Direct Image Feature Extraction and Multivariate Analysis for Crystallization Process Characterization

Application of shadowgraph imaging (SGI) particle characterisation data to interpret the impact of varying test conditions on powder dissolution and to develop an automated agglomeration identification method (AIM) in the USP flow-through apparatus

The aims of this work were 1) to explore the application of shadowgraph imaging (SGI) as a real time monitoring tool to characterize ibuprofen particle behaviour during dissolution testing under various conditions in the USP 4 flow-through apparatus and 2) to investigate the potential to develop an SGI-based automated agglomeration identification method (AIM) for real time agglomerate detection during dissolution testing. The effect of surfactant addition, changes in the drug mass and flow rate, the use of sieved and un-sieved powder fractions, and the use of different drug crystal habits were investigated. Videos at every sampling time point during dissolution were taken and analysed by SGI. The AIM was developed to characterize agglomerates based on two criteria - size and solidity. All detections were confirmed by manual video observation and a reference agglomerate data set. The method was validated under new dissolution conditions with un-sieved particles. Characterisation of particle dispersion behaviour by SGI enabled interpretation of the impact of dissolution test conditions. Higher numbers of early detections reflected greater dissolution rates with increased surfactant concentration, using sieved fraction or plate-shaped crystals, but was impacted by drug mass tested. An AIM was successfully developed and applied to detect agglomerates during dissolution, suggesting potential, with appropriate method development, for application in quality control.