Assessing microstructural critical quality attributes in PLGA microspheres by FIB-SEM analytics.

The distribution of the active pharmaceutical ingredient (API) within polymer-based controlled release drug products is a critical quality attribute (CQA). It is crucial for the development of such products, to be able to accurately characterize phase distributions in these products to evaluate performance and microstructure (Q3) equivalence. In this study, polymer, API, and porosity distributions in poly(lactic-co-glycolic acid) (PLGA) microspheres were characterized using a combination of focused ion beam scanning electron microscopy (FIB-SEM) and quantitative artificial intelligence (AI) image analytics. Through in-depth investigations of nine different microsphere formulations, microstructural CQAs were identified including the abundance, domain size, and distribution of the API, the polymer, and the microporosity. 3D models, digitally transformed from the FIB-SEM images, were reconstructed to predict controlled drug release numerically. Agreement between the in vitro release experiments and the predictions validated the image-based release modelling method. Sensitivity analysis revealed the dependence of release on the distribution and size of the API particles and the porosity within the polymeric microspheres, as captured through FIB-SEM imaging. To our knowledge, this is the first report showing that microstructural CQAs in PLGA microspheres derived from imaging can be quantitatively and predictively correlated with formulation and manufacturing parameters.

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.