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This work demonstrates a systematic development of a digital twin of a commercial active pharmaceutical ingredient (API) - Compound A - crystallization processes to be used for in-silico design of experiments (DoE) and process optimization. Eleven highly corelated kinetic parameters were estimated by exploiting an optimization approach with an evolutionary algorithm, to fit to concentration profile, mean size and the shape distribution of the product. Two more experiments were then performed to validate the model showing that the developed model could predict the product size and shape properties. The digital twin of the process was used to perform in silico design of experiments to understand the attainable operating space of the system and provide a framework for rapid quality-by-design (QbD). Subsequently the model was used in a dynamic optimization framework for the digital design of the API crystallization process to achieve the critical quality attributes (CQAs) without extensive experimentation. more...

The work proposes a combination of model-free and model-based quality-by-control (mfQbC and mbQbC) paradigms as a general framework for the optimal crystallization design with the aim of minimization of fines and agglomerates in the product. This framework provides an improved alternative to the current industrial practice, by the combined use of feedback control and mathematical models for rapid design of robust crystallization processes. The implementation of the proposed mbQbC framework enhanced the prediction of crystallization events and was able to achieve better control of the crystallization processes by providing controlled heating/cooling cycles, for deagglomeration and fines destruction. The information for parameter estimation and optimization calculations was collected from the experiments in mfQbC part, which additionally also provided a rapid design of robust operating conditions, which subsequently in the mbQbC approach were improved to achieve optimal operation. more...