Robust design of chemical processes with application to bio-manufacturing

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The introduction of an increasing number of bio-products, such as new monoclonal antibodies and new vaccines, has created a clear need for new robust and efficient bioprocesses to manufacture these products. mAbs (monoclonal antibodies) produced in mammalian cell cultures are currently a predominant bioproduct with world-wide sales of $45 billion in 2012 and with a predicted annual growth in demand of 9.5%. For instance, approximately 28 mAbs have been approved, e.g cetuximab (metastatic cancers), trastuzumab (breast cancer), while 350 more are in various stages of clinical trials. Vaccine manufacturing is another major strategic area that is being continuously re-energized by the introduction of new vaccines. This proposal investigates different aspects of robust design of bioprocesses and builds upon research conducted in the previous funding period in two areas: i) metabolic flux balance-based models and ii) simultaneous optimal control and design of chemical processes using imperfect models. The US Food and Drug Administration (FDA) has recently adopted the quality-by-design (QBD) approach whereby the entire bioprocess is a priori designed and certified to provide quality and quantity of the final product within a range of bounds referred to as the design space. In the QBD approach a process can be adjusted within the design space to accommodate variability in raw materials and disturbances in contrast with the traditional approach where the process is operated with mostly fixed operating recipes. To apply the QBD approach it is necessary to search for an optimal design and optimal operating conditions while taking into account a priori expected variability in inputs, e.g. raw materials, and inaccuracies of the models to be used for optimization and control. The long term objective of this research is to develop a method for model-based optimal designs of new bioprocesses or optimal upgrades of existing ones that are robust to model errors and process variations. By explicitly considering robustness to model error and process variability, the work closely fit the QBD approach. To achieve our long term goal, robust modeling, optimization and control tools will be developed via the following four projects: I. development of dynamic metabolic flux balance models (DMFB) for mammalian cells to be used for optimization. II. optimal design of bioprocesses tolerant to model errors and process variabilities. III. integration of DMFBs and multivariate statistical models for optimal design or upgrade of bioprocesses. IV. simultaneous optimal design and nonlinear model predictive control (NMPC) based on dynamic metabolic flux models. I am proposing to train 2 PhD’s (and additional 2 towards the end of the granting period) and 2 MASc’s in different areas of modeling, control and optimization of biotechnological processes. The work includes both theoretical developments in the areas of dynamic metabolic flux analysis, run-to-run optimization and nonlinear predictive control as well as experimentation to calibrate the proposed models and to validate the optimization procedures. Applications to a mammalian cell culture producing mAb and a whooping cough vaccine manufacturing process will be considered. Overall, the proposed model-based robust design tools will benefit the industry in two ways: i-by proposing an approach to bioprocess design that ensures predefined levels of productivity and quality as per the QBD idea and ii- by training HQP with versatile expertise in the application of process systems engineering tools (modeling, control, statistics and optimization) to biotechnological processes.