(607c) An Application of Reaction Engineering and Modeling In Quality by Design
AIChE Annual Meeting
Thursday, November 20, 2008 - 9:10am to 9:30am
Key aspects of a Quality by Design (QbD) approach include defining the design space and conducting a risk assessment of the process parameters. In this case study, we investigated a lactamization reaction using reaction modeling to perform a risk assessment and map the design space.
In the lactamization reaction, an organic base reacts with intermediate A to form intermediate B. In addition, the organic base and intermediate B can further react to form impurity C that is carried into the API product. A reaction model was built using data captured by FTIR and HPLC and then used to conduct numerous reaction simulations (?virtual experiments?) at various conditions. The results of the virtual experiments were used to generate response surfaces of the potentially critical reaction parameters (i.e., temperature, net amount of base, and reaction time). This allowed the proposed operating space to be visualized and foster process risk assessment. In addition, these results demonstrated that the reaction requires a delicate balance of the critical parameters to allow reaction completion and avoid high impurity formation. The reaction temperature and organic base equivalents were then optimized to maximize reaction conversion while ensuring that impurity C remained below specifications. The model also showed that lowering the batch temperature during reaction endpoint analysis significantly reduced the rate of impurity C formation.
In conclusion, reaction modeling and simulation software was shown to be a very powerful tool to not only deepen our knowledge of reaction kinetics but also to provide a means to simulate numerous experiments, test alternative processing procedures, and develop a design space based upon a scientific and risk-based approach. This work culminated in the establishment of an alternative process procedure for the upcoming production campaign that mitigates risk, reduces the number of critical parameters, and ensures final product quality.