Effect of Percent Relative Humidity, Moisture Content, and Compression Force on Light-Induced Fluorescence (LIF) Response as a Process Analytical Tool.

The effect of percent relative humidity (16-84% RH), moisture content (4.2-6.5% w/w MC), and compression force (4.9-44.1 kN CF) on the light-induced fluorescence (LIF) response of 10% w/w active pharmaceutical ingredient (API) compacts is reported. The fluorescent response was evaluated using two separate central composite designs of experiments. The effect of % RH and CF on the LIF signal was highly significant with an adjusted R (2) = 0.9436 and p < 0.0001. Percent relative humidity (p = 0.0022), CF (p < 0.0001), and % RH(2) (p = 0.0237) were statistically significant factors affecting the LIF response. The effects of MC and CF on LIF response were also statistically significant with a p value <0.0001 and adjusted R (2) value of 0.9874. The LIF response was highly impacted by MC (p < 0.0001), CF (p < 0.0001), and MC(2) (p = 0022). At 10% w/w API, increased % RH, MC, and CF led to a nonlinear decrease in LIF response. The derived quadratic model equations explained more than 94% of the data. Awareness of these effects on LIF response is critical when implementing LIF as a process analytical tool.

Effect of tapped density, compacted density, and drug concentration on light-induced fluorescence response as a process analytical tool.

The effect of tapped density, compacted density, and fluorescent drug concentration on the light-induced fluorescence (LIF) response is reported. The fluorescent response to powder mixtures containing 0.25%-10.00% w/w fluorescent active pharmaceutical ingredient (API) was evaluated over a density range of about 0.641-1.370 g/cm(3) . Blend concentrations up to 4.00% w/w API showed a linear trend in LIF response with increasing tapped and compacted density. API concentrations of 4.00% w/w or greater exhibited a negative parabolic trend in LIF response. The LIF responses were fitted to a quadratic model equation that included an interaction term between material density and API concentration (adjusted R(2) = 0.975 and p < 0.0001). All model terms were highly significant, including the material density-API concentration interaction (p < 0.0001). Being aware of the sensitivity of the LIF response to material density changes and the related changes in apparent concentration are important in implementing LIF as a process analytical tool for processes such as blending, roller compaction, and tableting.