This study quantifies the lubricating efficiency of two grades of crystalline vegetable-derived magnesium stearate (MgSt-V) using the DM(3) approach, which utilizes design of experiments (D) and multivariate analysis techniques (M3) to evaluate the effect of a material's (M1) molecular and macroscopic properties and manufacturing factors (M2) on critical product attributes. A 2(3) factorial design (2 continuous variables plus 1 categorical factor) with three center points for each categorical factor was used to evaluate the effect of MgSt-V fraction and blend time on running powder basic flow energy (BFE), tablet mechanical strength (TMS), disintegration time (DT), and running powder lubricant sensitivity ratio (LSR). Molecular characterization of MgSt-V employed moisture sorption-desorption analysis, (13)C nuclear magnetic resonance spectroscopy, thermal analysis, and powder X-ray diffraction. MgSt-V macroscopic analysis included mean particle size, specific surface area, particle morphology, and BFE. Principal component analysis and partial least squares multivariate analysis techniques were used to develop predictive qualitative and quantitative relationships between the molecular and macroscopic properties of MgSt-V grades, design variables, and resulting tablet formulation properties. MgSt-V fraction and blending time and their square effects showed statistical significant effects. Significant variation in the molecular and macroscopic properties of MgSt-V did not have a statistically significant impact on the studied product quality attributes (BFE, TMS, DT, and LSR). In setting excipient release specifications, functional testing may be appropriate in certain cases to assess the effect of statistically significant different molecular and macroscopic properties on product quality attributes.
Keywords: Basic flow energy; Critical material attributes; DM(3) approach; Excipients; Functionality related characteristics; Lubricant sensitivity ratio; Material science; Multivariate analysis; Partial least squares regression (PLS); Principal component analysis (PCA); QbD; Solid state NMR; Thermal analysis.
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