Correlative Image-Based Release Prediction and 3D Microstructure Characterization for a Long Acting Parenteral Implant.

Imaging-based characterization of polymeric drug-eluting implants can be challenging due to the microstructural complexity and scale of dispersed drug domains and polymer matrix. The typical evaluation via real-time (and accelerated in vitro experiments not only can be very labor intensive since implants are designed to last for 3 months or longer, but also fails to elucidate the impact of the internal microstructure on the implant release rate. A novel characterization technique, combining multi-scale high resolution three-dimensional imaging, was developed for a mechanistic understanding of the impact of formulation and manufacturing process on the implant microstructure. Artificial intelligence-based image segmentation and imaging analytics convert "visualized" structural properties into numerical models, which can be used to calculate key parameters governing drug transport in the polymer matrix, such as effective permeability. Simulations of drug transport in structures constructed on the basis of image analytics can be used to predict the release rates for the drug-eluting implant without running lengthy experiments. Multi-scale imaging approach and image-based characterization generate a large amount of quantitative structural information that are difficult to obtain experimentally. The direct-imaging based analytics and simulation is a powerful tool and has potential to advance fundamental understanding of drug release mechanism and the development of robust drug-eluting implants.

Investigating structural attributes of drug encapsulated microspheres with quantitative X-ray imaging.

The intra-sphere and inter-sphere structural attributes of controlled release microsphere drug products can greatly impact their release profile and clinical performance. In developing a robust and efficient method to characterize the structure of microsphere drug products, this paper proposes X-ray microscopy (XRM) combined with artificial intelligence (AI)-based image analytics. Eight minocycline loaded poly(lactic-co-glycolic acid) (PLGA) microsphere batches were produced with controlled variations in manufacturing parameters, leading to differences in their underlying microstructures and their final release performances. A representative number of microspheres samples from each batch were imaged using high resolution, non-invasive XRM. Reconstructed images and AI-assisted segmentation were used to determine the size distribution, XRM signal intensity, and intensity variation of thousands of microspheres per sample. The signal intensity within the eight batches was nearly constant over the range of microsphere diameters, indicating high structural similarity of spheres within the same batch. Observed differences in the variation of signal intensity between different batches suggests inter-batch non-uniformity arising from differences in the underlying microstructures associated with different manufacturing parameters. These intensity variations were correlated with the structures observed from higher resolution focused ion beam scanning electron microscopy (FIB-SEM) and the in vitro release performance for the batches. The potential for this method for rapid at-line and offline product quality assessment, quality control, and quality assurance is discussed.