(255d) Quality-by-Design (QbD) Case Study: Powder Blending Process Kinetics Evaluation

The objective of this project was to develop an integrated Quality-by-Design (QbD) approach for the evaluation of powder blending process kinetics and the determination of the blending end-point. A mixture design was created to include 26 powder formulations consisting of ibuprofen as the model drug and three excipient components (HPMC, MCC, and Eudragit L100-55). The mixer was stopped at various time points to enable NIR scan of the powder mixture for obtaining the time course of the blending process for each formulation. The determination of the blending process end-point was studied through three quantitative approaches: (1) Pure component spectra linear superposition method; (2) Characteristic peak method; (3) Moving block standard deviation method. While the first approach as a quick screening tool could help to tell whether there are any significant interactions between various formulation components, the second and the third approaches generated comparable prediction results. It was found that the plots of averages of absolute values of the relative prediction errors vs. blending time for independent powder formulations merged together first at around 3 minutes then after 15 minutes. This observation, together with the derived kinetics of the blending process from the third approach, suggests that the blending process experiences three distinct stages: (1) an initial rapid process to reach a quasi- end point within the first a few minutes; (2) demixing; and (3) a real blending end-point as characterized by an inflection point. ANOVA shows that the main components' compositions (Ibuprofen and MCC) are the most statistically significant variables (critical formulation/process variables) that impact the time to reach the blending end-point. This work as a QbD case study highlighted the critical importance of integration of Design of Experiments (DOE), Near infrared (NIR) process spectroscopy, and chemometrics to extract critical process information and generate essential process knowledge to enable real-time release of the blending process.