Combining experimental design and orthogonal projections to latent structures to study the influence of microcrystalline cellulose properties on roll compaction.

International Journal of Pharmaceutics(2011)

引用 31|浏览30
暂无评分
摘要
Roll compaction is gaining importance in pharmaceutical industry for the dry granulation of heat or moisture sensitive powder blends with poor flowing properties prior to tabletting. We studied the influence of microcrystalline cellulose (MCC) properties on the roll compaction process and the consecutive steps in tablet manufacturing. Four dissimilar MCC grades, selected by subjecting their physical characteristics to principal components analysis, and three speed ratios, i.e. the ratio of the feed screw speed and the roll speed of the roll compactor, were included in a full factorial design. Orthogonal projection to latent structures was then used to model the properties of the resulting roll compacted products (ribbons, granules and tablets) as a function of the physical MCC properties and the speed ratio. This modified version of partial least squares regression separates variation in the design correlated to the considered response from the variation orthogonal to that response. The contributions of the MCC properties and the speed ratio to the predictive and orthogonal components of the models were used to evaluate the effect of the design variation. The models indicated that several MCC properties, e.g. bulk density and compressibility, affected all granule and tablet properties, but only one studied ribbon property: porosity. After roll compaction, Ceolus KG 1000 resulted in tablets with obvious higher tensile strength and lower disintegration time compared to the other MCC grades. This study confirmed that the particle size increase caused by roll compaction is highly responsible for the tensile strength decrease of the tablets.
更多
查看译文
关键词
Experimental design,Microcrystalline cellulose,Orthogonal projections to latent structures,Principal component analysis,Roll compaction,Tablet manufacturing
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要