restructuring of agglomerates does not occur);


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Cr2O3

• Particles immediately and permanently adhere to cells at the bottom of the well upon arriving at the bottom, and are thereby removed from further influencing transport (i.e., by contributing to a concentration gradient driving diffusion upward).

Figure 3. Comparison of time required to deliver 90% of the administered dose (t90 ; h), calculated following the described dosimetry methodology. Reproduced with permission from Cohen et al. [53] .

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ZrO2 Printex Carbon VENGES VENGES VENGES VENGES VENGES EVONIK

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by displacement of medium by the particle, and the frictional or drag force. The net effect of these forces is dependent on particle size, shape and density, density of the media and can be calculated from the following equation (assuming spherical particles):

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Review Cohen, DeLoid & Demokritou improve accuracy. The VCM-ISDD methodology for calculating particle delivery to cells in vitro over time was recently validated against particle deposition data directly measured using a neutron-activated tracer particle system. Suspensions of radiolabeled CeO2, and SiO2-coated CeO2 particles were applied to transwell insert membranes with 3 μm pores, and the delivered dose (defined as the sum of particles deposited on and passed through the transwell membrane) was measured by gamma spectroscopy following 2-, 4- and 24-h incubation [53] . Close agreement between the measured delivered dose and VCM-ISDD calculated delivered dose observed at each time point highlights the utility and accuracy of this integrated methodology. The Distorted Grid (DG) Model recently reported by DeLoid et al. [Deloid G, Cohen J, Pyrgiotakis E et al. Advanced computational modeling for in vitro nanomaterial dosimetry, Submitted] addresses

the physical realities simplified by the last three of the ISDD’s aforementioned assumptions, namely, polydispersity, dissolution and variable interaction of particles at the bottom of the culture well. The DG model is a one-dimensional iterative finite element model based on a model previously developed to predict transport of protein systems in an analytical ultracentrifuge [70,71] . In this model, the cell culture well is divided along the vertical axis into multiple compartments, and the fundamental transport equations are used to iteratively simulate brief rounds of sedimentation and diffusion at the boundaries between successive compartments. The outputs include profiles of mass, surface area and particle number concentration along the vertical axis as a function of time (i.e., concentration as a function of time and vertical position), as well as the corresponding delivered dose (deposition) metrics at the bottom of the well. The iterative finite element design of the DG model permits direct simulation of polydisperse particle suspensions, as well as dynamic particle suspensions (i.e., in which agglomerates change in size over time due to dissolution) and realistic variable binding kinetics of particles at the bottom of the well. Experimental validation was performed by ‘flash freezing’ columns of ENM suspensions, slicing the columns into precise sections using a microtome, and measuring the optical absorbance of each section to arrive at concentration profiles for various time points. Close agreement was observed for both fast and slow settling materials [Deloid G, Cohen J, Pyrgiotakis E et al. Advanced computa-

tional modeling for in vitro nanomaterial dosimetry, Submitted] .

Such advanced models will provide more accurate dosimetry for realistic ENM suspensions, and allow nanomaterial researchers to consider spatial variability of particle concentration over time, and to investigate the impact of dynamic changes to agglomerate

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diameter and effective density over time on cellular interactions in vitro. A multidimensional computational fluid dynamics (CFD) model was also developed as an increasingly sophisticated and powerful tool for estimating particle deposition over time [Deloid G, Cohen J, Pyrgiotakis E et al. Advanced computational modeling for in vitro nanomaterial dosimetry, Submitted].

In this CFD model, particles are initially assigned to compartments of a 3D grid representation of the suspension column, and a solution of the Navier-Stokes equation is used to calculate the movement of individual particles between compartments. While being a powerful tool, the CFD model is highly computation-intensive, requiring expensive, sophisticated software and many hours (up to days) of compute time on multiplexed supercomputers to simulate transport for a single system. Outputs from the DG model were compared with the CFD model, and showed very close agreement for particle deposition over time for both fast and slow settling materi[Deloid G, Cohen J, Pyrgiotakis E et al. Advanced comals

putational modeling for in vitro nanomaterial dosimetry, Submitted] .

By contrast, the DG model requires only minutes of computation time on an average personal computer to complete the same task, and therefore may provide a more pragmatic tool for nanotoxicologists to address dosimetry. Impact of ENM properties on delivery to cells

As discussed above, agglomerate diameter directly influences the rates of diffusion [56,61] . The sedimentation and diffusion transport rates for gold nanospheres of known density (19.3 g cm-3), suspended in cell culture media typically used for in vitro nanotoxicity experiments (RPMI/10%FBS: viscosity = 0.00089 Pa s; density = 1.00 g cm-3), at 37°C, were calculated using Equations 1–4 for a range of primary particle diameters (Figure 4A) . The time required to travel 1 mm (approximately half the media column height used in a typical cell culture experiment) by sedimentation was estimated by dividing the transport rate by the distance, and was estimated for diffusion via Equation 3. Figure 4A demonstrates a clear size-dependency for which transport mechanism drives deposition in vitro, where gold nanospheres (ρ = 19.3 g cm-3) ≤40 nm in diameter are primarily driven by diffusion, and gold nanospheres (ρ = 19.3 g cm-3) >40 nm are primarily driven by sedimentation. For example, a 1 nm gold nanosphere is estimated to travel 1 mm by diffusion in 16 min versus approximately 1,500,000 min by sedimentation. By contrast, a 1000 nm gold nanosphere is estimated to travel 1 mm by diffusion in approximately 16,000 min versus only approximately 1 min by sedimentation. This concept is supported by a recent



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ENM density (g/cm3) Figure 4. Impact of engineered nanomaterial diameter and density on mass transport in vitro. (A) The theoretical time to transport nonagglomerating gold nanospheres of known density 1 mm is plotted against primary particle diameter. (B) The theoretical time to transport nonagglomerating gold nanospheres of known diameter 1 mm is plotted against effective density. ENM: Engineered nanomaterial.

study investigating cellular uptake of nonagglomerating gold nanospheres. Cho et al. [72] reported that


for nonagglomerating gold nanoparticles of hydrodynamic diameter 15 nm, diffusion is the prevailing


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Review Cohen, DeLoid & Demokritou means for transporting nanoparticles to cells in culture, while for nonagglomerating gold nanoparticles of hydrodynamic diameter 100 nm, sedimentation is the prevailing means for transport [72] . While effective density does not impact diffusion rates (see Equation 1), it has a direct linear effect on sedimentation rate (see Equation 4). Figure 4B illustrates the impact of effective density on sedimentation speed for gold nanospheres of constant diameter (50 nm). Nonagglomerating gold nanospheres that share a density with that of pure gold (19.3 g cm-3) are estimated to travel 1 mm by sedimentation in 595 min. In contrast, gold agglomerates with a hydrodynamic diameter of 50 nm and an effective density closer to that of the liquid media (2.15 g cm-3) travel 1 mm by sedimentation in 1,154,593 min. These data highlight the great influence of agglomerate density on particle transport in vitro, which has important implications for delivery to cells in culture. Selection of appropriate dose metrics Much debate has been and continues to be focused on the question of which dose metric (mass, particle number, surface area, etc.) is most appropriate for in vitro nanotoxicity [73,74] . In a typical in vitro toxicity study, however, dose is usually reported as the administered mass of ENM per unit volume of exposure media (e.g., μg ml-1) [32,74–77] . This practice ignores the tendency for ENMs in liquid suspension to agglomerate, [27,41] , diffuse and settle [72,73] as a function of exposure time. Furthermore, interactions between ENMs and cells or tissues are likely determined by interactions at the particle surface. Rushton et al. reported that ROS generation in alveolar macrophages in vitro was significantly correlated with polymorphonuclear neutrophil (PMN) counts in in vivo rat intratracheal instillation studies, when biological activity in both cases was expressed per unit administered particle surface area [76] . Surface reactivity refers to the capacity for particles to react with their local environment, resulting for example in the induction of reactive oxygen species (ROS) and inflammatory responses. Some studies report that the relative surface reactivity of various types of quartz or titania nanoparticles correlate well with inflammatory responses [77] . Particle surface charge influences electrostatic interactions with nearby proteins that make up the protein corona, which may influence cellular uptake and particle translocation in the lung [77] . It is clear that surface reactivity and surface charge influence biological responses, and must be considered in hazard screens for large panels of nanoparticles. For the purposes of correlating in vitro data with in vivo data for a given particle, or comparing

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toxicity of a particle of given reactivity and composition across various primary particle sizes, the delivered to cell or deposited particle surface area dose may provide a more accurate predictor of biological activity. Several groups have therefore recommended that the dose of ENMs be reported in relation to particle surface area [78–80] . It is important to note that particle transformations in cell culture media typically used for in vitro studies may alter the available surface area for biointeractions. Some groups have reported that formed agglomerates of nanoparticles in vitro exert different biological effects as compared with well-dispersed nanoparticles [81,82] . By contrast, Wittmaack recently reported significant correlation between multiple measures of in vitro toxicity and the density of SiO2 nanoparticle mass per area delivered to cells over the exposure duration [83] . To address these issues of particle transformations and delivery to cells over time, both the VCM-ISDD and DG models calculate delivered dose values in terms of mass, particle number and surface area delivered to cells over time, enabling nanotoxicologists to identify and choose the dose metrics most suitable for their particle-cell systems. Recently, a simplified dosimetric tool for nanotoxicologists referred to as relevant in vitro dose (RID) functions was introduced [53] . RID functions are simple mathematical equations, derived from the hybrid in vitro dosimetry methods presented above, that provide an easy way to roughly estimate delivered dose metrics in terms of mass, particle number and surface area for a given ENM-culture mediawell geometry system. RID functions were derived by fitting VCM-ISDD numerical model output data to a Gompertz sigmoidal function to obtain a material media specific deposition fraction constant (α), which is then used, along with agglomerate size (rH), agglomerate effective density (ρe) and culture well geometry parameters, to determine the corresponding mass, surface area and particle number metrics. These functions and values, recently reported for a large panel of industrially relevant nanomaterials [53] , provide simple tools that nanotoxicologists can use to approximate dosimetry in the absence of sophisticated fate and transport numerical models. Impact of solubility on dosimetry For partially soluble materials, changes in agglomerate diameter and effective density due to dissolution over time must be resolved and addressed in order to accurately estimate delivered dose. For greatest accuracy, mass loss due to dissolution, hydrodynamic diameter (dH ) and effective density (ρEV ), should be measured over the time of exposure. These time-resolved values should then be utilized by transport simulation models



Impact of dosimetry on hazard ranking of ENMs Diameter- and density-dependent variability in particle fate and transport in vitro will no doubt influence the interpretation and comparison of toxicity screens for large panels of materials. DeLoid et al. [56] and Cohen et al. [53] reported striking variability, among various metal and metal oxide ENMs, in the calculated time to deliver 90% of the total administered dose (see Figure 3). More recently, Pal et al. [84] compared administered and delivered (estimated using the VCM-ISDD model) dose-response curves for ‘slow settling’ and ‘rapidly settling’ ENMs. Marked differences between the slopes of administered and delivered dose-response curves were observed for ‘slow-settling’ ENMs, whereas only negligible corresponding differences were observed for ‘rapidly settling’ ENMs. Most importantly, these differences were also reflected in differences between the toxicological rank orders based on administered versus delivered dose. Based on cell viability in THP-1 cells as a function of administered dose, the ascending rank order (from low to high toxicity) of ENMs investigated was: TiO2 P25 < CeO2 < nAg < N110 < MnOx PALAS < Printex-90 < SWCHN-ox < nNi-Inco. In contrast, the rank order for cell viability as a function of delivered dose was: TiO2 P25 < CeO2 < nAg < MnOx PALAS < Ni-Inco < Printex-90 < N110 < SWCNH-ox. Furthermore, direct comparison of cell viability with in vivo lung inflammatory markers resulted in a stronger association for the delivered dose-response curve (R 2 = 0.97) than for the administered dose-response curve (R 2 = 0.64) (see Figure 5). While dosimetry may not be the final or only solution for improving and establishing in vitro methods that better correlate with in vivo results, these studies indicate that dosimetry cannot be ignored.


Bringing in vitro and in vivo doses to the same scale In order to ensure accurate and meaningful comparisons between the results of in vitro and in vivo studies, upon which the utility of in vitro toxicity screening is entirely dependent, ENM concentrations in in vitro nanotoxicity studies must be carefully selected such that the effective doses match those employed in the corresponding in vivo studies [3,28,33,85] . Numerical models, such as the widely used and well-accepted multiplepath particle dosimetry model (MPPD) [86,87] have long been employed to estimate the delivered in vivo dose of inhaled particles. Recent advancements to the MMPD have now extended its use to computational modeling of nanoparticle deposition in the mouse lung [88] . Demokritou et al. recently employed the MPPD model to estimate lung surface mass deposition per unit surface are associated with rodent in vivo inhalation exposures with CeO2 ENMs [3] . These mass deposition doses were then set as the target effective delivered doses for subsequent in vitro investigation. The integrated in vitro dosimetry approach outlined above (i.e., VCM-ISDD) was used to back-calculate the administered mass concentrations necessary to achieve the target in vitro delivered doses equivalent to those obtained from the MPPD model from the in vivo exposures. It is worth noting that the toxic2.5 Printex-90

Printex-90 2.0 PMN (×105) per µg

to accurately estimate delivered dose. Dissolution of 24 metal oxide ENMs in various cell culture media formulations has been previously reported [21] . Although most materials examined did not undergo significant dissolution, a few exhibited ≥10% dissolution following 24-h incubation (ZnO, CuO and WO3). Considering that mass loss due to dissolution will likely result in either a decrease in agglomerate effective density (as ENM material is removed from the agglomerates), or a decrease in agglomerate size, or both, dosimetry calculations for soluble materials based on effective densities and sizes measured immediately after sample preparation may result in an overestimation of delivered dose over time. In addition, for completion of dosimetry calculations, both the particulate and soluble components must be correctly identified, and considered separately, as previously described [69] .

Review

Y = 0.45.X R2 = 0.64

Ni Inco

Ni Inco

1.5

Y = 0.23.X R2 = 0.97

1.0 nAg

nAg

TiO2

0.5

CeO2

0.2 0

2

Administered dose Deposited dose 4

6

8

10

% Cell death per µg Figure 5. Comparison of in vitro cell death slopes to in vivo lung inflammation (polymorphonuclear neutrophil/microgram) for five low aspect ratio engineered nanomaterials. Much stronger association with PMN is observed for deposited dose slopes (R 2 = 0.97) as compared with administered dose (R 2 = 0.64), highlighting the importance of considering delivered dose to cells in in vitro nanotoxicology. PMN: Polymorphonuclear neutrophil. Reproduced with permission from Pal et al. [84] .


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Review Cohen, DeLoid & Demokritou ity results between the in vivo and in vitro studies were incompatible even at the estimated equivalent in vitro and in vivo doses [3] . These results underscore the challenge and potential pitfalls in extrapolating results from monocellular in vitro systems to whole animal systems. Importantly, the combined MPPDVCM-ISDD approach used in this study eliminated significant uncertainty regarding dosimetry, and the remaining questions land squarely in the realm of biology. More work is still needed to improve the sophistication and sensitivity of these methods, though these efforts toward improving the accuracy and relevance of in vitro dosimetry should provide an example for future nanotoxicology study. Other recently published studies that linked in vivo and in vitro doses following the same approach include studies of engineered nanomaterials released from printing equipment [89,90] . Improved exposure assessment for aerosolized ENMs in occupational and other settings are also becoming increasingly important for proper risk characterization. Measured aerosol concentrations and particle size distributions can be used to estimate deposited and retained mass doses of ENMs in the pulmonary alveolar region of humans and animals. A recent study by Gangwal et al. empoyed such an approach to determine human lung surface mass concentrations for a given ENM exposure scenario. These values were subsequently converted to testing solution mass concentrations for in vitro screening based on the cell culture well-bottom surface area [28] . Using this method, the equivalent in vitro dose for peak lung surface concentration following 24-h exposure to 1 mg m-3 aerosol concentration of TiO2 nanoparticles (ranging in thermodynamic diameter from 5 to 100 nm) was estimated to be 0.240 μg ml-1 (assuming a standard 96-well cell culture plate). Although this value represents a peak exposure value for humans in an occupational setting, it is considerably lower than doses typically tested in vitro [20,27,28,91] . Future studies should employ similar approaches to ensure that toxicity is assessed at doses relevant to human exposure. Importantly, this approach may only be appropriate for short-term or daily human exposures. Equating lifetime doses accumulated in the alveolar region following long-term chronic inhalation exposure with doses delivered all at once as a bolus in an in vitro system ignores differences in dose rate (which may span many orders of magnitude), and can be highly misleading. For example Gangwal et al. report a 45-year accumulated surface area dose equates to in vitro concentrations of 50–69 μg/ml, extremely high doses that should only be considered as the high-end limit of an in vitro investigation using a wide range of doses [29] .

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Future work

It is apparent that the lack of dosimetric considerations in most in vitro nanotoxicology studies introduces significant bias and hinders our ability to develop accurate and cost-effective screening strategies for ENMs. Adoption of integrated in vitro dosimetry methodologies such as those described here may help to improve the accuracy and reliability of in vitro screens. Most studies to date investigated toxicity only of freshly generated nanopowders. Future work must also examine nanomaterials at other stages across their life cycle (e.g., embedded within the matrix of composite materials, end of life waste streams, etc.). The nanotoxicology field is currently seeking ways to investigate links between exposures of particles released from nano-enabled products to human health hazards. Progress has been made in establishing sample collection protocols that enable direct testing of life-cycle released particulate matter (LCPM) in cellular and whole-animal models for human toxicity [92] . However, nanosized particle releases from composite materials often exist as a mixture of polydisperse size distributions and multiconstituent exposures. Hazard and risk characterization of LCPM will require evaluating the applicability of, and, as necessary, adapting current in vitro screening and dosimetry methodologies for these purposes. Another limitation to existing approaches is that computational modeling of particle fate and transport is based on the assumption of spherical particles or spherical agglomerates. The validity of such models for high aspect ratio materials such as carbon nanotubes or nonagglomerating nanorods has yet to be determined, and more work will likely be necessary to develop suitable methods for modeling the fate and transport of such materials in vitro. The typical in vitro approach of overlaying cells with predispersed nanomaterial suspensions differs fundamentally from most real-world environmental exposures, particularly from inhalation exposures, which could certainly confound attempts to extrapolate in vitro results to either in vivo experiments or human toxicity. One possible approach to producing more physiologically relevant inhalation exposures is to deliver aerosolized ENMs to cells cultured at an air–liquid interface [93–95] . However, commonly used aerosol generators used to disperse nanopowders for exposure to cells (e.g., nebulizers, fluidized beds and other venturi aspirator-type systems) are unable to consistently produce realistic nanosized aerosol distributions, and often lack the ability to accurately control and change exposure concentrations for dose–response investigations [3,96–98] . More work is necessary to further develop and integrate highly controlled and versatile aerosol exposure systems with physiologically relevant in vitro systems.



The mechanisms of nanoparticle–cell interactions that determine cellular uptake and internalized dose are not well understood, and more work is necessary to

Review

elucidate particle and cell properties that govern binding and uptake. The impact of such biokinetics on in vitro dosimetry are considered in more detail in recent

Executive summary • The growing number of engineered nanomaterials (ENMs) used in consumer and biomedical applications presents a major challenge for characterizing the potential human health hazards associated with exposure. • Due to the high cost and laborious nature of in vivo toxicity studies, most nanotoxicolgy efforts have focused on in vitro screening methods. • One major obstacle to the development of reliable in vitro screening methods is a lack of standardized and easy-to-use methods for considering dosimetry. • In spite of recent evidence demonstrating the importance of dosimetry on accurate interpretation of results from in vitro studies, dosimetry considerations are often ignored in the literature.

Current methodologies for in vitro dosimetry • ENM dispersion preparation and characterization: ––○ENM dispersion preparation is critical to generating reproducible particle exposures and accurate characterization of delivered to cell doses in vitro; ––○Several proposed dispersion preparation protocols are evaluated, and best practices for in vitro studies are summarized; ––○Minimal dispersion characterization requirements for accurate estimation of delivered to cell particle dose are proposed, and available characterization methods discussed. • Integrated in vitro dosimetry platforms: ––○Currently available numerical models for estimating particle delivery to cells in vitro based on particle dispersion characterization data are described. • Impact of ENM properties on delivery to cells: ––○The impact of particle agglomeration, size and effective density on delivery to cells is discussed; ––○Rates of particle sedimentation are impacted by both agglomerate diameter and effective density, while diffusion is impacted mainly by agglomerate diameter.

Selection of appropriate dose metrics • Various dose metrics including particle mass, surface area, number, in terms of administered and delivered values are discussed. • Particle interactions in liquid suspension may alter dose metrics, highlighting the importance of dispersion characterization for accurate dosimetry.

Impact of solubility on dosimetry • Partially soluble ENMs may exhibit dynamic properties in solution over time that can impact particle delivery to cells. • Particulate and soluble components of ENM dispersions must be identified and accounted for to accurately interpret cellular toxicity.

Impact of dosimetry on hazard ranking of ENMs • Consideration of delivered to cell dose improves accuracy when comparing in vitro results across large panels of ENMs, and improves concordance of in vitro results with effects measured in vivo.

Bringing in vitro & in vivo doses to the same scale • Concentrations used for in vitro study should be interpreted within the context of realistic human exposures. • Currently available numerical models facilitate determination of equivalent doses between in vitro and in vivo inhalation studies.

Future work • While much work to date has focused on freshly generated nanopowders, more work is necessary to characterize dosimetry of nanomaterials across their life cycle (e.g., embedded within the matrix of composite materials, end of life waste streams, etc.). • Further research is necessary to develop numerical models estimating dosimetry for high aspect ratio nanomaterials.

Conclusion • Accounting for dosimetry will improve the accuracy of parametric studies of nano-biointeractions in vitro. • Improved measures of dose in vitro may improve concordance with effects measured in vivo, a necessary step for establishing reliable in vitro hazard screening models. • Reducing uncertainty regarding in vitro dosimetry will enable comparison of results across multiple datasets available in the literature.



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Review Cohen, DeLoid & Demokritou work by Simko et al. [99] . A nanoparticle dose model was proposed based on the ionizing radiation dose model, comprising the deposited dose, defined as the total deposited particle surface area per tissue mass, the dose rate, defined as cellular uptake of particles over time, and the biokinetics. Physicochemical parameters including morphology, aspect ratio, surface properties, electron band gap energy level and dissolution rate, were accounted for using ENM-specific weighting factors. These weighting factors were applied to the deposited dose in order to determine an equivalent dose. The authors recommend incorporating additional organ- or cell-specific weighting factors (as they become available) in order to convert the equivalent dose to this kind of effective dose. Improved understanding of nanoparticle–cell interactions will further improve available conceptual models for in vitro dosimetry.

of integrated in vitro dosimetry methodology such as those described here will be a major step toward the development of inexpensive, accurate and reproducible in vitro screening assays, and a major advancement for nano-environmental health and safety research.

Conclusion Improving the accuracy of in vitro dosimetry will have positive impacts in several major areas of research. First, it should be possible from cellular studies to isolate and evaluate the effect of particle characteristics on cytotoxicity and uptake. Some groups have already reported that the time required to deliver ENMs to cells may be a rate-limiting factor in cellular internalization and translocation [60,96] . Better characterization of in vitro dose will also enable comparison of in vitro dose– response outcomes with effective doses and measured biological response patterns in vivo [3,85] . Importantly, reducing uncertainty regarding reported effective dose values will enable consolidation of nano-biointeraction results from various labs and biological systems into large libraries of comprehensive toxicity characterization data such as ‘ISA-TAB-Nano’ [23,100] . Adoption

Author contributions

Future perspective Incorporating dosimetry considerations into the design and interpretation of in vitro studies of nanobiointeractions will increase their accuracy and reproducibility, and also help to improve concordance with results measured in vivo. This will help to characterize potential human health hazards posed by engineered nanomaterials, as well as to study mechanisms related to cellular interaction and internalization relevant to the development of nanoparticle systems for drug delivery.

J Cohen conducted the literature review, assembled the figures and tables and co-wrote the manuscript. G DeLoid contributed to and reviewed the text, and co-wrote the manuscript. P Demokritou contributed to and reviewed the text, and co-wrote the manuscript.

Financial & competing interests disclosure This research project was supported by NSF Grant 1235806, and the Center for Nanotechnology and Nanotoxicology at The Harvard School of Public Health. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. toxicological characterization using a balb/c mouse model. Inhal. Toxicol. 25(9), 498–508 (2013).

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Review

3031

Review Cohen, DeLoid & Demokritou risk assessment of nanoparticles. Int. J. Environ. Res. Public Health 11(4), 4026–4048 (2014). •

3032

Proposes a nanoparticle dose model comprising the deposited dose, the dose rate and biokinetics and


incorporates organ-, tissue- and engineered nanomaterialspecific weighting factors. 100 National Cancer Institute.

https://wiki.nci.nih.gov/display/ICR/ISA-TAB-Nano


Abiotic soil changes induced by engineered nanomaterials: A critical review.

An integrated approach for the in vitro dosimetry of engineered nanomaterials.

Estimating the effective density of engineered nanomaterials for in vitro dosimetry.

Modeling physicochemical interactions affecting in vitro cellular dosimetry of engineered nanomaterials: application to nanosilver.

Mechanisms Underlying Cytotoxicity Induced by Engineered Nanomaterials: A Review of In Vitro Studies.

Environmental behavior of engineered nanomaterials in porous media: a review.

A critical evaluation of material safety data sheets (MSDSs) for engineered nanomaterials.

Tracing engineered nanomaterials in biological tissues using coherent anti-Stokes Raman scattering (CARS) microscopy - A critical review.

Overview of Risk Management for Engineered Nanomaterials.

Engineered Nanomaterials in the Environment.

Engineered multifunctional nanomaterials for multimodal imaging of retinoblastoma cells in vitro.

Species sensitivity distributions for engineered nanomaterials.

A review of the detection, fate and effects of engineered nanomaterials in wastewater treatment plants.

Decellularized and Engineered Tendons as Biological Substitutes: A Critical Review.

Comparative lung toxicity of engineered nanomaterials utilizing in vitro, ex vivo and in vivo approaches.

Pharmacological potential of bioactive engineered nanomaterials.

Tracking translocation of industrially relevant engineered nanomaterials (ENMs) across alveolar epithelial monolayers in vitro.

Barriers, pathways and processes for uptake, translocation and accumulation of nanomaterials in plants--Critical review.

Biological Responses to Engineered Nanomaterials: Needs for the Next Decade.

Nanomaterials for biosensing applications: a review.

In vitro dosimetry of agglomerates.

Toxicity Evaluation of New Engineered Nanomaterials in Zebrafish.

Ecotoxicological impact of engineered nanomaterials in bivalve molluscs: An overview.

The current state of engineered nanomaterials in consumer goods and waste streams: the need to develop nanoproperty-quantifiable sensors for monitoring engineered nanomaterials.