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IBC Poster Prize for "Collaborative Transformation of Biotherapeutics"
For the second year in a row, Bend Research has been awarded a poster prize at IBC's Cell Line Conference for the poster titled, "Collaborative Transformation of Biotherapeutics." Download a copy of the winning poster here and read the full abstract below.
The transformation of biotherapeutics will require a new level of collaboration among a wide variety of disciplines encompassing biology, engineering, advanced process control, signal processing, modeling, molecular biology, and higher mathematics. By leveraging knowledge from each of these disciplines, the design, optimization, and control of biotherapeutic production can be revolutionized. Since the characteristics of the product depend directly on the state of the cell population, quantifying and understanding the cell state is paramount to achieving process improvements, meeting quality-by-design (QBD) requirements, and shortening development timelines. Here, we present an emerging process-development methodology that is based on applying novel and existing bioreactor monitoring technologies to existing bioreactor processes, coupled with applied mathematics and data-integration techniques across disciplines. This approach employs the use of dielectric spectroscopy (DS), other enhanced process-analytical technologies (PATs), and cell-based bioreactor models with a simple, compact device that automatically obtains samples aseptically at specified intervals for off-line analysis. Cell-state information gained from these individual tools is then coupled using data-integration and applied-mathematics techniques to increase process understanding at the cellular level under different growth conditions. This poster presents a case study demonstrating a uniquely detailed look at cellular-level information to investigate apoptosis in CHO cells in bioreactors and shake flasks.
An “engineering-centric”, multi-disciplinary approach to understand “cell state” offers a unique platform for developing robust processes. The methodology described here offers an improved way to turn large raw data sets into useful guidance for process monitoring and development. This methodology enables better use of existing data, as well as strategies to generate more and higher-quality data sets to meet the dynamic nutrient requirements of cell cultures. In addition, it allows responsive control of the system, positively influencing the behavior of the cell population.
Authors: Lynn A. Davis, Lisa J. Graham, Brandon J. Downey, Jeffrey F. Breit, and Brian W. Russell
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