Deep-learning based in situ image monitoring crystal polymorph and size distribution: Modeling and validation

In situ monitoring and closed-loop control of the solution crystallization process are the modern trends for pharmaceutical development, in which the critical process parameters (CPPs) as well as the product critical quality attributes (CQAs) can be regulated and guaranteed during the manufacturing process. In this study, an in situ image monitoring methodology based on a state-of-the-art deep-learning model was developed to track the CQAs such as polymorph ratio, two-dimensional crystal size, and crystal shape in a solvent-mediated polymorphic transformation (SMPT) process. Coupled with the multidimensional process information, a 2D population balance model (PBM) was developed and validated using the results of the in situ image-based CQAs analysis. The 2D-PBM was solved using a high-resolution finite volume method (HR-FVM) which could provide a high dimensional particle-size distribution. Through the validation between the process image analysis and the 2D-PBM, the accuracy of image analysis was discussed, and the potential and challenges of in situ image analysis were proposed. This work aims to integrate the crystal polymorphism and two-dimensional crystal size distribution (2D-CSD) information in the SMPT process using intelligent microscopic image analysis and then to validate the results of neural network processing by solving the numerical solution of the multidimensional PBM.