(385b) Digital Design of the Continuous Crystallization of Atorvastatin Calcium

Controlling crystallization processes is crucial to avoid issues in downstream processes, such as filtration and drying, and to achieve the desired final product form.¹ Some important critical quality attributes (CQAs) of the crystalline products include crystal size distribution (CSD) and polymorphic form, which affect the performance of the final product, such as manufacturability and bioavailability.^1,2 Quality-by-Control (QbC) approaches are used in open or closed loop-controlled processes to regulate crystallization process with the desired CQAs.^3-5 These approaches are considered robust and intelligent designs for crystallization processes as they provide vital information about the system and produce an optimal operation profile to meet CQAs in the shortest possible time. The use of mathematical tools and computing power has made it possible to predict the behavior of the system under study.

The first part of this study focuses on conducting a robust digital design of a commercial active pharmaceutical ingredient (API), Atorvastatin calcium (ASC), in a three-stage continuous cooling crystallization system. Traditionally, ASC is mostly produced via batch crystallization, which poses challenges in terms of controlling the crystal size distribution and the crystal form of the product. However, as global competition increases, continuous manufacturing is gaining momentum for its potential to reduce costs, improve quality, and provide supply chain flexibility in pharmaceutical production.^5-8 To achieve a successful and reliable continuous manufacturing process for ASC, the benefits of digital design of the crystallization process are demonstrated. First, parameter estimation is performed using data-driven and model-based techniques to obtain the mathematical model of the crystallization processes. Then the model is used to estimate the feasible design and operating space for the system, along with assessing the effects of uncertainties in kinetic parameters and inlet seed distribution on the robustness of the design space. Furthermore, in order to simultaneously optimize the quality and efficiency of the crystallization process, a multi-objective optimization is performed with detailed analysis of the pareto front resulted. This approach aims to simultaneously minimize the coefficient of variation, ensuring more uniform products, and maximize crystal size, which is an important factor in achieving desired CQAs of the final product. Additionally, the approach considers techno-economic targets for the system, such as yield and productivity. By utilizing such multi-objective approach, the system can achieve an optimal balance between these various objectives, resulting in a more efficient and effective crystallization process.

References

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