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A soft sensor that combines data from Raman spectroscopy and off-gas analyzers with a dynamic mechanistic bioprocess model was investigated for online monitoring of the physiological characteristics of Penicillium chrysogenum fed-batch cultivations. A systematic workflow based on nonlinear observability analysis was established for accelerating and improving the process of soft-sensor development. Using in situ Raman spectroscopy, it was possible to perform accurate and frequent measurements of the penicillin concentration in the bioreactor, which were combined with measurements of respiratory rates. Using a particle filter algorithm, the soft sensor allowed for the online estimation of the biomass concentration, the specific growth rate, and the specific penicillin production rate.

The shift from empirical to science-based process development is considered to be a key factor to increase bioprocess performance and to reduce time to market for biopharmaceutical products in the near future. In the last decade, expanding knowledge in systems biology and bioprocess technology has delivered the foundation of the scientific understanding of relationships between process input parameters and process output features. Based on this knowledge, advanced process development approaches can be applied to maximize process performance and to generate process understanding. This review focuses on tools which enable the integration of physiological knowledge into cell culture process development. As a structured approach, the availability and the proposed benefit of the application of these tools are discussed for the subsequent stages of process development. The ultimate aim is to deliver a comprehensive overview of the current role of physiological understanding during cell culture process development from clone selection to the scale-up of advanced control strategies for ensuring process robustness.

Knowledge Management (KM) is a key enabler for achieving quality in a lifecycle approach for production of biopharmaceuticals. Due to the important role that it plays towards successful implementation of Quality by Design (QbD), an analysis of KM solutions is needed. This work provides a comprehensive review of the interface between KM and QbD-driven biopharmaceutical production systems as perceived by academic as well as industrial viewpoints. A comprehensive set of 356 publications addressing the applications of KM tools to QbD-related tasks were screened and a query to gather industrial inputs from 17 major biopharmaceutical organizations was performed. Three KM tool classes were identified as having high relevance for biopharmaceutical production systems and have been further explored: knowledge indicators, ontologies, and process modeling. A proposed categorization of 16 distinct KM tool classes allowed for the identification of holistic technologies supporting QbD. In addition, the classification allowed for addressing the disparity between industrial and academic expectations regarding the application of KM methodologies. This is a first of a kind attempt and thus we think that this paper would be of considerable interest to those in academia and industry that are engaged in accelerating development and commercialization of biopharmaceuticals.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-016-2043-9) contains supplementary material, which is available to authorized users.