A combined Quality by Design (QbD) and Discrete Element Model (DEM) simulation-approach is presen... more A combined Quality by Design (QbD) and Discrete Element Model (DEM) simulation-approach is presented to characterize a blending unit operation by evaluating the impact of formulation parameters and process variables on the blending quality and blending end point. Understanding the variability of both the API and the excipients, as well as their impact on the blending process are critical elements for blending QbD. In a first step, the QbD-methodology is systematically used to (1) establish the critical quality attribute content uniformity and to link this CQA to its surrogate blend homogeneity, (2) identify potentially critical input factors that may affect blending operation quality and (3) risk-rank these factors to define activities for process characterization. Subsequently, a DEM-simulation-based characterization of the blending process is performed. A statistical evaluation is finally presented, relating blend homogeneity of systems with low particle number to the regulatory requirements. Data are then used to map out a three-dimensional knowledge space, providing parameters to define a design space and set up an appropriate control strategy.
A combined Quality by Design (QbD) and Discrete Element Model (DEM) simulation-approach is presen... more A combined Quality by Design (QbD) and Discrete Element Model (DEM) simulation-approach is presented to characterize a blending unit operation by evaluating the impact of formulation parameters and process variables on the blending quality and blending end point. Understanding the variability of both the API and the excipients, as well as their impact on the blending process are critical elements for blending QbD. In a first step, the QbD-methodology is systematically used to (1) establish the critical quality attribute content uniformity and to link this CQA to its surrogate blend homogeneity, (2) identify potentially critical input factors that may affect blending operation quality and (3) risk-rank these factors to define activities for process characterization. Subsequently, a DEM-simulation-based characterization of the blending process is performed. A statistical evaluation is finally presented, relating blend homogeneity of systems with low particle number to the regulatory requirements. Data are then used to map out a three-dimensional knowledge space, providing parameters to define a design space and set up an appropriate control strategy.
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