(164c) Development of Process Analytical Technology (PAT) Methods for Online Control of Glycosylation Profiles During Monoclonal Antibody (MAb) Production

Monoclonal antibody (MAbs) production, the fastest growing sector of the biopharmaceutical industry (Farid, 2009), employs a highly complex and labor intensive process that often involves more than 10 production and purification steps, with processing times ranging from 6-9 months. The ultimate manufacturing objective is the production of therapeutically effective MAbs, with precisely specified product quality attributes and bioactivity, making it essential that the production process be designed and operated to meet these objectives consistently. Of the many parameters and cellular processes that affect the quality and bioactivity of MAbs, glycosylation, a post-translational modification in which carbohydrates are added to a protein, is arguably the most important (Fussenegger, 2002; Geyer, 2006). However, unlike other cellular processes such as DNA replication and protein production, glycosylation has no master template; glycan formation and attachment therefore are subject to variability and are often non-uniform. Because precise glycosylation patterns are essential for MAb effectiveness, the demands on today's quality assurance and effectiveness practices are predicated on ?making? product with the ?correct? glycosylation pattern. With current technology, however, it is only possible to determine glycosylation patterns post-production. As a result of the FDA's Quality Initiative in 2002, it is now no longer sufficient for biopharmaceutical manufacturers to test for final product quality post-production; rather, the FDA now requires the implementation of strategies to ensure consistent product quality during production. Such strategies, which will involve active monitoring and effective control of key process variables that are critical to product quality, are yet to be implemented fully in the biopharmaceutical industry for a variety of reasons, mostly attributable to the complexity of these bioprocesses, the non-availability of on-line measurements, and the lack of comprehensive control paradigms tailor-made for such processes.

Our goal is to develop such a comprehensive strategy for effective on-line, real-time control of biopharmaceutical manufacturing processes, and to demonstrate and validate it with a model system producing monoclonal antibodies. Our approach is based on a combination of multi-scale modeling (consisting of macro (bioreactor), meso (cell), and micro (Golgi apparatus) level models), hierarchical control, and state estimation. The experimental validation, proceeding simultaneously with the theoretical development, involves a novel bioreactor system equipped with an OPC interface. The presentation will include details of the process development and progress to date on the mathematical models. Preliminary results on in situ control of cellular metabolite concentrations will also be discussed.

References

Farid. 2009. Economic Considerations: Economic Drivers and Trade-Offs in Antibody Purification Processes. Supplement to BioPharm International. 22:37-42.

Fusseneger, M. and M. Betenbaugh. 2002. Metabolic Engineering II. Eukaryotic System. Biotech. and Bioeng. 79: 509-531.

Geyer, H. and R. Geyer. 2006. Strategies for analysis of glycoprotein glycosylation. Biochimica et Biophysica Acta 1764. 1853-1869.