Advances in Continuous Active Pharmaceutical Ingredient (API) Manufacturing: Real-time Monitoring Using Multivariate Tools
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The implementation of continuous processing technologies for pharmaceutical manufacturing has increased due to its potential to enhance supply chain flexibility, reduce the footprint of the manufacturing facility, and deliver more consistent quality. Additionally, it facilitates extensive, real-time monitoring by sensors and process analytical technology (PAT) tools without perturbing the process. In the presented case study, the use of multivariate tools for the real-time monitoring and retrospective review of a continuous active pharmaceutical ingredient (API) synthesis was evaluated from process development through to commercialization.
A multivariate statistical process monitoring (MSPM) approach summarizing variability in both quality critical (controlled flow rates, temperatures) and non-quality critical parameters (pressures, pump speeds, conductivity) was used to monitor three telescoped chemistry stages of a continuous API synthesis. Four different modeling strategies were presented addressing specific monitoring and analysis requirements during the pharmaceutical development lifecycle.
During development (R&D and commercial facility), the implemented multivariate monitoring resulted in the identification of potential failure modes, a deeper understanding of the natural process variability and accelerated root cause analysis for a recurrent reagent blockage. During manufacturing (commercial facility), the multivariate tool confirmed potential for predictive maintenance and early fault detection.
While the implemented control strategy based on parametric control and offline analytical testing provided the required quality assurance, the multivariate trends provided additional information on process performance. More specifically, they enabled more detailed process understanding during the development of the continuous API synthesis following quality by design (QbD) principles and demonstrated the potential for enhanced process performance during commercial manufacturing.
KeywordsMultivariate statistical process monitoring MSPM Continuous manufacturing PAT Process monitoring Multivariate analysis
We would like to thank Erik Johansson for the support on custom scaling of the process sensors. We would like to thank Ian Barylski for the informatics platform support.
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