Extensive amounts of data are generated starting from early development through commercialization of a biologics manufacturing process. Multivariate data analysis is a powerful technique which can be applied to this big data to further our process understanding. However, it is challenging to leverage this technique in the earliest stages such as clone selection and phase I process development, and throughout the development lifecycle. In this work we describe a methodology to perform multivariate data analysis at every stage of process development and use that information to inform continued activities. We also present a case study of its application.<br/>This standardized approach includes development of: (1) generic cell culture models used to monitor and compare the performance of different clones and phase I processes, (2) process-specific models based on phase III development to monitor and troubleshoot process characterization (PC) runs, (3) a scale-independent model built from PC and scale-down model qualification data to support scale-up tech transfer, (4) an enhanced scale-independent model using at-scale data for process performance qualification batch monitoring, and (5) a site-specific model for monitoring routine commercial manufacturing and beyond. Using this approach we will be leveraging data at every stage of process development and developing superior models for process monitoring and performance prediction.

Standardized approach for multivariate analysis of bioreactor data from early development to commercialization

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.