Spray dried dispersions (SDDs) have the potential to dramatically improve the oral bioavailability of drugs with poor water solubility. However, SDDs tend to have material attributes, such as small particle size, low bulk density, and poor flowability, which are undesirable for downstream processing such as tableting. The objective was to perform a comprehensive compaction characterization of both physical mixtures and SDDs consisting of itraconazole (ITZ) and hypromellose acetate succinate (HPMCAS) to elucidate process and material influences on compressibility and compactibility. We fabricated SDDs with 20% ITZ as a model BCS Class 2 drug and 80% HPMCAS as a polymer carrier. Results indicate that SDDs, as well physical mixtures of ITZ and HPMCAS, were easily deformable with similar compressibility profiles across all compression speeds. Analysis of Heckel plots revealed that yield pressures were fairly low for both physical mixtures and SDDs (43.97-59.75 MPa), indicative of ductile materials. SDDs had a much greater propensity to laminate, especially at higher compression speeds, compared to physical mixtures. This difference is likely due to the higher elastic recovery of SDDs. However, for intact tablets, the mechanical strength of compacts from SDDs tended to be higher than those produced from physical mixtures, likely due to the much smaller particle size of the SDDs. Importantly, examination of the compacts with differential scanning calorimetry did not detect any drug crystallization as a result of compaction. In conclusion, while spray drying did not significantly alter the compressibility of binary mixtures ITZ and HPMCAS, it dramatically impacted compactibility and tabletability, increasing elastic recovery, and making the mixtures more prone to lamination. However, at low compression speeds, SDDs produced tablets with higher tensile strength than physical mixtures.
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http://dx.doi.org/10.1016/j.ejps.2020.105556 | DOI Listing |
To characterize adolescent health behavior profiles and associations with mental health, mixture models using data from two assessment points ( = 201; Female = 53.7%, Time 1 -age = 15.4 years; Time 2 -age = 16.
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