Assessing the Interrelationship of Microstructure, Properties, Drug Release Performance, and Preparation Process for Amorphous Solid Dispersions Via Noninvasive Imaging Analytics and Material Characterization

Abstract

Purpose
The purpose of this work is to evaluate the interrelationship of microstructure, properties, and dissolution performance for amorphous solid dispersions (ASDs) prepared using different methods.

Methods
ASD of GDC-0810 (50% w/w) with HPMC-AS was prepared using methods of spray drying and co-precipitation via resonant acoustic mixing. Microstructure, particulate and bulk powder properties, and dissolution performance were characterized for GDC-0810 ASDs. In addition to application of typical physical characterization tools, we have applied X-Ray Microscopy (XRM) to assess the contribution of microstructure to the characteristics of ASDs and obtain additional quantification and understanding of the drug product intermediates and tablets.

Results
Both methods of spray drying and co-precipitation produced single-phase ASDs. Distinct differences in microstructure, particle size distribution, specific surface area, bulk and tapped density, were observed between GDC-0810 spray dried dispersion (SDD) and co-precipitated amorphous dispersion (cPAD) materials. The cPAD powders prepared by the resonant acoustic mixing process demonstrated superior compactibility compared to the SDD, while the compressibility of the ASDs were comparable. Both SDD powder and tablets showed higher in vitro dissolution than those of cPAD powders. XRM calculated total solid external surface area (SA) normalized by calculated total solid volume (SV) shows a strong correlation with micro dissolution data.

Conclusion
Strong interrelationship of microstructure, physical properties, and dissolution performance was observed for GDC-0810 ASDs. XRM image-based analysis is a powerful tool to assess the contribution of microstructure to the characteristics of ASDs and provide mechanistic understanding of the interrelationship.

Full text

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Pharmaceutical Research
https://doi.org/10.1007/s11095-022-03308-9
RESEARCH PAPER
Assessing theInterrelationship ofMicrostructure, Properties, Drug
Release Performance, andPreparation Process forAmorphous
Solid Dispersions Via Noninvasive Imaging Analytics andMaterial
Characterization
WeiJia1· Phillip D.Yawman2· KeyurM.Pandya1· KellieSluga1· TaniaNg1· DawenKou1· KarthikNagapudi1·
PaulE.Luner2,3· AidenZhu2· ShawnZhang2· HaoHelenHou1
Received: 28 February 2022 / Accepted: 27 May 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
Abstract
Purpose The purpose of this work is to evaluate the interrelationship of microstructure, properties, and dissolution perfor-
mance for amorphous solid dispersions (ASDs) prepared using different methods.
Methods ASD of GDC-0810 (50% w/w) with HPMC-AS was prepared using methods of spray drying and co-precipitation
via resonant acoustic mixing. Microstructure, particulate and bulk powder properties, and dissolution performance were
characterized for GDC-0810 ASDs. In addition to application of typical physical characterization tools, we have applied
X-Ray Microscopy (XRM) to assess the contribution of microstructure to the characteristics of ASDs and obtain additional
quantification and understanding of the drug product intermediates and tablets.
Results Both methods of spray drying and co-precipitation produced single-phase ASDs. Distinct differences in micro-
structure, particle size distribution, specific surface area, bulk and tapped density, were observed between GDC-0810 spray
dried dispersion (SDD) and co-precipitated amorphous dispersion (cPAD) materials. The cPAD powders prepared by the
resonant acoustic mixing process demonstrated superior compactibility compared to the SDD, while the compressibility of
the ASDs were comparable. Both SDD powder and tablets showed higher in vitro dissolution than those of cPAD powders.
XRM calculated total solid external surface area (SA) normalized by calculated total solid volume (SV) shows a strong cor-
relation with micro dissolution data.
Conclusion Strong interrelationship of microstructure, physical properties, and dissolution performance was observed for
GDC-0810 ASDs. XRM image-based analysis is a powerful tool to assess the contribution of microstructure to the charac-
teristics of ASDs and provide mechanistic understanding of the interrelationship.
KEY WORDS amorphous solid dispersion· coprecipitation· material characterization· microstructure-property-
performance-process interrelationship· spray drying
Abbreviations
ASD Amorphous solid dispersion
cPAD Co-precipitated amorphous dispersion
FaSSIF-V2 Fasted-state simulated intestinal fluid version
2
HME Hot-melt extrusion
SD Spray drying
SDD Spray dried dispersion
RAM Resonant acoustic mixing
VDD Vacuum drum drying
XRM X-ray microscopy
INTRODUCTION
In small molecule drug discovery and development port-
folios, approximately 75% of compounds are poorly water-
soluble and classified as Biopharmaceutical Classification
* Hao Helen Hou
hou.hao@gene.com
1 Small Molecule Pharmaceutical Sciences, Genentech Inc., 1
DNA Way, SouthSanFrancisco, California94080, USA
2 DigiM Solution LLC, 67 South Bedford Street, Suite 400
West, Burlington, Massachusetts01803, USA
3 Triform Sciences LLC, Waterford, Connecticut06385, USA
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Pharmaceutical Research
1 3
System (BCS) classes II and IV (1, 2). The pharmaceutical
industry has recognized the importance of solubility and
developed various strategies to either improve candidate
druggability or overcome poor solubility using medicinal
chemistry approaches and enabling formulation techniques
(3). Formulating poorly water-soluble compounds as amor-
phous solid dispersions (ASDs) is one of the most promising
approaches to enhance solubility and/or dissolution (4), and
membrane flux (5) to improve bioavailability (6).
ASDs can be manufactured via a number of technologies
which can be broadly categorized into melting/fusion-based
and solvent-based methods (7). In melting/fusion-based
methods, physical mixtures of drug and carrier are heated
above the melting or glass transition temperature, followed
by rapid cooling to solidify the drug in an amorphous state.
Adequate mixing to form a homogenous molecular solution
and sufficiently fast cooling are essential to the quality and
performance of ASDs formed via melting/fusion techniques.
Hot-melt extrusion (HME) (8, 9), especially the proprietary
Meltrex™ technique, and KinetiSolⓇ which uses a series
of rapidly rotating blades that generate a large amount of
energy to promote drug-carrier fusion without the need for
an external heating source (10–12), are representative pro-
cesses of melting/fusion-based technologies. Three-dimen-
sional (3D) printing (13, 14), microwave-induced in situ
amorphization (15, 16), and other melt-based methods (17,
18), have been investigated and used in ASD preparation.
In solvent-based methods, drug and carrier are dissolved in
a solvent system, followed by isolation of solid and solvent
removal. The amount of residual solvents in the ASD must
comply with regulatory guidelines since residual organic
solvents carry a toxicity liability and also plasticize the
ASD, which leads to the risks of phase separation and crys-
tallization. Therefore, secondary drying is typically applied
to remove the residual solvent to meet the acceptance criteria
as defined in International Council for Harmonization Q3C
(R8) guideline (19). Spray drying (SD) (20), electrospray-
ing (21), fluidized bed drying (22), supercritical fluids (23),
spray freeze-drying (24), and vacuum drum drying (VDD)
(25), are based on the principle of solvent evaporation. Pres-
ently, HME and SD are the most commonly used technolo-
gies for commercial-scale ASD production. There is exten-
sive literature on the advantages and limitations of these two
technologies (7, 26). Despite their prevalent application in
the pharmaceutical industry, researchers have consistently
made efforts in exploring novel manufacturing techniques to
circumvent the limitations of existing technologies.
Antisolvent co-precipitation is another solvent-based
approach in producing ASDs at both small and commer-
cial scales (6, 27). In this method, drug and polymer are
dissolved in a common solvent, and then introduced into
a common antisolvent to generate ASD via co-precipita-
tion. Solid precipitates are isolated (e.g., via filtration)
followed by solvent removal and subsequent drying pro-
cess. Co-precipitation is well suited for compounds with
low solubility in volatile organic solvents and exhibiting
high melting temperature and propensity for thermal deg-
radation at elevated temperatures (26). It is essential that
both drug and polymer are soluble in the common solvent
and insoluble in the common antisolvent, and the solvent
is miscible with the antisolvent. Ionic polymers which are
insoluble in acidified aqueous antisolvents are primarily
used for co-precipitation. Recent work has expanded the
application of this technology to water-soluble nonionic
polymers by using organic antisolvents (28), which makes
it a promising alternative to SD and HME.
Due to the unstable nature of amorphous materials and
the underlying principle for their formation, it is clear
that the manufacturing technology and process conditions
would impact ASD material characteristics and proper-
ties, and in turn in vivo performance (26, 29). Therefore,
rational selection of an appropriate manufacturing tech-
nology and fundamental understanding of the impacts of
process parameters on production efficiency, product qual-
ity, and performance are critical for delivering a successful
ASD product. Although there are extensive studies that
have investigated the choice of excipients and processes to
maintain solid state stability and enhance biopharmaceuti-
cal performance, there are limited studies that assessed the
effects of manufacturing technology on the microstructure
and particulate properties of ASDs, and downstream pro-
cessing in terms of powder flow and compaction. Davis
etal. (30) studied the impacts of SD and HME on powder
flow, compaction, and dissolution of an Itraconazole ASD.
Spray dried powder contained fine particles and exhibited
poorer powder flow, but showed better compactibility and
tabletability than milled extrudates consisting of larger and
denser particles. In vitro dissolution results revealed that
higher drug release was observed from tablets containing
spray dried powder than those containing milled extrudes.
Schönfeld etal. (31) investigated the downstream process-
ability of Ritonavir ASD prepared by HME, SD, and VDD.
The VDD material showed acceptable powder flow and
remarkable compactibility and tabletability, which makes
it suitable for direct compression. In contrast, the SD
material showed higher degree of elastic recovery during
the decompression phase, with strong capping and lami-
nation observed in compacts. In our previous work, we
studied the impact of method of preparation (i.e., SD and
co-precipitation) on the mechanical properties of an ASD
(32). Co-precipitated powders prepared by either overhead
mixing or resonant acoustic mixing (RAM) demonstrated
superior compression behavior compared to the SD mate-
rial. Because the processes of coprecipitation and SD
impact the physical attributes of the resultant materials,
in-depth characterization of their properties, at both the
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Pharmaceutical Research
1 3
bulk and particulate levels is necessary for understanding
how they perform in downstream processing resulting in
the final dosage form.
In addition to application of typical physical characteri-
zation tools, in this work we have applied X-Ray micro-
computed tomography to obtain additional understanding
of the drug product intermediates and tablets formed from
ASDs made by different techniques. X-Ray micro-computed
tomography (interchangeably termed MicroCT, XRCT or
μCT), has recently been used for microstructural assessment
of tablets (33, 34), granules (35), and spray dried disper-
sions (36). Advances in non-invasive 3D imaging allows
X-ray Microscopy (XRM) through AI-based image process-
ing to quantitatively interrogate API or spray dried disper-
sion domains and morphology in powder blends and tablets
(36–38).
In this study, ASD of GDC-0810 (Fig.1) and hydroxy-
propyl methylcellulose acetate succinate (HPMC-AS) was
chosen as the model system. GDC-0810 is a weak acid with
very low intrinsic solubility (<0.06mg/mL) (39). It is a BCS
class II compound with a molecular weight of 446.9g/mol,
pKa of 4.3, log P of 6.2, and melting point of 232°C. ASD
of GDC-0810 with HPMC-AS was prepared using methods
of SD and co-precipitation via RAM at different accelera-
tions. The objectives of this work were 2-fold: (1) evalu-
ate the interrelationship of microstructure, properties, and
dissolution performance for ASDs prepared using different
methods and (2) apply XRM to assess the contribution of
microstructure to the characteristics of ASDs.
Materials andMethods
Materials
GDC-0810 free acid was obtained from F. Hoffmann-La
Roche AG, Basel, Switzerland. HPMC-AS MF grade was
obtained from Shin-Etsu Chemical Co. (Tokyo, Japan).
Common pharmaceutical excipients for oral solid dosage
forms were used to prepare tablets in this study: microcrys-
talline cellulose (Avicel PH 102, FMC Biopolymer, Phila-
delphia, PA, USA), lactose monohydrate (Fast Flo® 316,
Foremost Farms, Rothschild, WI), croscarmellose sodium
(Disolcel® GF 1506, Mingtai Chemical Co. Ltd., Bah-Der
City, Taoyuan Hsien, Taiwan), colloidal silicon dioxide
(Aerosil 200, Evonik Corporation, Parsippany, NJ, USA),
and magnesium stearate (Ligamed MF-2-V, Peter Greven,
Venlo, Netherlands).
Fasted-state simulated intestinal fluid version 2 (FaSSIF-
V2) powder was purchased from Biore levant. com (London,
UK). The aqueous medium used in all experiments was
50mM pH6.5 sodium phosphate buffer. Buffer components
and all solvents (acetonitrile, dichloromethane, and metha-
nol) used in the study were purchased from Sigma-Aldrich
Co. (St. Louis, MO).
Methods
Preparation ofAmorphous Solid Dispersions (ASDs)
ofGDC‑0810
GDC-0810 (50% w/w) ASDs were prepared by the methods
of SD and co-precipitation.
(a) SD: GDC-0810 and HPMC-AS MF (1:1 weight ratio)
were dissolved in dichloromethane/methanol (1: 1
volume ratio) at a total solid load of 5% (w/v). The
solution was spray-dried using a Buchi B-290 mini-
spray dryer (Buchi Corp., New Castle, DE) at an inlet
temperature of 85°C, outlet temperature of 45°C, and
solution feed rate of 10mL/min. A two-fluid nozzle
with opening diameter of 0.5mm was used, and the
nozzle gas flow was 60mm (measured with a Q-Flow
meter from Vögtlin Instruments, Aesch, Switzerland).
Secondary drying was performed at 50°C and 160mbar
for 48hours using a vacuum oven (Thermo Fisher Sci-
entific Lindberg/Blue M Vacuum Oven VO914).
(b) Co-precipitation via RAM: The RAM platform was set
up as demonstrated in the previous work (32). GDC-
0810 and HPMC-AS MF (1:1 weight ratio) were dis-
solved in DMSO at 60°C at a total solid load of 15%
and the solution was then cooled to room temperature.
30mL of the DMSO solution was introduced into a
jacketed mixing vessel containing 400mL of 0.001N
HCl maintained at 5°C using a syringe pump at a rate
of 2mL/min. The mixing was performed at an accel-
eration of 40G or 80 G (where “G” is the force of
gravity) for a total of 45min. After the mixing was
completed, the suspension was then filtered, and the
filtrate was washed five times with 100mL of 0.001N
HCl. The solids were dried in a vacuum oven at 50°C
for 5days. After drying, powders were milled using a
Fig. 1 Molecular structure of GDC-0810 free acid
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Pharmaceutical Research
1 3
Comil (Model U5, Quadro®; Waterloo, Ontario, Can-
ada) with a 024R screen (610mm round hole) at the
impeller speed of 1500rpm.
Preparation ofASD Tablets
GDC-0810 ASD tablets at the dose strength of 100mg
(free acid equivalent) were prepared by a direct compres-
sion process. TableI lists the formulation composition of
GDC-0810 ASD tablets in this study. Each component was
manually sieved through a 30-mesh screen prior to use.
Each GDC-0810 ASD powder prepared using the method
mentioned above, was first blended with all excipients
except for magnesium stearate on a Turbula blender (Model
T2C, Glen Mills Inc., Clifton, NJ) at 67rpm for 10min.
The mixture was then blended with magnesium stearate at
the same speed for 3min. The final blend was compressed
into 15.4mm × 7.0mm capsule shaped tablets with a target
weight of 400 ± 5mg and a target hardness of 16 ± 2 kP
using a manual press (Model C, Carver Inc., Wabash, IN).
Characterization ofGDC‑0810 ASDs
X‑Ray Powder Diffraction (XRPD) Analysis Samples were
analyzed using a Rigaku Miniflex 600 benchtop diffrac-
tometer (Rigaku Corporation, Tokyo, Japan) with Cu Kα
radiation (40kV × 15mA) in the theta/2-theta configura-
tion (reflection mode). Samples were packed on a zero-back-
ground holder and scanned from 2 to 40° 2θ at a scan speed
of 2.0°/ min and a step size of 0.020° 2θ. Data were collected
under ambient conditions and analyzed using commercial
software (JADE, version 9, Materials Data Inc., Livermore,
CA).
Differential Scanning Calorimetry (DSC) Modulated DSC
experiments were carried out using a Q2000 differential
scanning calorimeter (TA Instruments, Newcastle, DE)
equipped with an RCS90 refrigerated cooling system. Nitro-
gen gas was used as the purge gas at a flow rate of 50mL/
min. High purity indium was used to calibrate temperature
and enthalpy of the instrument. Approximately 5–10mg
of ASD powder sample was packed in non-hermetically
crimped pans (Tzero™ aluminum pans and pin hole lids)
and heated from 5 to 200°C at 1°C/min using a tempera-
ture modulation of ±0.5°C every 60s. Data were analyzed
using commercial software (Universal Analysis 2000, TA
Instruments, Newcastle, DE). The value of glass transition
temperature (Tg) reported was the half height of the transi-
tion in the reversing heat flow signal.
Particle Size Distribution by Laser Diffraction The volume-
based particle size distribution (PSD) of ASD powders was
measured by laser diffraction using a Malvern Mastersizer
3000 equipped with the Hydro MV wet dispersion module
(Malvern Instruments Inc., Westborough, MA). The disper-
sant used was 0.1% Span 85 in heptane (v/v). Approximately
200mg of sample was suspended in approximately 4mL of
the dispersant. The slurry was then transferred dropwise to
the Hydro MV filled with the dispersant until an obscuration
of 10–20% was reached. The data were analyzed using a
Mie scattering measurement principle and a general-purpose
analysis model, with the refractive index of 1.39 (heptane)
used for all calculations. The resulting particle size distri-
butions were averaged from three measurements made on
each material.
Brunauer–Emmett–Teller Specific Surface Area Analysis The
specific surface area of ASD powders was determined by
nitrogen physisorption method using a Micromeritics ASAP
2460 surface area analyzer (Micromeritics Instrument Cor-
poration, Norcross, GA). Approximately 0.5g of each sam-
ple was first degassed at 25°C for 72h prior to the analy-
sis followed by nitrogen adsorption at −196°C. Data were
collected over a P/P0 range of 0.05–0.30, and the surface
area was calculated using the linear form of the Brunauer–
Emmett–Teller (BET) equation.
Bulk and Tapped Density Bulk density was determined by
measuring the volume of a known mass of powder in a grad-
uated cylinder. Tapped density was determined by mechani-
cally tapping the cylinder using a Tapped Density Tester
(Series JV 2000; Copley Scientific Limited, Nottingham,
UK) until there was no change in the volume. The volume
of the sample was then read directly from the cylinder and
used to calculate the tap density according to the relation-
ship: mass/volume.
Powder Compression Studies Compression experiments
were carried out using a servo-hydraulic compaction
Table I Composition of GDC-0810 ASD Tablet, 100 mg Dose
Strength
*GDC-0810 ASD powder includes spray dried dispersion (SDD), co-
precipitated amorphous dispersion (cPAD) prepared using RAM at
40G (RAM-40G) and 80G (RAM-80G), respectively
Component % w/w Amount
(mg/Tab-
let)
GDC-0810 ASD Powder* 50.0 200
Microcrystalline Cellulose 21.5 86
Lactose Monohydrate 21.5 86
Croscarmellose Sodium 5.0 20
Colloidal Silicon Dioxide 1.0 4
Magnesium Stearate 1.0 4
TOTAL 100.0 400
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Pharmaceutical Research
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simulator (HB100, Huxley Bertram Engineering Ltd., Cam-
bridge, UK). The compaction simulator measured punch
forces, displacements, and diewall pressure. Punch compli-
ance calibration was conducted in duplicates prior to test-
ing each material. Calibration was conducted by performing
bland compression experiments (i.e., by compressing the
punches against one another) using a load control profile
that constantly ramped the upper punch load from 0 to 45
kN over a 10s period and fixing the lower punch position.
The punch deformation was then calculated by subtracting
the lower punch position from the upper punch position as
a function of load. The deformation of the punch was then
added to the punch gap data collected during the instru-
mented die experiments. Die wall sensor calibration runs
(two at each sensor: lower, middle, and upper) were also
performed prior to the material compression using a 3mm
rubber slug compressed from 0 to 250MPa.
A cylindrical 10-mm diameter instrumented die was
used to measure pressure in the radial direction. Standard
TSM B 10mm flat round punches (Natoli Engineering, St
Charles, MO) were used for all experiments utilizing the
instrumented die. Punches and die were externally lubricated
by compressing magnesium stearate into a compact prior
to compressing each sample. After the magnesium stearate
compact was ejected, the die was filled with approximately
350mg of powder. The compression of each powder com-
menced with a gradual loading from 0.1 kN to 2 kN followed
by a constant rate loading at 250MPa/s to reach the target
peak load. In this study, the peak loads varied from 3.93 kN
(50MPa) to 19.65 kN (250MPa) at 3.93 kN (50MPa) incre-
ments. Decompression was carried out at 2500MPa/s until
approximately 0.1 kN of load remained. The compact was
held in the die for 2s and then ejected. During the course
of compression, force was applied from the upper punch
and the lower punch was kept stationary. Compression was
performed in duplicate at each target peak load.
Determination ofKey Compaction Properties
Tensile Strength of Round Compacts At least 24hours after
compression experiments, the weight of powder compacts
was determined using a balance (Model XPE205, Mettler
Toledo, Zurich, Switzerland), and out-of-die dimensions
were measured with a caliper (Model 537-400S, Mitutoyo,
Kawasaki, Japan). Tablet breaking force values were deter-
mined using a TA.HD Plus Texture Analyzer (Stable Micro
Systems, Texture Technologies, Hamilton, MA). Compacts
were fractured diametrically at a testing speed of 0.35mm/s.
Tensile strength of a round compact, σ, in MPa was cal-
culated using Eq. (1): (22)
where F is the breaking force (N), D is the diameter (m),
and t is the thickness (m) of a round compact.
Porosity of Round Compacts The porosity, ε, of a compact
was calculated by the following equation:
where ρ is the density which was calculated from the weight
and volume of the compact. ρt is the true density of the
powder, which was determined using a helium pycnometer
(AccuPyc II 1340, Micromeritics; Norcross, GA). Approxi-
mately 0.5–2.0g of each powder sample were filled in a
3.5-cm3 sample cup. Then true density measurement was
carried out at an equilibration rate of 0.0050 psig/min and
the number of purges was set to 5.
In‑Die Heckel Analysis The Heckel equation (40) was
derived based on the assumption that the densification of
the powder bed follows the first-order kinetics. It describes
the relationship between the powder bed porosity (ε) and the
applied compaction pressure (P) using Eq. (3):
where K is the slope of the linear region and A is the inter-
cept. The inverse of K represents the mean yield pressure,
Py. In this work, in-die Heckel analysis was performed using
compression data obtained with a maximum compaction
pressure of 250MPa to determine Py values. In-die poros-
ity of a compact was calculated from the minimum in-die
thickness measured by the compaction simulator and tablet
weight. The natural logarithm of the reciprocal of the in-die
porosity was plotted as a function of the applied compac-
tion pressure (P). Linear regression was applied to fit the
linear portion of the plot to obtain the slope by progressively
excluding data points until the R2 value greater than 0.99
were obtained. Measurements were performed in duplicate.
In Vitro Dissolution Testing
Powder Dissolution Dissolution profiles of GDC-0810 ASD
powders were acquired by using a μDiss Profiler™ (Pion
Inc., Billerica, MA). The dissolution medium, FaSSIF V2,
degassed and preheated to 37°C prior to use. Approximately
12mg of each ASD powder sample was added to a glass
vial, followed by adding 20mL of the medium. For each
vial, the magnetic stirrer was set at 300rpm and the tempera-
ture was maintained at 37°C. Up to eight photodiode array
(1)
𝜎
=
2F
10
6
𝜋Dt
(2)
𝜀
=1−
𝜌
𝜌
t
(3)
ln (1
𝜀)
=KP +
A
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Pharmaceutical Research
1 3
(PDA) spectrophotometers were employed, each with its
own dedicated fiber optic dip probe, center-positioned in the
vial to monitor real time absorbance of the drug in solution.
The detection wavelength selected was 365–375nm. The
path length of the UV fiber optic probes used was 10mm.
Data were collected up to 3hr. and analyzed for real-time
concentration-time profiles using AuPRO software (Pion
Inc.).
Tablet Dissolution Dissolution was performed using a stand-
ard USP type II dissolution vessel (Distek Symphony 7100,
North Brunswick, NJ). The medium was 500mL of FaSSIF
V2, degassed and preheated to 37°C using a Distek ezfill
4500. One tablet at dose strength of 100mg (GDC-0810 free
acid) was used in each vessel and dissolution was monitored
for 75minutes. The paddle speed was 75rpm up to 60min-
utes and was increased to 250rpm from 60 to 75minutes.
Automated sample volumes of 1.5mL were filtered through
10μm polyethylene in-line filter tips (Distek, Inc., North
Brunswick, NJ) at various time-points and collected in indi-
vidual HPLC vials for analysis.
Sample analysis was performed using an Agilent 1200
HPLC instrument (Santa Clara, CA) controlled by Empower
software (Waters Corporation, Milford, MA). Data acqui-
sition and processing was also performed using Empower.
The HPLC method used an Ascentis Express C18 column
(50 × 3.0mm, 2.7μm particle size) with 0.1% formic acid
in water/methanol (88:12v/v) as mobile phase A and 0.1%
formic acid in acetonitrile/methanol (88:12v/v) as mobile
phase B (MPB). A gradient program was employed, starting
with a 45% MPB hold from 0 to 1.4minutes followed by a
gradient of 45% to 100% MPB in 0.8minutes. Other HPLC
parameters used were a column temperature of 40°C, flow
rate of 1.2mL/min, UV detection at 310nm, an autosampler
temperature of 5°C, and an injection volume of 6μL. Sample
quantification was performed against an external standard
prepared in duplicate, with the second replicate serving as
a QC control.
Focused Ion Beam‑Scanning Electron Microscopy (FIB‑SEM)
All sample images were acquired using a Zeiss Auriga
FIB-SEM CrossBeam workstation (Carl Zeiss). SDD or
cPAD powder samples were mounted on an aluminum
stub with carbon tape and sputter coated with gold to
reduce charging. SEM imaging was performed with elec-
tron high tension (EHT) of 2kV, and ~ 5mm working
distance. A selected particle was milled with the focused
ion beam at 30kV/16nA.
X‑Ray Microscopy (XRM)
All sample images were acquired using a Zeiss Xradia Versa
520 XRM system (Carl Zeiss X-Ray Microscopy). The XRM
system was equipped with a sophisticated condenser and
objective lens design which allowed for the collection of
high resolution images on a region of interest within the
interior of the sample. SDD or cPAD powder samples were
placed in plastic vials and mounted to a rotational stage
between an X-Ray source and the detector. A low magnifi-
cation radiograph of the entire contents of the vial was first
acquired, based on which a smaller, representative region
of interest (ROI) was selected for scanning at a higher reso-
lution. Successive radiographs of this ROI were acquired
through rotation of the sample over 360-degrees, using an
X-ray source energy of 60keV. About 300 radiographic pro-
jection images were reconstructed into a stack of approxi-
mately 1000 images using a filtered back projection algo-
rithm with an effective voxel size of 1μm. Tablet samples
made from the powder sample were mounted directly to the
rotational stage and imaged in the same manner.
Artificial Intelligence‑Based Image Analysis
The XRM images were analyzed and quantified using DigiM
I2S™ cloud-based image analysis software (DigiM Solu-
tion, LLC, MA, USA). Data from the XRM images were
expressed in grayscale intensity values on a per pixel basis.
Pixel grayscale intensity corresponds to density where
bright grayscale contrast corresponds to high density mate-
rial, darker grayscale contrast corresponds to lower density
material, and black contrast corresponds to air voids or
porosity. The collection of pixels from the imaging signal
establishes a 3D density map of the different material com-
ponents in which each material phase is characterized by
a unique textural pattern. An artificial intelligence–based
image segmentation (AIBIS, DigiM Solutions LLC) algo-
rithm differentiates these unique textural patterns into vari-
ous material phases. The solid phase is then further seg-
mented into unique material components and interparticle
air is separated from intraparticle air or porosity. During an
AIBIS, a human analyst trains the AIBIS engine to recognize
the unique textural patterns through a 10 to 15min iterative
training on a small seed image (41). The results of the train-
ing set were then applied to additional images of a sample
automatically. The SDD powder sample was segmented
into two material phases: non-porous solid and interparticle
air. Both cPAD samples were segmented into four material
phases: non-porous solid, intraparticle solid, intraparticle
air (porosity), and interparticle air. Intra-particle solid and
intraparticle air compose a solid particle with porosity. 3D
rendering and visualization were generated using DigiM
I2S™ and 3D Slicer.
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The volume of a material phase was calculated as a
summation of all voxels assigned to that phase (via
AIBIS), multiplied by the physical dimension of a voxel.
For the SDD powder sample, a solid volume fraction was
calculated by the volume of non-porous solid divided by
the combined volume of interparticle air and non-porous
solid. For the cPAD powder samples, a total solid volume
fraction was calculated by combining the non-porous solid
and intra-particle solid phases. Porosity was also calcu-
lated as the volume of intraparticle air normalized by the
combined volume of intraparticle air, intraparticle solid,
and non-porous solid.
A number of particles reconstructed from 3D XRM
were often in contact with each other due to aggregation or
insufficient resolution to resolve the gap between adjacent
particles. An additional marker-based watershed algorithm
was used to separate particles in contact with each other.
This method determined the center of each particle using
a morphological erosion operation, then a distance map
from the center of each particle was calculated mimicking
the infill of a topological map with imaginary water. A
watershed line was defined to separate two particles where
the two watersheds met.
Once individual particles were identified, particle
size distribution (PSD) can be calculated. The volume of
each particle was converted into an equivalent spherical
diameter (ESD), based on which, the entire population
of particles can be represented as a PSD. Surface area
was also quantified via an algorithm based on the Crofton
formula. In integral geometry the Crofton formula relates
the length of a curve to the expected number of times a
random line intersects it. When applied to a discrete binary
image this method can be used to approximate surface area
by counting the intercept number of the object boundary
with a set of isotropic test lines (42). Using this method,
the total external surface of all solid material phases was
calculated for all powder samples and then normalized by
corresponding volume.
XRM Image Analysis andImage‑Based Permeability
Simulation forTablets
A similar AIBIS procedure was performed on the XRM data
for each tablet sample to segment porosity, based on which
permeability can be numerically computed. The image-
based permeability simulations were conducted using DigiM
I2S™. Pressure-driven fluid flow along three spatial direc-
tions were computed using a voxel-based computational
fluid dynamics (CFD) solver (43). Finite volume spatial
discretization was directly built on the porosity voxels of the
segmented 3D image data of the tablets. The Navier-Stokes
equations were then solved for flow of an incompressible
fluid. After the pressure and velocity fields are solved,
Darcy’s law was then used to calculate permeability. This
methodology has been comprehensively validated for phar-
maceutical materials (44) and geoscience (45) applications.
Results
Powder Characterization ofGDC‑0810 ASDs
Figure2 shows the XRPD patterns and reversing heat flow
calculated from the modulated DSC thermograms for the
amorphous form of GDC-0810, HPMC-AS MF polymer,
and their ASDs (1:1 weight ratio) prepared by different
methods, including the spray dried dispersion (SDD), and
co-precipitated amorphous dispersion (cPAD) via resonant
acoustic mixing (RAM) at the acceleration of 40G (RAM-
40G) and 80G (RAM-80G). The XRPD data indicate that
all the three ASD materials are amorphous. All three
ASDs showed a single glass transition temperature (Tg).
The Tg of the ASDs ranged from 105.9°C to 108.8°C, with
SDD having slightly higher Tg than RAM-40G and RAM-
80G materials. A negative deviation from ideality was
observed for the Tg of ASDs, with the values lower than
the Tg of the individual components (GDC-0810 ~ 125°C
Fig. 2 XRPD patterns (a) and
reversing heat flow calculated
from the modulated DSC ther-
mograms (b) for (A) amorphous
form of GDC-0810 (produced
by spray drying), (B) HPMC-
AS MF polymer (as received),
(C) RAM-80G, (D) RAM-40G,
(E) SDD
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Pharmaceutical Research
1 3
and HPMC-AS MF ~115°C). A similar behavior has previ-
ously been reported for AMG-517/HPMC-AS ASDs (46).
In a previous publication, we reported solid state NMR
(SSNMR) data which indicates that the GDC-0810 ASDs
prepared by spray drying and co-precipitation processes
via RAM are phase mixed (32). In addition to the SSNMR
data, the observation of a single Tg for all three ASDs,
indicates that GDC-0810 was molecularly dispersed in
HPMC-AS MF polymer in all cases.
The particle size distribution by laser diffraction, BET
specific surface area, specific surface area calculated from
the Sauter Mean Diameter (D3,2), and density data for
GDC-0810 ASD powders are summarized in TableII. The
particle size data of the two cPAD powders, RAM-40G
and RAM-80G, was fairly comparable, with the same D10
value. The D50 and D90 of RAM-40G powder was slightly
higher than that of RAM-80G powder. The SDD powder
was composed of much finer particles, with D50 and D90
of 8 and 16μm, respectively. The D90 of SDD powder
was even smaller than the D50 of both cPAD powders. In
spite of the smaller particle size of the SDD, its BET spe-
cific surface area value was approximately 10-fold lower
than that of the cPAD powders (RAM-40G and RAM-
80G). Calculation of the specific surface area based on the
D3,2 indicates the more expected relationship where the
smaller particle size material has the larger surface area.
In addition, the bulk density of the two cPAD powders was
approximately half of that of SDD. BET surface area, bulk
density, and SEM data indicate that the microstructure of
cPAD materials is highly porous.
Compressibility andCompactibility ofGDC‑0810
ASDs
Compressibility is defined as the ability of a powder bed to
decrease in volume under compaction pressure (47). It is
one of the important properties for characterizing powder
compaction behavior and is often used to quantify mate-
rial plasticity. Figure3a shows the compressibility plots
(i.e., the change of out-of-die compact porosity as a func-
tion of compaction pressure) for three GDC-0810 ASDs.
It appears that the compressibility plots for all three ASDs
overlaid quite well, especially for RAM-40G and RAM-
80G materials prepared using the co-precipitation method.
The porosity of SDD compacts was only marginally higher
at the same compaction pressure. The results suggest that
the compressibility of GDC-0810 ASDs was not affected
by their particulate properties (e.g., PSD, morphology, spe-
cific surface area) and microstructure. In addition, all three
GDC-0810 ASDs demonstrated good compressibility. As the
compaction pressure was lower than 150MPa, the porosity
of compacts reduced sharply with increasing the compaction
pressure, from approximately 0.4 to less than 0.15. As the
compaction pressure exceeded 150MPa, the porosity further
reduced, approaching to below 0.1 at the compaction pres-
sure of 250MPa.
Table II Summary of Physical Characterization Data for GDC-0810 ASD Powders Used in the Study. Values in parentheses indicate the stand-
ard deviations (n = 3)
Material PSD by Laser Diffraction BET Specific
Surface Area
(m2/g)
Specific Surface
Area via D3,2
(m2/g)
Bulk Den-
sity (g/cm3)
Tapped
Density (g/
cm3)
True
Density
(g/cm3)
D10 (μm) D50 (μm) D90 (μm) D3,2 (μm)
SDD 1.9 (0.1) 8.0 (0.1) 15.3 (0.4) 4.2 (0.1) 2.94 (0.03) 1.1 0.26 0.28 1.29
RAM-40G 5.1 (0.4) 25.2 (0.9) 104.0 (0.0) 12.4 (1.3) 34.04 (0.14) 0.4 0.11 0.18 1.32
RAM-80G 4.6 (0.0) 20.4 (0.7) 70.0 (6.8) 10.5 (0.3) 31.39 (0.21) 0.4 0.12 0.18 1.32
Fig. 3 Compressibility (a)
and compactibility (b) plots of
GDC-0810 ASDs (duplicate
compressions at each compac-
tion pressure). Lines on the (b)
plot are the exponential fitting
of the data
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To further quantify powder plasticity, in-die Heckel analysis
(Fig.S1) was performed to obtain the mean yield pressure,
Py. The in-die Py has been reported to be a reliable plasticity
parameter (48). As listed in TableIII, the average Py values of
GDC-0810 ASDs are within the range of 66–72MPa, which
are comparable to the Py value of Avicel PH 102 (66 MPa
(49)). The data suggests that GDC-0810 ASDs can be consid-
ered as ductile (plastic) materials, and show less resistance to
plastic deformation during compression.
Compactibility is defined as the ability of a powdered
material to be compressed into a compact of specific strength
during densification (47). It is most often expressed graphi-
cally in a plot of the compact tensile strength versus porosity.
Tablets with adequate mechanical strength are required to
withstand down-stream processing and handling. As com-
pactibility is generally independent of compaction speed, it
is considered a useful tool to predict the tensile strength of
tablets compressed at high speed using a rotary press dur-
ing scale up (50). Figure3b depicts the compactibility pro-
files for GDC-0810 ASDs. Both RAM-40G and RAM-80G
materials demonstrated superior compactibility compared
to SDD. As the porosity decreased from approximately 0.4
to 0.18, tensile strength of RAM-40G and RAM-80G com-
pacts increased exponentially from approximately 4MPa to
10MPa. However, as the porosity further decreased, ten-
sile strength did not change but plateaued around 10MPa.
In addition, no significant difference in compactibility
between RAM-40G and RAM-80G powders was observed.
Whereas, tensile strength of SDD compacts was consider-
ably lower at a given porosity. For example, tensile strength
was approximately 4MPa at the porosity of 0.1. The strength
of a compact is a reflection of interparticulate bonding that
has occurred during compaction, which relates to bonding
forces between individual particles, the number of bonding
points, contact surface area, and bond distribution in the
compact. In this work, substantially different compactibility
profiles observed between RAM materials and SDD suggest
that the interparticulate bonding formed in their compacts is
remarkably different, with much stronger bonding formed in
RAM-40G and RAM-80G compacts.
In Vitro Dissolution ofGDC‑0810 ASD Powders
andTablets
As described in the methods section, approximately 12mg
of each ASD sample of GDC-0810 and HPMC-AS MF
(1:1 weight ratio) was added to 20mL of FaSSIF V2
medium for powder dissolution testing. The target GDC-
0810 concentration in the medium is 300μg/mL, which
is above its crystalline solubility of 58μg/mL and below
its amorphous solubility of 352μg/mL in FaSSIF V2 (see
TableSI). Figure4 shows powder dissolution profiles for
three GDC-0810 ASDs. In the first 10min, all three GDC-
0810 ASD powders dissolved quickly with no difference in
dissolution rate observed, reaching approximately 100μg/
mL in the medium. Beyond this point, divergence in dis-
solution profiles for three materials occurred. SDD pow-
der further dissolved, reaching ~200μg/mL at 180min.
Whereas, two cPAD powders, RAM-40G and RAM-80G,
stopped further dissolving after 30min, with GDC-0810
concentration plateaued around 115μg/mL until the end
of the experiment. Additionally, no precipitation was
observed for all three ASD powders during the testing.
In agreement with powder dissolution results, drug
release from GDC-0810 SDD tablets was significantly
greater than tablets prepared from RAM-40G and RAM-
80G cPAD powders, as shown in Fig.5. RAM-80G tablets
showed slightly higher dissolution than RAM-40G tab-
lets. Note that the USP2 dissolution method used in this
study was under non-sink conditions, and hence, full drug
release from these tablets was not obtained within 75min.
The amount of GDC-0810 dissolved from SDD tablets
was approximately 50% at 75min, whereas, it was slightly
below 30% from RAM-40G and RAM-80G tablets.
Table III Mean Yield Pressure
(Py) Obtained from In-Die
Heckel Analysis for GDC-0810
ASD Powders Used in the
Study. Values in parentheses
indicate the standard deviations
(n = 2)
Material Py (MPa) R2
SDD 66.4 (0.3) 0.9999
RAM-40G 71.2 (3.2) 0.9998
RAM-80G 70.2 (0.3) 0.9999
Fig. 4 Powder dissolution data for GDC-0810 ASDs. Each point rep-
resents the mean (± std) of three experimental values
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XRM Imaging Analysis
Focused Ion Beam‑Scanning Electron Microscopy andX‑Ray
Microscopy
Surface imaging of the SDD and cPAD powder samples
via scanning electron microscopy revealed the samples to
have characteristic particles with distinct morphologies.
The SDD material showed wrinkled dense particles with a
collapsed sphere morphology (Fig.6a1, 6b1), whereas the
cPAD material showed large irregularly shaped particles
with significant surface porosity. High resolution FIB-SEM
cross-section images further revealed the SDD particles to
have a non-conformal geometry with no resolved porosity
(Fig.6a2), but the cPAD sample was characterized by a sig-
nificant porosity network (Fig.6b2and 6b3) as observed
by the spongy appearance in both SEM and XRM images.
While FIB-SEM enabled ultra-high resolution study of
the microstructure of individual particles within the pow-
der samples, X-ray microscopy study allowed for imaging
of a much larger field of view of the powder samples. The
enhanced field of view in XRM captured significantly more
particles and further revealed microstructure differences
between the samples. The SDD powder sampled (Fig.6a3)
showed small relatively uniformly sized primary particles
with no resolved porosity. Figure6b3shows a RAM-40G
cPAD sample at the same resolution and revealed two dis-
tinct solid material phases, dense solid particles with no
resolved porosity (similar to the particles observed in the
SDD sample) as well as large highly porous particles. The
2D XRM cross-sections of the tablets corresponding to SDD
and cPAD powder samples (Fig.6a4and 6b4) also revealed
microstructural differences between the tablets, most nota-
bly the SDD tablet appeared to have more abundant pores
(darkest grayscale intensity phase) than the cPAD samples.
Artificial Intelligence‑Based Image Analysis
As the SDD powder sample was observed to be com-
posed of a single solid phase with no resolved porosity, the
XRM dataset was segmented into two phases, solid and
Fig. 5 USP2 dissolution data for GDC-0810 ASD tablets. Each point
represents the mean (± std) of three experimental values
Fig. 6 Imaging modality comparison of powders and corresponding tablets: top row - spray dried dispersion (SDD), bottom row - co-precipi-
tated amorphous dispersion (cPAD). Key: (a1) surface SEM image of SDD powder, (a2) FIB-SEM cross-section image of SDD powder, (a3)
2D cross-section image of a 3D XRM scan of SDD powder, (a4) 2D cross-section image of a 3D XRM scan of SDD tablet with example pores
highlighted with red arrows. (b1) surface SEM image of RAM-40G cPAD powder, (b2) FIB-SEM cross-section image of RAM-40G cPAD
powder, (b3) 2D cross-section image of a 3D XRM scan of RAM-40G cPAD powder, (b4) 2D cross-section image of a 3D XRM scan of RAM-
40G cPAD tablet with example pores highlighted with red arrows
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interparticle air. Figure7a2shows a magnified field of view
from the 2D XRM cross-section of the SDD powder sample
shown in Fig.7a1. Circled in bright green is an example of
a solid particle and circled in light blue is a representative
region of intraparticle air. Figure7a3shows the final arti-
ficial intelligence–based image segmentation overlayed on
the grayscale image of the magnified 2D cross-section while
Figure7a4 shows a 3D rendering of the entire SDD powder
XRM volume after segmentation. Figures7b1-7b4 represent
the segmentation of a cPAD sample in the same fashion. In
Fig.7b2an example of the dense non-porous solid in the
cPAD samples are again circled in bright green and a region
of interparticle air is circled in light blue. The large porous
solid material unique to the cPAD samples were segmented
into two additional phases, porous solid (dark green), and
associated intraparticle pores (red). The segmented images
from the SDD and cPAD powder samples further highlight
the distinct microstructure differences between the samples.
TableIV highlights the results of quantification of pore
volume, pore size distribution, and solid surface area and
volume from the segmented XRM datasets. While no poros-
ity was resolved in the SDD powder sample, the RAM-40G
cPAD sample was revealed to have more than twice the
porosity than that of the RAM-80G cPAD sample (10.5%
and 3.9% respectively). Despite the higher pore volume in
the 40G sample, both cPAD samples were found to have
similar pore size distributions with comparable D10, D50, and
D90. Total solid external surface area and volume quantifi-
cation again distinguished the SDD sample from the cPAD
samples. When the calculated external surface area of the
total solid in each sample was normalized by the respective
total solid volume, the cPAD samples were found to have
comparable surface area to volume ratios of 0.439μm−1 for
the 40G sample and 0.393μm−1 for the 80G sample. The
SDD powder sample on the other hand had significantly
more surface area with a comparable solid volume resulting
Fig. 7 Microstructure morphology and image segmentation, top row is SDD powder results and bottom row is cPAD powder results. (a1) 2D
cross-section image of a 3D XRM scan of SDD sample. (a2) magnified region as highlighted in (a1), interparticle air highlighted by light
blue circle, solid materials highlighted by bright green circle. (a3) segmented interparticle air (blue), and solid material (bright green). (a4) 3D
reconstruction of solid material in SDD powder sample. (b1) 2D cross-section image of a 3D XRM scan of cPAD powder sample. (b2) magni-
fied region as highlighted in (b1) interparticle air highlighted by light blue circle, porous solid materials highlighted by dark green circles, and
intraparticle pores highlighted with a red circle. (b3) segmented interparticle air (blue), solid material (green), and porosity (red). (b4) 3D recon-
struction of all segmented phases in cPAD powder sample
Table IV Summary of Image-Based Characterization Data for GDC-0810 ASD Powders Used in the Study
Material Particle
Porosity (%)
Image-Based Pore Size
Distribution
Total Solid External
Surface Area SA (μm2)
Total Solid Vol-
ume SV (μm3)
Total Solid External Surface
Area/ Total Solid Volume SA/
SV
(μm−1)
D10
(μm)
D50
(μm)
D90
(μm)
SDD Powder 0 N/A N/A N/A 2.37 × 1082.67 × 1080.888
RAM-40G Powder 10.5 14 23 34 8.32 × 1071.90 × 1080.439
RAM-80G Powder 3.9 14 24 39 8.76 × 1072.23 × 1080.393
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1 3
in a surface area to volume ratio nearly double that of both
cPAD samples at 0.888μm−1.
In comparing some of the image-based quantification data
with powder dissolution data shown in Fig.4, some inter-
esting trends were observed. In Fig.8, powder dissolution
concentrations at 60minutes were plotted against particle
porosity as well as calculated surface area to volume ratio
(SA/SV), respectively. As seen in Fig.8a, powder dissolution
does not correlate well with the calculated pore volume as
the fastest dissolving SDD powder had no resolved porosity.
In Fig.8b on the other hand, powder dissolution data show
a strong correlation with the calculated total solid external
surface area (SA) normalized by calculated total solid vol-
ume (SV), with the amount of GDC-0810 dissolved increas-
ing with the increase of SA/SV ratios.
XRM Image Analysis andImage‑Based Permeability
Simulation forGDC‑0810 ASD Tablets
The tablet sample datasets were all segmented into two
phases, solid and pore. Based on the porosity segmentation
tablet pore volume and pore size distributions were calcu-
lated. As seen in TableV, the tablet samples had more com-
parable porosity than their associated powder with the trend
in porosity reversed. The SDD tablet had the highest poros-
ity at 17.6% followed by the RAM-80G tablet at 14.7% and
the RAM-40G tablet having the lowest porosity at 12.0%.
All three tablet samples were also revealed to have compa-
rable pore size distribution. The results of the image-based
permeability simulation followed a similar trend to calcu-
lated tablet porosity where the cPAD tablets were found to
have comparable permeabilities (slightly higher in the 80G
sample), while the SDD tablet was found to have a perme-
ability nearly twice that of both cPAD samples.
Tablet dissolution data were correlated with calculated
tablet porosity and permeability as shown in Fig.9 and again
showed strong correlations. As seen in Fig.9a, the percent-
age of GDC-0810 dissolved from ASD tablets at 60min
shows a strong correlation with calculated tablet poros-
ity. When plotting the same tablet dissolution data points
against calculated tablet permeability, the correlation is even
stronger than that seen with calculated porosity (linear fitting
R2 values of 0.95 and 0.92 respectively).
Discussion
Microstructure andParticulate Properties ofASDs
Manufacturing technology and operating conditions can
have a profound influence on particle microstructure and
particulate properties of ASDs, such as particle size, size
distribution, morphology, porosity, and surface texture/area.
In general, the HME process yields a low-porosity material
due to the reduction of free volume present in the polymer-
drug blends during the extrusion process. Particle size and
morphology of milled extrudes are typically dependent on
the downstream milling process conditions. Spray-dried
Fig. 8 (a) ASD Powder dissolu-
tion (GDC-0810 concentration
at 60min) versus XRM deter-
mined particle porosity and (b)
powder dissolution (GDC-0810
concentration at 60min) versus
XRM determined powder
external surface area to solid
volume ratio (SA/SV). Error bars
represent standard deviations
where n = 3
Table V Summary of Image-
Based Characterization and
Permeability Simulation Data
for GDC-0810 ASD Tablet
Used in the Study
Material Tablet Porosity
(%)
Image-Based Pore Size Distribution Permeability
Kn (Darcy)
Pore D10
(μm)
Pore D50
(μm)
Pore D90
(μm)
SDD Tablet 17.6 11 18 23 0.0104
RAM-40G Tablet 12.0 12 19 27 0.0041
RAM-80G Tablet 14.7 12 19 27 0.0068
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particles usually have smaller particle size and exhibit an
inflated or collapsed sphere shape (51). The co-precipitation
process generally yields large particles with irregular shape,
high porosity, and high surface area (52).
In this work, distinct differences in microstructure and
particulate properties were observed between GDC-0810
SDD and cPAD materials. SDD showed wrinkled dense
particles with a collapsed sphere morphology and exhibited
a single solid phase with no resolved porosity. This type of
morphology is based on the droplet drying mechanism of
film-forming polymers. When the solvent starts to evaporate
from the surface of droplets, an external solid layer (crust) is
formed, reducing the diffusion of the solvent to the particle
surface and thus the rate of evaporation. The inner solvent
diffuses through the crust to further evaporate. Shriveled
particles are formed when the partial pressure of the solvent
trapped in the particle is lower (53). Previous investigation
using FIB-SEM and XRM has shown that for a model sys-
tem (20% MK-A and 80% HPMC-AS), SDD particle mor-
phologies can range from hollow spheres with thin walls,
to raisin-like particles with thicker shell and reduced void
spaces, to solid particles with no internal voids(36). The
morphology was found to be sensitive to the process con-
ditions, in particular the outlet temperature. In the case of
spray drying at small scale for the preparation of GDC-0810
SDD in this work, the process conditions resulted in the
collapsed solid particle type. At the resolution of the XRM
data of about 0.5μm no porosity was observed and the parti-
cles do not have residual air pockets. In contrast, GDC-0810
cPAD materials produced in this study contained both dense
non-porous solid and large irregularly shaped particles with
significant surface porosity. In co-precipitation, the particle
formation occurs by extraction of the solvent by the anti-
solvent. Generally, the solvent is highly soluble in the anti-
solvent, so the extraction process is very efficient resulting
in amorphous particles with higher porosity compared to
spray drying. Process conditions, such as precipitation rate,
solvent to antisolvent ratio, temperature, and hydrodynamic
conditions, can affect the particle microstructure and prop-
erties (54). In this work, the impact of the amplitude of the
mechanical vibration, referred to as the system accelera-
tion during the RAM process, was explored. As listed in
TableIV, the acceleration of 40G resulted in co-precipitated
particles with higher porosity than 80G. Despite the differ-
ence in porosity, both cPAD samples were found to have
comparable pore size distributions in terms of D10, D50, and
D90. It is possible to further tailor the microstructure and
particulate properties of cPAD materials by varying other
conditions, e.g. precipitation rate and solvent to antisolvent
ratio.
Bulk Powder Properties ofASDs
Bulk powder properties critical to downstream process-
ing of ASDs, such as bulk and tapped densities, flow and
compaction properties, can be greatly influenced by their
microstructure and particulate properties. Bulk and tapped
densities are measures of consolidation propensity of a pow-
der bed under loose and tapped packing conditions respec-
tively. GDC-0810 SDD and cPAD materials showed 2-fold
difference in bulk density values due to their difference in
microstructure, particle size distribution, and particle mor-
phology. The SDD powder consisting of small wrinkled
dense particles with no resolved porosity promotes denser
packing and lower resistance to consolidation when com-
pared to the cPAD powders (RAM-40G and RAM-80G) that
contain large and irregularly shaped porous particles. This in
turn results in the higher bulk and tapped density of the SDD
powder when compared to the cPAD powders.
It has been well recognized that both intrinsic material
properties (surface functional end groups, surface energy,
elastic modulus, and plasticity) and particulate properties
(particle size, size distribution, morphology, and surface
roughness) can affect compaction properties of powders
(deformation/fragmentation, tensile strength) (55). The com-
pressibility plots (Fig.3a) of three GDC-0810 ASD powders
Fig. 9 (a) ASD tablet dissolu-
tion (%GDC-0810 dissolved at
60min) versus XRM deter-
mined tablet porosity and (b)
ASD tablet dissolution (%GDC-
0810 dissolved at 60min)
versusXRM determined tablet
permeability. Error bars repre-
sent standard deviations where
n = 3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Pharmaceutical Research
1 3
overlaid fairly well, indicating that particulate properties of
the three materials did not significantly impact the volume
reduction of the powder bed as a result of applied pres-
sure. In addition, comparable Py values obtained from in-
die Heckel analysis for the three GDC-0810 ASD powders
suggest that the three materials underwent similar deforma-
tion during the compression phase. On the other hand, as
shown in the compactibility plots (Fig.3b), RAM-40G and
RAM-80G materials demonstrated superior compactibil-
ity compared to SDD. Typically, for materials undergoing
plastic deformation, smaller particles result in stronger com-
pacts than larger particles. However, an opposite trend was
observed in the present work. Stronger compacts at a given
solid fraction were obtained with RAM-40G and RAM-80G
powders which consisted of larger particles. This observa-
tion suggests that particle size of ASD powders may not
be the material attribute which caused this effect. In com-
paction, particles are moved into closer proximity to each
other and interparticulate bonds may be established between
particles. The dominating interparticle bonding mechanisms
include solid bridges, mechanical interlocking, and inter-
molecular forces such as van der Waals forces, electrostatic
force, and surface liquid capillary force (56). The predomi-
nant interparticulate bonding force between solid surfaces
is the van der Waals force. Compact tensile strength is the
interplay between interparticulate bonding area and bond-
ing strength (57). Bonding strength is related to interpar-
ticulate bonding force and bonding area is dependent on
intrinsic mechanical properties, particulate properties, and
compaction conditions. The three GDC-0810 ASDs have the
same chemical nature, and thus comparable interparticulate
bonding force and bonding strength are expected. Thus, the
remarkable difference in their compactibility can be ascribed
to the difference in interparticulate bonding area. The large
BET specific surface area, highly porous structure, and espe-
cially high surface porosity of both RAM-40G and RAM-
80G powders (Fig.6 and TableIV) led to greater bonding
area between particles, and hence, stronger compacts.
Dissolution Performance ofASDs
For a given drug, dissolution performance of ASDs can be
influenced by particle size, particle morphology, surface
area, drug loading, polymer type, and surface energy. Parti-
cle size is important in controlling the drug release behavior.
The dissolution rates of ASDs were observed to be inversely
proportional to the particle size (58, 59). In general, smaller
particle size corresponds to higher surface area, according
to the Noyes-Whitney equation, higher surface area of the
dissolving solid leads to faster dissolution rate. However, the
impact of ASD particle size on its dissolution performance
can be complicated when crystallization or precipitation
occurs in the dissolution media, which ultimately affects
the extent and duration of supersaturation.
For GDC-0810 ASDs, both powder dissolution and tablet
dissolution results revealed that SDD exhibited higher dis-
solution than both cPAD materials, even though the BET
specific surface area of the cPAD powders (RAM-40G and
RAM-80G) was approximately 10-fold higher than that of
SDD, the dissolution performance does not correlate with
the BET specific surface area. Similar behavior was also
observed with spherical agglomerates of ferulic acid (60)
and other cPAD powders (61). One hypothesis is that the
internal surface area of pores present in GDC-0810 cPAD
powders are not accessible to the dissolution medium due
to the poor wettability, but accessible to nitrogen gas during
the BET surface area measurement. Note that GDC-0810
has a log P of 6.2 and is highly hydrophobic and shows poor
wettability. The surface chemical composition of particles
can affect their interaction with the dissolution medium.
The previous work has shown that depending on the ASD
manufacturing technology, hydrophobic components can be
enriched on the surface compared to the bulk and result in
lower wettability of powders (62).
Additionally, the particle porosity does not correlate
well with powder dissolution as the fastest dissolving SDD
powder had no resolved porosity (as seen in Fig.8b). In
contrast, the calculated total solid external surface area (SA)
normalized by calculated total solid volume (SV) obtained by
XRM image analysis shows a strong correlation with pow-
der dissolution data. These results indicate that dissolution
performance of GDC-0810 ASDs is likely dictated by solid
external surface area.
Application ofXRM andImage‑Based Analysis
In this study, three-dimensional non-invasive tomographic
imaging was applied to reveal physical properties at the
micro-scale that traditional characterization techniques are
not able to adequately elucidate. Application of state-of-
the-art artificial intelligence-based image segmentation and
analysis allowed extraction of quantitative microstructure
information from the XRM images that was then correlated
with dissolution behavior.
While visualization of the material differences between
the SDD and cPAD samples are readily observed in the FIB-
SEM images, application of XRM and subsequent image-
based analysis provides a quantitative and detailed per-
spective on how microstructure of these differently formed
materials contributes to their property-performance rela-
tionships. The use of XRM to provide distinction between
the external and internal features of SDD particulates and
subsequent influence on performance properties has recently
been reported (36, 37). In the case of the cPAD samples, the
ability of the XRM analysis to obtain a normalized surface
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Pharmaceutical Research
1 3
area to integrated solid volume ratio provides a target met-
ric for process improvement or reproducibility in contrast
to a standard particle size measurement from laser diffrac-
tion which can only represent the external component of
the particles.
The ability of using the XRM data to quantify both exter-
nal surface area of the particles and their porosities pro-
vided a better understanding of the differences in dissolu-
tion behavior between the SDD and cPAD powder samples.
Based on the experimentally obtained high BET surface
area, the cPAD material would be expected to dissolve faster.
However, this was not the case when the actual dissolution
of the cPAD powders were measured. In addition, the XRM
analysis showed that porosity of the cPAD 40G sample had
more than twice the pore volume fraction relative to the 80G
sample at 10.5% and 3.9%, respectively, with the SDD not
having a discernible pore volume. Using the XRM data, it
was possible to calculate the external particulate surface area
and normalize that by the respective total solid volume (spe-
cifically with correction to not include the pore volume) and
derive an external surface area to volume ratio for each pow-
der sample. When expressed using this metric, a good cor-
relation between external surface area to volume ratio and
dissolution concentration at 60min was observed (Fig.8b).
The SA/SV ratios were comparable to those calculated from
the Sauter Mean Diameter (D3,2) (c.f. TableII), a parameter
that can be used for estimation of surface area.
The microstructure analysis using the XRM data also pro-
vided insight into the tablet properties both from a material
property/compaction and dissolution behavior perspective.
Dissolution data for each of the tablet types was consistent
with their respective trends in powder dissolution. Three-
dimensional non-invasive tomographic imaging provided
visualization of the internal microstructure of the tablets,
in particular the subsurface pore network that can influence
fluid distribution and subsequent disintegration behavior.
Artificial intelligence–based image segmentation of the
XRM data enabled quantification of tablet porosity and pore
size distribution. Although the calculated tablet pore sizes
did not correlate with the dissolution results, pore volume
fraction showed a strong correlation (Fig.9a).
Liquid penetration is one of the critical parameters in the
tablet dissolution process and is strongly associated with
the physical properties of the tablet matrix and its inter-
action with fluid. Pore volume and pore size distribution
only describe the amount and size of pores, not how they
were interconnected or the ability of the pore network to
transmit fluid. Besides the pore structure itself additional
factors need to be considered when investigating liquid pen-
etration into a porous medium. While strongly tied to the
pore structure of the powder compact, tablet permeability
more accurately characterizes how fluid transmits through
the tablet. Advanced physical property modeling using
the quantified microstructure results was implemented to
numerically compute the permeability of each tablet based
on the segmented pore network in 3D. When permeability
was correlated with tablet dissolution at 60min, an improved
correlation was observed (R2= 0.95) when compared to the
correlation between dissolution and pore size (R2= 0.92).
The permeability impact on disintegration and subsequent
dissolution are convoluted by many parameters from for-
mulation, processing, and release kinetics. While study of
additional tablets would be required to more accurately cor-
relate permeability with tablet dissolution or disintegration,
the strong correlation observed here and in the literature
between tablet permeability and tablet dissolution further
reinforce the critically fundamental role of tablet microstruc-
ture in the many aspects of dissolution (63).
The various approaches accessible using the XRM
derived data to selectively separate components, pore vs.
solid regions and obtain quantitative internal and external
morphological descriptions of dosage form components is
expected to find increased utility in characterizing pharma-
ceutical systems. The application of AI to the 3D grey scale
images additionally allows for understanding of not only the
API component but also of the excipients and the intermedi-
ate powder or granule or the finished dosage form. Further-
more, the ability to then use the image based microstructural
data to “measure” porosity and permeability (38) and also
conduct mechanistically based performance simulations (64)
can reduce the need for time-consuming specialized ancil-
lary testing.
Conclusion
A systematic implementation of materials science tetrahe-
dron (MST) principle which depicts the interrelationship
among structure, property, performance and processing
(65) has been broadly recognized as an indispensable tool
in advancing pharmaceutical research and product devel-
opment. To study such interrelationship for ASDs, GDC-
0810 (50% w/w) with HPMC-AS ASDs were prepared using
methods of spray drying and co-precipitation via resonant
acoustic mixing at different accelerations. The application
of XRM image-based analysis provides a unique ability to
assess the contribution of microstructure to the character-
istics of ASDs and gain clearer mechanistic understanding
of the interrelationship among properties and performance.
GDC-0810 cPAD powders containing coarser particles dem-
onstrated superior compactability compared to the fine SDD
powder. This was attributed to their highly porous micro-
structure which promoted interparticulate bonding area
leading to stronger compacts. On the other hand, the SDD
powder showed greater extent of dissolution than both cPAD
materials. It was found that powder dissolution performance
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Pharmaceutical Research
1 3
did not correlate with the BET specific surface area or the
particle porosity. Instead, a strong correlation between the
external surface area to volume ratio obtained from XRM
image analysis and dissolution was observed. Deeper under-
standing of the interrelationship of structure-properties-per-
formance-process will provide insights when designing ASD
formulations, and guide the selection of manufacturing tech-
nology and the process optimization to deliver ASDs with
desired properties and performance. Downstream develop-
ment of the final dosage form of ASDs could be greatly
dependent on the material attributes of ASDs, especially
when high ASD loading in the final drug product is needed
to achieve the target dose without potential pill burden.
Therefore, improving ASD properties to meet the require-
ments for high-quality ASD drug products is one of the key
elements at the development stage. The interrelationship
of structure-properties-performance-process is the founda-
tion of a holistic approach for integrated drug development
and reflects a collaborative effort across multiple functions
within the technical development team throughout the entire
development cycle of a drug.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s11095- 022- 03308-9.
ACKNOWLEDGMENTS AND DISCLOSURES The authors declare that they
have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.
Funding This study was internally funded by Genentech Inc.
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