Systematic Model Builder, Model-Based Design of Experiments, and Design Space Identification for A Multistep Pharmaceutical Process � Toward Quality by Digital Design

This study aims at developing a holistic approach to establish robust mathematical models of integrated and interactive multistep processes, while systematically identifying the corresponding design space and acceptable operating region (AOR). The overall objective is to reduce the experimentation costs, enhance accuracy of integrated metathetical models, and deliver built-in quality assurance based on a new Quality by Digital Design (QbDD) paradigm. This methodology starts with the construction of a set of model candidates for different unit operations, based on the prior knowledge and inherent assumptions. Several model candidates of the integrated multistep process are considered. A model discrimination based on model prediction performance reveals the best integrated model for the multistep process. In the next step, the estimability analysis and model-based design of experiment (MBDoE) are implemented to deliver information-rich data and systematically refine the integrated model. With the acquisition of the new experimental data, the reliability and robustness of the multistep mathematical model is dramatically enhanced. A blending-tableting process is considered to validate the methodology. The model captures the effects of the blender as well as the composition and porosity of the tablet on the tablet tensile strength. Model discrimination and automated model refinement are then performed to identify and improve the optimal integrated model for this two-step process, and the enhanced model is applied for the design space identification under specified CQA targets and associated bounds.