Application of PAT in pharmaceutical manufacturing: model-based control of particle size distribution in anti-solvent aided crystallization

BACKGROUND: Crystallization plays a key role in determining the physiochemical properties of an active pharmaceutical solid, which are crucial for drug product performance as well as processibility in downstream operations. Precise control of crystallization parameters is hence necessary to maintain drug efficacy and consistency during active pharmaceutical ingredient (API) manufacturing. In our earlier work on crystallization of dexlansoprazole API, an empirical model was established with reactor temperature and addition time of anti-solvent identified as critical process parameters (CPPs) and particle size distribution (PSD) as critical quality attribute (CQA). In this study we demonstrate model-based control of particle size when disruptive incidences occur under a process analytical technology framework. RESULTS: Experiments highlighted the deviation in PSD (CQA) on performing a planned disruption event in the form of temporary shutting down the anti-solvent feeding dosing pump. Once the deviation had occurred due to process disruption, the desired set-point of particle size was achieved by making the desired changes estimated from model equations for individual CPPs. The change was signaled to the temperature control system or the dosing pump, which then set the revised process conditions in real time during crystallization. CONCLUSION: The study utilizes an empirical model to achieve the desired particle size by making suitable real-time changes in CPP in view of process disruption. The case study aligns with the paradigm of quality by design, which expects manufacturers to understand their processes and their products and have in-built controls to ensure that the resulting product consistently meets the approved specifications. (C) 2023 Society of Chemical Industry.

Automated summary

This study demonstrates the use of an empirical model to achieve the desired particle size by making suitable real-time changes in critical process parameters (CPPs) in view of process disruption. It aligns with the paradigm of quality by design, ensuring consistent product quality through process understanding and control.