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.

ICH M7 provides several risk-based control options to manage mutagenic and potentially mutagenic impurities (MI and PMIs) in the manufacture of pharmaceuticals. A Working Group in the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ, www.iqconsortium.org) performed a survey of pharmaceutical manufacturers to gain insight into the use and regulatory acceptance of mutagenic impurity control strategies and control options across the industry. Information on the regulatory acceptance of ICH M7 control strategies was collected on late-stage clinical and commercial programs with regulatory feedback from the FDA, EMA, and PMDA. The data show a preference for ICH M7 Option 4 as it allows the utilization of process knowledge to reduce analytical testing without any compromise on patient safety. This approach appeared globally acceptable when appropriately applied. The survey data collected show that the ability of a manufacturing process to purge impurities is a strong indicator of purity control, and the data provide additional evidence that the purge factor calculation as proposed by Teasdale et al. produces a conservative prediction of the purging ability of a process. As such, the use of predicted purge factors and purge ratio assessments relative to the required process purge provides a sound framework for mutagenic impurity controls. Experimental purge data may be generated to support Option 4 strategies when predicted purge factors offer insufficient assurance for process control.

No abstract

No abstract

No abstract