Supporting the implementation of chemometrics models in bioprocess from development to commercial stage

Manufacturing bioproducts involves using living organisms to produce a substance of interest. Cell culture is a crucial yet highly variable step. Monitoring, predicting, or even controlling the course of this growth requires data. For multiple reasons, the perspective of developing Process Analytical Technology based on chemometrics, rather than proceeding to actual samplings from the bioreactor, is strongly appealing and could lead to full digital twins of bioreactors. This paper addresses multiple roadblocks encountered along this modeling approach: proper model performance evaluation, limited amounts of data at process development stages, up-scaling between development and commercial stages, and regulatory concerns. These challenges are addressed by combining spectrometry data and offline readouts, simple and advanced machine learning techniques, and a good knowledge of the biomanufacturing activity and constraints. This paper uses vaccine data from multiple actual products at different scales to illustrate the considered developments.