Particle morphology refers to the external form, internal structure, and surface characteristics of individual particles within a pharmaceutical powder [22]-[24]. It encompasses multiple dimensions: particle size (expressed as equivalent circular diameter, Feret diameter, or Martin diameter), particle shape (described by parameters such as aspect ratio, circularity, convexity, and elongation), surface texture (roughness, porosity), and crystallographic structure (polymorphic form, degree of crystallinity) [25]. For pharmaceutical applications, the term “particle morphology” is generally used in a broader context than simple particle size, acknowledging that shape and surface properties exert independent and often synergistic effects on material behavior [26]. The various particle morphologies commonly encountered in pharmaceutical powders are depicted in Figure 1.

Figure 1. Common particle morphologies encountered in pharmaceutical powders. Each morphology exhibits distinct physicochemical and processing behavior.

For spherical particles, size is uniquely defined by diameter. However, pharmaceutical particles are rarely spherical, necessitating the use of equivalent diameter concepts [27]. Common size descriptors include: the equivalent volume diameter (dv), the equivalent surface area diameter (ds), the equivalent projected area diameter (EQPC), and Feret diameters (maximum and minimum distances between parallel tangent lines) [3]. The choice of size descriptor can significantly affect reported particle size distributions, as the same particle may yield different equivalent diameters depending on the measurement principle employed [28].

Particle size distributions (PSDs) are commonly expressed using percentile values (d10, d50 [median], and d90), along with span values [(d90 − d10)/d50] to characterize distribution width [29]. The polydispersity index (PDI), particularly used for nanoparticle systems, provides a dimensionless measure of the breadth of the size distribution, with values below 0.2 indicating monodisperse systems [30].

Particle shape profoundly influences powder behavior, yet remains more challenging to characterize than size. Standardized shape descriptors have been developed to enable quantitative comparison [3][31].

The aspect ratio ( AR = Length Width ) is defined as the ratio of the maximum Feret diameter to the minimum Feret diameter, providing a measure of particle elongation [3]. Values approaching 1.0 indicate equant (spherical/cubic) shapes, while higher values indicate increasing elongation. For pharmaceutical pellets, an upper limit of 1.1 on the aspect ratio has been recommended to ensure acceptable sphericity [31]. Circularity ( A = 4 π A P 2 , where A is the projected area, and P is the perimeter) measures the deviation from a perfect circle, with C = 1.0 for a perfect sphere [3][32]-[34]. Convexity ( C X = A A h u l l , Where A is the real projected area of the particle and total projected area of its convex hull) describes the ratio of particle area to convex hull area, indicating the presence of concavities. Elongation ( E = 1 − Width / Length ) quantifies the degree of stretching from a circular form [35]-[37]. Figure 2 illustrates particle shape descriptors, including aspect ratio, circularity, convexity, and elongation.

Figure 2. Key particle shape descriptors used in pharmaceutical characterization.

5.1.1. Optical Microscopy

Optical microscopy remains a fundamental tool for pharmaceutical particle characterization, offering direct visualization of particle size, shape, and surface features at magnifications up to ~1000× [42]. Polarized light microscopy enables the identification of crystalline particles by their birefringence properties, providing a simple method for distinguishing polymorphic forms [43]. The ICH Q6A guideline and pharmacopeial methods (USP <776>, Ph. Eur. 2.9.37) recognize optical microscopy as a primary method for particle size determination [18][44]. However, the technique is limited by its optical resolution (~0.5 µm) and the relatively small number of particles that can be examined in practice.

5.1.2. Scanning Electron Microscopy (SEM)

SEM provides high-resolution images of particle surfaces at magnifications from ~20× to >300,000×, revealing detailed morphological features including surface roughness, crystal faces, and agglomeration patterns [38][45]. The technique is particularly valuable for examining particles in the 10 nm to 1 mm range and is considered essential for comprehensive morphological characterization of pharmaceutical powders [46]. Energy-dispersive X-ray spectroscopy (EDX) coupled with SEM enables simultaneous analysis of chemical composition [47]. SEM has been widely used to characterize API crystal habits, assess milling effects, evaluate coating uniformity in microparticles, and verify nanoparticle morphology [48][49].

5.1.3. Transmission Electron Microscopy (TEM)

TEM is considered the “gold standard” technique for nanoparticle sizing and morphology characterization [38]. Operating at accelerating voltages of 80 - 300 kV, TEM can achieve atomic-scale resolution (<0.2 nm), providing detailed information on particle size, shape, internal structure, and crystallographic orientation [50]. For pharmaceutical nanoparticles, TEM images should be accompanied by size distribution histograms generated from at least 200 particles for average size determinations and >3000 particles for distribution width measurements [51]. High-resolution TEM (HR-TEM) enables the observation of crystal lattice fringes, confirming crystalline structures in nanoparticulate drug formulations [52].

5.1.4. Atomic Force Microscopy (AFM)

AFM provides three-dimensional surface topography at sub-nanometer vertical resolution by scanning a sharp probe across the particle surface [53]. Unlike electron microscopy, AFM operates under ambient conditions without requiring vacuum or conductive coating, making it particularly suitable for moisture-sensitive pharmaceutical materials [54]. The technique excels at measuring surface roughness, adhesion forces, and mechanical properties at the single-particle level. In pharmaceutical applications, AFM has been used to characterize API crystal-face properties, assess surface-energy heterogeneity, and study particle-particle interactions relevant to powder flow and agglomeration [55][56].

5.2.1. Laser Diffraction

Laser diffraction is the most widely used technique for particle size analysis in the pharmaceutical industry, covering a size range from ~0.01 to 3500 µm [1][57][58]. The technique measures the angular distribution of scattered light from a particle ensemble and uses Mie theory or the Fraunhofer approximation to calculate a volume-weighted particle size distribution. While offering excellent throughput and reproducibility, laser diffraction inherently assumes spherical-particle equivalence, providing no direct information about particle shape [59]. Pharmacopeial methods (USP <429>, Ph.Eur. 2.9.31) provide detailed guidance on method development and validation for laser diffraction measurements [60].

5.2.2. Dynamic Light Scattering (DLS)

DLS measures the hydrodynamic diameter of particles in suspension by analyzing the autocorrelation function of fluctuations in scattered light caused by Brownian motion [61]. The technique is particularly valuable for characterizing nanoparticles in the 0.3 nm to 10 µm range and is routinely used for quality control of pharmaceutical nanosuspensions, liposomes, and protein aggregates [62]. DLS provides the z-average diameter (intensity-weighted mean) and polydispersity index (PDI). However, the technique is sensitive to large particles or aggregates and does not provide shape information [63].

5.2.3. X-Ray Powder Diffraction (XRPD)

XRPD is a key solid-state analytical technique for identifying crystalline forms and assessing crystalline/amorphous phase composition in pharmaceutical solids [64]. While XRPD is not a direct particle-sizing method, it provides critical information on polymorphic form, crystallinity, and crystal structure, which may be associated with crystal habit and particle morphology. Preferred orientation effects in XRPD patterns can arise from anisotropic particle or crystal shapes, such as needle-like or plate-like morphologies, and may indicate non-random orientation of crystallites during sample preparation [65][66]. The Scherrer equation enables estimation of crystallite size from diffraction peak broadening, linking bulk diffraction measurements to nanoscale crystalline-domain dimensions rather than true particle size [67][68].

Recent advances have led to analytical tools that combine physical and chemical characterization in a single system. For example, morphologically directed Raman spectroscopy (MDRS) combines automated particle imaging with Raman microspectroscopy, enabling measurement of particle size and shape, along with Raman-based chemical identification of individual particles [69][70]. This approach has been applied to characterize API particle-size distribution in nasal suspension drug products and to support pharmaceutical product characterization. In particulate powder systems, combined automated image analysis and Raman spectroscopy can also classify particles at the individual-particle level based on both morphology and solid-form or chemical information.

Raman chemical imaging can chemically distinguish different ingredients in finished pharmaceutical products while simultaneously providing spatial information on particle distribution, size, and shape. For example, fiber-array-based Raman hyperspectral imaging has been used to monitor the size, shape, and distribution of particles of acetylsalicylic acid, acetaminophen, and caffeine in commercial analgesic tablets at a high spatial resolution of approximately 1 - 1.25 µm [71]. Raman chemical imaging has also been validated for estimating micrometer-scale API particle size in tablets using traceable polystyrene microsphere standards [41].

Process analytical technology (PAT) tools, such as focused beam reflectance measurement (FBRM) and particle vision measurement/in situ video microscopy (PVM), enable real-time or in-line monitoring of particle size, size distribution, and particle-shape-related attributes during pharmaceutical crystallization and particle-processing operations [72]-[74].

Deep learning image analysis is now used to monitor crystal type, size, and shape during crystallization [75]. However, widespread adoption of these advanced methods can be difficult due to high costs, the challenge of integrating new technology into existing processes, the need for expert staff, and a lack of clear regulatory guidance. Regulatory acceptance of advanced and AI-driven techniques is evolving. While traditional PAT tools such as laser diffraction, FBRM, and certain forms of spectroscopy have been recognized by regulatory authorities within Quality by Design frameworks, newer technologies like automated image analysis and deep learning models are still considered emerging. Current regulatory positions emphasize the need for robust method validation, clear demonstration of data integrity, and scientific justification when proposing novel techniques in regulatory filings. Some agencies have published draft guidance or concept papers acknowledging the potential of AI and advanced analytics, but formal, harmonized guidance is limited. Sponsors considering adopting these technologies should engage regulators early, document performance characteristics, and be prepared to address questions about model transparency and reproducibility. As regulatory experience grows, more explicit acceptance criteria and standards are expected to emerge.

A structured, phased approach supports the effective adoption of advanced particle morphology techniques within organizations. Initiating pilot studies in development laboratories can demonstrate the method’s benefits and assess its suitability. Cross-functional collaboration is essential for evaluating the compatibility of new technologies with existing workflows and determining optimal implementation points. For instance, limited-scale trials of advanced image analysis or parallel testing of morphologically directed Raman spectroscopy (MDRS) alongside current release methods can mitigate risk and facilitate staff training. Support and training from equipment suppliers further enhance implementation. Gradual, systematic deployment across products or sites enables continuous improvement and efficient resource allocation. Comprehensive documentation of feasibility assessments, performance metrics, and regulatory feedback underpins sustainable adoption. These strategies enable organizations to realize the advantages of advanced particle morphology analysis while minimizing operational disruptions.

When preparing regulatory submissions that include advanced particle morphology techniques, some practical steps can help ensure strong documentation and smooth interactions with agencies. Clearly document validation experiments for each measurement method, covering specificity, accuracy, precision, range, and robustness. Give detailed descriptions of calibration procedures, standard operating protocols, sample preparation, and instrument qualification. Include examples of typical results, system suitability criteria, and representative images if needed. Explain how the data are used to set specifications or support control strategies for critical quality attributes. It also helps to prepare answers in advance for possible agency questions, such as why certain characterization techniques were chosen or how data variability is handled. Keep well-organized records of all method changes, references to regulatory guidelines, and direct communications with agencies about method acceptance. This level of detail can make the review process smoother and help gain regulatory approval for new analytical methods.

The relationship between particle size and dissolution rate is described by the Noyes–Whitney equation:

where M is the mass dissolved, D is the diffusion coefficient, Ais the surface area, C_s is the saturation solubility, C_t is the concentration at time t, and h is the diffusion layer thickness [14]. Reducing particle size increases surface area (A), directly enhancing dissolution rate. For BCS Class II drugs, this relationship is of paramount importance [13][17]. Figure 5 shows simulated dissolution profiles demonstrating how decreasing particle size accelerates drug release, consistent with the Noyes-Whitney relationship.

Reduction in particle size consistently enhances dissolution performance and systemic exposure, particularly for poorly soluble drugs. Investigations involving aprepitant/MK-0869, rosuvastatin calcium, and esomeprazole formulations demonstrate that smaller particle size increases dissolution rate and pharmacokinetic exposure, as evidenced by higher Cmax and/or AUC values [76]-[78]. Therefore, particle size should be considered a critical material attribute when experimental data establish its direct impact on dissolution, absorption, or bioavailability for a specific drug product.

Figure 5. Simulated dissolution profiles showing the effect of particle size on drug release rate based on the Noyes–Whitney equation. Nanoparticles (<1 µm) exhibit the fastest dissolution, followed by micronized (1 - 5 µm), fine (5 - 50 µm), and coarse (>50 µm) particles. Data patterns are consistent with published findings [14][79].

Beyond size, particle shape influences dissolution through differential exposure of surface area. Rod-shaped particles of dipyridamole exhibited different dissolution rates compared to prismatic or tabular habits of the same compound [18]. Crystal morphology engineering has been shown to modify the exposed crystal faces, altering surface energy and wettability and, consequently, affecting dissolution kinetics [80].

Nanoparticle shape has emerged as a significant design parameter for drug delivery systems [81]. Studies by Banerjee et al. demonstrated that rod-shaped nanoparticles exhibit significantly higher cellular uptake and trans-epithelial transport in intestinal cell models than spherical particles of similar volume [82]. Nanoparticles smaller than 200 nm can traverse the intestinal mucus bilayer (pore size 10 - 200 nm) and be absorbed via endocytic, transcellular, and paracellular pathways [83]-[86]. Table 2 summarizes representative examples of how particle size affects dissolution and bioavailability.

Particle morphology is a primary determinant of powder flow behavior, which directly impacts manufacturing operations, including blending, die filling, and tablet compression [96][97]. Spherical particles generally exhibit superior flow properties compared to irregular or acicular particles due to reduced inter-particle mechanical interlocking [26]. The Carr Index and Hausner Ratio serve as empirical indicators of flow behavior but are influenced by both size and shape [98]. Different particle engineering routes can produce micron-sized API with similar size specifications but dramatically different bulk powder properties due to variations in particle shape, size distribution, and surface energetics [99][100].

Table 2. Summary of particle size effects on bioavailability and dissolution for selected pharmaceutical systems.

To assist formulators in diagnosing and resolving powder-flow problems related to particle morphology, the following three-step checklist is recommended:

Step 1: Assess particle shape (Aspect Ratio ≥ 1.5 or Circularity < 0.7)

Acicular (needle-like) or elongated particles mechanically interlock during flow, increasing the Carr Index above 25% and the Hausner Ratio above 1.35, indicating poor-to-very-poor flow (Carr, 1965). In blending and die-filling operations, this results in non-uniform die fill and weight variability in tablet presses. Corrective action: Consider milling to reduce the aspect ratio, or switch to a bottom-up particle-engineering technique (e.g., spray drying or controlled crystallization) that produces more equant (spherical) habits.

Step 2: Evaluate surface roughness and fines content (d10 < 10 µm)

High surface roughness and an elevated proportion of fine particles (<10 µm) increase inter-particle adhesive forces, leading to powder bridging, rat-holing in hoppers, and erratic feed rates. Micromeritics (2024) demonstrated that differences in particle shape were the dominant factor in Basic flowability energy (BFE) under low-stress blending conditions, while particle size dominated under consolidation (high-stress tablet pressing). Corrective action: Granulation, dry coating with colloidal silica (0.1 - 0.5% w/w), or blending with a spherical carrier can effectively reduce cohesion without altering particle size distribution.

Step 3: Confirm flow by pharmacopoeial methods before specification-setting

Measure the Angle of Repose (<25˚ = excellent flow; 26˚ - 30˚ = good; >40˚ = very poor), Carr Index, and Hausner Ratio. Where possible, supplement with a powder rheometer (BFE/specific energy ratio) to decouple the contributions of size versus shape to the overall flow behavior. Document which morphological parameter is the primary driver to enable targeted corrective action.

Crystal morphology engineering has been shown to be an effective tool for tailoring tablet performance [80]. Mirza et al. showed that modifying the crystal habit of erythromycin A dihydrate by adding hydroxypropyl cellulose during crystallization resulted in morphologies with significantly different compaction behavior [80]. Needle-shaped crystals tend to exhibit preferential orientation under compression, potentially leading to anisotropic tablet structures [101].

For inhaled drug delivery, particle morphology is arguably the most critical attribute determining therapeutic performance. The aerodynamic diameter, which depends on both geometric diameter and particle density, governs deposition in the respiratory tract [102]. Stokes’ law predicts the settling velocity of particles in air, and deviations from spherical shape require the application of dynamic shape factors [103]. Engineered low-density particles, porous particles, and elongated particles have been developed to optimize the aerodynamic diameter-geometric size relationship [104].

Table 3 outlines key quality attributes that are influenced by particle morphology, including dissolution, bioavailability, powder flow, compressibility, content uniformity, and aerodynamic performance

7.1.1. Mechanical Milling and Micronization

Jet milling, also known as air-jet or fluid-energy micronization, is a widely used pharmaceutical particle-size reduction technique for producing micronized drug powders. Fluid-energy milling can reduce drug particles from approximately 20 - 100 µm to below 10 µm, while jet milling for inhalation products commonly produces particles in the 1 - 20 µm range, with typical API particle sizes around 2 - 5 µm depending on the product target and material properties [105]-[108]. Wet media milling with zirconium oxide beads has been successfully applied to produce nanosuspensions with particle sizes below 300 nm while maintaining crystalline integrity [94].

7.1.2. High-Pressure Homogenization

High-pressure homogenization subjects particle suspensions to extreme shear, cavitation, and impact forces at pressures of 100 - 2000 bar [109]. The technique is particularly effective for producing drug nanocrystals and nanosuspensions of poorly soluble APIs [110].

7.2.1. Controlled Crystallization

Crystallization is the most critical unit operation in API manufacturing, accounting for approximately 90% of API production [111]. Crystal habit modification through additive-mediated crystallization offers a powerful approach to tailoring particle morphology. For example, hydroxypropyl cellulose has been used to modify the crystal habit of erythromycin A dihydrate [80]. Sonocrystallization, employing ultrasonic energy to control nucleation and growth, produces smaller crystals with narrower size distributions [55].

7.2.2. Spray Drying

Spray drying converts drug-containing liquids into dried powdered forms with engineered morphologies [112]. Nano spray drying extends the accessible particle size range to the submicron and nanoscale. Spray drying is particularly effective for producing amorphous solid dispersions (ASDs) [113].

7.2.3. Supercritical Fluid Processing

Supercritical fluid techniques, particularly using supercritical CO₂, offer an environmentally friendly approach to particle engineering [114]. The supercritical antisolvent (SAS) process, rapid expansion of supercritical solutions (RESS), and gas antisolvent (GAS) methods enable production of micro- and nanoparticles with controlled morphology and minimal residual solvent [114][115].

7.2.4. Emulsion Solvent Evaporation

The emulsion solvent evaporation method is a widely utilized bottom-up particle engineering technique for producing biodegradable polymeric micro- and nanoparticles from preformed polymers such as PLGA and PLA [116]. In the single oil-in-water (O/W) emulsion variant, a hydrophobic drug and polymer are dissolved in a volatile organic solvent, then emulsified into an aqueous phase stabilized by polyvinyl alcohol (PVA). The solvent is subsequently removed by evaporation, yielding smooth, spherical particles [117]. For hydrophilic drugs, the water-in-oil-in-water (W/O/W) double emulsion variant produces core-shell morphologies; however, encapsulation efficiency is generally lower due to drug partitioning into the external aqueous phase [118]. Particle size and morphology depend on homogenization energy, polymer concentration, surfactant concentration, and solvent evaporation rate [119]. A comparison of major particle engineering techniques and their corresponding pharmaceutical applications is presented in Table 4.

Table 4. Comparison of particle engineering techniques for pharmaceutical applications.

A practical control strategy for particle morphology may be organized into five elements. First, the development program should identify which morphological features are likely to influence product quality for the intended route of administration and formulation platform. Second, fit-for-purpose analytical methods should be selected to quantify those features with sufficient sensitivity, robustness, and relevance. Third, acceptable ranges or specifications should be defined based on development data, process capability, and clinical or biopharmaceutical relevance. Fourth, manufacturing processes should be designed and monitored so that morphology remains within the intended design space; this may include control of crystallization conditions, milling energy, homogenization pressure, spray-drying parameters, or granulation conditions. Fifth, ongoing lifecycle management should assess whether morphology remains under control during scale-up, site transfer, and post-approval changes.

Depending on the product, control can be achieved through different combinations of material controls and process controls. For example, direct-compression products may place greater emphasis on particle shape and flow-related descriptors, whereas poorly soluble immediate-release products may prioritize particle size distribution and surface area because these factors influence dissolution. In inhalation products, control strategies may need to extend beyond geometric particle size to include density, tendency to agglomerate, and aerodynamic behavior. This risk-based flexibility is important because the same morphological descriptor may be highly critical in one product type and only supportive in another.

ICH Q6A provides the foundational guidance for particle size specifications [19]. Decision Tree #3 in Q6A systematically guides the assessment of whether particle-size testing should be included in drug-substance specifications. ICH Q8(R2) encourages a QbD approach to identifying and controlling material attributes that impact drug product quality [9]. ICH Q9 (Quality Risk Management) provides tools for assessing and managing risks associated with particle-related quality attributes [10]. ICH Q10 emphasizes ongoing monitoring of particle-related controls throughout the product lifecycle [21]. ICH Q12 enables post-approval changes to particle specifications when supported by scientific understanding [121].

From a regulatory perspective, the control of particle morphology should be justified through a clear chain of evidence linking the selected morphological attribute to product CQAs and process performance. In practice, this means documenting why a given attribute is considered critical, how it is measured, what acceptance criteria are proposed, and how manufacturing controls maintain it within an acceptable range. When particle morphology is treated as a CMA, regulatory submissions are strengthened by demonstrating traceability from development studies through method selection, specification setting, process controls, and lifecycle monitoring. Such an approach is consistent with contemporary expectations for science- and risk-based pharmaceutical development.

Relevant regulatory guidelines and pharmacopeial standards for particle size and morphology are summarized in Table 5.

Table 5. Regulatory guidelines and pharmacopeial standards relevant to particle morphology.

Mirza et al. investigated the engineering of crystal morphology in erythromycin A dihydrate to tailor tableting performance [80]. Crystal habit modification was achieved by adding hydroxypropyl cellulose (HPC) during crystallization from solution. The predicted BFDH morphology was compared with the observed morphologies, and molecular simulations were used to analyze the arrangements on the dominant crystal faces [(002), (011), and (101)]. The study demonstrated that additive-mediated crystallization can selectively modify crystal habit for direct compression tableting.

A notable case study demonstrated the power of combined image analysis and Raman spectroscopy for process troubleshooting. During the manufacture of a solid dosage form, the HPLC assay showed API content in tablets was occasionally lower than in the precompression blend. Morphologically directed Raman spectroscopy revealed that API particles exhibited a distinct spherical morphology, unlike excipients, thereby enabling process optimization to minimize API loss during equipment start-up [68][69].

Piroxicam, a poorly soluble drug, has been investigated using multiple formulations and solid-state strategies, including particle-size control, salt formation, co-crystallization, and amorphous/polymeric solid dispersion. Particle-size and agglomeration studies showed that dissolution behavior of piroxicam powders and capsules can vary significantly even when the polymorphic form is unchanged, demonstrating that particle size, wetting, and agglomeration are important practical factors in dissolution performance [122]. Salt formation with ethanolamines has been reported to improve piroxicam bioavailability, while piroxicam co-crystals with carboxylic acid and phenolic conformers have been prepared and evaluated for solid-state properties, solubility, intrinsic dissolution rate, powder dissolution, and phase behavior [90]-[92]. PVP-based solid dispersions of piroxicam have also shown substantial dissolution enhancement, including solvent-prepared PVP K-30 dispersions and spray-dried or compressed-antisolvent piroxicam–PVP systems [123][124]. Taken together, these studies indicate that formulation strategy selection should not assume that advanced crystal engineering is always superior; instead, development should begin with comparative screening of simpler approaches such as particle-size control and proceed to salt formation, co-crystallization, or amorphous solid dispersion only when needed based on dissolution performance, physical stability, manufacturability, scalability, analytical control, regulatory expectations, intellectual-property position, and commercial feasibility. FDA guidance also supports the need for appropriate specification setting for particle-size control and additional classification/characterization considerations for pharmaceutical co-crystals. Piroxicam has been reported to form a succinic acid co-crystal, including an acetonitrile-solvated piroxicam–succinic acid co-crystal containing both zwitterionic and non-ionized piroxicam molecules [125].

Banerjee et al. systematically investigated the role of nanoparticle geometry in oral drug delivery using sphere-, rod-, and disc-shaped PLGA nanoparticles [82]. Using a triple co-culture model of intestinal cells (Caco-2/HT-29), the study demonstrated that rod-shaped nanoparticles exhibited significantly higher cellular uptake and transepithelial transport compared to spheres, regardless of the presence of active targeting moieties [82].

Deep learning-based image analysis methods have been developed for real-time, in situ monitoring of crystal polymorphs, size, and shape during solution crystallization [75]. Machine vision, combined with deep learning, enables non-invasive online monitoring of granule particle morphology during fluidized-bed granulation [126]. Data-driven frameworks for predicting pharmaceutical particle size distributions using kernel mean embedding methods show promise for model predictive control [127].

Continuous manufacturing requires real-time monitoring and control of particle attributes [128]. PAT frameworks incorporating in-line laser diffraction, FBRM, and machine vision enable feedback control of particle size in continuous processes [129]. Model-based control strategies represent the convergence of QbD and PAT principles [72]-[74].

Current characterization methods predominantly generate 2D projections of 3D particles. Advances in X-ray micro-computed tomography (µCT), electron tomography, and confocal microscopy are enabling true three-dimensional characterization of pharmaceutical particles [130].

Advances in 3D printing, microfluidics, and small-scale continuous processing are enabling on-demand manufacturing of personalized dosage forms with tailored particle attributes [131]. The development of compact, rapid-response analytical tools for particle characterization will be essential for enabling decentralized manufacturing models [132].

While the evidence reviewed here supports the utility of particle morphology characterization in pharmaceutical development, several important limitations affect the strength of the practical recommendations presented. First, morphology descriptors are not yet harmonized across laboratories and instrument platforms: aspect ratio, circularity, and convexity values obtained on one imaging system are often not directly comparable with those from another, limiting inter-study interpretation [ISO 9276-6:2008]. Second, the majority of characterization data in the published literature relies on 2D projected images—from optical microscopy, SEM, and automated image analysis—which cannot fully capture the three-dimensional geometry of irregular particles; this introduces systematic biases in shape parameter estimates, particularly for elongated or porous forms. Third, most bioavailability and dissolution studies demonstrate that the impact of particle morphology involves small numbers of formulations, single-drug compounds, or animal models; cross-study comparability is limited by differences in particle-size measurement methods, dissolution apparatus conditions, and animal species. Fourth, the case studies cited in Sections 9.1 - 9.4 are illustrative examples from specific compounds and should not be generalized without product-specific risk assessment. These constraints underscore the need for standardized shape descriptors, harmonized reporting practices, and adequately powered comparative studies before universal quantitative thresholds for particle morphology specifications can be recommended [133].