A near-infrared (NIR) calibration was developed using an efficient offline approach to enable a quantitative partial least-squares (PLS) chemometric model to measure and monitor the concentration of active pharmaceutical ingredients (API) in powder blends in the feed frame (FF) of a tablet press. The approach leveraged an offline "feed frame table," which was designed to mimic the full process from a NIR measurement perspective, thereby facilitating a more robust model by allowing more sources of variability to be included in the calibration by minimizing the consumption of API and other raw materials. The design of experiment (DOE) for the calibration was established by an initial risk assessment and included anticipated variability from factors related to formulation, process, environment, and instrumentation. A test set collected on the feed frame table was used to refine the PLS model. Additional fully independent test sets collected from the continuous drug product manufacturing process not only demonstrated the accuracy and precision of the model but also illustrated its robustness to material variability and process variability including mass flow rate and feed frame paddle speed. Further, it demonstrated that a calibration can be generated on the offline feed frame table and then successfully implemented on the full process equipment in a robust manner. Additional benefits of using the feed frame table include streamline model monitoring and maintenance activities in a manufacturing setting. The real-time monitoring enabled by this offline calibration approach can be useful as a key component of the control strategy for continuous manufacturing processes for drug products, including detecting special cause variations such as transient disturbances and enabling product collection/rejection based upon predetermined concentration limits, and may play an important role in enabling real-time release testing (RTRt) for manufactured pharmaceutical products.

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