Iterative Recipe Development Framework of Model-Free Quality-By-Control and Its Application for the Rapid Design of a Pharmaceutical Crystallization Process

The paper presents an iterative, model-free Quality-by-Control (QbC) framework as a strategic enabler for crystallization process development, facilitating both the selection of process analytical technology (PAT) tools and the implementation of robust control strategies. The framework employs a mechanism-oriented decision-making approach to guide the choice between Direct Nucleation Control (DNC), Supersaturation Control (SSC), or a hybrid of the two, based on the relative significance of growth and agglomeration phenomena. A practical rule of thumb is proposed for selecting appropriate control variables, leveraging inline data quality and temperature trajectory insights. The framework is demonstrated using a case study involving a commercial active pharmaceutical ingredient (API), with the objective of enhancing crystallinity and reducing agglomeration in the final product. Offline tools such as high-performance liquid chromatography (HPLC) and differential scanning calorimetry (DSC) are used to track crystallinity changes in systems with both amorphous and crystalline content. Factorial experimental design analyses revealed key operational parameters that influenced process performance. Unseeded crystallization resulted in encrustation and poor crystallinity, while high supersaturation with slower cooling and lower seed loading produced large needlelike crystals. Increasing seed loading and applying slower cooling rates significantly improved product crystallinity. Guided by the QbC framework and implemented through turbidity-based DNC (TDNC), the process demonstrated significant improvements in batch time, crystallinity, agglomeration control, and robustness against seed variability─outperforming traditional approaches. This work highlights the effectiveness of the proposed framework in enabling informed, rapid design of enhanced and resilient crystallization processes.