Application of Image-Based Particle Size and Shape Characterization Systems in the Development of Small Molecule Pharmaceuticals

Past, current, and future: Application of image analysis in small molecule pharmaceutical development

The often-perceived limitations of image analysis have for many years impeded the widespread application of such systems as first line characterisation tools. Image analysis has, however, undergone a notable resurgence in the pharmaceutical industry fuelled by developments system capabilities and the desire of scientists to characterize the morphological nature of their particles more adequately. The importance of particle shape as well as size is now widely acknowledged. With the increasing use of modelling and simulations, and ongoing developments though the integration of machine learning and artificial intelligence, the utility of image analysis is increasing significantly driven by the richness of the data obtained. Such datasets provide means to circumvent the requirement to rely on less informative descriptors and enable the move towards the use of whole distributions. Combining the improved particle size and shape measurement and description with advances in modelling and simulations is enabling improved means to elucidate the link between particle and bulk powder properties. In addition to improved capabilities to describe input materials, approaches to characterize single components within multicomponent systems are providing scientists means to understand how their material may change during manufacture thus providing a means to link the behaviour of final dosage forms with the particle properties at the point of action. The aim is to provide an overview of image analysis and update readers with innovations and capabilities to other methods in the small molecule arena. We will also describe the use of AI for the improved analysis using image analysis.

A Novel image processing technique for weighted particle size distribution assessment

The objective of the study was to create a reliable method that could be used to evaluate the particle size distribution of samples and pre-mixes in real-world situations, particularly those consisting of typical formulation blends. The goal was to use this method to assess the uniformity of the samples and ensure that they met the required quality standards. The researchers aimed to create a method that could be easily incorporated into the manufacturing process, providing a practical and efficient solution. This study demonstrates the use of ImageJ software to analyze the particle size distribution (PSD) of powders. The technique produces qualitative data from microscopy images and quantitative data from analysis of parameters including average diameter, D10, D50, D90, and standard deviation. The method was tested with various treatments, showing differentiating outcomes in all cases. The alternate technique provides a rapid and cost-effective method for PSD analysis, surpassing the limitations of sieve analysis. Extensive testing of the method, using a variety of sample types, including typical formulation blends, was performed. The results suggest that the method can effectively assess the morphology of changing materials during batch manufacturing and characterize uniformity in blends. The methodology has the capability to identify attributes related to PSD that are typically required to be monitored during manufacturing. The technique allows for accurate and reliable quantification of the attributes through image capture technology. The technique has future potential and has important implications for material science, powder rheology, pharmaceutical formulation development, and continual process monitoring.

Determining the Impact of Roller Compaction Processing Conditions on Granulate and API Properties: Impact of Formulation API Load

Previous work demonstrated that roller compaction of a 40%w/w theophylline-loaded formulation resulted in granulate consisting of un-compacted fractions which were shown to constitute between 34 and 48%v/v of the granulate dependent on processing conditions. The active pharmaceutical ingredient (API) primary particle size within the un-compacted fraction was also shown to have undergone notable size reduction. The aim of the current work was to test the hypothesis that the observations may be more indicative of the relative compactability of the API due to the formulation being above the percolation threshold. This was done by assessing the impact of varied API loads in the formulation on the non-granulated fraction of the final granulate and the extent of attrition of API particles within the non-granulated fraction. The influence of processing conditions for all formulations was also investigated. The results verify that the observations, both of this study and the previous work, are not a consequence of exceeding the percolation threshold. The volume of un-compacted material within the granulate samples was observed to range between 34.7 and 65.5% depending on the API load and roll pressure, whilst the API attrition was equivalent across all conditions.

Comparative Evaluation of the Powder and Tableting Properties of Regular and Direct Compression Hypromellose from Different Vendors

Hypromellose, a widely used polymer in the pharmaceutical industry, is available in several grades, depending on the percentage of substitution of the methoxyl and hydroxypropyl groups and molecular weight, and in various functional forms (e.g., suitable for direct compression tableting). These differences can affect their physicomechanical properties, and so this study aims to characterise the particle size and mechanical properties of HPMC K100M polymer grades from four different vendors. Eight polymers (CR and DC grades) were analysed using scanning electron microscopy (SEM) and light microscopy automated image analysis particle characterisation to examine the powder's particle morphology and particle size distribution. Bulk density, tapped density, and true density of the materials were also analysed. Flow was determined using a shear cell tester. Flat-faced polymer compacts were made at five different compression forces and the mechanical properties of the compacts were evaluated to give an indication of the powder's capacity to form a tablet with desirable strength under specific pressures. The results indicated that the CR grades of the polymers displayed a smaller particle size and better mechanical properties compared to the DC grade HPMC K100M polymers. The DC grades, however, had better flow properties than their CR counterparts. The results also suggested some similarities and differences between some of the polymers from the different vendors despite the similarity in substitution level, reminding the user that care and consideration should be given when substitution is required.

Continuous Manufacturing of Cocrystals Using 3D-Printed Microfluidic Chips Coupled with Spray Coating

Using cocrystals has emerged as a promising strategy to improve the physicochemical properties of active pharmaceutical ingredients (APIs) by forming a new crystalline phase from two or more components. Particle size and morphology control are key quality attributes for cocrystal medicinal products. The needle-shaped morphology is often considered high-risk and complex in the manufacture of solid dosage forms. Cocrystal particle engineering requires advanced methodologies to ensure high-purity cocrystals with improved solubility and bioavailability and with optimal crystal habit for industrial manufacturing. In this study, 3D-printed microfluidic chips were used to control the cocrystal habit and polymorphism of the sulfadimidine (SDM): 4-aminosalicylic acid (4ASA) cocrystal. The addition of PVP in the aqueous phase during mixing resulted in a high-purity cocrystal (with no traces of the individual components), while it also inhibited the growth of needle-shaped crystals. When mixtures were prepared at the macroscale, PVP was not able to control the crystal habit and impurities of individual mixture components remained, indicating that the microfluidic device allowed for a more homogenous and rapid mixing process controlled by the flow rate and the high surface-to-volume ratios of the microchannels. Continuous manufacturing of SDM:4ASA cocrystals coated on beads was successfully implemented when the microfluidic chip was connected in line to a fluidized bed, allowing cocrystal formulation generation by mixing, coating, and drying in a single step.

Morphological distribution mapping: Utilisation of modelling to integrate particle size and shape distributions

The aim of this work was to develop approaches to utilize whole particle distributions for both particle size and particle shape parameters to map the full range of particle properties in a curated dataset. It is hoped that such an approach may enable a more complete understanding of the particle landscape as a step towards improving the link between particle properties and processing behaviour. A 1-dimensional principal component analysis (PCA) approach was applied to create a 'morphological distribution landscape'. A dataset of imaged APIs, intermediates and excipients encompassing particle size, particle shape (elongation, length and width) and distribution shape was curated between 2008 and 2022. The curated dataset encompassed over 200 different materials, which included over 150 different APIs, and approximately 3500 unique samples. For the purposes of the current work, only API samples were included. The morphological landscape enables differentiation of materials of equivalent size but varying shape and vice versa. It is hoped that this type of approach can be utilised to better understand the influence of particle properties on pharmaceutical processing behaviour and thereby enable scientists to leverage historical knowledge to highlight and mitigate risks associated to materials of similar morphological nature.

The Role of Titanium Dioxide (E171) and the Requirements for Replacement Materials in Oral Solid Dosage Forms: An IQ Consortium Working Group Review

Titanium dioxide (in the form of E171) is a ubiquitous excipient in tablets and capsules for oral use. In the coating of a tablet or in the shell of a capsule the material disperses visible and UV light so that the contents are protected from the effects of light, and the patient or caregiver cannot see the contents within. It facilitates elegant methods of identification for oral solid dosage forms, thus aiding in the battle against counterfeit products. Titanium dioxide ensures homogeneity of appearance from batch to batch fostering patient confidence. The ability of commercial titanium dioxide to disperse light is a function of the natural properties of the anatase polymorph of titanium dioxide, and the manufacturing processes used to produce the material utilized in pharmaceuticals. In some jurisdictions E171 is being considered for removal from pharmaceutical products, as a consequence of it being delisted as an approved colorant for foods. At the time of writing, in the view of the authors, no system or material which could address both current and future toxicological concerns of Regulators and the functional needs of the pharmaceutical industry and patients has been identified. This takes into account the assessment of materials such as calcium carbonate, talc, isomalt, starch and calcium phosphates. In this paper an IQ Consortium team outlines the properties of titanium dioxide and criteria to which new replacement materials should be held.

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).

Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material

Soft material can undergo non-uniform deformation or change of shape upon processing. Identifying shape and its change is nevertheless not straightforward. In this study, novel image-based algorithm that can be used to identify shapes of input images and at the same time classify non-uniform deformation into various patterns, i.e., swelling/shrinkage, horizontal and vertical elongations/contractions as well as convexity and concavity, is proposed. The algorithm was first tested with computer-generated images and later applied to agar cubes, which were used as model shrinkable soft material, undergoing drying at different temperatures. Shape parameters and shape-parameter based algorithm as well as convolutional neural networks (CNNs) either incorrectly identified some complicated shapes or could only identify the point where non-uniform deformation started to take place; CNNs lacked ability to describe non-uniform deformation evolution. Shape identification accuracy of the newly developed algorithm against computer-generated images was 65.88%, while those of the other tested algorithms ranged from 34.76 to 97.88%. However, when being applied to the deformation of agar cubes, the developed algorithm performed superiorly to the others. The proposed algorithm could both identify the shapes and describe their changes. The interpretation agreed well with that via visual observation.

Image Analysis: A Versatile Tool in the Manufacturing and Quality Control of Pharmaceutical Dosage Forms

In pharmaceutical sciences, visual inspection is one of the oldest methods used for description in pharmacopeias and is still an important part of the characterization and qualification of active ingredients, excipients, and dosage forms. With the development of technology, it is now also possible to take images of various pharmaceutical dosage forms with different imaging methods in a size range that is hardly visible or completely invisible to the human eye. By analyzing high-quality designs, physicochemical processes can be understood, and the results can be used even in the optimization of the composition of the dosage form and in the development of its production. The present study aims to show some of the countless ways image analysis can be used in the manufacturing and quality assessment of different dosage forms. This summary also includes measurements and an evaluation of, amongst others, a less studied dosage form, medicated foams.

Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level

Solid form diversity of raw materials can be critical for the performance of the final drug product. In this study, Raman spectroscopy, image analysis and combined Raman and image analysis were utilized to characterize the solid form composition of a particulate raw material. Raman spectroscopy provides chemical information and is complementary to the physical information provided by image analysis. To demonstrate this approach, binary mixtures of two solid forms of carbamazepine with a distinct shape, an anhydrate (prism shaped) and a dihydrate (needle shaped), were characterized at an individual particle level. Partial least squares discriminant analysis classification models were developed and tested with known, gravimetrically mixed test samples, followed by analysis of unknown, commercially supplied carbamazepine raw material samples. Classification of several thousands of particles was performed, and it was observed that with the known binary mixtures, the minimum number of particles needed for the combined Raman spectroscopy - image analysis classification model was approximately 100 particles per solid form. The carbamazepine anhydrate and dihydrate particles were detected and classified with a classification error of 1 % using the combined model. Further, this approach allowed the identification of raw material solid form impurity in unknown raw material samples. Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level has its potential in accurate detection of low amounts of unwanted solid forms in particulate raw material samples.

Determining the Impact of Roller Compaction Processing Conditions on Granule and API Properties

The attrition of drug particles during the process of dry granulation, which may (or may not) be incorporated into granules, could be an important factor in determining the subsequent performance of that granulation, including key factors such as sticking to punches and bio-performance of the dosage form. It has previously been demonstrated that such attrition occurs in one common dry granulation process train; however, the fate of these comminuted particles in granules was not determined. An understanding of the phenomena of attrition and incorporation into granule will improve our ability to understand the performance of granulated systems, ultimately leading to an improvement in our ability to optimize and model the process. Unique feeding mechanisms, geometry, and milling systems of roller compaction equipment mean that attrition could be more or less substantial for any given equipment train. In this work, we examined attrition of API particles and their incorporation into granule in an equipment train from Gerteis, a commonly used equipment train for dry granulation. The results demonstrate that comminuted drug particles can exist free in post-milling blends of roller compaction equipment trains. This information can help better understand the performance of the granulations, and be incorporated into mechanistic models to optimize such processes.

Benefits of Fractal Approaches in Solid Dosage Form Development

Pharmaceutical formulations are complex systems consisting of active pharmaceutical ingredient(s) and a number of excipients selected to provide the intended performance of the product. The understanding of materials' properties and technological processes is a requirement for building quality into pharmaceutical products. Such understanding is gained mostly from empirical correlations of material and process factors with quality attributes of the final product. However, it seems also important to gain knowledge based on mechanistic considerations. Promising is here to study morphological and/or topological characteristics of particles and their aggregates. These geometric aspects must be taken into account to better understand how product attributes emerge from raw materials, which includes, for example, mechanical tablet properties, disintegration or dissolution behavior. Regulatory agencies worldwide are promoting the use of physical models in pharmaceutics to design quality into a final product. This review deals with pharmaceutical applications of theoretical models, focusing on percolation theory, fractal, and multifractal geometry. The use of these so-called fractal approaches improves the understanding of different aspects in the development of solid dosage forms, for example by identifying critical drug and excipient concentrations, as well as to study effects of heterogeneity on dosage form performance. The aim is to link micro- and macrostructure to the emerging quality attributes of the pharmaceutical solid dosage forms as a strategy to enhance mechanistic understanding and to advance pharmaceutical development and manufacturing processes.

Enhanced Understanding of Pharmaceutical Materials Through Advanced Characterisation and Analysis

The impact of pharmaceutical materials properties on drug product quality and manufacturability is well recognised by the industry. An ongoing effort across industry and academia, the Manufacturing Classification System consortium, aims to gather the existing body of knowledge in a common framework to provide guidance on selection of appropriate manufacturing technologies for a given drug and/or guide optimization of the physical properties of the drug to facilitate manufacturing requirements for a given processing route. Simultaneously, material scientists endeavour to develop characterisation methods such as size, shape, surface area, density, flow and compactibility that enable a stronger understanding of materials powder properties. These properties are routinely tested drug product development and advances in instrumentation and computing power have enabled novel characterisation methods which generate larger, more complex data sets leading to a better understanding of the materials. These methods have specific requirements in terms of data management and analysis. An appropriate data management strategy eliminates time-consuming data collation steps and enables access to data collected for multiple methods and materials simultaneously. Methods ideally suited to extract information from large, complex data sets such as multivariate projection methods allow simpler representation of the variability contained within the data and easier interpretation of the key information it contains. In this review, an overview of the current knowledge and challenges introduced by modern pharmaceutical material characterisation methods is provided. Two case studies illustrate how the incorporation of multivariate analysis into the material sciences workflow facilitates a better understanding of materials.

Correlation Characterization of Particles in Volume Based on Peak-to-Basement Ratio

We propose a new express method of the correlation characterization of the particles suspended in the volume of optically transparent medium. It utilizes inline digital holography technique for obtaining two images of the adjacent layers from the investigated volume with subsequent matching of the cross-correlation function peak-to-basement ratio calculated for these images. After preliminary calibration via numerical simulation, the proposed method allows one to quickly distinguish parameters of the particle distribution and evaluate their concentration. The experimental verification was carried out for the two types of physical suspensions. Our method can be applied in environmental and biological research, which includes analyzing tools in flow cytometry devices, express characterization of particles and biological cells in air and water media, and various technical tasks, e.g. the study of scattering objects or rapid determination of cutting tool conditions in mechanisms.

Measuring the sticking of mefenamic acid powders on stainless steel surface

This study proposes an approach for quantifying the amount of pharmaceutical powder adhering (quality attribute) to the metals surfaces. The effect of surface roughness (detrimental attribute) on the amount of powder sticking to a stainless steel surface for a model pharmaceutical material is also qualitatively determined. Methodology to quantify powder adhesion to surfaces utilises a texture analyser and HPLC. The approach was validated to qualitatively investigate effect of metal surface roughness on adhesion of mefenamic acid. An increase in metal surface roughness resulted in an increase in cohesion. By increasing the average roughness from 289nm to 407nm, a 2.5 fold increase in amount adhering to metal was observed, highlighting the role of surface roughness on adhesion. The simplicity in experimental design with no requirement of specialised equipment and operational ease makes the approach very easy to adopt. Further, ease in interpreting results makes this methodology very attractive.