High-Speed Imaging-Based Particle Attribute Analysis of Spray-Dried Amorphous Solid Dispersions Using a Convolution Neural Network.

Abstract

Spray drying is a well-established method for preparing amorphous solid dispersion (ASD) formulations to improve the oral bioavailability of poorly soluble drugs. In addition to the characterization of the amorphous phase, particle attributes of spray-dried intermediates (SDIs), including particle size, morphology, and microstructure, need to be carefully studied and controlled for optimizing drug product performance. Although recent developments in microscopy technology have enabled the analysis of morphological attributes for individual SDI particles, a high-throughput method is highly desirable. In this work, a fingerprinting method exploiting high-speed dynamic imaging, laser diffraction (LD), and a convolutional neural network (CNN) was developed to characterize and quantify size and morphological distributions of particles in batches of spray-dried ASDs. This imaging technology enables the generation of hundreds of thousands of single-particle images in a few minutes that are analyzed by both unsupervised and supervised CNN models. The unsupervised data mining analysis demonstrated that a batch of SDI is a mixture of diverse particle subpopulations with varying sizes and morphological attributes. Motivated by this observation, we developed a CNN model that enabled rapid computation of the volumetric composition of the distinct particle subpopulations in a SDI batch, thus generating a morphological fingerprint. We implemented this high-speed imaging-based particle attribute analysis method to investigate SDIs containing hypromellose acetate succinate as a model system. The CNN fingerprint results enabled quantification of the changes in the morphological distribution of SDI batches prepared with variations in the spray drying process parameters, and the results were in line with the LD and electron microscopy data. Our experiments and analysis demonstrate the robustness and throughput of this fingerprinting approach for quantifying particle size and morphological distributions of individual SDI batches, which can help guide spray drying process development and thereby enable the development of a drug product with more robust process and optimized performance.