Multivariate phase-dependent optimization of bioprocesses boosts performance and quality—Why timing (of exposure) matters

Abstract

Applying a single parameter set to describe complex mammalian kinetics often is too simplistic, as it fails to capture sensitive cell-to-environment interactions that may be exploited to optimize production performance. To resolve this time dependency, intra-experimental parameter shifts as part of design of dynamic experiments (DoDE) can be performed to study mammalian growth and production kinetics in fed-batch processes. This enables growth phase-dependent optimization, aligned with cellular requirements. Here, we provide a comprehensive, head-to-head comparison of our phase-dependent optimization approach with intra-experimental shifts of process parameters to a static optimization that retains parameter settings through the entire bioprocess. Showcasing a monoclonal antibody production process development scenario, the study examines growth phase-dependent effects of temperature (T) and dissolved oxygen (DO) together with time-invariant parameters for feed and seeding cell density. While the static optimization suggests settings near the center of the design space, phase-dependent optimization finds an optimum by shifting T and DO between the exponential growth, transition, and production phases. Overall, the phase-wise optimized process gives an experimentally validated ~30% increase in product titer while maintaining comparable product quality. Furthermore, the approach breaks the correlation between product titer and acidic charged variants: both depend on T but at different timeframes. Additionally, DoDE uncovers a crucial interaction between T and DO, with low T and high DO during the exponential growth phase, leading to strong lactate accumulation. The data demonstrate the advantages of phase-dependent optimization enabled by DoDE. The results may serve as a good practice example for follow-up research.