470558 A Comprehensive Statistics-Based Approach to Proactive Process Analysis Including Multivariate Analysis Methods

Monday, November 14, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Sara Koynov1, Paige Adack2, Conor McCurdy3, Orlaith Crowther3, Michelle Cleary3, Anthony Ruane3, Dearbhla Carr3, Eoin O'Brien3 and Jennifer Sun2, (1)Merck & Co., Inc., Kenilworth, NJ, (2)Merck & Co., Inc., (3)MSD

Following the initial design and validation of a pharmaceutical drug product manufacturing process, the process will undergo further monitoring. For example, those process parameters that are most likely to impact the critical quality attributes, may be monitored over a series of batches. The resulting data can be used to 1) satisfy continued process verification, as part of stage 3 PPQ, or other regulatory needs and 2) ensure the process is in control and increase robustness by proactively improving and optimizing the process (i.e. proactive process analysis).

The approach to process monitoring for continuous improvement purposes can be varied. An example of a comprehensive systematic, statistics-based approach to proactive process analysis is presented here. The approach includes using statistical process control to visualize and identify trends before an impact to quality occurs. Furthermore, the use of multivariate analysis methods, such as principal component analysis (PCA) and partial least squares (PLS), to identify interactions between process parameters and point of cause, in support of root cause investigations, is illustrated.

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See more of this Session: Poster Session: Pharmaceutical
See more of this Group/Topical: Pharmaceutical Discovery, Development and Manufacturing Forum