The first step to knowing what characteristics to model into a particulate system is to know and understand what the advantageous characteristics are. Historical information on particle properties has frequently been focussed on a narrow reading of particle size data, perhaps reducing particle size to a single numerical parameter such as D50 or D90. Of course such single parameters can cover an infinite number of distributions of particle size, and it is likely that these different distributions will have different performance in the key areas in which particle properties are the determinant of bulk powder characteristics, including (but not limited to) bulk flow and biopharmaceutics characteristics such as dissolution and absorption. Shape information, including shape distribution, is not captured at all in single parameter descriptions of particle size. The availability of novel particle size apparatus, which capture the size and shape of particles efficiently, mean that a much wider range of relevant information on particle properties can be built up. When these are correlated with such parameters as bulk flow or dissolution, greater understanding on the nature of the characteristics that scientists and engineers wish to design into particulate systems can be gained. Using the Morphologi apparatus our team gathered the size and shape properties of over 2000 batches of materials. These consisted of Active Pharmaceutical Ingredients (API's, excipients and intermediates), covering a wide range of the materials which are used in the development of oral solid dosage forms. This information was combined with detailed flow information from a subset of those batches. Using both machine learning and multivariate analysis techniques on the whole distribution (size and shape) of the particle properties it was possible to build models which predicted the flow properties of these materials, and identified those particle characteristics which would indicate those factors which led to successful flow characteristics in the bulk powder. These models can then be used to facilitate discussions with chemists and engineers about which parameters to target for materials being considered for different oral solid dosage form processes.

Selection and curation of data sets to allow particle properties modelling

ACS Fall 2021

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