Integrating Design of Experiments and Principal Component Analysis to Reduce Downstream Cost of Goods in Monoclonal Antibody Production

We present a framework to prioritize strategies for monoclonal antibody (mAb) second-generation process development, or post-approval optimization. Design of experiments (DoE), in conjunction with principal component analysis (PCA), were employed to identify process parameters that had the most impact on downstream purification cost of goods. Statistically significant parameters were identified through a DoE study, while the PCA characterization was applied as an independent tool to further elucidate the relative importance of these parameters. A stochastic approach incorporating process uncertainties was used to illustrate the distribution of downstream cost of goods under different process conditions. This framework offered insights on the relative contribution of each parameter to downstream cost of goods, and generated frequency distribution of the downstream cost of goods by incorporating process uncertainty. Such systematic approach to prioritize development strategies under compressed timelines could be useful for biopharmaceutical companies to achieve a competitive advantage.