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Particle Safety Assessment in Additive
Manufacturing: From Exposure Risks
to Advanced Toxicology Testing
Andi Alijagic
1
,
2
,
3
*, Magnus Engwall
1
, Eva Särndahl
2
,
3
, Helen Karlsson
4
,
Alexander Hedbrant
2
,
3
, Lena Andersson
2
,
3
,
5
, Patrik Karlsson
6
, Magnus Dalemo
7
,
Nikolai Scherbak
1
, Kim Färnlund
8
, Maria Larsson
1
†
and Alexander Persson
2
,
3
†
1
Man-Technology-Environment Research Center (MTM), Örebro University, Örebro, Sweden,
2
Inflammatory Response and
Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Örebro, Sweden,
3
School of Medical
Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden,
4
Department of Health, Medicine and Caring
Sciences, Occupational and Environmental Medicine Center in Linköping, Linköping University, Linköping, Sweden,
5
Department
of Occupational and Environmental Medicine, Örebro University, Örebro, Sweden,
6
Department of Mechanical Engineering,
Örebro University, Örebro, Sweden,
7
Absolent AB, Lidköping, Sweden,
8
AMEXCI AB, Karlskoga, Sweden
Additive manufacturing (AM) or industrial three-dimensional (3D) printing drives a new
spectrum of design and production possibilities; pushing the boundaries both in the
application by production of sophisticated products as well as the development of next-
generation materials. AM technologies apply a diversity of feedstocks, including plastic,
metallic, and ceramic particle powders with distinct size, shape, and surface chemistry. In
addition, powders are often reused, which may change the particles’physicochemical
properties and by that alter their toxic potential. The AM production technology commonly
relies on a laser or electron beam to selectively melt or sinter particle powders. Large
energy input on feedstock powders generates several byproducts, including varying
amounts of virgin microparticles, nanoparticles, spatter, and volatile chemicals that are
emitted in the working environment; throughout the production and processing phases.
The micro and nanoscale size may enable particles to interact with and to cross biological
barriers, which could, in turn, give rise to unexpected adverse outcomes, including
inflammation, oxidative stress, activation of signaling pathways, genotoxicity, and
carcinogenicity. Another important aspect of AM-associated risks is emission/leakage
of mono- and oligomers due to polymer breakdown and high temperature transformation
of chemicals from polymeric particles, both during production, use, and in vivo, including in
target cells. These chemicals are potential inducers of direct toxicity, genotoxicity, and
endocrine disruption. Nevertheless, understanding whether AM particle powders and their
byproducts may exert adverse effects in humans is largely lacking and urges
comprehensive safety assessment across the entire AM lifecycle—spanning from virgin
and reused to airborne particles. Therefore, this review will detail: 1) brief overview of the
AM feedstock powders, impact of reuse on particle physicochemical properties, main
exposure pathways and protective measures in AM industry, 2) role of particle biological
identity and key toxicological endpoints in the particle safety assessment, and 3) next-
generation toxicology approaches in nanosafety for safety assessment in AM. Altogether,
the proposed testing approach will enable a deeper understanding of existing and
Edited by:
Albert Duschl,
University of Salzburg, Austria
Reviewed by:
Ruibin Li,
Soochow University, China
Wan-Seob Cho,
Dong-A University, South Korea
*Correspondence:
Andi Alijagic
andi.alijagic@oru.se
†
These authors share last authorship
Specialty section:
This article was submitted to
Nanotoxicology,
a section of the journal
Frontiers in Toxicology
Received: 15 December 2021
Accepted: 06 April 2022
Published: 25 April 2022
Citation:
Alijagic A, Engwall M, Särndahl E,
Karlsson H, Hedbrant A, Andersson L,
Karlsson P, Dalemo M, Scherbak N,
Färnlund K, Larsson M and Persson A
(2022) Particle Safety Assessment in
Additive Manufacturing: From
Exposure Risks to Advanced
Toxicology Testing.
Front. Toxicol. 4:836447.
doi: 10.3389/ftox.2022.836447
Frontiers in Toxicology | www.frontiersin.org April 2022 | Volume 4 | Article 8364471
REVIEW
published: 25 April 2022
doi: 10.3389/ftox.2022.836447
emerging particle and chemical safety challenges and provide a strategy for the
development of cutting-edge methodologies for hazard identification and risk
assessment in the AM industry.
Keywords: industrial 3D printing, particle emissions, adverse outcome, inflammation, genotoxicity, endocrine
disruption, mechanism of action
INTRODUCTION
Additive manufacturing (AM) is a manufacturing technology
that in the last decade has completely revolutionized some
industries and continue to do so in many sectors. AM, as
opposed to subtractive manufacturing, such as machining,
only utilizes the material that is needed for the component,
with little waste material being produced. This is done by
slicing a 3D model into thousands of layers and adding
material in a layer-wise fashion to manufacture the
component. Many different AM technologies exist, utilizing
different processes and materials to build components. Some
of the most common types of AM machines currently use the
laser powder bed fusion (L-PBF) process, which use metal or
polymer microparticulate powder in small fractions that is melted
and fused locally by a laser (King et al., 2015). These machines
offer close to unparalleled versatility and design freedom, making
designs possible that were simply out of reach using conventional
methods of the past. However, while this technology has come far
in a short time, the industrial application of AM is a mere fraction
of what the conventional technologies represent. Part of this delay
is due to lack of experience and trust in the technology. To
overcome this, the AM community of the Swedish industries have
formulated a Strategic Research Agenda (SRA), in which key
strategic areas in the field of AM have been concretized. On top of
the list of the SRA are occupational health issues, design
competence, and quality and productivity.
“Those who cannot remember the past are condemned to
repeat it”. Looking at the world today, the words by George
Santayana are strikingly accurate. By all appearances, looking at
the general lack of publicly available research concerning
potential adverse health effects from working with AM, the
words are true also for this field. As depicted in Figure 1,
querying PubMed by using the search criteria “additive
manufacturing”and “3D printing”reveals a striking increase
of publishing on this topic over the last 20 years, reaching a peak
in 2020 with 5,424 publications. At the same time, the number of
publications investigating safety aspects of AM was growing
rather slowly, reaching 239 publications in 2020. While the
general understanding of the health effects imposed by
exposure to different elements, such as nickel, cobalt,
chromium, polymer additives, contaminants, and solvents, are
known, knowledge is lacking on the byproducts and particle size
ranges that are found in the AM industry. Long-term effects of
low dose exposures are virtually unexplored territories. The same
can be said about the exposure risks proposed by specific
operations in the daily work, since measurements have been
rarely done for particles at the nanoscale. By extension, no
guidelines exist that describe the exposure limits or safety
equipment requirements in AM, neither in Swedish nor in
European Union’s regulation.
Particulate materials in AM technology are found in powder
feedstocks for AM printers but can also be created during the AM
printing process (Taylor et al., 2021). Therefore, inhalation and
dermal exposure to feedstock particles or byproducts emitted
from AM printers may occur at different stages of the production,
from powder handling to printing post-processing, machine
cleaning, and maintenance. In addition, metals applied mainly
in directed energy deposition may be toxic or sensitizing.
Furthermore, chemical fumes emitted during the operation of
the AM printers could pose a significant risk for the occupational
health and safety. Many volatile organic chemicals (VOCs) are
known irritants with carcinogenic potential. Prolonged exposure
to VOCs can cause eye, nose, and throat irritations, headache, or
loss of coordination (Dobrzyńska et al., 2021).
The feedstock materials and particle emissions generated
during the AM processes could vary greatly regarding
toxicological potential, ranging from harmless to potential
occupational health risks that could result in a future tragedy,
as was seen with asbestos and silica. Therefore, this review will
detail: 1) overview of the AM feedstock powders, impact of reuse
on particle physicochemical properties, main exposure pathways
and protective measures in AM industry, 2) role of particle
biological identity and key toxicological endpoints in the
FIGURE 1 | Overview of the papers published over the last 20 years on
the topics “microparticle”and “nanoparticle safety,”“additive manufacturing”
and “3D printing,”and “additive manufacturing safety”and “3D printing
safety.”All publications were found via PubMed literature search.
Frontiers in Toxicology | www.frontiersin.org April 2022 | Volume 4 | Article 8364472
Alijagic et al. Particle Safety in Additive Manufacturing
particle safety assessment, and 3) next-generation toxicology
approaches in nanosafety for safety assessment in AM. The
herein proposed approach will enable a deeper understanding
of existing particle and chemical safety challenges and provide a
strategy for the development of cutting-edge methodologies for
proactive hazard identification and risk assessment in the AM
industry.
FEEDSTOCK POWDERS IN ADDITIVE
MANUFACTURING
One of the most important production categories in the AM
industry—L-PBF, such as selective laser sintering, electron beam
melting (EBM), and selective laser melting (SLM)—utilize laser
or electronic beam to selectively fuse polymeric, metallic, ceramic,
or composite particle powders layer-by-layer into desired
products according to their computer-aided design (CAD)
models (Yuan et al., 2019). Table 1 provides a short overview
of the most common polymeric, metallic, and ceramic powders
applied in AM. In addition, it summarizes different types of
reinforcements used in polymeric matrices.
IMPACT OF REUSE ON PARTICLES’
PHYSICOCHEMICAL PROPERTIES
Importantly, changes in the particle physicochemical features,
including size, shape, porosity, surface topography, interfacial
free energy, as well as chemical composition and surface
oxidation can profoundly affect biochemical mechanisms when
particles arrive at the biological interface (Rahmati et al., 2020).
This aspect of particle safety assessment is crucial in classifying
materials based on their toxic potential.
The reuse of powder in AM may affect the physiochemical
properties of the powder particles. In literature, the terms reuse
and recycling have been used interchangeably. However,
recycling refers to the production of new atomization
feedstock by remelting scrap materials or recover metal from
other manufacturing methods. Reuse of feedstock powder is done
when a single powder batch is used repetitively in an AM machine
through multiple cycles until the powder is out of specification or
predefined boundaries. In this section of the review, reused
powder particles of commonly used metals in AM, such as
stainless steel, alloys of aluminum, titanium and nickel-based
alloys are in focus.
There are several factors, such as material type and number of
reuse cycles, that may affect the physiochemical properties of the
powder particles. Slotwinski et al. (2014) noted the presence of
metal oxides on both virgin and reused 17-4 SS stainless steel
powder particles. The powder was reused eight times in a L-PBF
process. However, the study showed that there was no significant
difference between the virgin and reused powder particles in
terms of metal oxide type and concentrations. Gorji et al. (2019)
investigated the reusability of 316L stainless steel powder, reused
over 10 times in the L-PBF process. The study showed that the
reused powder had greater surface oxidation and higher
concentration of metallic oxides. Heiden et al. (2019)
discovered significant changes in surface composition, oxide
thickness, and magnetic properties of reused 316L stainless
steel powder particles. Virgin and reused powder were
investigated, and the powder was used through 30 build cycles
in the L-PBF process. They showed that surface oxygen content,
iron oxide Fe
3
O
4
thickness, powder magnetic susceptibility and
TABLE 1 | Chemical composition of powder particles applied in the AM industry-- >Class of particles/Chemical composition.
Class of particles Chemical composition of particles
Polymeric powders Semi-crystalline thermoplastics Polyamides (PAs), polypropylene (PP), polyaryletherketone (PAEK), polyethylene (PE), polybutylene
terephthalate (PBT)
Amorphous thermoplastics Polycarbonate (PC) and polystyrene (PS), Acrylonitrile Butadiene Styrene (ABS)
Thermoplastic elastomers Ester-based polyurethane (PU)
Biocompatible polymers Polyvinyl alcohol (PVA), polycaprolactone (PCL), polyhydroxybutyrate-co-hydroxyvalerate (PHBV),
polylactide (PLA), polyglycolide (PGA)
Metallic powders Titanium alloys Ti6Al4V (α-βtitanium alloy)
Nickel alloys NiCr19Fe19Nb5Mo3 (precipitation nickel-base superalloy), NiCr22Fe18Mo
Aluminum alloys AlSi10 Mg (hypoeutectic Al–Si casting alloy), AlCu4Mg1, AlCu3.5LiAgMg, AlCu6Mn,
AlMg4.5Mn0.7, AlZn6MgCu, AlZn4.5Mg1, Al5.6Zn-2.5Mg-1.6Cu-0.23Cr
Steel alloys 22Mo9Nb, X5CrNiCuNb 16–4, X2CrNiMo 17–12-2, 25CrMo4, 16MnCr5, 42CrMo4, X5CrNiCuNb
17–4, X3NiCoMoTi 18-9-5, X40CrMoV5-1
Cobalt alloys CO212, CO502, CO90, Co49Fe2V
Copper alloys OFHC Cu, HC Cu, Cu10Al, Cu10Sn, Cu15Sn
Ceramic powders Oxide and non-oxide advanced
ceramics
Alumina (Al
2
O
3
), zirconia (ZrO
2
), silicon carbide (SiC), tungsten carbide (WC), boron carbide (B
4
C),
silicon nitride (Si
3
N
4
), aluminum nitride (AlN), zirconium diboride (ZrB
2
)
Polymer-derived ceramics SiC, Si
3
N
4
, silicon oxynitride (SiON), silicon oxycarbide (SiOC), silicon carbonitride (SiCN), boron
nitride (BN) and boron carbonitride (BCN)
Ceramic matrix composites Carbon fibers/carbon matrix (C
f
/C), C
f
/SiC matrix, SiC
f
/SiC
Reinforcement of polymeric
matrices
Metallic fillers Aluminum and carbon steel
Ceramic/glass fillers Silica, glass beads, clays, and oxides
Carbon-based fillers Carbon black, carbon nanotubes (CNTs), graphite and graphene
Organic additives Polycarbonate (PC) and polystyrene (PS)
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Alijagic et al. Particle Safety in Additive Manufacturing
magnetic moment increased with reuse, the latter due to increase
in δferrite. Similar findings were reported in a more recent study
by Wang et al. (2021), where powder particles, reused at least
once, were investigated.
Another metal, commonly used in AM, that has high tendency
for surface oxidation is aluminum. An oxide layer of Al
2
O
3
is
formed when oxygen is chemisorbed and, therefore, correct Al-
based powder handling is crucial to avoid oxidation (Nie et al.,
2016;Riener et al., 2021). Cordova et al. (2019) displayed an
increase of oxygen content from 0.005 to 0.01 wt% after six reuse
cycles of gas atomized AlSi10Mg powder in a L-PBF process.
Inconel 718 and Ti6Al4V were also included in the study, but the
oxygen content only varied for the reused AlSi10Mg powder. In
contrast, Maamoun et al. (2018) showed that the chemical
composition and surface oxide content were identical for
virgin and reused AlSi10Mg powder after 18 reuse cycles in
L-PBF. They proposed that AlSi10Mg powder could be reused
if proper sieving was applied.
Titanium is a highly reactive metal toward oxygen, and
Nandwana et al. (2016) showed that the oxygen content
increased when Ti6Al4V metal powder was reused five times
in an EBM process. Similar findings were reported by Tang et al.
(2015). They studied reused Ti6Al4V metal powder through 21
build cycles and showed that the oxygen content increased
progressively with powder reuse cycles. In addition, Popov
et al. (2018) showed that after many reuse cycles, Ti6Al4V
metal powder particles display a variety of defects, including
satellites, bonded particles, elongated and non-spherical particles,
“super-balls,”and particle agglomerates (Figure 2).
In addition to Ti6Al4V, Nandwana et al. (2016) included the
nickel-based alloy Inconel 718 in the study on powder reusability
in EBM. In contrast to Ti6Al4V, only minor changes in chemistry
of the virgin and reused powder were found, and the oxygen
content increased from 0.014 to 0.016 wt%. Similar findings were
reported by Gruber et al. (2019) as the average oxygen level
increased from 146 ppm to 266 ppm after 14 build cycles.
Additionally, they observed Al-rich nanosized particles
forming on the surface of the powder already after the first
reuse cycle. The particles tended to grow with increasing
number of build cycles. Ardila et al. (2014) investigated the
reuse of Inconel 718 in the L-PBF process but reported no
significant change in chemical composition other than a minor
oxidation of nickel. Wang et al. (2021) showed that there was no,
or only minor, differences in surface oxide composition and metal
release pattern of reused powder in a comparison to virgin
powder in their study of reusability of Inconel 718 and
18Ni300, utilizing L-PBF.
ADDITIVE MANUFACTURING EMERGING
SAFETY CHALLENGES: PARTICLE AND
CHEMICAL EMISSIONS
As all work environments, AM work environments are controlled
by occupational hygiene regulations to ensure the workers safety.
Regarding airborne particles and chemicals, gravimetric or VOC
analyzing techniques are today used to ensure that threshold limit
values for specific compounds in air are not exceeded (Ljunggren
et al., 2019;Runström-Eden et al., 2021). However, it has been
shown that the gravimetric measurements are not sufficient when
assessing exposure-related health risks; mainly because particle
sizes vary from 10 nm to 65 µm or larger, and due to different AM
work activities entail different emissions and thereby exposure
risks. Therefore, particle-counting instruments are suggested as
complement to gravimetric measurements, mainly to be able to
identify particularly hazardous process steps (Ljunggren et al.,
2019). Exposure assessment, health hazards of particles, possible
chemical hazards as well as implications for risk assessment and
management in metal AM work environments have recently been
reviewed (Chen et al., 2020;Dobrzynska et al., 2021;Leso et al.,
2021). An increasing body of evidence supports that the micro
and nanosized particles and/or VOCs are present in the AM
environments. Figure 3 depicts possible particle emission sources
in the AM process chain and the most common human exposure
pathways.
Pathways of the Particle Emissions
Open metal powder handling, such as powder input into a
machine that is not equipped with an enclosed powder
handling system, entails particle emissions in sizes from 10 nm
up to the largest declared sizes of the feedstock powder. In
addition, an increase of micro and nanosized particles has
been found in reused Inconel 939 powder after SLM printing,
compared to virgin powder (Mellin et al., 2016;Graff et al., 2017).
Studies of high-energy techniques, such as SLM, have indicated
that the nanosized particles are created during the AM process;
with a subsequent release into the working environment when the
chamber door is opened (Ljunggren et al., 2019). Information is
still limited regarding exact number and sizes of particles emitted
FIGURE 2 | Impact of reuse on the properties of microparticle feedstock
powders in AM. Large energy input during production usually elicits formation
of satellites (small particles attached to the surface of bigger particles), bonded
particles, non-spherical and elongated particles, tightly bound
agglomerates, particles with rough/irregular surface topography or “super-
ball”particles with increased size in comparison to the virgin particles.
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Alijagic et al. Particle Safety in Additive Manufacturing
at specific AM process steps but when studying SLM printing
with Hastelloy X alloys, Ljunggren et al. (2019) found that most
gravimetric analyses performed in the metal AM facilities were
within occupational exposure limits (OELs). The background
number of particles, with an average size of 50–100 nm, were
<20,000 particles/cm
3
, which is rather low compared to other
metalworking facilities or outdoor environments. However,
elevated numbers of nanosized particles (50–100 nm) were
detected during post processes, such as sawing of printed
products from the construction plate or de-powdering of
finished products, reaching approximately 50,000 particles/
cm
3
. Even though the levels of micro and nanosized particles
are substantially lower in AM than for example welding
environments, the importance not to neglect the larger
inhalable particles (<15 µm) present in AM environments
must be pointed out - as they as well may pose a health risk.
Importantly, a study of health effects in the same AM workers, as
described above by Ljunggren et al. (2021), confirmed biological
uptake of metals present in the powders.
Regarding particle emissions in polymer printing
environments, a study by Väisänen et al. (2019) showed
that particle concentrations were highest (2,070-81,890
particles/cm
3
) during manufacturing with methods where
plastics were thermally processed. Another study by Zisook
et al. (2020) showed that submicron particles, predominantly
nanoparticles, were produced during material extrusion
printing using ABS at approximately 12,000 particles/cm
3
above background. After subtracting the mean background
concentration, the mean concentration for material extrusion
printing operations correlated with a calculated emission rate
of 2.8 × 10
10
particles/min
−1
under the conditions tested.
During processing of parts produced using material jetting
(MJ) or L-PBF, particle emissions were generally negligible.
This indicates that airborne emissions associated with AM
operations are variable, depending on printing and parts
handling processes, raw materials, and ventilation
characteristics. In addition, Runström-Eden et al. (2021)
have recently studied particle and VOC emissions from
four different printing techniques: L-PBF, material
extrusion (ME), MJ, and vat photopolymerization. The
most significant emissions of particles in the size range
10 nm to 1 µm were found during ME printing.
Background levels before the start of the printer were
between 5,000 and 10,000 particles/cm
3
, while particle
numbers outside the printer during printing ranged
between 5,000 and 15,000 particles/cm
3
with isolated peaks
at 50,000 particles/cm
3
. Inside the printer hood, emissions of
500,000 particles/cm
3
were detected, and during post
processing, emissions of 75,000-300,000 particles/cm
3
were
found; indicating that negative exposure-related health effects
cannot be ruled out.
Pathways of the Volatile Chemical
Emissions
VOCs are organic substances with a boiling point of 50–320°C.
Aromatic and aliphatic hydrocarbons, esters, ketones, and
aldehydes constitute this group of substances. These
FIGURE 3 | Particle and chemical exposure risks in AM. At different stages of the AM process chain particle and chemical emissions may occur, including input of
feedstock powders, high-energy production, product post-processing, machine cleaning, and maintenance. Potential particle exposure routes for exposed AM workers
involve inhalation, ingestion, and skin absorption.
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Alijagic et al. Particle Safety in Additive Manufacturing
compounds are used in solvents, paints, glues, degreasing
agents, etc. but are also part of gasoline and other mineral
oil products. In the AM industry, the materials entering the
machines may release VOC gases or vapors, especially when
using polymeric materials heated during the printing process.
The printed products might be warm when taken out of the
printers; thereby continue to emit VOCs. The types of VOCs
that would be emitted depends on the printing material,
printing method, and the pre- and post-activities. When,
for example, using ABS plastic material in AM, the main
substance emitted from the printers was styrene (Dobrzyńska
et al., 2021).OtherVOCsidentified during AM when using
ABS plastic material were acrylonitrile, acetonitrile, methyl
methacrylate, propylene glycol, methyl styrene, cumene,
cyclohexanone, ethylbenzene, toluene, butanol, and acetone
(Wojtyla et al., 2020). In addition, post-processing of the 3D-
printed products, including excess material removal, curing,
heat-treatment, support removal, machining, surface finish
processes (e.g., bead-blasting), vapor smoothing (using
acetone, ethyl acetate, etc.), coloring, can also lead to the
potential VOC exposure. Exposures to VOCs is of concern for
workers because some of these chemicals are respiratory and
mucous membrane irritants (He et al., 2015).
Opening industrial-scale FDM™3Dprinterdoorsafter
printing, removing desktop FDM™3Dprintercoversduring
printing, acetone vapor polishing (AVP) and chloroform
vapor polishing (CVP) tasks all resulted in transient
increases in levels of VOCs. Personal exposures were 380
to 6,470 μg/m
3
for acetone during AVP and 180 μg/m
3
for
chloroform during CVP (Du Preez et al., 2018). Väisänen et al.
(2019) showed that VOC concentrations, were low
(113–317 μg/m
3
) during manufacturing where plastics were
thermally processed, and during vat photopolymerization.
However, Zisook et al. (2020) showed that total VOC
concentrations of MJ and multi jet fusion methods were
higher (1,114–2,496 μg/m
3
), probably due to material and
binder spraying, where part of the spray can become
aerosolized. Chemical treatment of 3D-printed objects was
found to be a severe VOC source as well. Formaldehyde was
detected in low concentrations (3–40 μg/m
3
) in all printing
methods except for MJ, in addition to several other carbonyl
compounds. Measurements by using VOC sensors at four
Swedish AM facilities using polymeric printing materials and
different printing methods showed the varied levels of VOCs
during different production activities. When comparing the
different printing techniques, and feedstock materials, MJ had
the highest concentration of VOC (3,200 µg/m
−3
). High peak
exposures, 18,000–99,000 μg/m
3
, were measured during
cleaning of the printer when post-processing printed
material (Runström-Eden et al., 2021). Importantly, an
increase in VOC was observed in the evening and a
decreaseinthemorning,roughlythesametimeasthe
changes in ventilation settings occur. This finding
strengthens the fact that AM facilities should implement
adequate preventive measures (high-efficiency filters in AM
printing machines, encapsulated processes, printers with
hoods, etc.) to reduce particle and VOC exposure risks.
PROTECTIVE MEASURES IN ADDITIVE
MANUFACTURING: CASE OF INDUSTRIAL
FILTERS
The particles used in AM are often in the range of 20–100 μm, but
the emissions are primarily from used and degraded particles of
smaller size. Therefore, it is necessary to use high efficiency
particle filters in the printing machines, at least of class H13,
as final filters to guarantee a separation efficiency of 99.95%.
However, it is necessary to restrict the air speed at low levels, since
efficiency is strongly related to air speed when the major capture
mechanism for nanosized particles in fiber filters is diffusion due
to Brownian motion (Nelson, 2020). It is also important to have
an indoor filtration system with high air output that prevent
accumulation of small particles and gases in the indoor air.
For example, AM printing machines that process stainless
steel and aluminum have dust cartridges that are cleaned by
compressed air pulses, to keep the concentration of particles low
in the process air. It is primarily argon gas that is circulating from
the machine to the filters and vice versa. Excess process air is first
passing through a hepa H13 filter before it is released outside the
building. It is difficult to perform any internal measurements of
the process since the system has a pressure of 0.5 bar and sensors
will shut off the machine if the pressure decreases. However, our
preliminary measurements of external printer air from one AM
facility (unpublished data) show that the particle concentration
(Ø >0.15 µm), measured with Welas 2000 (Palas instrument), was
low in all measuring locations and the same was observed for the
larger particles above 1 µm (Figure 4). Parallel measurements
with the NanoTracer (Philips) indicated higher concentration of
nanosized particles, with an average particle diameter of 80 nm.
This supports that nanosized particles are emitted in the AM
occupational setting, and it is therefore of utmost importance to
setup working layouts, including air-ventilation with proper
filters, to ensure protection and reduce exposure risks for the
AM workers.
IMPACT OF PARTICLES’PROPERTIES AND
ACQUIRED BIOLOGICAL IDENTITY ON
CELL RECOGNITION AND UPTAKE
Understanding the interaction of particles with a biological
system (i.e., biomolecules, organelles, cells, tissues) is a
fundamental challenge in the particle safety research (Boraschi
et al., 2020;Himly et al., 2020). The outcome of these interactions
governs particle fate, including recognition, internalization,
distribution within the cells, and activation of different
signaling pathways.
When the particle enters the biological microenvironment
(i.e., blood, lung fluid or experimental cell culture media), its
surface is covered by a layer of adsorbed proteins that form a so-
called protein corona (Figure 5). The protein corona alters the
particle size, aggregation potential, and surface chemistry, giving
the particle a biological identity that is different from its synthetic
physicochemical identity (Ke et al., 2017). The corona further
determines the biological responses by mediating the interaction
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Alijagic et al. Particle Safety in Additive Manufacturing
of the particle with biomolecules (proteins, metabolites),
membrane receptors, and physical barriers (lung epithelial
cells, endothelial cells). The protein corona is a direct function
of the particles’physicochemical properties, biological
microenvironment, and the duration of exposure (Lundqvist
et al., 2008). Each biological microenvironment has a distinct
set of proteins that interact in a specific way with the particles’
surface. Protein adsorption to particles does not always require
direct contact with the particle surface and may instead occur via
protein-protein interactions (Li and Lee, 2020). In addition,
proteins can undergo conformational changes that together
with nonspecific protein-protein interactions may be
interpreted as a danger-associated signal (Corbo et al., 2016);
initiating an immune response that may develop into a successful
defensive response or an uncalibrated inflammatory reaction
(Boraschi et al., 2020). Moreover, proteins adsorbed to a
particle surface are in a constant state of flux, thereby causing
changes in the composition of the protein corona because of
continuous desorption/adsorption or by the so-called Vroman
effect. The Vroman effect states that the adsorbed proteins can be
replaced during time by proteins with higher affinity for the
particle surface, even when present in smaller amount
(Angioletti-Uberti et al., 2018).
As protein adsorption occurs at the interface between the
particle and the biological microenvironment, surface charge,
topography, surface area, and size, are features governing protein
interactions (Richtering et al., 2020). Notably, hydrophobic
particles with charged surfaces tend to adsorb more proteins,
causing their denaturation to a greater extent (Pustulka et al.,
2020). For example, polystyrene nanoparticles with increasing
negative charge and hydrophobicity increase the total protein
adsorption (Rahman et al., 2013). Research has also found that
protein binding capacity of the particle surface positively
correlates with the rate of particle internalization by cells. For
example, particles that easily adsorb plasma proteins strongly
interact with tissue-resident macrophages, causing rapid blood
clearance and accumulation in the liver and spleen. A special
subset of plasma proteins called opsonins (Wright and Douglas,
1904;Cockram et al., 2021), facilitate the recognition and
internalization of different particles by macrophages
(Mirshafiee et al., 2016). Still, protein adsorption can have the
opposite effect on the internalization as the adsorbed proteins in
some cases can inhibit the interaction between cellular receptors
and the encountered particle surface (Cai et al., 2019).
Generally, particles are readily internalized by phagocytic
cells. In some cases, it has been shown that pattern recognition
receptors (PRRs), such as scavenger receptors (SRs) and Toll-
like receptors (TLRs) participate in the recognition and
internalization, and NOD-like receptors (NLRPs) interact
with particles following internalization, and it is crucial to
note that the properties of the protein/biomolecular corona
dictate the type of interaction between particles and PRRs
(Boraschi et al., 2020). Another important aspect influencing
the particle fate is size. Increase in the overall size of the particle,
alters protein adsorption capacity and subsequent interaction
potential with biological barriers (Clift et al., 2008;Walkey et al.,
2012). For example, particles with a diameter ≤500 nm as well as
rod/fiber-shaped particles were preferentially taken up by
dendritic cells compared to larger particles or those with
cubicorsphericalshape(Casals et al., 2011). Herd et al.
(2013) investigated cellular uptake of three silica particle
constructs: worm-like (232 × 1,348 nm), cylindrical (214 ×
428 nm), and spherical (178 nm). They found that the
internalization rate depended on particle geometry. The
authors linked the variations they found to the different
internalization mechanisms undergone by particles with
different geometries. Smaller particles (≤500 nm) are mainly
internalized via pino or micro-pinocytosis and via clathrin- or
FIGURE 4 | Particles in the AM occupational setting. The particle size distribution was quite similar in all measurement locations. Some larger particles (>1 µm)
could be found in the entrance hall and in the process air.
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Alijagic et al. Particle Safety in Additive Manufacturing
caveolin-mediated endocytosis, while particles larger than
500 nm are mainly internalized by phagocytosis (Behzadi
et al., 2017;Foroozandeh and Aziz, 2018). In this context,
high-energy input and powder reuse in AM significantly
alter particle physicochemical properties and by that, most
probably, change particle fate, recognition, and cellular
responses.
By focusing on particles’physicochemical characteristics and
growing body of safety studies, materials scientists now have a
better grasp on the relationships between the particles’
physicochemical features and their hazard/safety profiles.
Hence, it is expected that an integration of design synthesis
and safety assessment will foster particles safer-by-design by
considering both applications and later safety/hazard
implications (Morose, 2010;Lin et al., 2018). Proposed
strategies to reduce hazard for particles comprise coating,
control of size, doping, managing shape and crystallinity,
reducing the presence of substances at the surface of particles
that contribute to hazard, reduced persistence, and substitution
(Geraci et al., 2015;Reijnders, 2020). Until today, a few safer-by-
design strategies that have been implemented to make safer metal
oxide, carbon-based, silica, and rare Earth oxide nanoparticles.
These examples demonstrated the key aspect of particles safer-by-
design, i.e., knowing which physicochemical characteristics
contributed to toxicity and how to remediate them by rational
design (Lin et al., 2018). This approach may have significant
implications when designing novel materials for the AM.
TOXICOLOGICAL ENDPOINTS IN THE
PARTICLE SAFETY ASSESSMENT
Inflammation
The correlation between occupational and environmental particle
exposure and human respiratory disease has been known for a
long time and in 1995 Seaton et al. (1995) put forward a
hypothesis that lung exposure to particles mediated alveolar
inflammation as a central common denominator for systemic
effects, including cardiovascular disease (CVD). This idea was
further developed by Sjögren (1997) describing the connection
between occupational dust exposure, inflammation, and ischemic
heart disease. In addition to effects on hormonal and blood
pressure regulation, particle exposure is known to generate
both local and systemic inflammatory responses, where the
systemic inflammatory reactions are likely contributing to the
strong relationship between particle exposure and CVD (Golia
et al., 2014). While extensive and acute exposure can lead to
bursts of proinflammatory mediators, it is the low-grade
inflammation due to long-term exposure that is most
associated with particle exposure. It is further important to
FIGURE 5 | Particle’s biological identity. Particles entering protein-rich biological environments are swiftly covered by proteins forming the so-called protein corona
or mechanical interface between particles and cell receptors. Particle physicochemical properties are directly dictating the composition of the protein corona and the
extent to which proteins adsorb/desorb from the particle’s surface. The protein corona may block or promote receptor recognition and particle internalization by the cells.
Particle’s size determines the mechanism of the internalization, and it may involve pinocytosis, endocytosis (caveolin- or clathrin-mediated) and phagocytosis.
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Alijagic et al. Particle Safety in Additive Manufacturing
note that nanosized particles, in comparison to larger inhalable
fractions, may leak into the blood circulation and cause direct
effects in tissues—distant from the immediate exposure site,
including organs and blood vessel walls (Miller et al., 2017).
Focus of this review section is on the respiratory inflammation.
In that context, particle size is an important factor for their toxic
potential that affects two critical factors: the main region of
exposure in the respiratory tract, and the surface area of the
particles. As already mentioned, the surface of a particle plays a
detrimental role in its toxicity, allowing interactions of the
particle to biological structures, such as membranes and
proteins. Therefore, higher surface area to mass ratio
potentiates the toxicity of a particle. The size also determines
how far the particles can reach in the respiratory tract, and the
capacity to leave the deposition tissue and exit into the blood
stream. The respiratory tract can be divided into three regions for
particle exposures: 1) the upper respiratory tract, also called the
extrathoracic region, comprising the mouth and nasopharynx, 2)
the tracheobronchial or thoracic region, comprising the trachea,
main bronchi, and bronchi of the conducting airways, and 3) the
alveolar region, comprising the bronchioles and alveolar sacs and
ducts. The particle fractions corresponding to each respiratory
region include: 1) the inhalable fraction ( <100 µm) representing
particles mainly reaching nose and mouth, and 2) the thoracic
fraction ( <10 µm) that can pass the larynx, and the respirable
fraction ( <4 µm) that can reach the alveoli. For studies on
ambient particle exposures, PM2.5 is often used, which
corresponds to the respirable particle fraction. In addition, the
ultrafine particle fraction corresponds to the nanoparticle range
of particles less than 100 nm in size. For further reading, the
current sampling criteria to assess exposures of the different
respiratory regions is thoroughly reviewed by Vincent (2005).
Deposition of the ultrafine or nanosized particles in the
respiratory tract is not uniform but is size dependent. The total
deposition increases as the size decreases, reaching near 100%
deposition for 1 nm particles. However, for the smallest particles,
the main deposition is in the extrathoracic region, in contrast to
particles in the 10–100 nm range for which deposition in the
alveolar space dominates (Geiser and Kreyling, 2010). It is also
important to consider that the actual particle size can be affected by
agglomeration of nanoparticles and adsorption of water molecules
(Szałaj et al., 2019). Nanosized particles may further cross cellular
membranes, leave the alveolar space through the alveolar
epithelium and reach the bloodstream, where they may cause
adverse effects on the cardiovascular system as well as distant
organs (Geiser et al., 2005;Schulz et al., 2005;Miller et al., 2017).
Mediators of Inflammation
Inflammation is closely associated with the soluble factors,
chemokines, cytokines, alarmins, lipid mediators, reactive
oxygen/nitrogen species and acute phase proteins modulating
the response, and these factors provide useful tools for the
evaluation of the immunogenicity/immunotoxicity of particles.
Cytokines and chemokines have central modulating effects to
the activity of many cells and can be produced by most cell types
in our bodies but are most strongly correlated with
immunocompetent cells (Turner et al., 2014). Inflammatory
mediators are pleiotropic in nature, and depending on the
signaling landscape in the microenvironment, many effects
overlap, synergize, and antagonize each other. In general, high
levels of the proinflammatory mediators are associated with
immunological activation to the degree that it is immunotoxic
and cell and tissue damaging. Soluble inflammation markers are
readily quantifiable with standard techniques, including
antibody-based immunoassays, whereas lipid mediators, with
some exceptions, require a mass spectrometric approach for
detection.
Inflammasomes are multiprotein complexes that are
composed upon cellular sensing of danger. Among the various
inflammasomes, NLRP3 inflammasomes are the best described
and their activity results in the activation and release of
Interleukin-1β(IL-1β) and Interleukin-18 (IL-18), among the
most potent proinflammatory soluble factors identified. The
NLRP3 inflammasome can be activated in response to various
stimuli, including extracellular ATP, K
+
ionophores, heme,
pathogen-associated RNA, bacterial and fungal toxins and/or
components, as well as endogenous and exogenous particulate
matter (Kanneganti et al., 2006;Mariathasan et al., 2006;
Martinon et al., 2006;Hornung et al., 2008;Lee et al., 2016).
The NLRP3 inflammasome has been found to be activated by
organic, inorganic, metal, and elemental nanosized particles of
various shapes (Schanen et al., 2009;Meunier et al., 2012;Yang
et al., 2012). The dispersion state and physicochemical
characteristics of the particles, such as size, aspect ratio, and
surface charge and functionalization, are important factors to
determine how potent the particle is in provoking NLPR3
inflammasome activation (Sun et al., 2013;Wang et al., 2017).
Mechanistic studies revealed that NLRP3 inflammasome
activation induced by long aspect ratio nanomaterials involves
lysosomal damage, and subsequent cathepsin B release that serves
as a signal for the assembly of the NLRP3 inflammasome (Wang
et al., 2012). The size effect of particles on NLRP3 inflammasome
activation has been examined by several studies, though
contradictory results were obtained. Sharp et al. (2009)
compared polystyrene particles of 430 nm, 1 µm, 10 µm, and
32 µm in diameter in inducing IL-1βrelease in dendritic cells
and showed that smaller particles were more potent in promoting
IL-1βproduction due to efficient internalization (Sharp et al.,
2009). Inflammasome activation can be determined through an
array of assays; ranging from study of composition of the complex
microscopically or detection using western blot, and
quantification of the active protease activity (caspase-1) using
fluorescent probes coupled to a caspase-1 cleave site.
Cell Death and Immunomodulation
Many of the cellular responses to particle exposures are
dependent on uptake and internalization of the particle, e.g.,
through phagocytosis, endocytosis, or pinocytosis. The cellular
response following uptake is believed to be governed by the size
and shape of the solid particle, where sharp and spiky shapes can
cause damage to membranes and thus result in a greater impact
on the cell responses compared to spherical and rod-shaped
particles. Also plate shaped particles (nanosheets) has been
shown to exert a more potent toxicity than more conformed
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shaped particles (Cai et al., 2018;Xu et al., 2020). Much of the
described adverse cellular responses are related to interaction
with, or destabilization of, cellular membranes either by oxidation
or other interaction with phospholipids or sheer abrasive stress
on the membrane. Following internalization of particles,
lysosomal destabilization with cathepsin leakage into the
cytosol has been described as a major effect in particle-
induced cellular events, involving mitochondrial membrane
destabilization and ROS generation, often leading to cell death.
In addition, in neutrophils, a wide range of particles have been
described to provoke formation of neutrophil extracellular traps
(NETs) (Desai et al., 2017). NETs were initially described as an
effective weapon against bacteria, fungi, viruses, and further
pathogens, a list now supplemented with recent discoveries
that particulate matter is capable of inducing NETs formation
(Brinkmann et al., 2004;Skallberg et al., 2019).
Clinical Samples
To investigate the direct effects of exposure to the human body,
investigations of inflammatory markers in exposed people, e.g., in
occupational settings is often the approach. However, low
exposures are generally not reflected in readouts of acute
inflammation, such as chemokines and proinflammatory
cytokines, C-reactive protein (CRP), or in cellular readouts,
such as inflammasome activity and ROS. Dilution effects
makes it also challenging to measure local mediator
production in e.g., serum, where the factors present in the
circulation are of tissue origin and have thus been extensively
diluted. The local effects in the exposed tissue microenvironment
may however still contain biologically relevant levels of mediators
but this is most often out of reach for today’s investigations.
Direct lung measures, including fractional exhaled nitric oxide
(FeNO) is an established measure of inflammatory activity in the
airways. NO can be produced by most immunocompetent as well
as tissue cells in the lung as a response to endogenous mediators,
such as chemokines and cytokines as well as by exogenous
irritants, e.g., as bacterial toxins, viruses, or allergens (Watkins
et al., 1997;Pechkovsky et al., 2002). In a study by Gümperlein
et al. (2018), 26 healthy adults in a single-blinded, randomized,
cross-over design were exposed to emissions of a desktop 3D
printer using fused deposition modeling (FDM) for 1 h (high
UFP-emitting ABS vs. low-emitting polylactic acid (PLA)). The
results showed difference in the time course of FeNO, with higher
levels after ABS exposure.
The non-ciliated Club cells (formerly known as Clara cell),
containing the most P-450 activity in the lungs, are known for
their vulnerability to toxic insults. These cells are the source of the
lung epithelium specific biomarker Club cell secretory protein 16
(CC16), also known as CC10 and uteroglobin. CC16 exerts anti-
inflammatory properties involved in the airway inflammatory
homeostasis, and the molecule can passively diffuse across the
bronchoalveolar-blood barrier into serum (Milne et al., 2020).
Elevated concentrations of CC16 in serum and urine thus provide
a useful biomarker for evaluation of the integrity of the lung
epithelial barrier and airway inflammation (Arsalane et al., 2000),
whereas diminished levels indicate severe lung injury (Kropski
et al., 2009). The correlation between PM2.5 exposure and
elevated CC16 levels in serum and urine of have been
indicated in several studies; however, some studies report
inverse correlations possibly due to the homeostatic nature of
the inflammatory factor as well as differences in the content and
composition of the particulate matter (Timonen et al., 2004;
Jacquemin et al., 2009;Vattanasit et al., 2014;Andersson et al.,
2019).
Further, standard clinical measurements, such as total white
blood cell counts and 5-part differential counts (neutrophils,
lymphocytes, monocytes, eosinophils, basophils) providing
neutrophil-to-lymphocyte ratio (NLR) as well as the
lymphocyte-to-monocyte ratio (LMR) may further provide
useful tools bearing evidence of the result of elevated systemic
inflammatory status able to predict cardiovascular disease events
(Prats-Puig et al., 2015;Huang et al., 2018).
Ex vivo
When quantification of factors in serum, urine, or exhaled air is
not sufficient to evaluate the inflammatory status, ex vivo
experimentation of cells isolated from exposed individuals may
provide valuable insight into the condition of the cellular
components. One approach is to extract cells from exposed
individuals, e.g., through blood sampling and cell isolation, to
investigate the cellular responses in an isolated model. By this, the
cells inflammatory activity can be evaluated directly, for instance
by flow cytometric quantification of the monocyte activation
marker CD11b, which has been found to positively associate
with PM2.5 exposure (Karottki et al., 2015). The ex vivo approach
offers a possibility to study not only soluble mediators, but also
detection of changes of the cellular inflammation, including
inflammasome activation, CD-marker, and receptor expression
as well as inflammation-associated readouts, such as
mitochondrial depolarization, ROS generation, lysosomal
destabilization, acidification, and cell death in a certain cell
population. In addition, the extracted cells can further be
experimentally provoked by additional inflammatory stimuli to
investigate their altered potential to induce an inflammatory
response. Such approaches have been used to study
inflammasome activation in monocytes of particle-exposed
iron foundry workers (Westberg et al., 2019;Hedbrant et al.,
2020) and TLR expression in dust-exposed pig farmers
(Sahlander et al., 2010). However, the control populations are
difficult to identify for evaluation of the results due to the
heterogeneity in the healthy population with regards to
inflammatory mediators and mechanisms and each
experimental design needs thereby to provide its own controls.
In Vitro
To be able to scrutinize the molecular and cellular mechanisms
involved in inflammatory responses to particle exposure, in vitro
cell models are very useful. In addition to primary human cells,
several cellular models of human origin are readily available,
including the bronchial and alveolar epithelial cells: A549, BEAS-
2B, NHBE, 16HBE14o-, HBE135-E6E7, H441, NCI-H292, Calu-
3, NuLi-1, CuFi-1, and immunocompetent cells such as the
monocytic cell lines THP-1 and U937 as well as primary cells
isolated from healthy volunteers. All these models readily lend
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themselves to studies of cellular effects of particle exposure,
including inflammatory potency in an airway relevant context.
A study by Farcas et al. (2019) investigated the effect of emissions
generated from a commercially available 3D printer inside a
chamber, while operating for 1.5 h with ABS or PC filaments and
collected in cell culture medium. Characterization of the culture
medium revealed that repeat print runs with an identical filament
result in the emission of various amounts of particles and VOCs.
Mean particle sizes in cell culture medium were 201 ± 18 nm and
202 ± 8 nm for PC and ABS, respectively. At 24 h post-exposure,
both PC and ABS emissions induced a dose dependent
cytotoxicity, oxidative stress, apoptosis, necrosis, and
production of proinflammatory cytokines and chemokines in
human small airway epithelial cells (SAEC). Similarly, in vitro
assays involving rat alveolar macrophages (NR8383, CRL 2192)
and human tumorigenic lung epithelial cells (A549) showed toxic
responses following the exposure to particles emitted from PLA
and ABS. Interestingly, PLA-emitted particles elicited higher
response levels than ABS-emitted particles at comparable mass
doses (Zhang et al., 2019).
Furthermore, different culture strategies to better mimic the in
vivo situation are available. To mimic the lung tissue, several
strategies can be used, as reviewed by Heinen et al. (2021),
including culture at the air-liquid interphase (ALI), fluidics
systems to mimic shear stress, mechanical stretching systems
to mimic breathing movement and organ-on-a-chip methods.
Genotoxicity
Alterations of the genetic material can lead to severe health
defects, as mutations in cells may elicit cancer and contribute
to development of chronic diseases. Genotoxic events can occur at
the DNA, chromosome, or whole genome level. Before
genotoxicity testing, it is necessary to know the cytotoxicity of
the tested particles and to establish the LC
50
(lethal concentration
at which 50% of cells die) in order to determine the appropriate
range of exposure concentrations (Kohl et al., 2020). Due to the
specific physicochemical features of particles, many standardized
methods are inappropriate for the genotoxicity testing of
particles. Thus, in the test strategy, the mammalian gene
mutation test is recommended together with the micronucleus
assay or γH2AX assay (Doak et al., 2012). In addition, different
assays exist that assess intermediate endpoints, such as various
types of DNA damage and novel markers measured with omics or
epigenetics (Ventura et al., 2018). In recent years, there has been a
strong effort to develop more complex, in vivo-like in vitro
models based on 3D structures either of a single cell or co-
cultures of two or more cell types. The application of these models
in toxicology provides reliable data that are more relevant for
evaluating genotoxicity in humans than standard single cell 2D
culture models. Moreover, several existing genotoxicity testing
methods, which are amenable to high content screening
approaches have been identified (Collins et al., 2017).
The genotoxicity of particles and mechanisms leading to
transient or permanent genetic alterations has been widely
studied (Magdolenova et al., 2015;Doak and Dusinska, 2017;
Nelson et al., 2017;Singh et al., 2019). Studies show that particle
genotoxicity can result from two key mechanisms: primary
(direct or indirect) or secondary genotoxicity. In primary
genotoxicity, direct action of nanostructures on nucleic acids
and induction of DNA damage could occur (Rezaei et al., 2021),
whereas secondary genotoxicity usually implies a pathway of
genetic damage resulting from the oxidative DNA attack by
reactive oxygen/nitrogen species (ROS/RNS), generated during
particle-elicited inflammation (Schins and Knaapen, 2007). For
some particle types, one of these mechanisms might apply;
however, for some particles both mechanisms can occur
simultaneously. To define if primary genotoxicity is direct or
indirect, it is crucial to understand the particle internalization
mechanisms and the potential entrance into the nucleus. Particles
of only a few nanometers in size can penetrate the nucleus via
nuclear pores. Still, studies demonstrate the presence even of the
larger particles in the nuclear compartment, showing that there
could exist other pathways for nuclear internalization, e.g.,
intracellular processes resembling endocytosis (Kazimirova
et al., 2020). During mitosis, the nuclear membrane is
dissolved, allowing particles to enter the nucleus as it is
reformed. When particles enter the nucleus, the interaction
with DNA is considered a possible mechanism for direct
genotoxicity. However, secondary genotoxicity is the main
mechanism of particle genotoxicity, and it is mediated via ROS
molecules produced by immune cells. Commonly, particles
can trigger an oxidative burst when internalized by phagocytic
cells. This event is an initial inflammatory/defense mechanism
against invasion of non-self materials, including particles.
However, in the case clearance of particles fails, it can lead to a
chronic inflammatory response. Secondary genotoxicity cannot be
studied with standard in vitro approaches and has so far only been
investigated in vivo following chronic inflammation caused by
activation of macrophages and/or neutrophils (Bartek et al., 2010).
Importantly, shape, size, surface topography and chemical
composition of the particle as well as the biotransformation and
corona all play important roles in particle genotoxicity (Cowie
et al., 2015;Xu et al., 2020). The same was confirmed in studies on
genotoxicity of different metal particles (Vales et al., 2015;
Rodriguez-Garraus et al., 2020). In the case of silver
nanoparticles of same size genotoxicity and mutagenicity
depend on their surface properties and charge (Huk et al.,
2015). In addition, Gábelová et al. (2017) demonstrated that
surface chemistry directly affects genotoxicity of iron oxide
nanoparticles. Furthermore, (bio)transformation of particles,
including chemical, physical, and biological processes could
significantly affect the genotoxic potential of particles.
Particle transformation during the AM processing is an important
consideration in the AM particle safety assessment since AM powders
are commonly reused, and as a result, their physicochemical features
may be significantly altered, which in turn may vary their genotoxic
potential. Additionally, formation and emission of mixed-size
particles, like nanosized condensate particles or microsized spatter
with very irregular shape, high degree of agglomeration, and increased
surface oxidation may potentiate genotoxicity in the human cells.
Finally, there is a huge gap in studying particle genotoxicity in
relation to additives, monomers from polymer breakdown, metal
ions, and biodegradation products that may be released from
particles, both in the environment and in the target cells (Figure 6).
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Endocrine Disruption
Disruption of the endocrine function causes hormonal
imbalance, influencing the development and occurrence of
metabolic disorders. Most common approaches in assessing
endocrine disruption involve in vitro screening assays designed
to detect binding of disruptors to estrogen, androgen,
glucocorticoid, aryl hydrocarbon (Ah), and peroxisome
proliferator-activated receptors (PPARs). Currently available
data support the notion that different types of particles can
alter physiological activities of the endocrine tissues, as
reviewed by Iavicoli et al. (2013). The reproductive systems of
males and females are the endocrine organs that have received
most attention. In the male reproductive system, particles can
affect cell viability in gonadal tissues, testicular morphology, and
the spermatogenesis. In the female reproductive system, particles
cause toxic effects on ovarian structural cells and impaired
oogenesis, and follicle maturation. In addition, particles elicit
significant alterations in normal sex hormone levels both in
females and males.
Additional important aspects of the possible roles of particles
in the endocrine disruption are molecular mechanisms of action
underlying the adverse effects. In several papers, oxidative stress
was outlined as the main damaging mechanism (Zhu et al., 2009;
Bai et al., 2010;Gao et al., 2012;Hou and Zhu, 2017). Also, silver
nanoparticles have been found to inhibit cell proliferation by
disrupting the proliferation signaling cascade of spermatogonial
stem cells (Braydich-Stolle et al., 2010) with similar mechanism
also observed for silicon carbide nanowires on ovarian cells (Jiang
et al., 2010). Apart from those mechanisms, direct genotoxicity
has been reported as an initiating event in endocrine disruption
(Gromadzka-Ostrowska et al., 2012). For example, different
studies confirm that particle-induced endocrine toxicity, very
often, could occur due to particle mediated alteration of gene
expression, leading to changes in activities of proteins and
enzymes involved in sex hormone biosynthesis, metabolism,
and release (Li et al., 2009;Gosso et al., 2011;Gao et al., 2012;
Li et al., 2012). Additional hypothesis can be put forward, some
endocrine disrupting particles could alter functions of the
endocrine system due to their ability to bind to the hormone
receptors (estrogen, androgen, thyroid receptors, etc.) and play a
significant role in initiating adverse endocrine effects. For
example, the estrogenic effects elicited in human cells may be
dependent on particle interaction with various receptors, leading
to activation/deactivation of the estrogenic signaling pathway. A
study by Jain et al. (2012) reported that the release of ionic
cadmium may contribute to the metalloestrogenic effects
(estrogenic effect of a metal) of quantum dots (QDs) with a
cadmium core. Study demonstrated that in vitro cadmium-
containing QDs induce cellular proliferation, estrogen receptor
αactivation, and biphasic phosphorylation of protein kinase B
FIGURE 6 | Particle-induced effects in the human cells. Both in the extracellular and intracellular milieu, particles potentially release additives, monomers, or metallic
ions (depending on the particle chemical composition). After recognition and internalization, particles affect cell physiology in numerous ways by involving extensive
signaling leading to changes in the expression of target genes, resulting in inflammation, endocrine disruption, genotoxicity, or cytotoxicity. Moreover, metal ions released
during dissolution of metal particles may induce oxidative stress that may trigger inflammation and/or cytotoxicity.
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Alijagic et al. Particle Safety in Additive Manufacturing
(AKT) and extracellular signal-regulated kinases (ERK1/2),
comparable with 17β-estradiol. These findings suggest that
certain cadmium-containing nanocrystals are endocrine
disruptors, with effects exceeding those induced by ionic
cadmium or 17β-estradiol.
Apart from endocrine disruptive effects on sex hormones,
particles may influence different metabolic pathways (Priyam
et al., 2018). Gurevitch et al. (2012) demonstrated the potential
role of titanium dioxide nanoparticles in the etiology of
metabolic/endocrine disorders, such as obesity and insulin
resistance. They showed that the exposure of Fao rat
hepatoma cells to titanium dioxide nanoparticles altered
insulin response and induced insulin resistance by interfering
with insulin-signaling and by indirect inflammatory activation of
macrophages.
In the AM environment, particles may cause endocrine
disruption per se, but release of additives, monomers, metals,
bound chemicals, and biodegradation products (especially from
the polymeric/plastic particles) should not be neglected as an
additional and so far, unexplored mechanism of inducing
endocrine disruption, as shown in Figure 6. That issue should
be properly addressed in order to complete a particle safety
assessment framework and to move forward in the safer-by-
design approach.
NEXT-GENERATION TOXICOLOGY TOOLS
FOR THE PARTICLE SAFETY ASSESSMENT
High-Throughput Cell Profiling
Alongside the development of the classical toxicological
endpoints and targeted assay for the evaluation of the toxicity,
recent advances in the development of a non-targeted assay called
Cell Painting (Bray et al., 2016) are making this technique a
promising tool for toxicological studies.
Cell Painting assay employs a set of fluorescent dyes to stain
the cells’different compartments. Cells are treated in multi-well
plates with the chemicals of interest (one chemical per well), at
one or several concentrations of each tested chemical, for
24–48 h. After fixation and staining, fluorescent images of the
cells are captured at the relevant for each dye, wavelengths using a
high-content screening system. CellProfiler (https://cellprofiler.
org/), a cell image analysis freeware developed by the Carpenter
Lab at Broad Institute, is used to measure ≈3200 morphological
features, like size, shape, texture, intensity, etc. from the
individual cells, thus producing complex feature profiles for
each treatment (Carpenter et al., 2006;McQuin et al., 2018).
Extracted morphological profiles can be used for the
characterization of the chemicals, and for elucidation of their
mechanisms of action.
Screening of 462 environmental chemicals from the ToxCast
library in a concentration-response mode using Cell Painting
assay with U-2 OS cells showed that approximately two-thirds of
the tested chemicals were administered at a concentration dose
that is comparable to the corresponding in vivo potency. The rest
of the chemicals over-predicted the in vivo dose, which could be
partially explained by the lacking expression of the molecular
targets for those chemicals in U-2 OS cells (Nyffeler et al., 2020).
Considering the last limitation of the U-2 OS cells, other cell lines
may be used and are being tested with this assay (Bray et al., 2016;
Willis et al., 2020). The combination of phenotypical profiles with
other descriptors, like chemical descriptors, gene expression,
knock-out data, etc., and with machine learning algorithms for
image characterization and clustering increases predictivity of the
chemical toxicity by the Cell Painting assay (Chavan et al., 2020;
Way et al., 2021).
Interaction of cells with particles has an impact on the cell
morphology (Lipski et al., 2008), thus the application of high-
content screening by employing approaches, like Cell Painting, to
study the toxicity and mechanisms of particle action is of great
interest. Complementing high-content screening with additional
methods (e.g., omics technologies and machine learning) offers
an excellent tool for a detailed deconvolution of particle
mechanisms of action in cells. Recently, we have successfully
applied Cell Painting integrated with lipidomics, metabolomics
and unsupervised learning for the detection of cell response
signatures to (nano)particles released in metal AM
(unpublished data).
Multiomics Testing Strategies
The omics technologies include transcriptomics, proteomics,
metabolomics, epigenomics, and genomics. By using these
techniques, it is possible to identify, not only toxic but also
the adaptive responses to toxicants at low exposure levels
putting cells or organisms under stress or inflammation, which
is often the case with low-dose and chronic particle exposures.
Early identification of altered cellular conditions at low doses is
crucial for elucidating mechanisms of action as the toxicity will
occur when the compensation and repair systems are exhausted.
Particles usually interact with multiple structures and biomolecules
in the cells and perturbate multiple signaling pathways impacting
different cellular processes, including proliferation, cell death,
oxidative stress, inflammation, and membrane integrity, and it
is usually difficult to deduce the pathway of toxicity from the
regulation pattern (Fröhlich, 2017;Hartung et al., 2017). Hence,
systems toxicology integrates a system-wide response, such as
transcriptomics, metabolomics, and proteomics data to capture
a variety of omics data and metadata to comprehensively address
particle toxicity.
The transcriptome consists of the entire set of transcripts or
mRNAs present in a cell or an organism. Gene expression
profiling outlines the expression level of mRNAs at a given
time point by DNA microarrays, next generation RNA
sequencing, subtraction hybridization, differential display, or
serial analysis of gene expression. Even so, a known limitation
of transcriptomics lies in the fact that changes in gene expression
do not correlate directly with the phenotypic changes (Nikota
et al., 2015;Fröhlich, 2017). Proteomics defines the analysis of
functionally and structurally related proteins, thereby providing
more direct information on cellular responses than
transcriptomics, and can capture non-transcriptionally
regulated responses, including fast modification or changes in
cellular localization of proteins. Proteomics also plays an
important role in identification of regulated pathways and
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Alijagic et al. Particle Safety in Additive Manufacturing
identification of proteins adsorbed to the particle’s surface
(protein corona) (Monopoli et al., 2011;Vogt et al., 2015). In
comparison to transcriptomics and proteomics, which provide
information of potential particle hazards, metabolomics directly
identifies phenotypic changes that occurred under particle
exposure by analyzing variations in carbohydrate, lipid, and
amino acid patterns. Metabolomics is not organism-specific
and does not have a fixed code (Ramirez et al., 2013), offering
the great opportunity to profile the entire metabolome either as a
footprint (extracellular metabolites) or as a fingerprint
(intracellular metabolites).
Numerous in vitro and in vivo studies have assessed particle
effects by omics techniques. Table 2 summarizes some of the
in vitro studies by using an omics approach, with a focus on lung
and immune cell models. These studies emphasize the
importance of pre-condition analyses prior to the omics,
including a simple and straightforward cytotoxicity screening
to determine the particle concentration range. Such approach is
crucial because highly cytotoxic particle concentrations should be
avoided as apoptotic/necrotic cells provide only limited
information on particle regulatory mechanisms. Fröhlich
(2017) summarizes limitations that hinder the broad use of
omics technologies in particle toxicology, including lack of
standardized methods for particle exposure (sample pre-
treatment, cell type, complex medium composition) and
relevant concentration range. Such limitations should be taken
into consideration when designing and performing particle
toxicity studies.
Machine Learning
Increasing number of simple and composite particulate materials
is produced in various conformations (Winkler and Le, 2017;
Lamon et al., 2019). Particle safety assessment is extremely time-,
labor-, animal-, and cost-consuming (Nel et al., 2013). Large and
complex datasets generated in high-content screening assays and
omics studies are ideal for modeling and analysis by modern
machine learning methods. For example, unsupervised machine
learning may reduce dimensionality of the Cell Painting and
omics datasets. This will provide visual representation of the
secondary data analysis that will be helpful in predicting similar
mechanisms of action and classification of the particles based on
their hazardous potential. In addition, machine-identified cell
states will reveal how particle-specific effects correspond to the
known disease states and known mechanisms of the toxin action.
In addition, data driven modeling techniques based on supervised
machine learning have concurrently risen in importance,
sophistication, and utility, driven largely by the availability of
these massive datasets. Larger training datasets usually result in
models with high prediction accuracies and large domains of
applicability (Winkler, 2020).
The physicochemical characteristics of particles must be
encoded as mathematical entities called descriptors that are
used by machine learning to generate predictive models
(Young et al., 2012;Mikulskis et al., 2019). Particles have
certain issues that make finding useful descriptors more
difficult than for single molecules/chemicals. These depend on
the size and shape distribution of particles, their tendency to
aggregate/agglomerate, and affinity to interact with biomolecules
creating corona that modulates the biological characteristics of
particles (Ke et al., 2017). Commonly, particle descriptors are
obtained from the whole particle and include particle diameter,
surface, aspect ratio, number of atoms, number of surface atoms,
potential energy of surface atoms, descriptors for surface coating,
zeta potential, solubility, etc. (Epa et al., 2012;Winkler, 2020).
Traditional machine learning methods include linear and
nonlinear regression, artificial neural networks, various types
of decision trees (Svetnik et al., 2003), Bayesian networks
(Aguilera et al., 2011), support and relevance vector machines
(Burden and Winkler, 2015), and genetic algorithms (Winkler
and Le, 2017). For example, Liu et al. (2013) reported
TABLE 2 | Brief overview of the in vitro studies characterizing the effects of particles on the human cell transcriptome, proteome, and metabolome.
Omics
technique
Particle type Experimental
model(s)
Dose and
timepoint
Regulated pathway(s) References
Transcriptomics Iron oxide (Fe
3
O
4
) RAW264.7,
Hepa1–6
30–100 μg/ml;
4–48 h
Immune effects, cell death, homeostatic processes Liu et al. (2014)
Silicon dioxide (SiO
2
) A549 50–600 μg/
ml; 2 h
Inflammation, apoptosis, matrix metalloproteinases Fede et al. (2014)
Iron oxide (Fe
3
O
4
) KG1a, HL60 50 μg/ml; 72 h Lipid metabolism, antioxidation, Hypoxia-inducible factor-1
(HIF-1) signaling pathways
Luo et al. (2020)
Proteomics Titanium dioxide (TiO
2
) BEAS-2B 10 μg/ml; 24 h Stress response, metabolism, adhesion, cytoskeleton
dynamics, cell growth, cell death, cell signaling
Ge et al. (2011)
Silicon dioxide (SiO
2
) A549 100 μg/ml; 24 h Apoptosis, cytoskeleton, oxidative stress response, protein
synthesis
Okoturo-Evans et al.
(2013)
Silicon dioxide (SiO
2
) A549 0.1–6μg/cm
2
,24
and 72 h
Rho signaling cascade, cytoskeleton remodeling,
endocytosis, inflammation, coagulation system pathway,
oxidative stress
Pisani et al. (2015)
Metabolomics Aluminum oxide (Al
2
O
3
) HBE 50–500 μg/
ml; 24 h
Apoptosis, oxidative stress, mitochondrial function Li et al. (2015)
Copper oxide (CuO) A549 5–40 μg/ml;
4–24 h
Oxidative stress, hypertonic stress, apoptosis Boyles et al. (2015)
Carbon black
nanoparticles (CBNPs)
A549 70 μg/ml, 48 h Energy, amino acid, and lipid metabolism Hou et al. (2020)
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Alijagic et al. Particle Safety in Additive Manufacturing
classification machine learning models of the effect of 44 iron
oxide core nanoparticles on aortic endothelial, vascular smooth
muscle, hepatocyte, and monocyte/macrophage cells, using four
different biological assays. Their two-class models had relatively
high accuracies >78%. Recently, a study by Huang et al. (2020)
reported use of a Quantitative Structure-Activity Relationship
(QSAR) models to predict IL-1βrelease in THP-1 cells following
the exposure to metal oxide nanoparticles. QSAR models based
on IL-1βwere able to predict the inflammatory potential of
nanoparticles allowing for computational assessment of
particles’inflammatory potential depending on their
physicochemical characteristics.
Importantly, machine learning approaches are critically
dependent on large datasets for model training and validation,
generation of relevant descriptors that represent physicochemical
properties of particles, robust training of models, and use of the
models to predict characteristics of novel materials (Winkler,
2020). By this in mind, machine learning models hold an
excellent potential for the safer-by-design approach when
designing novel particulate materials, including feedstocks
applied in AM.
Adverse Outcome Pathway Networks as a
Promising Tool for the Particle Safety
Assessment
Large datasets obtained in in vitro assays, high-content screening,
and omics studies are excellent inputs to build adverse outcome
pathway (AOP) networks (Figure 7). When first described in the
context of ecotoxicological risk assessment, an AOP was defined
as “a conceptual construct that portrays existing knowledge
concerning the linkage between a direct molecular initiating
event (MIE) and an adverse outcome (AO),”by capturing the
sequential chain of causally linked Key Events (KEs) at different
levels of biological organization—from molecules to organism
level (Vinken, 2018;Halappanavar et al., 2020;Nymark et al.,
2021). Detailed guidelines are established by the OECD (2016),
and a large database of AOPs describing various adverse
outcomes of relevance to human health is available (https://
aopkb.oecd.org/). Recently, AOP development in the field of
particle toxicology has particularly focused on case study-
based and data mining approaches, with the aim to develop
new AOPs or refine existing AOPs based on one or several model
stressors. The data mining approach is employed when there is
sufficient high-content and high-throughput information, such as
omics data, available to identify KEs and to support the
development of AOPs (Gerloff et al., 2017;Nymark et al.,
2018;Halappanavar et al., 2020).
In general, AOPs developed for chemicals should be applicable
to particles (Halappanavar et al., 2020). Different biological
pathways and adverse outcomes (AO) induced by
nanoparticles are shown to share similarities with those
triggered by chemicals, albeit with a lack of detailed
understanding of the MIE (Gerloff et al., 2017).
Mechanistically, when deposited in the lungs, particles induce
oxidative stress, inflammation, genotoxicity, and cytotoxicity
(Oberdörster et al., 2007). Metal oxide nanoparticles are
demonstrated to induce toxic effects similar to specific
occupational hazards (e.g., silica) (Napierska et al., 2010).
Therefore, in principle, toxicity pathways and key biological
events describing chemical-induced AOs should be applicable
in the case of particles.
An example of an AOP network is the development of lung
cancer due to the occupational exposure to fibers and particles,
such as asbestos (Nymark et al., 2008). The underlying
mechanism is initiated by the interaction of particles with lung
FIGURE 7 | Next-generation toxicology testing for the particle safety assessment. Integration of the bioassay toolbox, high-content screening, omics technologies,
and machine learning models offers excellent input in the development of AOP networks and description of MIEs, KEs, and AOs.
Frontiers in Toxicology | www.frontiersin.org April 2022 | Volume 4 | Article 83644715
Alijagic et al. Particle Safety in Additive Manufacturing
macrophages; leading to frustrated phagocytosis. This event is
described as the MIE in this AOP network. Furthermore,
frustrated phagocytosis, and consequential biopersistence of
particles, causes lung inflammation, characterized by increased
release of proinflammatory cytokines (this is key event 1 (KE1))
and by increased invasion of leukocytes into lungs (this is KE2).
Intensified cytokine release and functional alterations of the
immune cells lead to an increased production of ROS (this is
KE3) (Mittal et al., 2014). The ROS is acting as a secondary
messenger and mediator of inflammation leading to DNA
damage and gene mutation (this is KE4) in lung epithelial
cells (Hiraku et al., 2015). Proliferation (this is KE5) is a
highly surveilled process that maintains tissue homeostasis.
However, when proliferation checkpoints are impaired, an
increase of proliferation is detected, which is one of the
hallmarks of cancer (Hanahan and Weinberg, 2011). The
uncontrolled proliferation of lung cells usually evokes increase
of mutations in oncogenes or tumor suppressors, and inevitably
induce the development of cancer (this is the AO).
Importantly, AOP networks offer a great next-generation
toxicology tool for particle safety assessment that will enable
deeper understanding of mechanisms involved in particle
toxicity. More precisely, AOP networks can be used to inform
the design and development of targeted in vitro assays, and for
generating quality data necessary for human health risk
assessment of particles (Halappanavar et al., 2020).
CONCLUSION AND OUTLOOKS
AM has entered in a dynamic development phase in the industry.
Feedstock powders and particles formed and emitted during AM
processes may pose potential occupational risk for the workers
and potentially end users. To mitigate risks, it is important to
design comprehensive safety assessment framework that will map
particle exposures, characterize captured particles, understand
mechanisms by which particles act to induce adverse effects, and
to translate findings into an in vivo setting and identify long-term
and low-dose exposure health effects. However, particle safety
assessment is facing challenges in collection of the airborne
particle emissions in the AM facilities. Due to small size and
technical limitations of the collection instruments, it is very hard
to collect enough nanosized particles for toxicological testing.
While optimizing these methods, safety assessments should focus
on feedstock powders, impact of powder reuse, and particles
captured into air-ventilation filters that can be recovered in
sufficient quantities for biological testing. To address biological
effects of AM particles, a completely new approach of next-
generation toxicology testing is emerging. It includes a systemic
approach with advanced in vitro and in silico models, high-
content screening, omics technologies, big data approaches,
and development of AOPs as mechanistic tools in particle
safety assessment. The authors of this article, consider this
review an opportunity to: 1) highlight particle exposure-
related risks in AM, 2) to define well-established
toxicological endpoints relevant for AM particle
assessment, and 3) to propose next-generation toxicology
testing approaches. Together, the proposed approach will
enable a deeper understanding of existing and emerging
particle and chemical safety challenges and provide a
strategy for the development of cutting-edge methodologies
for hazard identification and risk assessment in the AM
industry.
AUTHOR CONTRIBUTIONS
All authors have made a substantial, direct, and intellectual
contribution to the work and approved the final version for
the publication.
FUNDING
This work was supported by the Swedish Knowledge Foundation
(Grants No. 20190107 and 20160019).
ACKNOWLEDGMENTS
Figures 2,3,5–7were created by BioRender.com.
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