Comparative binder efficiency modeling of dry granulation binders using roller compaction

Abstract

Roller compaction parameters’ impact on granules and tableting properties of coprocessed Avicel^® DG [ADG], a physical mixture of the two components at the same composition present in ADG [PADCP], and microcrystalline cellulose and Kollidon^® VA-64 Fine physical mixture [KVA64] was quantified by analysis of variance (ANOVA) and multivariate methods. Roller force, roller gap, and roller speed levels were selected for evaluation. A 3³ full-factorial experimental design with three center points for roller force, roller gap, and roller speed was used. The response parameters studied were granule-to-fines (GF) ratio, compressibility index (CI), tablet thickness (TT), tablet friability (TF), tablet breaking force (TBF) and disintegration time (DT). A model acetaminophen tablet formulation was roller granulated and tableted at 10 kg scale. Principal component analysis of ADG and PADCP formulations were separated from KVA64 formulations, indicating different granule and tableting properties were binder dependent. This difference in binder performance was also confirmed by ANOVA. The ANOVA also showed that there were no statistical performance differences between coprocessed ADG and its comparable physical blend with the exception of TT. Principal component regression (PCR) analyses of ADG and PADCP revealed that these excipients exhibited a statistically significant negative effect on granules-to-fine (GF) ratio, TT, TBF, and DT. KVA64 demonstrated a positive effect on these parameters. The KVA64 physical mixture demonstrated an overall better performance and binding capability. This study strongly suggests that there is no performance advantage of coprocessed Avicel^® DG when compared to a physical mixture of the two components at the same composition.

Keywords:

• Excipients
• dry granulation
• compression
• powder technology
• tableting
• roller compaction
• spray-dried and coprocessed excipients
• continuous pharmaceutical processing
• Avicel DG
• Kollidon VA-64
• analysis of variance
• multivariate analysis
• principal component analysis
• principal component regression

Disclosure statement

Authors declare that part of this work has been presented as a poster to 2014 AAPS Annual Meeting held in San Diego, CA.