Investigation of factors affecting isolation of needle-shaped particles in a vacuum-agitated filter drier through non-invasive measurements by Raman spectrometry

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.

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.

A Novel Computational Approach Coupled with Machine Learning to Predict the Extent of Agglomeration in Particulate Processes

Solid particle agglomeration is a prevalent phenomenon in various processes across the chemical, food, and pharmaceutical industries. In pharmaceutical manufacturing, agglomeration is both desired in unit operations like wet granulation and undesired in unit operations such as agitated filter drying of highly potent active pharmaceutical ingredients (API). Agglomeration needs to be controlled for optimal physical properties of the API powder. Even after decades of work in the field, there is still very limited understanding of how to quantify, predict, and control the extent of agglomeration, owing to the complex interaction between the solvent and the solid particles and stochasticity imparted by mixing. Furthermore, a large size of industrial scale particulate process systems makes it computationally intractable. To overcome these challenges, we present a novel theory and computational methodology to predict the agglomeration extent by coupling the experimental measurements of agglomeration risk zone or "sticky zone" with discrete element method. The proposed model shows good agreement with experiments. Further, a machine learning model was built to predict agglomeration extent as a function of input variables, such as material properties and processing conditions, in order to build a digital twin of the unit operation. While the focus of the present study is the agglomeration of particles during industrial drying processes, the proposed methodology can be readily applied to numerous other particulate processes where agglomeration is either desired or undesired.

On the Breakage of High Aspect Ratio Crystals in Filter Beds under Continuous Percolation

Purpose

This work details experimental observations on the effect of liquid flow percolating through packed beds of crystals to elucidate how the filtration pressure severely alters the size distribution and crystal shape. Pressure filtration is widely used in the pharmaceutical industry, and frequently results in undesired size distribution changes that hinder further processing.

Methods

The percolation methodology presented fixes fluid flow through a bed of crystals, resulting in a pressure over the bed. X-ray computed tomography (XCT) provided detailed observations of the bed structure. Detailed 2D particle size data was obtained using automated microscopy and was analysed using an in-house developed tool.

Results

Crystal breakage is observed when the applied pressure exceeds a critical pressure: 0.5–1 bar for ibuprofen, 1–2 bar for β-L glutamic acid (LGA) and 2–2.5 bar for para amino benzoic acid (PABA). X-ray computed tomography showed significant changes in bed density under the applied pressure. Size analysis and microscope observations showed two modes of breakage: (i) snapping of long crystals and (ii) shattering of crystals.

Conclusion

LGA and PABA have a similar breakage strength (50 MPa), ibuprofen is significantly weaker (9 MPa). Available breakage strength data may be correlated to the volumetric Gibbs free energy. Data from 12 and 35 mm bed diameters compares well to literature data in a 80 mm filter; the smaller, easy to operate percolation unit is a versatile tool to assess crystal breakage in filtration operations.

Electronic supplementary material

The online version of this article (10.1007/s11095-020-02958-x) contains supplementary material, which is available to authorized users.

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.