Characterization of Pharmaceutical Tablets by X-ray Tomography

Evaluation of Individual and Crystal Population Dissolution Rates by Time-Resolved X-ray Microtomography

The intrinsic dissolution rate (IDR) is an important parameter in pharmaceutical science that measures the rate at which a pure crystalline active pharmaceutical ingredient dissolves in the absence of diffusion limitations. Traditional IDR measurement techniques do not capture the complex interplay between particle morphology, fluid flow, and dissolution dynamics. The dissolution rate of individual particles can differ from the population average because of factors such as particle size, surface roughness, or exposure of individual crystal facets to the dissolution medium. The aim of this work was to apply time-resolved X-ray microtomography imaging and simultaneously measure the individual dissolution characteristics of a large population of crystalline particles placed in a packed bed perfused by the dissolution medium. Using NaCl crystals in three different size fractions as a model, time-resolved microtomography made it possible to visualize the dissolution process in a custom-built flow cell. Subsequent 3D image analysis was used to evaluate changes in the shape, size, and surface area of individual particles by tracking them as they are dissolved. Information about the particle population statistics and intrabatch variability provided a deeper insight into the dissolution process that can complement established IDR measurements.

Microstructure Formation and Characterization of Long-Acting Injectable Microspheres: The Gateway to Fully Controlled Drug Release Pattern

Long-acting injectable microspheres have been on the market for more than three decades, but if calculated on the brand name, only 12 products have been approved by the FDA due to numerous challenges in achieving a fully controllable drug release pattern. Recently, more and more researches on the critical factors that determine the release kinetics of microspheres shifted from evaluating the typical physicochemical properties to exploring the microstructure. The microstructure of microspheres mainly includes the spatial distribution and the dispersed state of drug, PLGA and pores, which has been considered as one of the most important characteristics of microspheres, especially when comparative characterization of the microstructure (Q3) has been recommended by the FDA for the bioequivalence assessment. This review extracted the main variables affecting the microstructure formation from microsphere formulation compositions and preparation processes and highlighted the latest advances in microstructure characterization techniques. The further understanding of the microsphere microstructure has significant reference value for the development of long-acting injectable microspheres, particularly for the development of the generic microspheres.

Just-in-time deep learning for real-time X-ray computed tomography

Real-time X-ray tomography pipelines, such as implemented by RECAST3D, compute and visualize tomographic reconstructions in milliseconds, and enable the observation of dynamic experiments in synchrotron beamlines and laboratory scanners. For extending real-time reconstruction by image processing and analysis components, Deep Neural Networks (DNNs) are a promising technology, due to their strong performance and much faster run-times compared to conventional algorithms. DNNs may prevent experiment repetition by simplifying real-time steering and optimization of the ongoing experiment. The main challenge of integrating DNNs into real-time tomography pipelines, however, is that they need to learn their task from representative data before the start of the experiment. In scientific environments, such training data may not exist, and other uncertain and variable factors, such as the set-up configuration, reconstruction parameters, or user interaction, cannot easily be anticipated beforehand, either. To overcome these problems, we developed just-in-time learning, an online DNN training strategy that takes advantage of the spatio-temporal continuity of consecutive reconstructions in the tomographic pipeline. This allows training and deploying comparatively small DNNs during the experiment. We provide software implementations, and study the feasibility and challenges of the approach by training the self-supervised Noise2Inverse denoising task with X-ray data replayed from real-world dynamic experiments.

Multiscale X-ray imaging and characterisation of pharmaceutical dosage forms

A correlative, multiscale imaging methodology for visualising and quantifying the morphology of solid dosage forms by combining ptychographic X-ray computed nanotomography (PXCT) and scanning small- and wide-angle X-ray scattering (S/WAXS) is presented. The methodology presents a workflow for multiscale analysis, where structures are characterised from the nanometre to millimetre regime. Here, the method is demonstrated by characterising a hot-melt extruded, partly crystalline, solid dispersion of carbamazepine in ethyl cellulose. Characterisation of the morphology and solid-state phase of the drug in solid dosage forms is central as this affects the performance of the final formulation. The 3D morphology was visualised at a resolution of 80 nm over an extended volume through PXCT, revealing an oriented structure of crystalline drug domains aligned in the direction of extrusion. Scanning S/WAXS, showed that the nanostructure is similar over the cross section of the extruded filament, with minor radial changes in domain sizes and degree of orientation. The polymorphic forms of carbamazepine were qualified with WAXS, showing a heterogeneous distribution of the metastable forms I and II. This demonstrates the methodology for multiscale structural characterization and imaging to enable a better understanding of the relationships between morphology, performance, and processing conditions of solid dosage forms.