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

PURPOSE: The purpose of this work is to evaluate the interrelationship of microstructure, properties, and dissolution performance for amorphous solid dispersions (ASDs) prepared using different methods. METHODS: ASD of GDC-0810 (50% w/w) with HPMC-AS was prepared using methods of spray drying and co-precipitation via resonant acoustic mixing. Microstructure, particulate and bulk powder properties, and dissolution performance were characterized for GDC-0810 ASDs. In addition to application of typical physical characterization tools, we have applied X-Ray Microscopy (XRM) to assess the contribution of microstructure to the characteristics of ASDs and obtain additional quantification and understanding of the drug product intermediates and tablets. RESULTS: Both methods of spray drying and co-precipitation produced single-phase ASDs. Distinct differences in microstructure, particle size distribution, specific surface area, bulk and tapped density, were observed between GDC-0810 spray dried dispersion (SDD) and co-precipitated amorphous dispersion (cPAD) materials. The cPAD powders prepared by the resonant acoustic mixing process demonstrated superior compactibility compared to the SDD, while the compressibility of the ASDs were comparable. Both SDD powder and tablets showed higher in vitro dissolution than those of cPAD powders. XRM calculated total solid external surface area (SA) normalized by calculated total solid volume (SV) shows a strong correlation with micro dissolution data. CONCLUSION: Strong interrelationship of microstructure, physical properties, and dissolution performance was observed for GDC-0810 ASDs. XRM image-based analysis is a powerful tool to assess the contribution of microstructure to the characteristics of ASDs and provide mechanistic understanding of the interrelationship.

Investigation of Quantitative X-ray Microscopy for Assessment of API and Excipient Microstructure Evolution in Solid Dosage Processing.

Assessment and understanding of changes in particle size of active pharmaceutical ingredients (API) and excipients as a function of solid dosage form processing is an important but under-investigated area that can impact drug product quality. In this study, X-ray microscopy (XRM) was investigated as a method for determining the in situ particle size distribution of API agglomerates and an excipient at different processing stages in tablet manufacturing. An artificial intelligence (AI)-facilitated XRM image analysis tool was applied for quantitative analysis of thousands of individual particles, both of the API and the major filler component of the formulation, microcrystalline cellulose (MCC). Domain size distributions for API and MCC were generated along with the calculation of the porosity of each respective component. The API domain size distributions correlated with laser diffraction measurements and sieve analysis of the API, formulation blend, and granulation. The XRM analysis demonstrated that attrition of the API agglomerates occurred secondary to the granulation stage. These results were corroborated by particle size distribution and sieve potency data which showed generation of an API fines fraction. Additionally, changes in the XRM-calculated size distribution of MCC particles in subsequent processing steps were rationalized based on the known plastic deformation mechanism of MCC. The XRM data indicated that size distribution of the primary MCC particles, which make up the larger functional MCC agglomerates, is conserved across the stages of processing. The results indicate that XRM can be successfully applied as a direct, non-invasive method to track API and excipient particle properties and microstructure for in-process control samples and in the final solid dosage form. The XRM and AI image analysis methodology provides a data-rich way to interrogate the impact of processing stresses on API and excipients for enhanced process understanding and utilization for Quality by Design (QbD).

Correlative Image-Based Release Prediction and 3D Microstructure Characterization for a Long Acting Parenteral Implant.

Imaging-based characterization of polymeric drug-eluting implants can be challenging due to the microstructural complexity and scale of dispersed drug domains and polymer matrix. The typical evaluation via real-time (and accelerated in vitro experiments not only can be very labor intensive since implants are designed to last for 3 months or longer, but also fails to elucidate the impact of the internal microstructure on the implant release rate. A novel characterization technique, combining multi-scale high resolution three-dimensional imaging, was developed for a mechanistic understanding of the impact of formulation and manufacturing process on the implant microstructure. Artificial intelligence-based image segmentation and imaging analytics convert "visualized" structural properties into numerical models, which can be used to calculate key parameters governing drug transport in the polymer matrix, such as effective permeability. Simulations of drug transport in structures constructed on the basis of image analytics can be used to predict the release rates for the drug-eluting implant without running lengthy experiments. Multi-scale imaging approach and image-based characterization generate a large amount of quantitative structural information that are difficult to obtain experimentally. The direct-imaging based analytics and simulation is a powerful tool and has potential to advance fundamental understanding of drug release mechanism and the development of robust drug-eluting implants.

Characterization of the Morphological Nature of Hollow Spray Dried Dispersion Particles Using X-ray Submicron-Computed Tomography.

Graphical Abstract.

Understanding the Impact of Microstructures on Reconstitution and Drying Kinetics of Lyophilized Cake Using X-ray Microscopy and Image-Based Simulation.

The drying time of lyophilization and resultant cake microstructure are dependent on each other as water and solvent leave a lyophilized cake. The drying rate affects the size, distribution, and tortuosity of the pores as these macropores evolve during the primary drying phase, which in return impact the further removal of water and solvent from the cake throughout the drying period. This interplay results in a microstructure that determines the reconstitution time for a given formulation. The current study employs advanced X-ray Microscopy (XRM) coupled with mathematical models to correlate the microstructure with the drying kinetics and the reconstitution time. The normalized diffusion coefficients, derived from the reconstructed 3D microstructure of the cake, correlate with the solid content of the pre-lyophilization solution and agree with the mass transfer coefficients from a semi-empirical drying model built with lyophilization process data. Specifically, a solution with less solid content leads to a lyophilized cake with larger pores, thinner walls, and a greater pore volume compared to a solution with more solid content. Consequently, models from the microstructure and drying experiments reveals faster mass transfer independently. While the mass transfer models from the cake structure and the lyophilization process data accurately represents the drying kinetics, both models are inadequate to describe the reconstitution process due to the significant impact from formulation ingredients that alter the mass transfer mechanism via solubility and wettability. In summary, X-ray microscopy imaging and mathematical models are powerful tools that provide insights into the lyophilization process from a new angle.

Investigating structural attributes of drug encapsulated microspheres with quantitative X-ray imaging.

The intra-sphere and inter-sphere structural attributes of controlled release microsphere drug products can greatly impact their release profile and clinical performance. In developing a robust and efficient method to characterize the structure of microsphere drug products, this paper proposes X-ray microscopy (XRM) combined with artificial intelligence (AI)-based image analytics. Eight minocycline loaded poly(lactic-co-glycolic acid) (PLGA) microsphere batches were produced with controlled variations in manufacturing parameters, leading to differences in their underlying microstructures and their final release performances. A representative number of microspheres samples from each batch were imaged using high resolution, non-invasive XRM. Reconstructed images and AI-assisted segmentation were used to determine the size distribution, XRM signal intensity, and intensity variation of thousands of microspheres per sample. The signal intensity within the eight batches was nearly constant over the range of microsphere diameters, indicating high structural similarity of spheres within the same batch. Observed differences in the variation of signal intensity between different batches suggests inter-batch non-uniformity arising from differences in the underlying microstructures associated with different manufacturing parameters. These intensity variations were correlated with the structures observed from higher resolution focused ion beam scanning electron microscopy (FIB-SEM) and the in vitro release performance for the batches. The potential for this method for rapid at-line and offline product quality assessment, quality control, and quality assurance is discussed.

Control of drug release kinetics from hot-melt extruded drug-loaded polycaprolactone matrices.

Sustained local delivery of meloxicam by polymeric structures is desirable for preventing subacute inflammation and biofilm formation following tissue incision or injury. Our previous study demonstrated that meloxicam release from hot-melt extruded (HME) poly(ε-caprolactone) (PCL) matrices could be controlled by adjusting the drug content. Increasing drug content accelerated the drug release as the initial drug release generated a pore network to facilitate subsequent drug dissolution and diffusion. In this study, high-resolution micro-computed tomography (HR µCT) and artificial intelligence (AI) image analysis were used to visualize the microstructure of matrices and simulate the drug release process. The image analysis indicated that meloxicam release from the PCL matrix was primarily driven by diffusion but limited by the amount of infiltrating fluid when drug content was low (i.e., the connectivity of the drug/pore network was poor). Since the drug content is not easy to change when a product has a fixed dose and dimension/geometry, we sought an alternative approach to control the meloxicam release from the PCL matrices. Here, magnesium hydroxide (Mg(OH)2) was employed as a solid porogen in the drug-PCL matrix so that Mg(OH)2 dissolved with time in the aqueous environment creating additional pore networks to facilitate local dissolution and diffusion of meloxicam. PCL matrices were produced with a fixed 30 wt% meloxicam loading and variable Mg(OH)2 loadings from 20 wt% to 50 wt%. The meloxicam release increased in proportion to the Mg(OH)2 content, resulting in almost complete drug release in 14 d from the matrix with 50 wt% Mg(OH)2. The porogen addition is a simple strategy to tune drug release kinetics, applicable to other drug-eluting matrices with similar constraints.

Release Mechanisms and Practical Percolation Threshold for Long-acting Biodegradable Implants: An Image to Simulation Study.

The development of long-acting drug formulations requires efficient characterization technique as the designed 6-12 months release duration renders real-time in vitro and in vivo experiments cost and time prohibitive. Using a novel image-based release modeling method, release profiles were predicted from X-Ray Microscopy (XRM) of T0 samples. A validation study with the in vitro release test shows good prediction accuracy of the initial burst release. Through fast T0 image-based release prediction, the impact of formulation and process parameters on burst release rate was investigated. Recognizing the limitations of XRM, correlative imaging with Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) was introduced. A water stress test was designed to directly elucidate the formation of pores through polymer-drug-water interplay. Through an iterative correction method that considers poly(lactic-co-glycolic acid) (PLGA) polymer degradation, good agreement was achieved between release predictions  using FIB-SEM images acquired from T0 samples and in vitro testing data. Furthermore, using image-based release simulations, a practical percolation threshold was identified that has profound influence on the implant performance.  It is proposed as an important critical quality attribute for biodegradable long-acting delivery system, that needs to be investigated and quantified.

Assessing microstructural critical quality attributes in PLGA microspheres by FIB-SEM analytics.

The distribution of the active pharmaceutical ingredient (API) within polymer-based controlled release drug products is a critical quality attribute (CQA). It is crucial for the development of such products, to be able to accurately characterize phase distributions in these products to evaluate performance and microstructure (Q3) equivalence. In this study, polymer, API, and porosity distributions in poly(lactic-co-glycolic acid) (PLGA) microspheres were characterized using a combination of focused ion beam scanning electron microscopy (FIB-SEM) and quantitative artificial intelligence (AI) image analytics. Through in-depth investigations of nine different microsphere formulations, microstructural CQAs were identified including the abundance, domain size, and distribution of the API, the polymer, and the microporosity. 3D models, digitally transformed from the FIB-SEM images, were reconstructed to predict controlled drug release numerically. Agreement between the in vitro release experiments and the predictions validated the image-based release modelling method. Sensitivity analysis revealed the dependence of release on the distribution and size of the API particles and the porosity within the polymeric microspheres, as captured through FIB-SEM imaging. To our knowledge, this is the first report showing that microstructural CQAs in PLGA microspheres derived from imaging can be quantitatively and predictively correlated with formulation and manufacturing parameters.

Development of hot-melt extruded drug/polymer matrices for sustained delivery of meloxicam.

For effective resolution of regional subacute inflammation and prevention of biofouling formation, we have developed a polymeric implant that can release meloxicam, a selective cyclooxygenase (COX)-2 inhibitor, in a sustained manner. Meloxicam-loaded polymer matrices were produced by hot-melt extrusion, with commercially available biocompatible polymers, poly(ε-caprolactone) (PCL), poly(lactide-co-glycolide) (PLGA), and poly(ethylene vinyl acetate) (EVA). PLGA and EVA had a limited control over the drug release rate partly due to the acidic microenvironment and hydrophobicity, respectively. PCL allowed for sustained release of meloxicam over two weeks and was used as a carrier of meloxicam. Solid-state and image analyses indicated that the PCL matrices encapsulated meloxicam in crystalline clusters, which dissolved in aqueous medium and generated pores for subsequent drug release. The subcutaneously implanted meloxicam-loaded PCL matrices in rats showed pharmacokinetic profiles consistent with their in vitro release kinetics, where higher drug loading led to faster drug release. This study finds that the choice of polymer platform is crucial to continuous release of meloxicam and the drug release rate can be controlled by the amount of drug loaded in the polymer matrices.

Microstructure, Quality, and Release Performance Characterization of Long-Acting Polymer Implant Formulations with X-Ray Microscopy and Quantitative AI Analytics.

Long-acting implants are typically formulated using carrier(s) with specific physical and chemical properties, along with the active pharmaceutical ingredient (API), to achieve the desired daily exposure for the target duration of action. In characterizing such formulations, real-time in-vitro and in-vivo experiments that are typically used to characterize implants are lengthy, costly, and labor intensive as these implants are designed to be long acting. A novel characterization technique, combining high resolution three-dimensional X-Ray microscopy imaging, image-based quantification, and transport simulation, has been employed to provide a mechanistic understanding of formulation and process impact on the microstructures and performance of a polymer-based implant. Artificial intelligence-based image segmentation and image data analytics were used to convert morphological features visualized at high resolution into numerical microstructure models. These digital models were then used to calculate key physical parameters governing drug transport in a polymer matrix, including API uniformity, API domain size, and permeability. This powerful new tool has the potential to advance the mechanistic understanding of the interplay between drug-microstructure and performance and accelerate the therapeutic development long-acting implants.

Characterizing the Impact of Spray Dried Particle Morphology on Tablet Dissolution Using Quantitative X-Ray Microscopy.

For oral solid dosage forms, disintegration and dissolution properties are closely related to the powders and particles used in their formulation. However, there remains a strong need to characterize the impact of particle structures on tablet compaction and performance. Three-dimensional non-invasive tomographic imaging plays an increasingly essential role in the characterization of drug substances, drug product intermediates, and drug products. It can reveal information hidden at the micro-scale which traditional characterization approaches fail to divulge due to a lack of resolution. In this study, two batches of spray-dried particles (SDP) and two corresponding tablets of an amorphous product, merestinib (LY2801653), were analyzed with 3D X-Ray Microscopy. Artificial intelligence-based image analytics were used to quantify physical properties, which were then correlated with dissolution behavior. The correlation derived from the image-based characterization was validated with conventional laboratory physical property measurements. Quantitative insights obtained from image-analysis including porosity, pore size distribution, surface area and pore connectivity helped to explain the differences in dissolution behavior between the two tablets, with root causes traceable to the microstructure differences in their corresponding SDPs.

Characterization of Spray Dried Particles Through Microstructural Imaging.

Spray drying is commonly used to produce amorphous solid dispersions (ASD) to improve the bioperformance of poorly water-soluble drugs. In this study, imaging techniques such as focused ion beam-scanning electron microscopy (FIB-SEM) and X-ray microcomputed tomography (XRCT) were used to study the microstructure of spray dried (SD) particles. Spray drying at higher outlet temperature (Tout) was found to produce more spherical hollow particles with smooth surface and thinner walls, while more raisin-like particles with thicker walls were generated at lower Tout. For the first time, an artificial intelligence-facilitated XRCT image analysis tool was developed to make quantitative analysis of thousands of particles individually possible. The particle size distribution through XRCT image analysis is generally in line with what is measured by laser diffraction. The image analysis reveals envelope density as a more sensitive physical attribute for process change than conventional bulk/tap density. Further, the tensile strength of SD particle compacts correlates with the particle wall thickness, and this is likely caused by the larger interparticle contact area generated by more deformation of particles with thinner walls. The knowledge gained here can help enable SD particle engineering and drug product with more robust process and optimized performance.