Track: Formulation and Delivery - Chemical - Formulation - Drug Substance Properties
Category: Poster Abstract
Particle Analysis within Drug Products: Novel Methods in Quantification using 3D Microscopy
Purpose: Particle morphology and particle size distribution are critical physico-chemical parameters and important to characterize during the development of pharmaceutical products. In particular, the control and optimization of active pharmaceutical ingredient (API) particles in the final drug product is essential to its dissolution and release performance. An understanding of the particle size, shape, and spatial relationship is important for interpreting performance differences and root causes of failure. However, there is a striking lack of suitable approaches to investigate the size and shape of API particles within the final product. This poster will present a novel image-based platform to address particle size and shape analysis both for loose particle raw materials as well as particles inside final products. The application of automated image analysis with artificial intelligence (AI) image segmentation and the use of tomographic imaging techniques converted qualitative high-resolution images into quantitative data.1 Additionally, new mechanistic understandings of performance based on three-dimensional (3D) imaging analysis will be discussed. Methods: Three-dimensional micro-imaging, including X-Ray Microscopy (XRM) and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM), were the primary techniques used for imaging, either as standalone or together correlatively. These 3D imaging approaches were chosen to evaluate particle size and morphology within a drug product, based on previous studies reported.2. A suite of AI-based methods was applied to analyze particle features from images. These approaches allowed segmentation of particles of various material phases, including those with similar contrast. Further, these algorithms were processed within a cloud-based platform in which specific parameters and controls were applied to images.3 The sample volumes were reconstructed based on the 3D images and particle morphology evaluated both qualitatively and quantitatively. Using this platform allowed partial automation of the analysis process, with the aim of reducing computing resources and human interaction needed. Results: The particle size distribution of a loose particle sample was analyzed with both laser diffraction and image-based approaches with comparable results. Further, an ibuprofen drug pellet was chosen to evaluate the image-based approach for particle morphology and distribution analysis of internal API. As demonstrated in Figure 1, the FIB-SEM tomographic images provided an internal view of API particles and microstructure features within a drug pellet. In another performed study, the API particle size distribution was quantified, and its volume was reconstructed in 3D. The characterization of API particles inside the drug product is unique to 3D tomographic imaging and is not achievable through laser-diffraction methods. Conclusion: The presented image-based methods in particle size and shape analysis opens a new window for pharmaceutical scientists, helping them to qualitatively and quantitatively study particles as starting materials as well as inside final drug products. Through AI-based segmentation and a cloud-based platform, the analysis process of these images was greatly accelerated and partially automated. The application of 3D tomographic techniques was found to be incredibly useful in evaluating the particle characteristics and distribution within the studied drugs. Unique to microscopy, 3D analysis can be key in ensuring that the particles maintain their designed characteristics in the final product, as well as understanding how their distribution impacts performance. Like other particle analysis techniques, image-based characterization faces its own limitations. Particularly, obtaining representative sampling is inherently limited by the time and resources needed in electron and x-ray based microscopy. This challenge can be conquered through a careful design of experiment and correlative analysis.
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Zhang, S., Byrnes, A. P., Jankovic, J., & Neilly, J. (2019). Management, Analysis, and Simulation of Micrographs with Cloud Computing. Microscopy Today, 27(2), 26-33.