During the regulatory requested process validation of pharmaceutical manufacturing\nprocesses, companies aim to identify, control, and continuously monitor process variation and its\nimpact on critical quality attributes (CQAs) of the final product. It is difficult to directly connect the\nimpact of single process parameters (PPs) to final product CQAs, especially in biopharmaceutical\nprocess development and production, where multiple unit operations are stacked together and\ninteract with each other. Therefore, we want to present the application of Monte Carlo (MC)\nsimulation using an integrated process model (IPM) that enables estimation of process capability\neven in early stages of process validation. Once the IPM is established, its capability in risk\nand criticality assessment is furthermore demonstrated. IPMs can be used to enable holistic\nproduction control strategies that take interactions of process parameters of multiple unit operations\ninto account. Moreover, IPMs can be trained with development data, refined with qualification\nruns, and maintained with routine manufacturing data which underlines the lifecycle concept.\nThese applications will be shown by means of a process characterization study recently conducted at\na world-leading contract manufacturing organization (CMO). The new IPM methodology therefore\nallows anticipation of out of specification (OOS) events, identify critical process parameters, and take\nrisk-based decisions on counteractions that increase process robustness and decrease the likelihood\nof OOS events.
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