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156 3D Printing of Pharmaceutical and Drug Delivery Devices: Progress from Bench to Bedside
8.2.2 X-Ray Powder Diffraction (XRPD)
The evaluation of the crystallinity of a drug in the 3D-printed object can be decisive for its cor-
rect functioning; in this sense, X-ray powder diffraction (XRPD) is a simple and highly recom-
mended technique. The principle of this analytical instrumentation is based on the orderly and
patterned arrangement of the atomic structure of crystals. Thereby, an X-ray beam impinges the
solid sample at specific angles, and then they are scattered according to the organisation of
electrons in the atoms, defining the diffraction patterns [25]. This signal is converted into peaks,
where its position is used to determine the size and shape of the crystal, while the intensity of
the diffraction peak determines the atomic position within the crystal [26].
One of the great advantages of producing pharmaceuticals by 3D printing is the ability
of this technology to render and maintain the amorphous profile of the drug, conditioning
its release to the embedded matrix [27]. In this way, XRPD should be applied in preformu-
lation studies of 3D pharmaceutical dosages in order to anticipate the amorphisation of the
drug and even to check the possible formation of new crystalline phases [24, 28].
The crystalline profile commonly observed in drugs is shown in Figure 8.5. The disap-
pearance of characteristic peaks of the original crystal and the appearance of new signals
Figure 8.5 Hypothetical XRPD diffractograms of the drug and three different physical mixtures.
The characteristic peaks of the drug original crystal are shaded. Physical mixture 1 describes the
disappearance of peaks corresponding to drug crystal structure and the appearance of new
peaks different from its original crystalline form, suggesting the formation of a new polymorph of
the drug. Physical mixture 2 represents the presence of some background noise with the
disappearance of some peaks, indicating partial amorphisation of the drug. Physical mixture 3
exemplifies the disappearance of all peaks corresponding to the drug’s crystalline form and an
intense baseline noise, indicating complete amorphisation of the drug.
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Analytical Characterisation of 3D-Printed Medicines 157
on the diffractogram are unmistakable signs of changes in the crystal organisation, i.e.,
polymorphism (physical mixture 1; Figure 8.5) [29]. Moreover, changes in baseline, such
as the intense presence of noise with non-peak identification, indicate a partially or entirely
drug amorphisation (physical mixtures 2 and 3; Figure8.5) [28, 30].
Therefore, this technique should be explored together with thermal analysis to identify
pharmaceutical matrices and excipients that can amorphise the drug and do not trigger the
appearance of polymorphs. When observed in physical mixtures, this response is beneficial
to developing 3D pharmaceuticals, since additive manufacturing processing tends to inten-
sify the drug–excipient interactions, stabilising and enhancing the previously observed
amorphous character [31]. This was followed, for example, with indomethacin, which
remained amorphous in the polymer Kollidon
®
VA 64 after processing by SLS 3D printing
[27] and with griseofulvin that even with the intense stress of FDM 3D printing remained
amorphous in tablets produced using hydroxypropyl cellulose [32].
8.2.3 Infrared Spectroscopy
The analytical techniques based on vibrational spectroscopy in the infrared region include
near-infrared (NIR) and mid-infrared (MIR). Particularly, MIR spectrometry, which uses
lower wavelengths (400–4,000 cm
–1
), can recognise hydrophilic and hydrophobic func-
tional groups. Notably, the region of 900–1,500 cm
–1
is ideal for identifying the chemical
structure of drugs [33], making this technique one of the most demanded analytical tests for
a preformulation study [34, 35].
The identification of functional groups is based on detecting interatomic chemical bonds.
First, infrared incident light passes through the sample, which triggers the vibration of
specific molecular bonds due to infrared energy absorption. Then, this signal is transmitted
to a detector and is converted into a spectrum (Figure 8.6) [36]. A mathematical model is
required to decode the analytical signal. The most used procedure for this purpose is Fourier
transformation [37, 38].
Therefore, FTIR is a straightforward methodology to be applied as a compatibility
screening tool, since the vibrational alterations detected by this method allow the identifi-
cation of possible intermolecular interactions between the formulation components. The
reduction in peak intensity or the appearance or disappearance of some peaks indicates
interactions between the drug and the excipient [33, 37].
Figure 8.7 shows hypothetical FTIR spectra in which alterations in the peaks correspond
to the characteristic functional groups of the molecule, signaling the chemical interac-
tion between the components. Moreover, based on this analysis, not only polymer–drug
Figure 8.6 Schematic illustration of spectrum detection by infrared spectroscopy, from
infrared light radiation to sample response and data detection.
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158 3D Printing of Pharmaceutical and Drug Delivery Devices: Progress from Bench to Bedside
interaction is inferred by also its chemical stability in order to assist the screening of the
best excipients [24]. Indeed, the disappearance of a peak following the non-appearance of
a new one that could correspond to a new bond with that atom indicating degradation and
loss of molecular structure [39].
8.2.4
Hot-Stage Microscopy (HSM)
Organoleptic changes such as colour and shape, even if unrelated to a chemical modifica-
tion of the sample, constitute quality deviations and must be anticipated in preformulation
studies. Hot-stage microscopy (HSM) is a widely used technique to assess sample
morphology changes during heating. Therefore, it is a valuable tool to complement the
other analyses herein presented, in which melting, solubilisation, crystallisation, and
possible degradation or polymorphism events can be noticed [13]. In this characterisation,
the sample is placed in a small hot-stage cell with a microscope that allows the visualisa-
tion of the morphology of thermal events (Figure 8.8) [40].
Some changes resulting from heat exposure are not observed in the thermal analysis,
since its low concentration does not generate an analytical signal [41, 42]. Thereby, the
evaluation of drug behaviour through HSM becomes even more interesting, especially for
Figure 8.7 Hypothetical FTIR spectra of the drug and three different physical mixtures. The
shaded peaks correspond to the characteristic functional groups of the drug paracetamol.
Physical mixture 1 represents practically unchanged drug spectra, indicating little or no
interaction between the components. Physical mixture 2 exemplifies an enlargement and
reduction of the peak intensity, indicating changes in the molecular structure due to inter- or
intramolecular interactions, maintaining the chemical stability of the drug. Physical mixture 3
represents the disappearance of peaks corresponding to the drug, indicating its instability and
incompatibility with the excipient.
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Analytical Characterisation of 3D-Printed Medicines 159
the preformulation of 3D products developed by categories that explore temperature varia-
tion, such as material extrusion, material jetting, and vat photopolymerisation.
Furthermore, some events observed by DSC, TGA, and even XRPD that are difficult to
understand can have their interpretation assisted with the images obtained by HSM [43].
Thus, this analysis can be a complementary tool to other analytical instrumentations [44].
8.2.5 Customizsd Sample Preparation for the Preformulation Protocol
Additive manufacturing uses different materials and energy sources than conventional
technology to produce pharmaceutical products. Therefore, innovative steps can be intro-
duced in the preparation of samples trying to simulate the processing stress that the mate-
rial will be exposed to when printed, e.g., using heating or light (Figure 8.1). This will
provide a proper application of the above-mentioned analytical techniques in a preformula-
tion study of 3D pharmaceutical dosage forms.
In this direction, a protocol for preformulation studies for FDM 3D printing proposed heat-
ing the samples for 5 minutes at a predetermined temperature in order to simulate the thermal
stress of the hot-melt extrusion (HME) process, followed by cooling at room temperature and
then heating again at a specific temperature for 2minutes, aiming to simulate the heating of the
print step [10]. Moreover, another work performed a similar double-heating protocol for
HME-FDM systems whose extrusion process is more accelerated, in which the samples were
heated for 2 minutes at each chosen temperature [24]. In both studies, the time–heat exposure
was classified as greater than residence processing time to anticipate, in the worst possible
scenario, the repercussion of physical-chemical changes in the sample due to thermal stress.
8.3 In-Process Characterisations
The 3D printing of drug products involves mostly continuous processes with few produc-
tion steps. However, particularly in the case of FDM, the drug-loaded filaments are inter-
mediate products whose viscoelastic and mechanical characteristics are decisive for correct
printing and should therefore be closely monitored. Accordingly, establishing in-process
control protocols that fulfill this purpose is mandatory as part of the quality assurance sys-
tem for pharmaceutical production.
Figure 8.8 Schematic illustration of hot-stage microscopy, showing the morphological analysis
of the crystal melting.
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160 3D Printing of Pharmaceutical and Drug Delivery Devices: Progress from Bench to Bedside
8.3.1 Mechanical Analysis
Mechanical evaluation is commonly used on filaments for FDM 3D printing, since it is
crucial information to assist printability requirements and successful feedability. In fact,
most pharmaceutical polymers are not designed to suit 3D technology; in this way, extruded
polymeric filaments without other adjuvants are most likely too hard or too soft, which
hamper the traction and printing processes [45]. Therefore, in order to choose the ideal fila-
ment for printing, mechanical properties are assessed before the printing step, as an in-
process control [46].
This evaluation can be performed using a universal testing machine or a texture analyser.
Both instruments equipped with the proper geometry cell can perform three-point bending,
resistance, and elongation tests, which aim to measure the maximum stress and force
required to fracture the filament (Figure 8.9) [47–49].
The three-point bending and the resistance tests use compressive forces. The first test is
performed on a selected piece of the filament, while the resistance test is carried out
throughout the entire sample (Figure 8.9). This difference makes bending tests more rec-
ommendable since the reduced sample volume leads to a lower probability of finding errors
and generating large statistical deviations, especially in the case of sensitive materials such
as brittle filaments [46].
Figure 8.9 Schematic illustration of mechanical evaluation performed in a universal testing
machine or a texture analyser. The different analyses commonly performed to measure the
maximum stress and force required to fracture the filament are represented.
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Analytical Characterisation of 3D-Printed Medicines 161
On the other hand, elongation tests, also known as tensile tests, use extensional forces to
stretch the filament and measure the fracture point (Figure 8.9). Considering that the filaments
are pulled by the gears of an FDM 3D printer, the majority of force applied under the samples
are tension forces. Thus, this type of test would be more recommendable due to the high simi-
larity with the mechanical feeding process of the FDM 3D printer [50]. However, the analyti-
cal deviation of this test is very high due to the force being performed throughout the entire
sample, as explained before, which requires an increased number of replicates [46].
Regardless of the experimental chosen protocol, the result is represented in force-
displacement or stress-strain graphs. The latter is the most used, as it provides extensive
information about the material’s mechanical properties (Figure 8.10A). In stress-strain
diagrams, the first part of the curve is formed by a linear growth (elastic deformation),
followed by a necking behaviour, in which small changes in stress cause pronounced
variation in strain (plastic deformation) (Figure 8.10A) [51].
The difference between these two deformations lies in the material’s ability to recover.
When stress is employed in a solid material, its chemical bonds stretch [52]. Then, when
the force is removed, the intra-atomic bonds can relax and recover, return to their original
form (elastic deformation), or be irreversibly destroyed and maintain their new conforma-
tion (plastic deformation) [53].
Breaking stress higher than 8.5 MPa has been described for filaments capable of sup-
porting the traction of a FDM 3D printer. Hence, this value is considered the lower stress
limit to FDM materials [54]. Moreover, the area under the curve provides information
about the material’s toughness, i.e., larger areas correspond to ductile materials and smaller
areas to brittle materials (Figure 8.10A) [55, 56]. In general, ductile samples have elastic
and plastic deformation, while brittle materials only deform elastically. Additionally, this
variable is related to fracture stress. Still, it is not totally dependent on it, as it is possible to
obtain a ductile material that fractures at small stress values [57].
In this sense, to choose the ideal filament for printing, it is necessary to observe not only
the maximum fracture point but all the deformation behaviour until filaments break. In
Figure 8.10 Hypothetical stress-strain curves from the mechanical evaluation (A) indicating
elastic and plastic deformation, toughness (area under the curve), and fracture point of brittle
(fragile) and ductile (resistant) materials. (B) Example of the ideal curve for polymeric filaments
used in FDM 3D printers.
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162 3D Printing of Pharmaceutical and Drug Delivery Devices: Progress from Bench to Bedside
general, ideal filaments to support the traction and properly feed a FDM 3D printer have
elastic deformation up to the limit stress values (8.5 MPa) and that are not highly ductile,
since the polymer matrix needs to be able to deform to create the desired 3D structure, and
then recover, aiming to solidify the final drug product (Figure 8.10B) [54].
8.3.2 Rheological Analysis
As mentioned before, pharmaceutical 3D objects are developed based on resins and polymers
(Figure 8.1); hence, the formulations are extremely viscoelastic. In this way, rheological
evaluation, especially oscillatory shear rheology, measured by a rheometer, is an indispensa-
ble tool to guide the production of 3D products, since it provides information on the deforma-
tion profile of viscoelastic compounds in different thermal-shear conditions [58].
In FDM 3D printing, the viscoelastic behaviour of the filament needs to provide ade-
quate flow to the printer nozzle; however, its complex viscosity cannot be too low, other-
wise the material will overly deform and could clog the printer [59]. On the other hand, for
light-based vat photopolymerisation, the complex viscosity of the formulation needs to be
as low as possible to allow adequate recoating of the liquid monomer on the polymerised
underlayer [60]. Still, lower viscosity values have been described as a desirable condition
for material jetting to achieve the appropriate spray of droplets [61].
To correctly assess the formulation’s processability and characterise its components, it is
necessary to ensure that the molecular structure will not be damaged or destroyed during
the analysis, allowing reliable results. This chemical stability is difficult to be established
with solid materials since the extreme rigidity can make materials brittle, leading to fragile
intramolecular bonding forces, which can break during oscillation tests and disable the
evaluation, especially at temperatures in which the material remains solid [62].
Thus, the first step of the evaluation is to establish linear viscoelastic response (LVR)
and non-linear measurement intervals [63]. The strain is the rheological variable that will
control this phenomenon, representing the amplitude in which the deformation can occur.
Thus, changes in the strain inside the LVR of the material do not modify the rheological
response. In contrast, changes in the strain outside the LVR imply a non-constant deforma-
tion with a risk of molecular destruction, making the analysis unfeasible [64].
The determination of the LVR is performed by an amplitude (strain) sweep test, in which
the applied amplitude is increased at a fixed frequency, allowing to observe a transition
between the linear and non-linear regimes (Figure 8.11) [65]. The LVR is different for each
material, being extensive for liquid compounds and short for solid compounds, making
high strain values easily accepted by fluids, while the molecular control of elastic materials
is more demanding [66, 67].
Methods that work with low strain values are known as the small amplitude oscillatory
shear (SAOS), while those that work with high strain values are known as the large ampli-
tude oscillatory shear (LAOS) [68]. As the LVR of solid materials is always formed by low
strain values, the SAOS methodology is often chosen to characterizs pharmaceutical sys-
tems developed by extrusion or 3D printing.
For materials characterisation, rheological measurements of viscosity, modulus, and tan-
gent of angle δ are evaluated as a function of frequency and temperature variations [58, 66].
Amongst all the numerous analyses that can be performed on an oscillatory rheometer, the
frequency sweep test is the most popular [69–71].
In this experimental technique, temperature and strain are constant, and an increasing
frequency is applied to the material in order to evaluate their moduli (G' and G'') and
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Analytical Characterisation of 3D-Printed Medicines 163
complex viscosity (Figure 8.12) [72, 73]. This provides the time-dependent behaviour of a
sample, since low frequencies simulate slow motion or slow processing conditions (dark
grey region Figure 8.12), and high frequencies simulate fast motion or fast processing con-
ditions (light grey region. Figure 8.12) [74, 75].
When applied to FDM, frequency sweeps are proven methods to choose the printing
speed, as they can collect information on the behaviour and inner structure of the formula-
tion under predefined procedure parameters (shear and temperature are constant) and
assess the time required for the material to deform optimally [76]. Additionally, this experi-
mental tool is useful to choose the processing temperature in material jetting 3D printing
[61]. Several possible processing temperatures are selected in most research, and the fre-
quency sweep analysis is performed in these different isotherms. Therefore, the condition
that provides adequate deformation in a short time and low temperature is considered ideal
for printing [77–79].
Rheological temperature ramp analysis is a widely explored test in materials science for
evaluating polymers suitable for 3D printing [80, 81]. Indeed, such oscillatory assay has
been increasingly used to characterise polymer-based materials for SLA, material jetting,
and FDM [66, 82, 83]. In this experiment, rheological patterns are monitored according to
a temperature ramp (Figure 8.13).
In practice, the viscoelastic behaviour of the material is evaluated as a function of tem-
perature in order to assess its deformation capacity at different heat profiles to enable the
Figure 8.11 Hypothetical graphical representation of an amplitude sweep analysis in which
the loss modulus (G") and storage modulus (G') are plotted. The dark grey region indicates
the linear viscoelastic response and the strain values that form the SAOS method. In contrast,
the light gray region indicates the non-linear viscoelastic response and the strain values that
form the LAOS method.
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164 3D Printing of Pharmaceutical and Drug Delivery Devices: Progress from Bench to Bedside
Figure 8.13 Hypothetical graphical representation of a temperature ramp analysis loss
modulus (G"), storage modulus (G'), and complex viscosity (η*) are plotted.
Figure 8.12 Hypothetical graphical representation of a frequency sweep analysis in which
the loss modulus (G''), storage modulus (G'), and complex viscosity (η*) are plotted. The dark
grey region indicates the low frequency values used to represent slow processing conditions,
while the light grey region indicates the high frequency values used to describe fast processing
conditions.
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Analytical Characterisation of 3D-Printed Medicines 165
ideal definition of thermal and stress for the printing process [84]. For pharmaceutical sys-
tems developed by FDM, the elastic deformation (marked by G') cannot be too high, other-
wise, the polymer will not deform; consequently, the polymer-drug interaction is impaired.
However, if the viscous behaviour (marked by G'') is greater, the deformation will be so
intense that the polymer will not recover and solidify after extrusion or printing [58].
Moreover, for assessing the complete deformation of the formulation, complex viscosity
and complex modulus are monitored as they gather information about the resistance of
flow and deformation, respectively, considering the viscoelasticity of the material [10].
Additionally, for characterisation purposes, this analysis is commonly performed to assess
the glass transition region (T
g
) and melting point (T
m
) of the polymer [85, 86].
For a solid polymer, the glass transition range starts at the first signs of materials soften-
ing, and it ends when the molten state has been completely reached (grey dotted line
Figure8.14) [87]. In this way, the T
g
temperature is established approximately in the mid-
dle of the T
g
range, and it can change according to the proposed mathematical method of
interpretation: temperature onset of G' decay [88], peak preceding G'' decay [89], or peak
of tan δ [90]. Since tan δ considers both viscous and elastic deformation, it is the most
described method for calculating T
g
temperature.
The melting point in a rheological temperature ramp analysis is classified as the crosso-
ver temperature (G' = G") [85]. Figure 8.14A shows the viscoelastic profile of an amor-
phous polymer, indicating its T
g
temperature, while Figure 8.14B shows the viscoelastic
profile of a crystalline polymer, showing T
g
and T
m
.
Conclusively, besides supporting the identification of the best process conditions, a tem-
perature ramp analysis can also evaluate a crystalline profile, decomposition, compatibility,
and capacity for molecular interaction [28, 46]. Moreover, the intrinsic characterisation
proposed by this rheological test can provide information during and after the material is
stressed, since the rheometer is able to return to ambient conditions while continuing to
monitor the rheological variables.
Figure 8.14 Hypothetical graphical representation of a temperature ramp analysis in which
the loss modulus (G''), storage modulus (G'), and tangent of the angle (Tan δ) are plotted. Tan
δ peak indicates the glass transition temperature (T
g
) of (A), an amorphous polymer, and (B) a
crystalline polymer, in which the melting point (T
m
) is also represented and indicated by the
modulus crossover.
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