Evaluating Co-precipitated Amorphous Solid Dispersion Formulations

Microstructure CQAs
Original Publication Title

Assessing the Interrelationship of Microstructure, Properties, Drug Release Performance, and Preparation Process for Amorphous Solid Dispersions Via Noninvasive Imaging Analytics and Material Characterization

The purpose of this work is to evaluate the interrelationship of microstructure, properties, and dissolution performance for amorphous solid dispersions (ASDs) prepared using different methods. ASD of GDC-0810 (50% w/w) with HPMC-AS was prepared using methods of spray drying and co-precipitation via resonant acoustic mixing. Microstructure, particulate and bulk powder properties, and dissolution performance were characterized for GDC-0810 ASDs. In addition to application of typical physical characterization tools, we have applied X-Ray Microscopy (XRM) to assess the contribution of microstructure to the characteristics of ASDs and obtain additional quantification and understanding of the drug product intermediates and tablets. Both methods of spray drying and co-precipitation produced single-phase ASDs. Distinct differences in microstructure, particle size distribution, specific surface area, bulk and tapped density, were observed between GDC-0810 spray dried dispersion (SDD) and co-precipitated amorphous dispersion (cPAD) materials. The cPAD powders prepared by the resonant acoustic mixing process demonstrated superior compactibility compared to the SDD, while the compressibility of the ASDs were comparable. Both SDD powder and tablets showed higher in vitro dissolution than those of cPAD powders. XRM calculated total solid external surface area (SA) normalized by calculated total solid volume (SV) shows a strong correlation with micro dissolution data. Strong interrelationship of microstructure, physical properties, and dissolution performance was observed for GDC-0810 ASDs. XRM image-based analysis is a powerful tool to assess the contribution of microstructure to the characteristics of ASDs and provide mechanistic understanding of the interrelationship.

Wei Jia, Phillip D. Yawman, Keyur M. Pandya, Kellie Sluga, Tania Ng, Dawen Kou, Karthik Nagapudi, Paul E. Luner, Aiden Zhu, Shawn Zhang, and Hao Helen Hou

Published with Genentech


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