Monitoring small-scale bioreactor studies for media development using polarized total synchronous fluorescence spectroscopy (pTSFS) and synchronous light scattering (SyLS)

Biopharmaceutical process development often involves the use of small-scale bioreactors (SSBR) for optimizing media formulations and process conditions during scale up to commercial scale production. Two key process parameters (CPP) used in SSBR studies are protein titre and viable cell density (VCD). Here, we explore the efficacy of parallel polarized total synchronous fluorescence spectroscopy (TSFS_||) and Synchronous Light Scattering (SyLS_||) to qualitatively monitor these CPPs and quantitatively predict titre and VCD for a large-scale cell culture media optimization SSBR study. The study involved 71 different media formulations (50+ components each), and the bioprocess was run for 13 days or more. Samples were extracted at set times (Day 0, 3, 9, and 13) and clarified by centrifugation. TSFS_|| spectra showed significant emission changes along with increased light scatter over the course of the bioprocess. SyLS_|| measurements strongly correlated with particle size data obtained from Dynamic Light Scattering but did not correlate well with VCD probably because of the centrifugation-based sample preparation. Statistical and principal component analysis (PCA) of the pTSFS data showed that spectral variation was greater between media formulations than due to the evolving bioprocess. This prevented the development of accurate global prediction models for media performance (e.g., predicting product titre at day 9 from media spectra measured at day 0). However, classification methods were successfully used to select media subsets with better quantitative prediction accuracy based on spectral similarities. A practical binary (high/low performance) classification model based on Support Vector Machines was generated for media formulation screening. Combining emission and scatter measurements with multivariate data analysis provides a more holistic, multi-attribute bioprocess monitoring method that minimizes the need to use different offline analytical methods. This methodology can be used to monitor process trajectories and deviations, and ultimately be used to predict bioprocess CPPs when implemented on production scale processes where there is much less compositional variation in the media. We believe this SSBR-pTSFS/SyLS approach will provide a valuable resource to develop the design/parameter space for in-process monitoring at production scale from early-stage process/media development studies.