Derisking Crystallization Process Development and Scale-Up Using a Complementary, “Quick and Dirty” Digital Design

Abstract

Despite the spread of digital (model and AI-based) techniques, the industry-standard pharmaceutical crystallization design and scale-up is still based on experiments’ design (DoE). Many orthogonally designed and usually relatively lightly monitored experiments are performed as a part of it. The final design/scale-up is inherently truncated by experimental and statistical modeling errors and assumptions, compromising the reliability of the calculated design space (DS). This study proposes to apply process modeling in a complementary way: utilize the experiments from the DoE to calibrate an application-driven model, quantify its accuracy, and use it─in parallel with the statistical interpretation of the DoE─to design the process. Both the DoE and model-based DS determination involve workflow-specific assumptions, simplifications, and errors, but the overlap between the independent results may be considered a derisked DS. We demonstrate this workflow on the design of a fed-batch salting-out crystallization for a commercial active pharmaceutical ingredient (API). The model was identified based on product particle size distribution data of a DoE set from a small-scale reactor (0.25 L) and a manufacturing batch (ca. 4000 L). Although reactors with intermediate volumes are also generally applied as a part of scale-up, included in the presented case study, those were not included in the model development and verification. The kinetic equations were taken from our previously developed cooling crystallization model of the same API. After calibration and accuracy evaluation, the critical process parameters were determined using interpretable machine learning via Shapley diagrams, and the DS was mapped and visualized using Monte Carlo sampling-based simulations. The DS was validated for 0.25 L experiments. The model-based DS was somewhat narrower than the DoE-based DS on a small scale. The DS determined for plant-scale crystallization can guide the manufacturing-scale process design and operation. The extrapolation capabilities of the model were stressed by external validation by defining and validating experimentally the DS for a 1 L crystallization. These results indicate that models developed in this application-centric way can enhance the robustness of the processes, and the modeling branch does not add any risk. In the worst-case scenario, if the modeling fails, one still has the results from the traditional design approach.