Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals.

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.

Workflow for multi-analyte bioprocess monitoring demonstrated on inline NIR spectroscopy of P. chrysogenum fermentation.

Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate analysis has been applied in bioprocesses for a couple of decades. Nevertheless the papers published in this field are case-specific and do not focus on providing the community generic workflows to conduct experiments, especially as a standard Design of Experiment (DoE) for a multi-analyte process might require overwhelming amount of measurements. In this paper, a workflow for feasibility studies and inline implementation of FT-NIR spectrometer in multi-analyte fermentation processes is presented. The workflow is applied to Penicillium crysogenum fermentation, where the similarities in chemical structures and growth trends between the key analytes together with the aeration and growing fungi make the task challenging: first, the pure analytes are measured off-line with FT-NIR and clustered using principal component analysis. To study the separability of the gained clusters, a DoE approach by spiking is applied. The multivariate modelling of the separable analytes is conducted using the off-line and inline data followed by a comparison of the properties of the different models. Finally, the model output constraints are set by means of outlier diagnostics. As a result, biomass, penicillin (PEN), phenoxyacetic acid (POX), ammonia and biomass were shown to be separable with root mean square error of predictions of 2.62 g/l, 0.34 g/l, 0.51 g/l and 18.3 mM, respectively. Graphical abstract Flowchart illustrating the workflow for feasibility studies and implementation of models for inline monitoring of Ammonia, Biomass, Phenoxyacetic acid and Penicillin.

Quantitative feature extraction from the Chinese hamster ovary bioprocess bibliome using a novel meta-analysis workflow.

The scientific literature concerning Chinese hamster ovary (CHO) cells grows annually due to the importance of CHO cells in industrial bioprocessing of therapeutics. In an effort to start to catalogue the breadth of CHO phenotypes, or phenome, we present the CHO bibliome. This bibliographic compilation covers all published CHO cell studies from 1995 to 2015, and each study is classified by the types of phenotypic and bioprocess data contained therein. Using data from selected studies, we also present a quantitative meta-analysis of bioprocess characteristics across diverse culture conditions, yielding novel insights and addressing the validity of long held assumptions. Specifically, we show that bioprocess titers can be predicted using indicator variables derived from viable cell density, viability, and culture duration. We further identified a positive correlation between the cumulative viable cell density (VCD) and final titer, irrespective of cell line, media, and other bioprocess parameters. In addition, growth rate was negatively correlated with performance attributes, such as VCD and titer. In summary, despite assumptions that technical diversity among studies and opaque publication practices can limit research re-use in this field, we show that the statistical analysis of diverse legacy bioprocess data can provide insight into bioprocessing capabilities of CHO cell lines used in industry. The CHO bibliome can be accessed at http://lewislab.ucsd.edu/cho-bibliome/.

Observability analysis of biochemical process models as a valuable tool for the development of mechanistic soft sensors.

By enabling the estimation of difficult-to-measure target variables using available indirect measurements, mechanistic soft sensors have become important tools for various bioprocess monitoring and control scenarios. Despite promising higher process efficiencies and increased process understanding, widespread application of soft sensors has been stalled by uncertainty about the feasibility and reliability of their estimations given present process analytical constraints. Observability analysis can provide an indication of the possibility and reliability of soft sensor estimations by analyzing the structural properties of first-principle (mechanistic) models. In addition, it can provide a criteria for selection of suitable measurement methods with respect to their information content; thereby leading to successful implementation of soft sensors in bioprocess development and manufacturing environments. We demonstrate the utility of observability analysis for two classes of upstream bioprocesses: the processes involving growth and ethanol formation by Saccharomyces cerevisiae and the process of penicillin production by Penicillium chrysogenum. Results obtained from laboratory-scale cultivations in addition to in-silico experiments enable a comparison of theoretical aspects of observability analysis and the real-life performance of soft sensors. By taking the expected error of measurements provided to the soft sensor into account, an innovative scaling approach facilitates a higher degree of comparability of observability results among various measurement configurations and process conditions.

Advanced Development Strategies for Biopharmaceutical Cell Culture Processes.

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 in the QbD paradigm: manufacturing of biotech therapeutics.

In the quality by design (QbD) paradigm, global regulatory agencies have introduced the concepts of quality risk management and knowledge management (KM) as enablers for an enhanced pharmaceutical quality system. Although the concept of quality risk management has been well elucidated in the literature, the topic of KM has received relatively scant attention. In this paper we present an opinion on KM in the QbD paradigm as it relates to the manufacturing of biotech therapeutic products. Both academic and industrial viewpoints have been considered and key gaps have been elucidated. The authors conclude that there is an urgent need for the biotech industry to create efficient KM approaches if they wish to be successful in QbD implementation.