Use of similarity scoring in the development of oral solid dosage forms

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.

Manufacturing classification system in the real world: factors influencing manufacturing process choices for filed commercial oral solid dosage formulations, case studies from industry and considerations for continuous processing

Following the first Manufacturing Classification System (MCS) paper, the team conducted surveys to establish which active pharmaceutical ingredient (API) properties were important when selecting or modifying materials to enable an efficient and robust pharmaceutical manufacturing process. The most commonly identified factors were (1) API particle size: small particle sizes are known to increase risk of processing issues; (2) Drug loading in the formulation: high drug loadings allow less opportunity to mitigate poor API properties through the use of excipients. The next step was to establish linkages with process decisions by identifying publicly-available proxies for these important parameters: dose (in place of drug loading) and BCS class (in place of particle size). Poorly-soluble API were seen as more likely to have controlled (smaller) particle size than more highly soluble API. Analysis of 435 regulatory filings revealed that higher doses and more poorly-soluble API was associated with more complex processing routes. Replacing the proxy factors with the original parameters should give the opportunity to demonstrate stronger trends. This assumption was tested by accessing a dataset relating to commercial tablet products. This showed that, for dry processes, a larger particle size was associated with higher achievable drug loading as determined by percolation threshold.

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.