RUI WHEATON*, AHSAN MUNIR*, SRI N.G. GOURISETTI, CHRISTOPHER VANLANG,
HUANCHUN CUI, THOMAS ERDENBERGER, JOSEPH SHULTZ, BRIAN TO
*Corresponding authors
National Resilience, Inc., La Jolla, California, United States

Abstract

To enable robustness, facilitate in silico process characterization, and enable an efficient process control strategy, we are developing a digital twin framework using data-driven and mechanistic modeling approaches. The Digital Twin will be transformational in bringing significant value across process lifecycle from real time prediction to cyber-resiliency.
In this paper, we present a visionary perspective on the potential of digital twin technology to assist in biopharmaceutical manufacturing. Our focus is on outlining the strategic framework for a digital twin platform and discussing the conceptual development of a digital shadow for an integrated continuous process. Such digital shadow can be used to determine the impact of expected disturbances, deviations, and uncertainties on product quality. The vision is to use residence time distribution analysis to identify the duration of product diversion in response to the deviation, and allow product impacted by disturbance to be diverted without impacting the reminder of the batch.

REAL TIME CONTROL FOR CONTINUOUS PROCESS
In biopharmaceutical manufacturing, the FDA has advocated the need for enhanced online monitoring and control methods to ensure consistent product quality throughout manufacturing processes (1, 2). A parallel effort is echoed by the European Medicines Agency (EMA), which has issued recommendations on real-time release testing (3). As a result, the biopharmaceutical industry is placing significant emphasis on the development of process monitoring and automated process control strategies. One notable approach, proposed by Myerson et al., involves the development of a digital twin system with relevant variables, running in parallel with operations to predict critical quality attributes in real time (4). Other works include Lu et al. presented a case study involving the shadowing of major unit operations, such as a perfusion bioreactor, packed bed chromatography separation train, and in-line dilution, using mechanistic models (5). Feidl et.al. combined mechanistic models with Raman Spectroscopy to predict the breakthrough curve and monoclonal antibody (mAb) concentration in a chromatography protein capture step (6).