Determination of process variables affecting drug particle attrition within multi-component blends during powder feed transmission

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.

Ten years of the manufacturing classification system: a review of literature applications and an extension of the framework to continuous manufacture

The MCS initiative was first introduced in 2013. Since then, two MCS papers have been published: the first proposing a structured approach to consider the impact of drug substance physical properties on manufacturability and the second outlining real world examples of MCS principles. By 2023, both publications had been extensively cited by over 240 publications. This article firstly reviews this citing work and considers how the MCS concepts have been received and are being applied. Secondly, we will extend the MCS framework to continuous manufacture. The review structure follows the flow of drug product development focussing first on optimisation of API properties. The exploitation of links between API particle properties and manufacturability using large datasets seems particularly promising. Subsequently, applications of the MCS for formulation design include a detailed look at the impact of percolation threshold, the role of excipients and how other classification systems can be of assistance. The final review section focusses on manufacturing process development, covering the impact of strain rate sensitivity and modelling applications. The second part of the paper focuses on continuous processing proposing a parallel MCS framework alongside the existing batch manufacturing guidance. Specifically, we propose that continuous direct compression can accommodate a wider range of API properties compared to its batch equivalent.

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.

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.

Blend Segregation in Tablets Manufacturing and Its Effect on Drug Content Uniformity-A Review

Content uniformity (CU) of the active pharmaceutical ingredient is a critical quality attribute of tablets as a dosage form, ensuring reproducible drug potency. Failure to meet the accepted uniformity in the final product may be caused either by suboptimal mixing and insufficient initial blend homogeneity, or may result from further particle segregation during storage, transfer or the compression process itself. This review presents the most relevant powder segregation mechanisms in tablet manufacturing and summarizes the currently available, up-to-date research on segregation and uniformity loss at the various stages of production process—the blend transfer from the bulk container to the tablet press, filling and discharge from the feeding hopper, as well as die filling. Formulation and processing factors affecting the occurrence of segregation and tablets’ CU are reviewed and recommendations for minimizing the risk of content uniformity failure in tablets are considered herein, including the perspective of continuous manufacturing.

Analytical method development for characterizing ingredient-specific particle size distributions of nasal spray suspension products

Particle size characterization for active pharmaceutical ingredients (APIs) in nasal spray suspension products presents unique challenges because both the API and excipient particles are present in the final dosage form. Currently, an established method is lacking because traditional particle sizing technologies do not distinguish the chemical identity of the particles. In this study, a non-destructive, ingredient-specific particle sizing method was developed for characterization of mometasone furoate (MF) nasal spray suspensions using Morphology Directed Raman Spectroscopy (MDRS). A five-step method development procedure was used in this study: sample preparation, particle imaging and morphology analysis, particle Raman measurements and classification, morphology filter selection, and minimum number of particles determination. Wet dispersion sample preparation method was selected to ensure that the particles were measured in their original suspended state. A training set containing over 10,000 randomly-selected particles, including both the API and excipient particles, was used to gain a comprehensive understanding of particle size, shape, and chemical ID for the nasal spray suspension. Morphology and Raman measurements were performed on each particle in the training set. The measurement results suggested that the aspect ratio and intensity mean filter combination was an appropriate morphology filter setting to selectively target API particles and exclude most of excipient particles. With further optimization of the morphology filter cutoff values and determination of minimal number of particles to be measured, the total measurement time was reduced from 90 hours to 8 hours. The morphologically screening strategy ultimately allowed us to create a time-efficient practical API-specific particle size distribution (PSD) methods for nasal spray suspensions. This study shows that MDRS is a fit for purpose analytical technique for determining ingredient-specific PSDs of the pharmaceutical formulation studied in this work.

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.

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.