(668d) Engineering Considerations on Modeling for Pharmaceutical Process Analytical Technology (Pat) Applications

Recent FDA presentations and media reports indicated that process understanding is critical to enhance the manufacturing efficiency and reduce the likelihood of producing products of poor quality. The FDA's Process Analytical Technology (PAT) Guidance also highlights the importance of process understanding and process control. Focusing on pharmaceutical manufacturing processes may open up unprecedented opportunities and challenges for engineering disciplines, such as applying fundamental engineering principles to process/product design, process scale-up, process monitoring, and process control. Process modeling as an enabling tool for linking various stages in the manufacturing pipeline will play a key role during the implementation of PAT in pharmaceutical development, manufacturing, and quality assurance, and is also being reflected in recent ICH (International Conference on Harmonization) Guidelines of Q8 and Q9. In this regard, specific case studies can be very helpful.

In this work, two case studies are presented to illustrate how engineering principles and modeling tools can be utilized to enhance pharmaceutical process understanding and to help achieve Quality-by-Design from the product/process design perspective.

The first case study deals with the multivariate modeling of a tablet dissolution process. In conjunction with the multivariate statistical modeling, classical engineering principles of mass transfer and Fick's diffusion law were applied successfully to accomplish two goals: modeling the tablet dissolution process and identifying critical process variables for this process.

The second case study is based on the scale-up of a multi-phase agglomeration mixing system. It will illustrate how chemical engineering scale-up methodologies can be used to cope with the scale-up issues in mixing and agglomeration systems. In this case, the minimal time required to produce spherical agglomerates, tE , and final size of the agglomerates, dp, as measured by an off-line imaging system, were analyzed by applying a combination of multiple linear regression analysis and statistical analysis of variance to determine the dependence of tE and dp on the independent variables. Correlations were established to describe how processing parameters, such as power input per unit volume (P/V) and impeller tip speed (S), impact the process outcomes measured by tE and dp. By selecting a suitable scale-up rule, the mixing tank was scaled-up successfully by testing on three different tanks whose diameter ranged from 11.2 to 24.0 cm. The relevance of the chemical engineering scale-up methodologies as applied to pharmaceutical process understanding will be discussed.