467571 Application of Process Monitoring Data Management Systems to Make Multiple Incremental Improvements to Cell Cultures Processes at the Manufacturing Scale

Friday, November 18, 2016: 4:21 PM
Continental 4 (Hilton San Francisco Union Square)
Christopher C. VanLang1, Erin K. Abbott1, June Axelson2 and Hugh Graham1, (1)Drug Substance Manufacturing Sciences and Technology, Bristol-Myers Squibb, East Syracuse, NY, (2)Manufacturing Sciences and Technology Process Monitoring and Analytics, Bristol-Myers Squibb, East Syracuse, NY

Process monitoring data management systems have been important tools to capture the large number of parameters and conditions that influence both the product quality and productivity of cell culture processes. Recent improvements in process analytical technologies and refinements in process control strategies have improved the biopharmaceutical industry’s ability to gather large, multidimensional data sets that can be used for statistical process monitoring. In this work, we discuss the implementation of a data management platform and its application to build understanding around mammalian cell culture processes at the manufacturing scale. The database comprises of the end to end collection of process information from vial thaw to drug substance release for several commercial biologics produced using mammalian cell culture.

We explore the use of this database to make several incremental improvements to the cell culture process through a series of case studies including responses to health authority requests, technology transfers between multiple facilities, and evaluations of upstream parameters to understand their impact on final drug substance output. These case studies highlight the gains in process knowledge that can be achieved using these data management tools and illustrate some of the challenges in developing these capabilities. While data management tools facilitated the rapid retrieval of available data, there were several obstacles associated with data structure, data acquisition, and the subsequent data analysis. The current work demonstrates the successful application of data management tools to improve the performance and robustness of existing cell culture processes at the manufacturing scale.

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