Modeling the Downstream Processing of Monoclonal Antibodies Reveals Cost Advantages for Continuous Methods for a Broad Range of Manufacturing Scales.

The biopharmaceutical industry is evolving in response to changing market conditions, including increasing competition and growing pressures to reduce costs. Single-use (SU) technologies and continuous bioprocessing have attracted attention as potential facilitators of cost-optimized manufacturing for monoclonal antibodies. While disposable bioprocessing has been adopted at many scales of manufacturing, continuous bioprocessing has yet to reach the same level of implementation. In this study, the cost of goods of Pall Life Science's integrated, continuous bioprocessing (ICB) platform is modeled, along with that of purification processes in stainless-steel and SU batch formats. All three models include costs associated with downstream processing only. Evaluation of the models across a broad range of clinical and commercial scenarios reveal that the cost savings gained by switching from stainless-steel to SU batch processing are often amplified by continuous operation. The continuous platform exhibits the lowest cost of goods across 78% of all scenarios modeled here, with the SU batch process having the lowest costs in the rest of the cases. The relative savings demonstrated by the continuous process are greatest at the highest feed titers and volumes. These findings indicate that existing and imminent continuous technologies and equipment can become key enablers for more cost effective manufacturing of biopharmaceuticals.

Optimized Continuous Multicolumn Chromatography Enables Increased Productivities and Cost Savings by Employing More Columns.

The advantages of continuous chromatography with respect to increased capacity are well established. However, the impact of different loading scenarios and total number of columns on the process economics has not been addressed. Here four different continuous multicolumn chromatography (MCC) loading scenarios are evaluated for process performance and economics in the context of a Protein A mAb capture step. To do so, a computational chromatography model is validated experimentally. The model is then used to predict process performance for each of the loading methods. A wide range of feed concentrations and residence times are considered, and the responses of operating binding capacity, specific productivity, and the number of process columns are calculated. Processes that are able to add more columns proved to be up to 65% more productive, especially at feed concentrations above 5 g L-1 . An investigation of the operating costs shows that discrete column sizing and process performance metrics do not always correlate and that the most productive process is not necessarily the most cost effective. However, adding more columns for the non-load steps at higher feed concentrations allows for overall cost savings of up to 32%.