Identifying IR Tablet Formulation Ingredients with Correlative Microscopy

Publication Title

Quantitative Structural and Compositional Elucidation of Real‑World Pharmaceutical Tablet Using Large Field‑of‑View, Correlative Microscopy‑Tomography Techniques and AI‑Enabled Image Analysis

Purpose

The purpose of this study is to present a correlative microscopy-tomography approach in conjunction with machine learning-based image segmentation techniques, with the goal of enabling quantitative structural and compositional elucidation of real-world pharmaceutical tablets.

Methods

‍Specifically, the approach involves three sequential steps: 1) user-oriented tablet constituent identification and characterization using correlative mosaic field-of-view SEM and energy dispersive X-ray spectroscopy techniques, 2) phase contrast synchrotron X-ray micro-computed tomography (SyncCT) characterization of a large, representative volume of the tablet, and 3) constituent segmentation and quantification of the imaging data through user-guided, iterative supervised machine learning and deep learning.

Results

‍This approach was implemented on a real-world tablet containing 15% API and multiple common excipients. A representative volumetric tablet image was obtained using SyncCT at a 0.36-µm resolution, from which constituent particles and pores were fully segmented and quantified. As validation, the derived tablet formulation composition and porosity agreed with the experimental values, despite the micrometer-scale particle and pore sizes. The approach also revealed the formation of ordered mixture inside the tablet. Notably, the image-derived size distributions of both the agglomerated microcrystalline cellulose and its primary particulate units matched the laser diffraction-based measurements of the as-is material. Key pore attributes including the pore size distribution, spatial anisotropy, and pore interconnectivity were also qualified.

Conclusion

‍Overall, this study demonstrated that the correlative microscopy-tomography approach, by leveraging phase contrast SyncCT and AI-based image analysis, can deliver new, practically-useful structural and compositional information and facilitate more efficient formulation and process development of tablets.

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