(383aq) Generalized Workflow for Model-Free Quality-By-Control: Recipe Development and Its Implementation in Pharmaceutical Crystallization

Crystallization is widely employed across chemical, food, and pharmaceutical industries, with over 90% of active pharmaceutical ingredients utilizing it to control essential particle properties, crucial for achieving desired product quality attributes. Purity, yield, crystal size, polymorphism, and shape are key crystallization development considerations in pharmaceutical manufacturing also known as critical quality attributes (CQAs), as they impact the bioavailability and manufacturability of the final product. Additionally, controlling agglomeration is vital due to risks associated with impurity inclusion and downstream processing challenges. Quality-by-control (QbC) strategies, utilizing feedback control and process analytical technology (PAT) tools, offer efficient customization of the crystallization process to meet pharmaceutical standards, preferred over traditional methods for their economic use of time and materials [1, 2, 3, 4]. Direct nucleation control (DNC) and supersaturation control (SSC) techniques
are examples of such QbC strategies [5, 6]. However, the lack of explicit recommendations for selecting techniques and process analytical technology (PAT) tools may present challenges in implementing the model-free approach.

This study introduces a generalized iterative framework specifically designed for the model-free Quality-by-Control (QbC) approach, aiming to streamline the implementation process across various scenarios by providing systematic guidelines for PAT tool selection and incorporating a mechanism-oriented decision-making scheme for choosing between supersaturation control (SSC) and direct nucleation control (DNC) techniques, including addressing complexities associated with polymorphic or crystallinity control. The significance of this framework is exemplified through its application to an industrial active pharmaceutical ingredient, compound K, from Takeda Pharmaceuticals, serving as a pertinent case study. This compound presents multiple challenges, including slow growth kinetics, highly agglomerated final products, a high aspect ratio, extreme sensitivity to seeding conditions, and crystallinity issues. As a result, this compound provides an ideal demonstration of the substantial advantages offered by the model-free QbC framework compared to the quality-by-design (QbD) recipe developed previously, with notable emphasis on the time saved during the process design phase.

Application of this framework to the case study highlights the importance of offline tools like differential scanning calorimetry (DSC) for monitoring crystallinity changes amidst varying content from amorphous to crystalline with respect to varying crystallization operating conditions. Exploring the experimental operating space for batch cooling crystallization of compound K reveals that unseeded crystallization provides a disadvantage of encrustation due to high supersaturation and also produces amorphous content that is undesirable. Slower cooling rates and lower seed loading under high supersaturation favours larger needle-like particles. Reduced crystallinity at high initial concentration can be addressed by increasing seed loading and slower cooling rates favoured cases with lower initial concentration. Implementation of turbidity-based DNC (TDNC), guided by the framework, enhances batch time, crystallinity, agglomeration severity, and sensitivity to seed uncertainty compared to traditional approaches, emphasizing the efficacy of this framework in informed decision-making for enhanced crystallization processes.

References

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