(189a) A Comprehensive Sensitivity Analysis for Risk Assessment of a Pharmaceutical Crystallization Process

In pharmaceutical industries, crystallization is one of the most critical unit operations. Performance of a crystallization process can be improved by effective use of state of the art science knowledge, engineering principles and tools. As emphasized by regulatory mechanism, the quality of a pharmaceutical product should be built-in or should be by design, rather than being tested. A comprehensive understanding of the process is essential in order to build the quality in both process and product. For this purpose, process systems engineering methods and tools can significantly support and speed up process understanding and development by means of mechanistic modeling, integrated process analytical technology (PAT) tools and control strategies, identification of uncertainties in the process and quantification of process risk assessment. Therefore, in this study we developed an in silico tool for a pharmaceutical crystallization process to perform a global uncertainty/sensitivity analysis for process risk assessment. Identification of operational variables with uncertainties in the process and analysis of how these process variables affect the performance of the crystallization process and outcome are critical for a risk based approach. Thus, the consistency of the process and probability of failing to meet desired quality requirements can be assessed. To this end, several uncertainties in the process design space of the crystallization process were identified. A comprehensive uncertainty and sensitivity analysis was performed to quantify the influence of these variabilities as well as to obtain importance ranking in order to help to assess the risks in the process. In conclusion, this study shows that in silico tools provide a fast, effective and cost-efficient platform to pharmaceutical industries to enhance their process and product developments through investigating design spaces, testing the feasibility of different strategies and assessing process risks.

Acknowledgment

We would like to thank the Danish Council for Independent Research (DFF) for financing the project with grant ID: DFF-6111600077B.