Research Article

Characterization of Controlled Release Microspheres Using FIB-SEMand Image-Based Release Prediction

Shawn Zhang,1,3 Dan Wu,2 and Liping Zhou1

Received 11 April 2020; accepted 22 June 2020; published online 14 July 2020

Abstract. For polymer-based controlled release drug products (e.g. microspheres andimplants), active pharmaceutical ingredient distribution and microporosity inside the polymermatrix are critical for product performance, particularly drug release kinetics. Due to thedecreasing domain size and increasing complexity of such products, conventional character-ization and release test techniques are limited by their resolution and speed. In this study,samples of controlled release poly(lactic-co-glycolic acid) microspheres in the diameter rangeof 30–80 μm are investigated with focused ion beam scanning electron microscope imaging at20 nm or higher resolution. Image data is quantified with artificial intelligence-based imageanalytics to provide size distributions of drug particles and pores within the microspheresample. With an innovative image-based numerical simulation method, release profiles arepredicted in a matter of days regardless of the designed release time. A mechanisticunderstanding on the impact of porosity to the interplays of drug, formulation, process, anddissolution was gained.

KEY WORDS: Focused ion beam scanning electron microscopy; Image-based release simulation; Drugparticle inside microsphere; Microporosity; Quantitative charactorization.

INTRODUCTION

Controlled release (CR) drug products have beendeveloped to meet specific therapeutic targets, controltoxicology effects, and improve patient compliance. Theyare typically formulated using carrier(s) of specific physicaland chemical properties, such as biodegradable poly(lactic-co-glycolic acid) (PLGA) (1,2). The active pharmaceuticalingredient (API) is dispersed into the PLGA matrix toachieve a desirable release profile. In vitro release testingmethods, necessary for product development, batch release ofproduct, and in vitro-in vivo correlations, typically requiresignificant effort for the development of appropriate assaysand analytical methods (3). Additionally, if acceleratedin vitro testing methods are not available, the time requiredfor batch release of long-acting drug products, sometimes upto several months or years, can be problematic for thedevelopment and commercialization. Furthermore, due totheir microscale domain size and increasing complexity,polymeric controlled release formulations are challenging tocharacterize by conventional analytical methods. For exam-

ple, microspheres used in a controlled release (CR) formula-tion are often 30–80 μm in diameter. To ensure sustainedrelease over a period of weeks or months, API domains orthe microporous network in a microsphere are often assmall as a few tens of nanometers (4,5). Bulk measurementsoften fail to reveal the mechanisms of drug-formulation-process-dissolution interplay complicated by small domainsize. Conventional particle imaging techniques such asconfocal microscopy, Raman imaging (6), and X-Raymicro-computed tomography (7–9) lack the required reso-lution to resolve small API and porosity domains inside amicrosphere. Consequently, the development of alternativeapproaches to understand the essential quality microstruc-ture attributes that affect drug release and performance areof critical importance. Electron microscopy (EM) stands outas an indispensable tool in CR drug characterization toreveal the properties of the internal microstructures ofmicrosphere samples, such as API domain size distributionand microporosity, and correlate them with the bulkbehavior. The most commonly used microscopy techniquesfor examining the shape and the morphology of the matrixinclude transmission electron microscopy (TEM) (10) andscanning electron microscopy (SEM) (11,12). Although suffi-ciently high resolution is achievable by these EM platforms,their investigations are limited to either the particle surface or avery thin slice of the sample. While cryo-cleaved cross-sectioning (10) makes it possible to image the inside of aparticle, mechanical damage is typically produced during the

1DigiM Solution LLC, 67 South Bedford Street, Suite 400 West,Burlington, Massachusetts 01803, USA.

2 Bausch Health Companies, 400 Somerset corporate Blvd, Bridge-water, New Jersey 08807, USA.

3 To whom correspondence should be addressed. (e–mail:shawn.zhang@digimsolution.com)

AAPS PharmSciTech (2020) 21: 194DOI: 10.1208/s12249-020-01741-w

1530-9932/20/0500-0001/0 # 2020 American Association of Pharmaceutical Scientists

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process, preventing accurate and quantitative investigation ofthe internal microstructures.

Focused Ion Beam (FIB) SEM is a new-generationelectron microscopy imaging platform that supports 3Dnano-tomography imaging. Gallium ion FIB can removematerial from the sample at a precision as high as a fewnanometers without disrupting microstructures in the nextlayer (13). A clean, artifact-free cross-section can thus beprepared for high-resolution field emission SEM imaging.Iterative FIB milling and SEM imaging at 3–50 nm resolutionproduces a stack of SEM images that can be reconstructedinto a 3D volume. Chemical composition can be confirmedusing energy dispersion X-ray spectroscopy (EDS) (14).

Image-based analytics have been demonstrated as avaluable technique for the characterization of complexpharmaceutical systems (4,6,7). Due to the limited imagedata of microsphere formulations, quantitative analysis andmodeling, essential to understand the role of the microstruc-tures in the drug-formulation-process-dissolution interplay, issparse at best. Further, investigations of the relationshipbetween microstructures and API release are limited.

In this paper, we describe an innovative image-basedcharacterization method for PLGA microsphere characteri-zation. FIB-SEM provided the necessary high resolution tovisualize the API and microporosity morphology insidemicrosphere samples. API particle distribution uniformityand pore morphology were qualitatively assessed. An artifi-cial intelligence (AI)-based algorithm further segmented theimages into different material phase domains. Quantitativeinformation was computed including volume fractions, spatialdistribution homogeneity, and particle/pore size distributions(15). An image-based release simulation algorithm predictedmonolithic release profiles using the drug distribution ex-tracted from microsphere FIB-SEM images. The impact ofmicroporosity was investigated to demonstrate this approachas a powerful and time-efficient alternative for CR micro-sphere performance characterization.

METHODS

Materials

Microsphere drug samples were formulated using PLGApolymer and compound A, the API. Poly(D, L-lactide-co-glycolide) (50:50 lactic acid: glycolic acid; inherent viscosity:0.44 dL/g) was obtained from Evonik Industries (Darmstadt,Germany). Compound A was purchased from Cipan(Castanheira do Ribatejo, Portugal). Five different batches(D382, D383, D396, D495, and D460) of compound A PLGAmicrospheres were prepared as follows. First, compound Aparticles were milled down to reduce the particle size of theAPI. Polymer solution was prepared by dissolving PLGA inmethylene chloride. A compound A suspension was preparedby mixing milled compound A particles in the polymersolution. A phase separation agent, dimethicone, was addedto induce phase separation under further mixing, formingmicrospheres. The resulting mixture was then transferred intoa container containing a hardening agent, cyclomethicone, tocontinue hardening the microspheres. The solidified micro-spheres were collected using a stainless steel screen andrinsed with fresh cyclomethicone. Finally, the collected

microspheres were dried under vacuum to remove residualsolvent. They were screened to control size uniformity. Allmicrosphere samples were loaded with approximately 23–24% weight/weight crystalline API.

Laser Diffraction

Particle size distributions of microspheres and APIparticles were determined with a Sympatec HELOS LaserParticle Size Analyzer. Microspheres were first suspended inthe suspension medium that contained squalene and sorbitanmonooleate to form a pre-suspension. The pre-suspensionwas sonicated for about 15 min to break visible aggregation ofparticles. The pre-suspension was then added dropwise to atest cuvette containing suspension medium, while allowingsufficient mixing time between additions for optical concen-tration level to stabilize. The addition was continued until anobscuration level between 9% and 12% was reached prior tothe performance of the measurement. Samples were preparedin triplicates. Each measurement was repeated three times.Crystalline API particles were determined using the sameinstrument, except the dry powder sample was sprayed in theair before laser diffraction measurements were taken.

In vitro Dissolution Study

Approximately 4.5 mg of the microspheres were weighedand placed between two 25-μm screens. The screens wereheld together by two pieces of donut-shaped Teflon, forminga release cell. Six tubes were prepared for each sample. Eachcell was placed into a borosilicate glass tube (25 mm in outerdiameter, and 15 cm in length) containing 10 mL of a pH 4.2phosphate buffer previously equilibrated at 37°C. The tubeswere then placed in a tube rotator, which was placed in aconvection incubator maintained at 37°C. The tubes wererotated for 4 h, before the solution was removed andanalyzed by HPLC. The amount of drug released wasmeasured to derive an initial release rate between 0 and 4 h.

Focused Ion Beam - Scanning Electron Microscope (FIB-SEM)

An Auriga FIB-SEM (Carl Zeiss Microscopy, USA)workstation was used for FIB milling and SEM imaging ofindividual microsphere cross-sections. The principle of the FIB-SEM technique is illustrated in Fig. 1a, at three stages, before FIBmilling, the first FIB-milled cross-section where an SEM imagewas taken, and the 500th FIB milled cross-section where an SEMimagewas taken. The intermediate FIBmilling and SEM imagingsteps are omitted. The sample stage was tilted by 54 degrees,exposing the sample to an ion beam column pointing perpendic-ularly at the sample. An electron beam concurrently imaged aside view of the sample. For microspheres samples, the micro-spheres were scattered on a carbon adhesive tape which was fixedon the SEM stub (Fig. 1b). On a selected microsphere (Fig. 1c),cross-sectioning was carried out using 30-keV focused Gallium(Ga) ion beam of various currents. SEM imaging was thenconducted at a 54-degree tilt angle, using a 2-keVelectron beam.The secondary electron (SE) and energy selective backscattered(EsB) electron detectors were used for SEM imaging. To obtainoptimal contrast, each image was collected using a mixture of

194 Page 2 of 14 AAPS PharmSciTech (2020) 21: 194

40% SE signal and 60% EsB signal. The 2D FIB-SEM scancollected a single image of a cross-section at 20-nm resolution.When a 3D FIB-SEM scan was conducted over an 8–12-hinterval, a total of 800–1000 FIB-SEM slices were collected at 20-nm resolution and 20-nm slice thickness. Figure 1d shows theremains of the same microsphere sample as Fig. 1c after FIB-SEM milling and scanning were completed. Due to the non-conductive nature of PLGA, and pharmaceutical materials ingeneral, damage from electron charging and the focused ionbeam are common problems requiring careful mitigation. Spe-cially designed sample sputter coatings were used to controlcharging and maintain sample stability over the hours ofautomated, continuous FIB milling and SEM imaging. Anelectron beam with high energy, commonly used in materialcharacterization, was avoided to prevent introducing perturba-tions that could lead to structural, morphological, or physical formchanges on the API, the polymer, or the porosity.

Energy Dispersion X-Ray Spectroscopy (EDS)

An energy dispersive X-ray spectrometer (EDS) (OxfordInstrument, USA) equipped on the Zeiss Auriga FIB-SEMwas used to acquire X-ray spectra for elemental identification.To minimize electron–sample interaction volume, hence alsoimproving spatial resolution in elemental analysis, a 5-keVelectron beam was used.

Image Analytics

Image analytics were performed using DigiM™ I2Scloud-based image analysis platform (DigiM Solution,USA). The large number of high-resolution 2D imagescollected from FIB-SEM imaging allowed digitization of thedrug sample into a 3D volume. Data from images wereexpressed in grayscale values. In order to evaluate micro-structures of each material phase, image segmentation ofmaterial regions was a critical step before these images couldbe quantified or used for prediction. Drug and porosityphases were segmented using an artificial intelligence (AI)-based image processing algorithm with supervised machinelearning (15) in both 2D and 3D. Domain size distributionsfor API, polymer, and porosity were computed.

FIB-SEM can introduce certain imaging artifacts. Due tothe rasterization nature of FIB milling over a plane withmaterials that were heterogeneous in density, vertical stripesforming a “curtaining” artifact were visible. A Fouriertransformation was applied to remove the repetitive stripesafter the images were mathematically transformed from thespatial domain to the frequency domain. During FIB milling,20-nm thickness between slices was specified for each FIBcut. The instrument, however, cannot guarantee that eachslice is exactly 20-nm thick. The diameter of the Ga beamitself had some fluctuations, while the sample it milled wascomposed of inhomogeneous materials. The iterative millingand imaging over a few hours were subjected to smallperturbations from either the environment or from thesample itself (e.g., a center of gravity change due to removalof material, de-gassing, and de-hydration in vacuum). Theseeffects caused a fluctuation of thickness ranging from 5 to20%. The smaller the thickness, the greater the fluctuationpercentage. The images from slice to slice were also slightly

misaligned. The thickness fluctuation and slice misalignmentare collectedly called “drifting artifacts.” As part of the imageanalytics software workflow, an intensity-match optimizationalgorithm was applied to correct the drifting artifacts, byiteratively moving two adjacent slices until a maximumintensity overlap was identified. The DigiM™ I2S softwarewas used to accurately correct these imaging artifacts. Theresult of these corrections is shown in Fig. 6. Thesecorrections were essential to the reliability of image segmen-tation, and subsequently to the accuracy of any furtherquantification and modeling. Examples of these artifacts andtheir corrections on specific samples will be presented in theresult section.

Image-Based Pressure-Driven Permeability Simulation

All image-based simulations were conducted usingDigiM™ I2S. When 3D FIB-SEM data was collected,pressure-driven permeability along three spatial directionswere computed using a voxel-based computational fluiddynamics (CFD) solver. Permeable phases were alterednumerically to include porosity only, API phase only, orcombined, to study three networks as follows: the network ofthe API phase, the network of the porosity phase, and thenetwork of a combined phase. These digital experimentsallowed the investigation of the degree of connectivity forthese three networks, and to understand the impact ofporosity.

The permeable phase was reconstructed from theoriginal image resolution, as opposed to being reduced to apore network model (PNM). Finite volume spatialdiscretization was directly built on the voxels of the seg-mented 3D image data. Navier-Stokes equations were thensolved with an implicit pressure explicit momentum scheme,using the following government partial differential equations,

∇•u ¼ 0∇p ¼ μ∇2u− u•∇ð Þuþf ð1Þ

where u is the fluid velocity vector, p is the pressure, μ is thedynamic viscosity, and f is the body force vector which is setto zero in the simulations reported in this paper. Theboundary conditions of the cubic computational domain arespecified on the four faces that are perpendicular to the flowdirection, and on the interior wall of the pore space, no-flowboundary conditions were used. On the two faces that areparallel to the flow direction, pressure inlet and outlet werespecified.

Simulations were conducted on a distributed-memory,parallel cluster with a web browser user interface. After thepressure and velocity fields are solved, Darcy’s law was usedto evaluate the absolute permeability for all three directions,

kn ¼ un μΔxΔp ð2Þ

where n denotes the direction of flow, and Δx and Δp are thelength and pressure drop across the sample, respectively.

Using volume averaging, tensor forms of Equations 4and 5 can be derived using periodic boundary conditions. The

Page 3 of 14 194AAPS PharmSciTech (2020) 21: 194

resulting intrinsic permeability tensor K does not depend onthe flow conditions and describes the anisotropy of thesystem. Validation results against theoretical models andstandard glass bead packaging models are comparable withprevious efforts using similar approaches.

When a scalar permeability is needed, the intrinsicpermeability tensor collapses into a scalar by taking the threeeigen values, ke0, ke1, and ke2, derived from the permeabilitytensor using

kmag ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffik2e0 þ k2e1 þ k2e22

qð3Þ

The CFD approach to compute permeability is a directnumerical simulation using a finite volume discretizationtechnique (16). In comparison, a PNM approach is a reducedorder modeling with simplifications both in pore geometryand consequently in numeric.

Image-Based Release Profile Prediction

With the reconstructed 3D microstructure system inwhich API particles were dispersed into the polymer matrix,a drug release profile was predicted with a novel numericalmethod employing percolation theory and Fick’s Second Law.The release profile simulation had three components asfollows: a percolation simulation, a direct numerical

simulation for the effective diffusivity coefficient, and arelease time conversion with a geometry-dependent drugrelease model.

Percolation Simulation Model

Starting with the 3D reconstruction of the drug APInetwork, the first component of the drug release simulationwas a voxel-based percolation simulation using a monolithicrelease mechanism to simulate the initial release when thePLGA polymer degradation is not significant. Understandingthe initial release behavior is an important intermediate stepto characterize a complex release system such as a PLGAmicrosphere. Furthermore, a monolithic model also helps toestablish an end member scenario to compare differentformulation parameters when PLGA properties are constant.

When a microsphere is in initial contact with the releaseenvironment, e.g., body fluids, API molecules in the particleson the surface layer first dissolve into the fluid and diffuse outof the microsphere (17,18). The rate of this initial release ismostly constrained by the solubility limit of the APImolecule. In our simulation, interconnected drug particleswere released layer by layer from the exterior surface to theinterior locations of the drug release system (17,18), followinga spherical coordinate system. The discrete increment of drugreleased from each layer that the model can simulate wasdictated by the resolution of the image data. Through thispercolation simulation in the spherical coordinate system, the

(a)

(c) (d)

(b)

100μm

10μm

Before mill

Mill #1

Mill #500

36o

10μm

Fig. 1. FIB-SEM overview using sample D396 as an example. a FIB-SEM principle diagram. b SEM image of microspheresample on carbon tape. c Single microsphere sample. d Microsphere sample after FIB-SEM experiment

194 Page 4 of 14 AAPS PharmSciTech (2020) 21: 194

amount of drug released from each outer layer of drugparticles that are in contact with dissolution fluid can beevaluated as a fraction of total drug. The microstructures ateach intermediate release stage were determined. Figure 2ashows a 2D cross-section of the 3D microsphere sample, at anintermediate release time instance t. To simplify analysis,porosity in the microsphere prior to release was left outintentionally. The space vacated by the API particles becamepores filled with dissolution fluid (Fig. 2a), creating a porouspolymer media (pores shown in green-blue) between theremaining API (blue) inside the microsphere and the releaseenvironment outside the microsphere (black). PLGA poly-mer domain is displayed in dark blue. The API remaininginside the microsphere was released through the porouspolymer media. The thickness of the porous polymer layerthus increased with time, leading to increased tortuosity,decreased effective diffusivity, and a decreased release rate.In Fig. 2a, an infinitesimal drug layer being released at timeinstance t is displayed in red and exaggerated using severalpixels from the actual layer with the thickness of one voxelfor better visualization.

This percolation simulation produced a non-dimensional drugrelease profile, with the voxel distance to microsphere surface asthe horizontal axis, and the percentage of the amount of drugreleased as the vertical axis. During the percolation simulation,each of the intermediate release stage microstructures weregenerated in 3D. Similar to the depiction of Fig. 2a, theseintermediate microstructures included the outer ring of porousmedia and the inner core with remaining API.

Direction Numerical Simulation Model for EffectiveDiffusivity Coefficient

In the second step of the release simulation, the porouslayer from the intermediate release stage was used as theinput of direct numerical simulation for effective diffusivitygoverned by Fick’s Second Law, Equation 4,∂C∂t

¼ Dbulk∇2C ð4Þ

where Dbulk is the diffusion coefficient of the API in thesolvent, C is the concentration field of the API in the solvent,and t is numerical time in computational integration. Finitevolume discretization with Dirichlet boundary conditions wasused, where the inlet and outlet concentrations were specifiedas Cin 0 1.0 and Cout 0 0.0. This simulation produced a scalarconcentration field throughout the porous layer. Figure 2billustrates a magnified view of a small region of the simulationdomain, where C was solved for all green-blue pixelscorresponding to the pore space in a spherical coordinatesystem of the equivalent effective media. Using the resultingconcentration distribution, an effective diffusivity coefficientwas derived according to Fick’s First Law rearranged in Eq. 5,

Deff ¼ j!

∇!C

ð5Þ

where j!

was the mass flux of the API and ∇!C was the

effective API concentration gradient between inlet and outletderived using volume integration.

Drug Release Model

In the final step, the numerical, voxel-based release timeinterval was converted into physical time using modifiedHiguchi’s model (19). The model was expressed in a sphericalcoordinate system, and rearranged for time conversion inEquation 6:

T ið Þ ¼ R2

2CsC0

� �D ið Þ

1− 1−M ið ÞM∞

� �23

−23

M ið ÞM∞

� �" #ð6Þ

where T is physical time, i is numerical time in percolationsimulation (which determines the porous media domainwhere Equation 4 was solved, hence is different from t inEquation 4), R is the radius of the microsphere sample, Cs isthe total drug amount per unit volume of solution, C0 is theinitial drug amount per unit volume of solution, and M ið ÞM∞ is thefraction of drug released expressed in numerical time i(determined from percolation simulation with the assumptionof no change in the solid drug particle density when theparticles are still inside the microsphere). D(i) is the effectivediffusivity coefficient corresponding to the porous media ringat numerical time i, determined using direct numericalsimulation discussed above. Further details of the releasesimulation method can be found in a published patentapplication (20).

Layer of drug (red) currently being release at �me

instance T(i)

Sample with unreleased drug (blue)

Porous media a�er drug release (pores are greenish blue)

(a)

Pores

(b)

release

environment

release

environment

Cout

CinPolymer

Fig. 2. Monolithic drug release at one time instance; a a 2D cross-section of 3D image data of a microsphere sample. b Voxelcomputational cell used for direct numerical simulation of effectivediffusivity coefficient. Color legend: black, release environment; darkblue, PLGA polymer; blue, API particles; greenish blue, poresvacated by API release; red, API particles being release at oneinfinitesimal time instance (exaggerated)

Page 5 of 14 194AAPS PharmSciTech (2020) 21: 194

Degradation of the PLGA material during the releasewas excluded from this study based on two considerations.First, the initial dissolution rate of the API was assumed to behigher than the polymer degradation rate (21). Furthermore,the formulation under investigation had a fairly high drugloading, providing a percolation network with good intercon-nectivity. Hence, it was assumed that API diffusion domi-nated the initial phase of its release. PLGA degradation onlybecame significant at the later phase of the release (22).Second, the inclusion of PLGA degradation complicated thevalidation of the initial model. The PLGA degradation effecthas been studied and will be reported in a separatepublication.

RESULTS

Table 1 summarizes the five samples studied in thisproject, along with the image-based analysis conducted,corresponding drug loading, and other analyzed parameters.The results discussion below uses D396 as the primaryexample; however, FIB-SEM 2D cross-section, 2D imageanalysis, and EDS studies were conducted on all five samples.

Due to the small size of the microsphere samples (D50around 50 μm as shown in Table 1), cutting the microspheresmechanically was challenging. The PLGA material matrixwas soft and delicate, which further complicated cuttingwithout introducing artifacts or sample damage. Figure 3demonstrates FIB-SEM as an advantageous cross-sectioncharacterization tool. Figure 3a is an SEM image of a cross-section of a microsphere sample prepared by mechanicalcutting. Residual surface topology due to mechanical fractur-ing obscured the details of the microstructure. In comparison,Fig. 3b shows an SEM image of the cross-section prepared byFIB cutting. Different contrasts corresponding to variousmaterial phases are clearly and accurately visualized.

The FIB-SEM cross-section image of the microsphereshowed characteristic grayscale for each material phase, asindicated in Fig. 3b by areas with different contrast. Thegrayscale contrasts representing different material phaseswere further confirmed using EDS. Using D396 as anexample, Fig. 4a shows the bottom half of a microspherecross-section, where spot EDS, area averaged EDS, andelemental map were analyzed. The API molecule contains ahydrochloride counter ion. In Fig. 4b, the abundance ofchlorine matching the light gray regions in Fig. 4a confirmedcorrespondence to the API domain. The dark gray regionsabsent of chlorine signal corresponded to the PLGA polymer.The API phase also had a higher degree of oxygen than the

PLGA polymer phase, as suggested by the oxygen map(Fig. 4c). Due to the use of an organic solvent containingsilicon, it was useful to monitor silicon to determine theexistence of any residual silicon solvent and its distribution(Fig. 4d). Both API and polymer phases contained carbon;hence, the porosity space seen in Fig. 4a was confirmed by thecarbon map (Fig. 4e). Spot and area EDS on numerouslocations were conducted. Four of the spot EDS locations,highlighted as circles in Fig. 1a, are summarized in Fig. 4f.Spots 1 and 3 were on the material phase with bright contrast.Strong Cl signal and the presence of N signal were associatedwith these unique elements in the API. Spots 2 and 4 did notshow N signal, and very weak Cl signal. They were eitherpure polymer or mostly polymer with a very small amount ofAPI. An area EDS corresponding to the white box in Fig. 4aare also reported in Fig. 4f. The spot and area EDS resultswere consistent with the EDS map observations. Once phaseswere confirmed, further imaging on samples did not requireEDS analysis, unless a new contrast phase was identified.

EDS in combination with FIB-SEM aided in understand-ing the distribution of elements, and ultimately variousphases, throughout the microsphere sample. Figure 5 showstwo EDS measurements, one on the exterior surface of amicrosphere, the other on the FIB-SEM cross-section of thesame microsphere. The signals of gold and gallium, from thesputter coating and FIB plasma residue respectively, werenormalized away. Silicon signal was observed on the exteriorsurface of the microsphere but not the interior, suggestingremaining residual silicon solvent mostly on the surface. Dueto its hydrophobic nature, the presence and distribution of theresidual silicon solvent on the exterior could impact the initialwater uptake, hence influencing the drug release. On the FIB-SEM cross-section, a slightly stronger chlorine signal wasobserved than the exterior, suggesting higher density of APIper unit area inside the microsphere. The nitrogen signal wasalso associated with the API.

Through FIB-SEM imaging of sampleD396, a 3D volume ofthemicrosphere sample, approximately 20 μmby 20 μmby 12 μmin three dimensions, was digitized with 630 images, which was thebasic building unit volume for subsequent analysis and simulation.Figure 6a shows an example of a “curtaining artifact,” which wascorrected with the Fourier transformation algorithm (Fig. 6b).Figure 6c shows “drifting artifacts,” which were only obvious onthe XZ plane, perpendicular to the imaging plane XY. It wascorrected by an intensity-match optimization algorithm (Fig. 6d).

Upon phase confirmation and artifact correction, mate-rial phases from FIB-SEM images can be better elucidatedand reconstructed. Figure 7a annotates an API particle,

Table 1. Batches of Microspheres Studied

Column A B C D

Sample ID Microsphere size (μm) Initial releaserate (μg/mg/h)

Drug loading (w/w%) Image-based analysis conductedD10 D50 D90

D382 34 54 79 163 23.4 SEM, FIB, 2DD383 36 52 69 85 23.2 SEM, FIB, 2DD396 32 46 65 143 23.5 SEM, FIB, EDS, 3D, release simulationD459 31 44 58 105 23.2 SEM, FIB, EDS, 2DD460 30 41 55 172 23.6 SEM, FIB, EDS, 2D

194 Page 6 of 14 AAPS PharmSciTech (2020) 21: 194

corresponding to the lightest gray phase, which is distributedin the darker gray polymer matrix. The pores are the darkestin the gray scale. Two types of pores were observed in themicrosphere based on their geometry and interaction with theAPI and polymer. Inter-phase pores were spherical andisotropic, lining the interface between API and polymerdomains. These submicron-sized pores were likely associatedwith the annealing process or drying process. Polymer pores,on the other hand, were observed in the polymer domain.These pores had a high aspect ratio and were anisotropic,likely associated with the pockets of residual solvent and theirevaporation. Through a software algorithm using supervisedAI learning, API and porosity phases were segmented intored and blue phases respectively as shown in the cross-sectionimage Fig. 7b, corresponding to Fig. 7a. The remaining voxels

corresponding to PLGA polymer were rendered as transpar-ent. Further reconstruction of the API and porosity in 3D, asshown in Fig. 7c and d, found a calculated volume percentageof 41% and 23%, respectively.

Based on the 3D reconstruction of the API phase, APIparticle size distribution in the microsphere was determined.Table 2 compares the API particle size distributions (PSD)obtained from image analysis with that from laser diffractionmeasurements. It is important to keep in mind that the PSDmeasured by laser diffraction was from the sample of raw APImaterial, while the PSD determined by image analysis wasbased on API particles inside the final microsphere product.Overall, these two particle size measurements are compara-ble. This confirmed that there was no significant change oforiginal API particle size during the microsphere manufactur-

(a) (b)Drug Par�cle

Porosity

PLGA

Fig. 3. Comparison of microsphere cross-section technique. a Mechanical cut; b FIB cut

(a)

(b) (c)

(d)

(e)

APIPorosityPolymer

Element wt%

C N O Si Cl

Spot 1 63 2 6 0 29Spot 2 82 0 14 1 3Spot 3 64 2 7 0 27Spot 4 72 0 22 4 2Area 75 3 16 1 5

Area

Spot 1Spot 2

Spot 3

Spot 4

(f)

Fig. 4. Confirmation of material phases with EDS map. a Cross-section of a bottom half of a microsphere sample. bChlorine distribution map on the same area as a. d Oxygen map. c Silicon map. e Carbon map. f Spot and area EDScorresponding to the annotations in (a)

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ing process. The results of the D90 measurements wereparticularly interesting. Laser diffraction measurementsdepended on the dispersion of the sample in the measure-ment chamber. Aggregation of powder and particle sampleslikely led to a larger D90 measurement with the laserdiffraction approach. A more comprehensive comparison ofimage and laser diffraction PSD results requires additionaleffort, as making an apple to apple comparison is challengingdue to differences in measurement, sample numbers, particlemorphology issues, and background calculations and

assumptions. Obtaining a ground truth accuracy can be verychallenging in these regards (23).

After image segmentation and phase confirmation, majormicrostructure properties were calculated. Table 3 comparesthe microstructure properties computed based on 2D and 3Dimage data for sample D396. The 2D FIB-SEM analysisprovided quick initial characterization of the internal micro-structure. Using a relatively large 2D image at one or severalcross-sections, the API volume fraction and API particle sizedistribution were evaluated. These results were comparable

Fig. 5. EDS surface of D396 a surface; b cross-section

Fig. 6. FIB-SEM imaging artifacts and corrections. a XY plane with curtaining artifacts. b Sameimage as a with curtaining correction. c XZ plane with stage drifting artifacts. d Same image as bwith stage drifting alignment

194 Page 8 of 14 AAPS PharmSciTech (2020) 21: 194

with that from 3D analysis. On the other hand, the 2D studywas intrinsically limited in the characterization of transportproperties such as diffusivity and permeability. The charac-terization of microstructure phases with less frequent appear-ance also required more sampling volume, as available in the3D FIB-SEM study.

The API phase occupied 41% of the microsphere involume (Fig. 7b) whereas the porosity was 23% (Fig. 7c).Both phases were found to be distributed homogeneouslythroughout the microsphere sample. The API volume

fraction reported was converted to a weight percentage of25.3%, after a density-based volume to weight conversion,and a correction of porosity. Considering uncertainties frommicrosphere to microsphere variation and lot to lot variation,the drug weight compares reasonably well with the expected23–24% drug loading.

It is clear in Table 3 that porosity played a significant rolein predicted transport properties. The effective diffusivityincreased by over a factor of 3 when porosity was consideredtogether with the API domain distribution. Likewise, thepermeability increased by a factor of more than one order ofmagnitude when porosity was considered.

Figure 8 shows several parameters essential to charac-terize internal microstructures of the microspheres. Figure 8acompares the particle size distributions from three samples.The PSD for these samples, D382, D383, and D396, werevery similar. This further confirmed that the API particles inthe microsphere product were consistent with the particlesfrom the starting API material. Figure 8a presents the poresize distribution for sample D396. Pore size was a few timessmaller than the API particle size. When the API phase and

Fig. 7. FIB-SEM image analysis and 3D reconstruction. a One example 2D FIB-SEM cross-section image and primarymaterial phase identification; b segmentation of API (red) and porosity (blue); c 3D reconstruction of API phase; (d) 3Dreconstruction of porosity phase

Table 2. Comparison of API PSD from Image Analysis and LaserDiffraction

Unit:(μm) Laser Image

D10 0.5 0.7D50 1.6 1.7D90 4.3 3.3

Page 9 of 14 194AAPS PharmSciTech (2020) 21: 194

porosity phase were combined, a virtual phase was generated.Figure 8a suggested that porosity phase did not significantly

alternate the size of this combined phase. Figure 8b shows thedistributions of surface area frequency for three scenarios of

Table 3. AI-Based Image Analysis Summary of Batch D396

D396 2D 3D

No. of images 1 630Image size (number of pixels) 2048 × 1536 1054 × 1045 × 631API volume fraction (%) 40 41Porosity (%) 17 23Number of API particles analyzed 69 342API D10 (μm) 0.85 0.65API D50 (μm) 1.85 1.70API D90 (μm) 3.85 3.25Pore D10 (μm) n/a 0.42Pore D50 (μm) n/a 0.64Pore D90 (μm) n/a 0.9Polymer domain D10 (μm) n/a 0.9Polymer domain D50 (μm) n/a 1.4Polymer domain D90 (μm) n/a 2.1Diffusivity (with API only, fraction of Dbulk) n/a 0.172Diffusivity (with Pore only, fraction of Dbulk) n/a 0.173Diffusivity (with API and porosity, fraction of Dbulk) n/a 0.620Permeability (×10−15 m2) without porosity n/a 0.3Permeability (×10−15 m2) with porosity n/a 6.0

Fig. 8. Summary of quantitative analysis. a Domain size distribution comparison among D382, D383, and D396. b Surfacearea distribution of different phases of D396. c Capillary pressure distribution from porosimetry simulation on porosityphase of D396 in three Cartesian directions X, Y, and Z. d Pore throat distribution corresponding to c

194 Page 10 of 14 AAPS PharmSciTech (2020) 21: 194

sample D396 as follows: porosity phase, API phase, and APIcombined with porosity phase. It shows that inclusion ofporosity notably impacted the distribution of surface area.Peak surface area occurred at 0.9-μm equivalent sphericaldiameter (ESD) for 4.5% of surface areas, increased from0.6-μm ESD for less than 3% of surface areas. The surfacearea is important to consider in evaluating material transport,particularly when surface dynamics such as polymer degra-dation become significant. Despite having a small effect onthe total spherical diameter, inclusion of porosity criticallychanged the surface area, which explained the significantdiffusivity and permeability increases.

Figure 8c and d were computed using a porosimetrymodule from DigiM I2S™. The module simulated a MercuryIntrusion Capillary Porosimetry (MICP) technique using theYoung-Laplace equation (24). Figure 8c shows the pore spacebeing progressively occupied by intruding fluid as capillarypressure increased. The capillary pressure required to pene-trate the pore space was dictated by the smallest openings ofthe pore space, i.e., pore throats. The inserts (black indicatesempty pore space; gray indicates pore space occupied byintruding fluid) visualize fluid saturations at three intermedi-ate pressure steps. Figure 8d is the pore throat distributionderived from this porosimetry simulation. Pore throat sizepeaked at 0.16 μm, four times smaller than the average porebody size of 0.64 μm (Table 3). Pore throat was the limitingfactor in mass transfer inside the microsphere, thus having abigger impact on API release. Porosimetry simulation resultsin all three directions were comparable, confirming theisotropic character of the pore network of the sample.

Two different batches (D396 and D459) of PLGAmicrospheres were analyzed by FIB-SEM and compared(Fig. 9). Figure 9a and b show the conventional low-resolution surface SEM for each batch. Similar external

morphology was observed without notable differences. Whencomparing the corresponding cross-sections prepared by FIB-SEM, Fig. 9c and d, major microstructure differences wereapparent, particularly the distribution of API domains. Adrug-free zone near the external microsphere surface wasobserved on sample D459, while more uniform drug distri-bution was identified throughout the cross-section of sampleD396. The existence of the drug-free zone near the externalsurface requires a longer time for water penetration andoffered an explanation to the different release behaviors offormulation D459, which had a retardation in its initialrelease. The measured initial in vitro release rate of D396(Table 1) was 40% higher than that of D459, as the APIparticles were closer to the microsphere surface. In additionto the explanation of the release retardation, this comparisonconfirmed the change in the final drug product which had ledto a closer examination on potential changes in themanufacturing process.

Permeability is an important transport parameter,particularly for release systems that are pressure gradientdriven. A pressure gradient can be due to osmosispressure, or from capillary force of dissolution fluidimbibition for strong hydrophilic porous matrix. Table 4compares the permeability of D396 computed with andwithout the inclusion of porosity. In both cases, the APIphase was assumed to be released, leaving behind aporous network. When porosity was considered, theporosity phase was combined with the API phase, forminga larger porous network. When only the API network wasconsidered, the permeability was fairly small and isotropicin all three directions. When the 23% porosity volumewas added to the percolation network, permeabilityincreased by a factor of 20, along with a more anisotropicbehavior.

5

4μm

100μm

(a) (b)

(c) (d)

Fig. 9. FIB-SEM cross-section reveals microstructure differences that correlate with differentrelease behaviors. a D396 surface SEM. b D459 surface SEM. c D396 FIB-SEM cross-section. dD459 FIB-SEM cross-section

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Release simulations required 3D data and wereconducted on sample D396. Figure 10a shows the releaseprofiles predicted. When both the API and porosity wereconsidered, the simulation predicted continuous release upto 3 weeks, consistent with the designed release period ofthis microsphere drug formulation. The release ratederived from our simulation was 158 μg/mg/h. It com-pared reasonably well with the 143 μg/mg/h measuredexperimentally on this particular sample, and with theaverage release rate of 133.6 μg/mg/h of all samplesmeasured experimentally, with a standard deviation of33.5 μg/mg/h. For controlled release formulations, it ispreferable to reduce porosity to achieve a longer releasetime. The second release profile in Fig. 10a corresponds toa hypothetical scenario where the porosity of the micro-sphere sample was numerically eliminated. Instead ofreaching 50% drug release in 3 days, it took 16 days toreach the same release amount when porosity wasremoved from the release percolation network.

Since the release simulation computed effective diffusiv-ity coefficient at each and every time instance, Fig. 10b plotsthe time varying effective diffusivity coefficient for bothrelease simulation scenarios. The effective diffusivity coeffi-cient decreased sharply during the initial 20% of the releaseperiod, due to the rapid increase of the thickness of theporous layer. Once the thickness of the porous layer becamelarge enough, the porous media reached representative length

scale. Further increase of the porous layer thickness did notsignificantly change the effective diffusivity coefficient.

Figure 11 visualizes an intermediate release time at T 05 days in the monolithic release simulation corresponding tothe case where porosity was digitally removed from T0samples (Fig. 10c). The pores vacated by the released APIare shown as blue in 3D and white in 2D cross-sections. Twocross-sections are displayed in Fig. 11. The 3D rendering ofthe microsphere with pores vacated by the released API werecut open to allow the visualization of the remaining APIinside, which is displayed in red on the 2D cross-sections.

DISCUSSIONS AND CONCLUDING REMARKS

Using the PLGA microsphere as an example, this paperhas demonstrated an innovative image-based microstructurecharacterization workflow for controlled release pharmaceu-tical samples. With careful design of experiments (DoE)factoring in sample representativeness, sample preparation,image resolution, appropriate sample stability control, andartifact mitigation, FIB-SEM is an excellent cross-sectiontool. FIB-SEM can reveal the internal microstructures of apharmaceutical sample with unprecedented resolution andaccuracy, particularly in comparison to traditional cross-section techniques that involve mechanical methods (e.g.,Fig. 3). Information obscured at the micro-scale, whichtraditional characterization approaches fail to divulge due toa lack of resolution, can be acquired. The EDS detector cancollect X-ray signals and identify elemental composition onFIB-SEM cross-section surfaces, providing a correlationbetween structural contrast and chemical compounds.

While obtaining high-quality images is an importantstarting point to visualize the internal microstructures ofmicrospheres, vital characterization requires the applicationof AI image analysis to quantify internal microstructuralfeatures. This paper demonstrates, for the first time to theauthors’ knowledge, that API particles inside the finalmicrosphere product can be verified, quantified, and com-pared with the starting API material. The analysis cancharacterize each individual material phase separately or

Table 4. Permeability Determination of D396 with 3D FIB-SEMImages

Permeability(× 10−15 m2)

API alone; no porosityconsidered volumefraction 41%

API and porositycombined, volumefraction 64%

Kx 0.2 4Ky 0.2 4Kz 0.2 2Kn0

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiK2xþK2yþK2z2

q0.3 6

Fig. 10. DigiM I2S predicted monolithic release profile of a microsphere formulation. a Simulation of drug release profilesusing 3D API percolation network reconstructed from FIB-SEM volume in spherical coordinates, with and without porosity.b Predicted effective diffusivity coefficient over a month timeframe

194 Page 12 of 14 AAPS PharmSciTech (2020) 21: 194

pool them in various combinations in the digital imagingspace. Transport properties, such as permeability, effectivediffusivity coefficient, and porosimetry, can be computeddirectly from image data. These properties are difficult, ifnot impossible, to measure using a physical laboratoryapproach. All properties have been determined from thesame sample, which is not possible in physical laboratorytesting. For lab-based tests, specimens of the same batch mustbe prepared for each and every test. Sample-to-samplemeasurement uncertainty is thus increased. The accumulatedamount of sample required is much higher, which furtherincreases the difficulty and measurement cost.

The microstructures of pharmaceutical samples recon-structed via imaging facilitate an unprecedented investiga-tion of release mechanisms and what-if scenarios. Usingthe image-based characterization framework, the porosity impacton release has been systematically studied. The quantification andclassification of porosity provides a direct evaluation of its variousimpacts on the percolation network, permeability, effective diffu-sivity, and release profiles. Microporosity in the studied PLGAmicrosphere samples has been found to have a significant impacton predicted permeability, increasing this parameter by more thanan order of magnitude. When the porosity is suppressed, therelease of 50% of drug has been extended from 3 days to almost12 days. In actual formulation and process optimization, this totallypore-free long release would never be achievable. However, theimage-based simulation framework predicts what the longestpossible release could be. The end-member scenarios provideimportant guidance to the optimization effort in process engineer-ing. The in vitro test in this study is limited to a release interval ofonly 3 days, restricting evaluation of the controlled releaseperformance and characteristics at late time. In comparison, latestage release profile was obtained with little extra cost using theimage-based release modeling approach, reducing cost and timeassociated with long-lasting in vitro tests.

As with any emerging analytical method, challengesand limitations should be noted. Due to the high-resolution FIB-SEM provides, the size of the sample that

can be studied is limited to 50–100 μm in each dimension.As a feasibility study, this project selected severalmicrospheres from each lot of sample, then visuallyverified consistency in API and porosity microstructuresusing 2D FIB-SEM cross-sections. 3D FIB-SEM andsubsequent analysis were completed on one microspheresample that was considered to be representative. Due tothe limited number of samples studied, the authorscaution that any results reported in the paper should beconsidered suggestive rather than conclusive. For a morecomprehensive study, it is important to consider a designof experiment that ensures sample representativeness.Correlative imaging in conjunction with a multi-scaleapproach is often needed to confirm microsphere tomicrosphere uniformity. The large data size and intensivenature of computation from both AI image processing anddirect numerical simulations increase the demand oncomputing infrastructure. A cloud framework with flexibleand accessible browser user interface and a parallelizedbackend computing library accelerated by graphics pro-cessing unit offers the necessary scalability.

The simulation method based on percolation theory islimited to the release prediction of formulations which havedrug loadings high enough to meet or exceed percolationthreshold, i.e., the API domain along with the pre-existingporosity forms an interconnected network. When drugloading is low, the API release mechanism changes funda-mentally; hence, the polymer excipient matrix behavior hasto be considered. For microsphere formulations usingbiodegradable polymers such as PLGA, the most challeng-ing part in drug release characterization is the dynamicchanges of internal structure over the release period. Thesmall domain size and microstructure further complicate theprocess. A continuous monitoring of the microstructureover the release period will provide additional insight tounderstand and predict the release, particularly at laterstages after the initial release burst dominated by APIdissolution. Additional factors impacting drug release, suchthe interaction between polymer and API, as well aswettability, require further study.

Due to the unavailability of in vitro release profile, it wasnot possible to validate the full release profile predicted bythe model against experimental data at the time of preparingthis manuscript. Since only initial release rate was available,the predicted release profile was converted to the corre-sponding release rate, which matched the experimentalrelease rate within the standard deviation of the experimentaldata. Although, as discussed previously, the prediction wasdone on only one microsphere sample, efforts had been madeto ensure representativeness to the author’s best capacity atthe time of study. Some sample to sample variability isexpected and to be quantified. Therefore, the preliminaryrelease simulation result reported in this report should beconsidered as a proof of concept. Continuous effort onvalidation of the predicted transport parameters and releaseprofile is ongoing with several other long-acting drugdevelopment projects including microspheres and implants.This is to further address sample to sample variability thecomparison between simulated data and in vitro measure-ments, the impact of polymer degradation through imaging

Fig. 11. Visualization of partial API release at T 0 5 days, where 40%of API was released. The T0 sample of pre-release API phasecorresponded to Fig. 7c.

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the microsphere samples before and after the dissolution test(25), and the potential correlation expansion to in vivoprediction.

The method reported in this paper can be applied toother controlled release systems including implants, depots,semi-solid formulations, and combination products. Image-based analytics and simulations provide a new set of tools fordrug delivery system characterization, design, andmanufacturing. The prediction of drug release can provideguidance for formulation design and development veryefficiently, which consequently reduces the cost and time forproduct development and manufacturing.

ACKNOWLEDGMENTS

The authors acknowledge the constructive feedbackfrom the reviewers and editors, and the editorial assistanceof Mr. Josh Lomeo from DigiM Solution LLC.

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Characterization of Controlled Release Microspheres Using FIB-SEM and Image-Based Release PredictionAbstractINTRODUCTIONMETHODSMaterialsLaser DiffractionInvitro Dissolution StudyFocused Ion Beam&newnbsp; - Scanning Electron Microscope (FIB-SEM)Energy Dispersion X-Ray Spectroscopy (EDS)Image AnalyticsImage-Based Pressure-Driven Permeability SimulationImage-Based Release Profile PredictionPercolation Simulation ModelDirection Numerical Simulation Model for Effective Diffusivity CoefficientDrug Release Model

RESULTSDISCUSSIONS AND CONCLUDING REMARKSReferences