Structure, morphology and surface properties of α-lactose monohydrate in relation to its powder properties

The particulate properties of α-lactose monohydrate (αLMH), an excipient and carrier for pharmaceuticals, is important for the design, formulation and performance of a wide range of drug products. Here an integrated multi-scale workflow provides a detailed molecular and inter-molecular (synthonic) analysis of its crystal morphology, surface chemistry and surface energy. Predicted morphologies are validated in 3D through X-ray diffraction (XCT) contrast tomography. Interestingly, from aqueous solution the fastest growth is found to lie along the b-axis, i.e. the longest unit cell dimension of the αLMH crystal structure reflecting the greater opportunities for solvation on the prism compared to the capping faces leading to the former's slower relative growth rates. The tomahawk morphology reflects the presence of β-lactose which asymmetrically binds to the capping surfaces creating a polar morphology. The crystal lattice energy is dominated by van der Waals interactions (between lactose molecules) with electrostatic interactions contributing the remainder. Predicted total surface energies are in good agreement with those measured at high surface coverage by inverse gas chromatography, albeit their dispersive contributions are found to be higher than those measured. The calculated surface energies of crystal habit surfaces are not found to be significantly different between different crystal surfaces, consistent with αLMH's known homogeneous binding to drug molecules when formulated. Surface energies for different morphologies reveals that crystals with the elongated crystal morphologies have lower surface energies compared to those with a triangular or tomahawk morphologies, correlating well with literature data that the surface energies of the lactose carriers are inversely proportional to their aerosol dispersion performance.

Surface Analysis-From Crystal Structures to Particle Properties

Understanding the surface properties of particles is crucial for optimizing the performance of formulated products in various industries. However, acquiring this understanding often requires expensive trial-and-error studies. Here, we present advanced surface analysis tools that enable the visualization and quantification of chemical and topological information derived from crystallographic data. By employing functional group analysis, roughness calculations, and statistical interaction data, we facilitate direct comparisons of surfaces. We further demonstrate the practicality of our approach by correlating the sticking propensity of distinct ibuprofen morphologies with surface and particle descriptors calculated from a single crystal structure. Our findings support and expand upon previous work, demonstrating that the presence of a carboxylic acid group on the {011} facet leads to significant differences in particle properties and explains the higher electrostatic potential observed in the block-like morphology. While our surface analysis tools are not intended to replace the importance of chemical intuition and expertise, they provide valuable insights for formulators and particle engineers, facilitating informed, data-driven decisions to mitigate formulation risks. This research represents a significant step toward a comprehensive understanding of particle surfaces and their impact on products.

Predicting the Strength of Cohesive and Adhesive Interparticle Interactions for Dry Powder Inhalation Blends of Terbutaline Sulfate with α-Lactose Monohydrate

Grid-based systematic search methods are used to investigate molecule-molecule, molecule-surface, and surface-surface contributions to interparticle interactions in order to identify the crystal faces that most strongly affect particle behavior during powder blend formulation and delivery processes. The model system comprises terbutaline sulfate (TBS) as an active pharmaceutical ingredient (API) and α-form lactose monohydrate (LMH). A combination of systematic molecular modeling and X-ray computed tomography (XCT) is used to determine not only the adhesive and cohesive interparticle energies but, also the agglomeration behavior during manufacturing and de-agglomeration behavior during delivery after inhalation. This is achieved through a detailed examination of the balance between the adhesive and cohesive energies with the XCT results confirming the blend segregation tendencies, through the particle-particle de-agglomeration process. The results reveal that the cohesive interaction energies of TBS-TBS are higher than the adhesive energies between TBS and LMH, but that the cohesive energies of LMH-LMH are the smallest between molecule and molecule, molecule and surface, and surface and surface. This shows how systematic grid-search molecular modeling along with XCT can guide the digital formulation design of inhalation powders in order to achieve optimum aerosolization and efficacy for inhaled medicines. This will lead to faster pharmaceutical design with less variability, higher quality, and enhanced performance.

Microstructural insight into inhalation powder blends through correlative multi-scale X-ray computed tomography

Dry powder inhalers (DPI) are important for topical drug delivery to the lungs, but characterising the pre-aerosolised powder microstructure is a key initial step in understanding the post-aerosolised blend performance. In this work, we characterise the pre-aerosolised 3D microstructure of an inhalation blend using correlative multi-scale X-ray Computed Tomography (XCT), identifying lactose and drug-rich phases at multiple length scales on the same sample. The drug-rich phase distribution across the sample is shown to be homogeneous on a bulk scale but heterogeneous on a particulate scale, with individual clusters containing different amounts of drug-rich phase, and different parts of a carrier particle coated with different amounts of drug-rich phase. Simple scalings of the drug-rich phase thickness with carrier particle size are used to derive the drug-proportion to carrier particle size relationship. This work opens new doors to micro-structural assessment of inhalation powders that could be invaluable for bioequivalence assessment of dry powder inhalers.

Development of a Digital Twin for the Prediction and Control of Supersaturation during Batch Cooling Crystallization

Fine chemicals produced via batch crystallization with properties dependent on the crystal size distribution require precise control of supersaturation, which drives the evolution of crystal size over time. Model predictive control (MPC) of supersaturation using a mechanistic model to represent the behavior of a crystallization process requires less experimental time and resources compared with fully empirical model-based control methods. Experimental characterization of the hexamine-ethanol crystallization system was performed in order to collect the parameters required to build a one-dimensional (1D) population balance model (PBM) in gPROMS FormulatedProducts software (Siemens-PSE Ltd.). Analysis of the metastable zone width (MSZW) and a series of seeded batch cooling crystallizations informed the suitable process conditions selected for supersaturation control experiments. The gPROMS model was integrated with the control software PharmaMV (Perceptive Engineering Ltd.) to create a digital twin of the crystallizer. Simulated batch crystallizations were used to train two statistical MPC blocks, allowing for in silico supersaturation control simulations to develop an effective control strategy. In the supersaturation set-point range of 0.012-0.036, the digital twin displayed excellent performance that would require minimal controller tuning to steady out any instabilities. The MPC strategy was implemented on a physical 500 mL crystallizer, with the simulated solution concentration replaced by in situ measurements from calibrated attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. Physical supersaturation control performance was slightly more unstable than the in silico tests, which is consistent with expected disturbances to the heat transfer, which were not specifically modeled in simulations. Overall, the level of supersaturation control in a real crystallizer was found to be accurate and precise enough to consider future adaptations to the MPC strategy for more advanced control objectives, such as the crystal size.

A Review of Particle Size Analysis with X-ray CT

Particle size and morphology analysis is a problem common to a wide range of applications, including additive manufacturing, geological and agricultural materials' characterisation, food manufacturing and pharmaceuticals. Here, we review the use of microfocus X-ray computed tomography (X-ray CT) for particle analysis. We give an overview of different sample preparation methods, image processing protocols, the morphology parameters that can be determined, and types of materials that are suitable for analysis of particle sizes using X-ray CT. The main conclusion is that size and shape parameters can be determined for particles larger than approximately 2 to 3 μm, given adequate resolution of the X-ray CT setup. Particles composed of high atomic number materials (Z > 40) require careful sample preparation to ensure X-ray transmission. Problems occur when particles with a broad range of sizes are closely packed together, or when particles are fused (sintered or cemented). The use of X-ray CT for particle size analysis promises to become increasingly widespread, offering measurements of size, shape, and porosity of large numbers of particles within one X-ray CT scan.

A Digital Mechanistic Workflow for Predicting Solvent-Mediated Crystal Morphology: The α and β Forms of l-Glutamic Acid

The solvent-mediated crystal morphologies of the α and β polymorphic forms of l-glutamic acid are presented. This work applies a digital mechanistically based workflow that encompasses calculation of the crystal lattice energy and its constituent intermolecular synthons, their interaction energies, and their key role in understanding and predicting crystal morphology as well as assessing the surface chemistry, topology, and solvent binding on crystal habit growth surfaces. Through a comparison between the contrasting morphologies of the conformational polymorphs of l-glutamic acid, this approach highlights how the interfacial chemistry of organic crystalline materials and their inherent anisotropic interactions with their solvation environments direct their crystal habit with potential impact on their further downstream processing behavior.