Particle Property Characterization and Data Curation for Effective Powder Property Modeling in the Pharmaceutical Industry.

Computational modeling, machine learning, and statistical data analysis are increasingly utilized to mitigate chemistry, manufacturing, and control failures related to particle properties in solid dosage form manufacture. Advances in particle characterization techniques and computational approaches provide unprecedented opportunities to explore relationships between particle morphology and drug product manufacturability. Achieving this, however, has numerous challenges such as producing and appropriately curating robust particle size and shape data. Addressing these challenges requires a harmonized strategy from material sampling practices, characterization technique selection, and data curation to provide data sets which are informative on material properties. Herein, common sources of error in particle characterization and data compression are reviewed, and a proposal for providing robust particle morphology (size and shape) data to support modeling efforts, approaches for data curation, and the outlook for modeling particle properties are discussed.

Atomic Force Microscopy to Identify Dehydration Temperatures for Small Volumes of Active Pharmaceutical Ingredients.

The environmental conditions associated with changing the hydration state of active pharmaceutical ingredients (API) are crucial to understanding their stability, bioperformance, and manufacturability. Identifying the dehydration event using < 1µg of material is an increasingly important challenge. Atomic Force Microscopy indentation mapping is implemented at controlled temperatures between 25-100°C, for nanoscale volumes of hydrated APIs exhibiting distinct dehydration behavior and anhydrous APIs as controls. For caffeine hydrate and azithromycin dihydrate, the relative mechanical modulus increases ~10-fold at dehydration temperatures. These are confirmed by conventional macroscopic measurements including Variable Temperature Powder X-ray Diffraction, Thermogravimetric Analysis, and Differential Scanning Calorimetry. Conversely, no such mechanical transition is observed for anhydrous ibuprofen or a proprietary anhydrous compound. AFM-based mechanical mapping is therefore demonstrated for small-volume determination of temperature-induced solid-state dehydration events, which may enable spatially or temporally mapping for future studies of dehydration mechanisms and kinetics, as a function of commercially relevant nanoscale heterogeneities.

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.