Importance of Raw Material Attributes for Modeling Ribbon and Granule Properties in Roller Compaction: Multivariate Analysis on Roll Gap and NIR Spectral Slope as Process Critical Control Parameters
This study discusses the effect of formulation composition on the physical characteristics and drug release behavior of controlled-release formulations made by roller compaction. The authors used mixture experimental design to study the effect of formulation components using diclofenac sodium as the model drug substance and varying relative amounts of microcrystalline cellulose (Avicel), hydroxypropyl methylcellulose (HPMC), and glyceryl behenate (Compritol). Dissolution studies revealed very little variability in drug release. The t70 values for the 13 formulations were found to vary between 260 and 550 min. A reduced cubic model was found to best fit the t70 data and gave an adjusted r-square of 0.9406. Each of the linear terms, the interaction terms between Compritol and Avicel and between all three of the tested factors were found to be significant. The longest release times were observed for formulations having higher concentrations of HPMC or Compritol. Tablets with higher concentrations of Avicel showed reduced ability to retard the release of the drug from the tablet matrix. Crushing strength showed systematic dependence on the formulation factors and could be modeled using a reduced quadratic model. The crushing strength values were highest at high concentrations of Avicel, while tablets with a high level of Compritol showed the lowest values. A predicted optimum formulation was derived by a numerical, multiresponse optimization technique. The validity of the model for predicting physical attributes of the product was also verified by experiment. The observed responses from the calculated optimum formulation were in very close agreement with values predicted by the model. The utility of a mixture experimental design for selecting formulation components of a roller compacted product was demonstrated. These simple statistical tools can allow a formulator to rationally select levels of various components in a formulation, improve the quality of products, and develop more robust processes.