Abstract
Modular plants using intensified continuous processes represent an appealing concept for the production of pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes; besides, it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and is based on a compact nuclear magnetic resonance (NMR) spectrometer. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including robust evaluation of sensor data. Beyond monitoring the product quality, online NMR data was used in a new iterative optimization approach to maximize the plant profit and served as a reliable reference for the calibration of a near-infrared (NIR) spectrometer. The overall approach was demonstrated on a commercial-scale pilot plant using a metal-organic reaction with pharmaceutical relevance.Graphical abstract
Highlights
The pharmaceutical industry is making considerable efforts to establish continuous manufacturing of active pharmaceuticalSimon Kern and Lukas Wander contributed to this work.Electronic supplementary material The online version of this article contains supplementary material, which is available to authorized users.Online quality monitoring and model-based control of critical quality attributes (CQAs) are required to ensure the desired product quality and to run a continuous process in an optimal way [6]
Peak signals within the spectra of compact nuclear magnetic resonance (NMR) devices tend to spread and overlap due to the weaker magnetic field of permanent magnets compared to instruments with higher magnetic field strengths
A spectral deconvolution method was applied that relies solely on pure component NMR spectra of the substances expected in the chemical process
Summary
The effort to develop such integrated control solutions slows down the implementation of new continuous API processes considerably and hampers flexibility when making many different products. Process analytical methods such as online near infrared (NIR), UV/VIS, or Raman spectroscopy are typically employed for online quality monitoring [7,8,9]. The calibration of such instruments is usually expensive and time consuming. A calibration model correlating spectral sensor data to the corresponding chemical compositions has to be fitted, validated, and maintained
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