A process digital twin framework for biopharmaceutical manufacturing

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).

In response to the increased demand for higher productivity and lower capital costs through equipment utilization optimization, continuous manufacturing has emerged as an advanced alternative to traditional batch processing (7). The FDA’s emphasis for integration of continuous processes within biopharmaceutical manufacturing has further catalyzed the industry’s adoption of this approach (8). In the context of continuous processes, implementation of robust control strategies has an essential role in ensuring process stability. Product quality consistency over time is critical as process perturbations can impact a larger product pool accumulated over time. Rooted in thermodynamics and kinetics, the mechanistic model based digital twin approach enhances the understanding of process dynamics. This advanced insight empowers the formulation of robust control strategies that effectively mitigate the impacts of process variability and disturbances originating from raw materials or upstream unit operations (9).