Challenges have already been studied in the past

in nanotoxicology

The variety of available nanomaterials and their
diversity pose a great challenge for nanotoxicology, considering their
different sizes, shapes and production processes (Johnston et al., 2013,
which makes finding the right mix that will represent exposure scenarios best a
daunting task. Furthermore, this justifies the need to improve the currently
used methodologies in order to achieve faster screening classification of
nanomaterials in terms of risk (Nel et al., 2013). Such high performing
processes should ideally also offer insights on the specific mechanisms and
allow for pathway-driven testing (Nel et al., 2013).

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Moreover, detailed characterization is required in
order to associate specific attributes with toxicological results. Usually some
basic particle characterization is offered by the manufacturer, but additional
characterization of the behavior in physiological fluid is required, in order
to explore the way in which particles form their biological identity (example:
the creation of the bio-corona).

The unique nature and attributes of nanomaterials can
intervene with the common toxicological assays, altering the results of the
screening process and under circumstances possible leading to false detections.
The interactions between carbon- or metal-based nanoparticles and test systems have
already been studied in the past (Monteiro-Riviere et al., 2009, Kroll et
al., 2012). An evaluation of the level of interference should be carried
out routinely for each new material, because the results can’t be grouped and
are unique for each one. Performing two or more tests with similar purpose
should also add to the validity and reliability of the outcomes.

A frequent issue in nanotoxicology evaluations is the practice
of using high, unrealistic dosages (Krug and Wick, 2011). This is somewhat
driven by the inclination of editors to favor the publication of positive
results and is preventing scientific growth in the field (Krug and Wick, 2011).

In order to achieve actual progress towards
comprehending the full extent of nanotoxicology, realistic doses should be used
in both in vivo and in vitro testing. Furthermore, most studies
tend to focus on the short-term results while in fact it’s the chronic, low-dosage
exposure tests that will be most useful in the long term.

The nanosafety research field has not seen much
progress over the past 15 years (Krug, 2014). In spite of the incremental
amount of research works (over 10000), there are still extensive gap, resulting
in uncertainties about the safety of nanomaterials. As mentioned, an important
issue are the high doses that offer some information but can’t be depended
on for a toxicological evaluation. The limited amount of reference materials
and necessary control information are some other aspects that prevent an
accurate comparison between research results for risk evaluation (Krug, 2014).



There are various pathways of exposure to nanomaterials, such as inhalation,
ingestion or skin contact. One of the most common occurrence of inhalation
exposure is in an occupational setting, thus making the lungs one of the
important targets. Because of their size, nanoparticles can pierce through them
deeper than other particles. The depth of exposure determines the severity of
the toxicity (Oberdörster et al., 2005). Studies in live rats
showed that exposure to 10 nm Ag nanoparticles led to higher concentration and increased
lung inflammation as opposed to exposure to 410 nm Ag nanoparticles (Braakhuis et
al., 2014a). The deposition mechanisms in the case of nanoparticles are
affected by diffusion, while larger sizes are affected by impaction, blockage
and gravitational settling (Oberdörster et al., 2005).

There are different mechanisms addressing particle exposure based on the
deposition location. Macrophage clearance is preferred for those in the
alveolar area, followed by progressive motion towards the mucociliary
escalator. The estimated half-time is about 700 days for humans (Oberdörster et
al., 2005). The macrophage clearance is quite effective for microparticles,
but can only clear about 20% of nanoparticles, which allows for interaction
with epithelial and interstitial tissue (Oberdörster et al., 2005). Another
scenario to consider is the transfer of nanoparticles through the lung-blood
barrier. It is generally not likely to occur, but should be taken into account
as a result of a long term exposure to and accumulation (Krug, 2014). Translocation
is also affected by particle size, based on studies using iridium (Kreyling et
al., 2009) and gold (Sadauskas et al., 2009) nanoparticles of
varying sizes. There are also findings of translocated Ag nanoparticles (Patchin et al., 2016) in the brain of rats through the olfactory bulb, which
makes the brain a secondary mark of inhalation exposure (Oberdörster et al.,
2004). Such results are a cause of concern due to prior findings suggesting
that Ag nanoparticles may affect neurons in vitro (Cooper and Spitzer, 2015).

Further research reports that carbon nanoparticles could also
translocate to the brain to a through sensory nerve endings (Oberdörster et
al., 2004), while MnO used similar pathways and resulted in inflammation (Elder
et al., 2006). A 2016 study discovered magnetite nanoparticles in human
brains, connecting it with the appearance of Alzheimer disease (Maher et al.,

Nanoparticles could also get access to the central nervous system and
the brain through the blood stream (Cupaioli et al., 2014). These pathways
are more relevant in terms of drug delivery for biomedical applications though.



Physico-chemical properties

with all materials, interaction with organisms and possible toxicity are
affected by the properties of said material. Such properties include shape, size,
surface charge, coating, structure, composition and more. Even the slightest
alteration in these properties could affect biological responses, it is imperative
to thoroughly perform a nanoparticle characterization along with the hazard evaluation
(Fadeel et al., 2015).



has an important effect on the behavior of nanoparticles because with smaller
particles, there appear to be a higher amount of atoms at the surface, which
are more reactive (Auffan et al., 2009). Surface area also depends on
the size and any changes are proportional to the changes in size, for the same
mass (Hubbs et al., 2013). Ag nanoparticles have displayed such
size-dependent toxicity (Braakhuis et al., 2014a, Wang et al.,
2014). Taking into account the
varying stability in the distribution medium, it is essential to differentiate
the normal particle size from the size of aggregates and agglomerates in the
exposed biological system. Lastly, agglomeration and sedimentation have
also been found to have an effect on the toxicity (Cho et al., 2011). Size
also influences the uptake process (Krug and Wick, 2011, Kuhn et al.,

Lastly, diffusion is a passive mechanism which was studied.
for quantum dots (Wang et al., 2012) and gold nanoparticles (Lin et
al., 2010). Generally, a combination of mechanisms may result in
nanoparticle uptake. It is should be noted that it is not properly regulated
compared to uptake of other particles like for example metal ions, which is a
cause of concern due to possible high bioavailability of toxic metals (Krug and
Wick, 2011).



Nanomaterials can be categorized as low-aspect-ratio
nanoparticles (LARN) consisting of spherical, cubic, prismatic etc.
nanoparticles, versus the high-aspect-ratio nanomaterials (HARN) consisting of nanowires
and nanotubes (Colognato, 2012). The similarity of the effects of HARN to those
of asbestos poses serious questions about the toxicity of HARN. Tran et al. created
a hypothetical framework to predict HARN toxicity using pathogenic fibers under
the following three conditions:

The HARN dimension should be thin, allowing for deposition
in the lower airways;

A high enough deposition of HARN is achieved;

Biopersistence (Tran et al., 2011).


Contrary to asbestos fibers, carboxylated single-walled
carbon nanotubes (SWCNTs) were found to be subjected to enzymatic degradation followed
by a decline of lung inflammation (Kagan et al., 2010), SWCNTs combined
with polyethylene glycol on the other hand, displayed an enzymatic degradation when
in the presence of myeloperoxidase (Bhattacharya et al., 2014). Ag
nanowires of 1.5 and 8 ?m were reportedly found to be more toxic than
nanospheres (Stoehr et al., 2011). Furthermore, in regards to TiO2
nanomaterials, only the long (> 15 ?m) nanobelts or nanospheres caused
inflammatory reactions in alveolar macrophages (Hamilton et al., 2009).



Surface charge

The surface charge is characterized by the zeta potential
and is identified by the electric current between the particle surface and the distribution
medium (Cho et al., 2012). Considering that cellular membranes have
electric potential, it becomes evident that a possible surface charge of a
nanoparticle can affect how the particles interact with organisms and thus adjust
the toxicity profile


Fröhlich E. looked into surface charge and its effect on
cellular uptake and suggested that cationic particles have a tendency to disturb
a cell’s membrane and cause higher levels of toxicity, as opposed to anionic
particles which are more likely to cause apoptosis (Fröhlich, 2012) and
lysosomal damage (Nel et al., 2009).
The zeta potential of metal
and metal oxide nanoparticles in acidic environments has been associated with lung
inflammation (Cho et al., 2012). In biological systems the surface
charge of nanoparticles is subject to change because of the adsorption of
biomolecules and creation of the bio-corona (Monopoli et al., 2012).



While other attributes also play an important role in defining
the extent of a toxic outcome, the chemical composition is equally significant.
According to their composition, nanomaterials can be at a first glance categorized
as metal or carbon based or polymeric nanoparticles. They can have a variety of
coatings and functionalities. Studies showed that Copper and Zinc based nanomaterials
display the highest acute toxicity when compared to for example with Titanium and Cerium based nanomaterials (Cho et
al., 2010, Lanone et al., 2009). Purity may impact toxicity as well even
though it is often neglected in toxicity studies. Lastly, there are reports
that nanomaterials could be tainted by endotoxins or organic residues (Crist et
al., 2013).


The ‘bio-corona’

A bio-corona is formulated by the adsorption of
biomolecules on a nanomaterial’s surface. This phenomena occurs due to high
free energy at the surface (Monopoli et al., 2012). Depending on the
adsorbed molecules, the bio-corona could be protein or lipid based etc. A
protein corona has a “hard” and “soft” part consisting of tightly and loosely connected
molecules respectively (Docter et al., 2015). The composition of the
bio-corona is dependent on the material and its other properties as well as the
type of biological system (Westmeier et al., 2016). The bio-corona affects
the nanoparticle’s colloidal stability along with the toxicological result. For
example, the formation of the protein corona was found to affect the uptake of
Ag nanoparticles (Shannahan et al., 2015, Monteiro- Riviere et al.,



1.3.2 Common toxicity endpoints



Cytotoxicity is usually one of the first finding when attempting
to perform in vitro toxicity testing. There are multiple cytotoxicity
tests available and the selection of assay should be done after considering the

(i) Endpoints of interest

(ii) Cell death of interest

(iii) Potential interaction between nanoparticles and the


As mentioned earlier, use of unrealistic doses is a common
pitfall of many studies (Krug, 2014). However, for an initial assessment of cytotoxicity,
we might need to resort in high does in order to achieve cell death and get a
first look of the cytotoxicity profile. The cytotoxicity assessment is a not an
accurate measurement of nanoparticle toxicity but can prove quite useful for rating
purposes or determining the doses for other endpoints.


Oxidative stress

The oxidative stress is a frequently used model for the explanation
of toxic effects of inhaled particles, also applicable to nanomaterials (Nel et
al., 2006). Nanomaterials can induce oxidative stress through the following

Directly, due to the presence of reactive groups at the
surface that may transfer electrons to oxygen molecules resulting in the formulation
of superoxide radicals (Nel et al., 2006, Shvedova et al., 2012)

Dissolution, (metal nanoparticles) with consecutive release
of metal ions that could catalyze Fenton and/or Haber-Weiss reactions (Manke et
al., 2013)

Indirectly, after particle interaction with phagosomes,
lysosomes and mitochondria etc. (Xia et al., 2006)

Indirectly, as a result of antioxidant depletion (Manke et
al., 2013) ??1 


When higher Reactive oxygen species (ROS) levels are
reached, inflammation and eventually cell death occur. Furthermore, ROS can
directly bind with DNA and cause genotoxicity as well as provoke protein or
lipid oxidation which will result in altered cellular functionality (Manke et
al., 2013). ROS however also acts as a cellular messenger, managing a variety
of processes (Sauer et al., 2001). It is hence possible that some
nanoparticles with antioxidant attributes might cause a ROS imbalance thus
alter cellular functions.



Lung inflammation was closely associated to oxidative
stress and has been reported for a variety of nanomaterials (Braakhuis et
al., 2014b). Attributes such as size, shape and composition are important determinants
for the results of lung inflammation. In general, HARNs displayed a higher probability
to cause lung inflammation (Braakhuis et al., 2014b). Lung inflammation
can be examined in vivo by executing a cytological analysis (Cho et
al., 2010). It was found that intratracheal instillation of metal oxide
nanoparticles in mice lead to specific inflammatory motifs; exposure to Nickel
Peroxide nanoparticles caused mild lung inflammation over 24-hour period, while
Copper Oxide nanoparticles had severe results over a 24-hour period that
resolved almost completely after 4 weeks (Cho et al., 2010).



Nanoparticles could lead to DNA damage through various genotoxic
mechanisms (Magdolenova et al., 2014). Unrepaired or incorrectly repaired
damage could in turn lead to mutations that may encourage cancer development.
Primary genotoxicity can be induced by both direct and indirect processes and
is easier to analyze in vitro.

Direct primary genotoxicity could happen after close
interaction of nanoparticles with the DNA may occur either upon entry or during
cell division. (Magdolenova et al., 2014). Theoretically, particles with
a size of ~5 nm could penetrate the nucleus, while larger particles, up to 40
nm, could be carried over inside it by interacting with the nuclear pore
complex (Wente and Rout, 2010). During mitosis, nanoparticles could cause breaks
or loss of chromosomes (Magdolenova et al., 2014). Such cases affect the
genomic instability negatively, which is a deciding factor for cancer development
(Giam and Rancati, 2015).

Furthermore, nanoparticles could provoke indirect genotoxicity
via several mechanisms (Magdolenova et al., 2014), such as:

Interaction with DNA repair proteins (Jugan et al.,

Interference with the mitotic spindle and cell cycle
control checkpoints(Huang et al., 2009)

ROS creation may cause oxidative DNA damage and DNA strand

Depletion of antioxidants such as glutathione, superoxide
dismutase and catalase (Sharma et al., 2009)

 ??1afto exei poly sygkekrimenes orologies kai den mporesa na allakso