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A Process characterization is a regulatory imperative for process validation within the biopharmaceutical industry. Several individual steps must be conducted to achieve the final control strategy. For that purpose, tools from the Quality by Design (QbD) toolbox are often considered. These tools require process knowledge to conduct the associated data analysis. They include cause and effect analysis, multivariate data analysis, risk assessment and design space evaluation. However, this approach is limited to the evaluation of single unit operations. This is risky as the interactions of the operations may render the control strategy invalid. Hence, a holistic process evaluation is required. Here, we present a novel workflow that shows how simple data analysis tools can be used to investigate the process holistically. This results in a significant reduction of the experimental effort and in the development of an integrated process control strategy. This novel QbD workflow is based on a novel combination of risk assessment and integrated process modeling. We demonstrate this workflow in a case study and show that the herein presented approach can be applied to any biopharmaceutical process. We demonstrate a workflow that can reduce the number of factors and increase the amount of responses within a Design of Experiments (DoE). Consequently, this result demonstrates that experimental costs and time can be reduced by investing more time in thoughtful data analysis.

Graphical abstract, Highlights • Mathematical model for Vero cell growth in batch bioreactors. • Mathematical model for poliovirus proliferation on Vero cells. • Oxygen uptake rate as process analytical technology for simple process monitoring., The production of poliovirus vaccines in adherent Vero cells in batch bioreactors usually consists of a two-step upstream process: (1) Vero cell cultivation on microcarriers and (2) poliovirus proliferation. In this study we developed a mathematical model to describe this two-step process. We introduced the calculation of the oxygen uptake rate (OUR) and a correction of measurement for the sampling effect in order to ensure the high quality data sets. Besides the data of the OUR, we selected glucose concentration, Vero cell concentration and the virus titer for daily in process control to evaluate the progress of the process. With the selected data sets, the described model can accurately describe poliovirus production by Vero cells. Several other regular in process control samples (e.g. lactate concentration, ammonia concentration, and amino acids concentration) were excluded from the model, simplifying the process control analysis and minimizing labor.

Root cause analysis (RCA) is one of the most prominent tools used to comprehensively evaluate a biopharmaceutical production process. Despite of its widespread use in industry, the Food and Drug Administration has observed a lot of unsuitable approaches for RCAs within the last years. The reasons for those unsuitable approaches are the use of incorrect variables during the analysis and the lack in process understanding, which impede correct model interpretation. Two major approaches to perform RCAs are currently dominating the chemical and pharmaceutical industry: raw data analysis and feature-based approach. Both techniques are shown to be able to identify the significant variables causing the variance of the response. Although they are different in data unfolding, the same tools as principal component analysis and partial least square regression are used in both concepts. Within this article we demonstrate the strength and weaknesses of both approaches. We proved that a fusion of both results in a comprehensive and effective workflow, which not only increases better process understanding. We demonstrate this workflow along with an example. Hence, the presented workflow allows to save analysis time and to reduce the effort of data mining by easy detection of the most important variables within the given dataset. Subsequently, the final obtained process knowledge can be translated into new hypotheses, which can be tested experimentally and thereby lead to effectively improving process robustness.

Bioprocess development generates extensive datasets from different unit operations and sources (e.g. time series, quality measurements). The development of such processes can be accelerated by evaluating all data generated during the experimental design. This can only be achieved by having a clearly defined data logging and analysis strategy. The latter is described in this manuscript. It consists in a combination of a feature based approach along with principal component analysis and partial least square regression. Application of this combined strategy is illustrated by applying it in an upstream processing (USP) case study. Data from the development and optimization of an animal component free USP of Sabin inactivated poliovirus vaccine (sIPV) was evaluated. During process development, 26 bioreactor runs at scales ranging from 2.3 to 16 L were performed. Several operational parameters were varied, and data was routinely analyzed following a design of experiments (DoE) methodology. With the strategy described here, it became possible to scrutinize all data from the 26 runs in a single data study. This included the DoE response parameters, all data generated by the bioreactor control systems, all offline data, and its derived calculations. This resulted in a more detailed, reliable and exact view on the most important parameters affecting bioreactor performance. In this case study, the strategy was applied for the analysis of previously produced data. Further development will use this data analysis methodology for continuous enhancing and accelerating process development, intensified DoE and integrated process modelling.

Highlights • Vero cells were grown in batch, semi-batch, perfusion and recirculation strategies. • At high cell densities (to 5 × 106 cells mL−1) cells were infected with poliovirus. • Increased cell densities allowed 3 fold increase in d-antigen yield. • Cell specific d-antigen yields were lower at higher cell densities. • The semi-batch cultivation strategy is most promising for optimization., Vero cells were grown adherent to microcarriers (Cytodex 1; 3 g L−1) using animal component free media in stirred-tank type bioreactors. Different strategies for media refreshment, daily media replacement (semi-batch), continuous media replacement (perfusion) and recirculation of media, were compared with batch cultivation. Cell densities increased using a feed strategy from 1 × 106 cells mL−1 during batch cultivation to 1.8, 2.7 and 5.0 × 106 cells mL−1 during semi-batch, perfusion and recirculation, respectively. The effects of these different cell culture strategies on subsequent poliovirus production were investigated. Increased cell densities allowed up to 3 times higher d-antigen levels when compared with that obtained from batch-wise Vero cell culture. However, the cell specific d-antigen production was lower when cells were infected at higher cell densities. This cell density effect is in good agreement with observations for different cell lines and virus types. From the evaluated alternative culture methods, application of a semi-batch mode of operations allowed the highest cell specific d-antigen production. The increased product yields that can easily be reached using these higher cell density cultivation methods, showed the possibility for better use of bioreactor capacity for the manufacturing of polio vaccines to ultimately reduce vaccine cost per dose. Further, the use of animal-component-free cell- and virus culture media shows opportunities for modernization of human viral vaccine manufacturing.

Risk assessments (RAs) are frequently conducted to assess the potential effect of process parameters (PPs) on product quality attributes (e.g., a critical quality attribute (CQA)). To evaluate the PPs criticality the risk priority number (RPN) for each PP is often calculated. This number is generated by the multiplication of three factors: severity, occurrence, and detectability. This mathematical operation may result in some potential errors due to the multiplication of ordinal scaled values and the assumption that the factors contribute equally to the PPs criticality. To avoid these misinterpretations and to assess the out of specification (OOS) probability of the drug substance, we present a novel and straightforward mathematical algorithm. This algorithm quantitatively describes the PPs effect on each CQA assessed within the RA. The transcription of severity and occurrence to model effect sizes and parameters distribution are the key elements of the herein developed approach. This approach can be applied to any conventional RA within the biopharmaceutical industry. We demonstrate that severity and occurrence contribute differently to the PP criticality and compare these results with the RPN number. Detectability is used in a final step to precisely sort the contribution of each factor. To illustrate, we show the misinterpretation risk of the PP critically by using the conventional RPN approach.

We report the large-scale production of VHH antibody fragments, raised against the azo-dye RR-6, by the yeast Saccharomyces cerevisiae. The secretion efficiency of the VHH fragments was correlated with their overall hydrophobicity. With a 15 m3 fed-batch fermentation, 1.3 kg VHHs were produced. Purification was done by removal of biomass and product concentration. During induction, the specific production rate qp was 0.213 g.[kg dryweight]−1.h−1.

Worldwide efforts to eradicate polio caused a tipping point in polio vaccination strategies. A switch from the oral polio vaccine, which can cause circulating and virulent vaccine derived polioviruses, to inactivated polio vaccines (IPV) is scheduled. Moreover, a manufacturing process, using attenuated virus strains instead of wild-type polioviruses, is demanded to enhance worldwide production of IPV, especially in low- and middle income countries. Therefore, development of an IPV from attenuated (Sabin) poliovirus strains (sIPV) was pursued. Starting from the current IPV production process based on wild type Salk strains, adaptations, such as lower virus cultivation temperature, were implemented. sIPV was produced at industrial scale followed by formulation of both plain and aluminium adjuvanted sIPV. The final products met the quality criteria, were immunogenic in rats, showed no toxicity in rabbits and could be released for testing in the clinic. Concluding, sIPV was developed to manufacturing scale. The technology can be transferred worldwide to support post polio-eradication biosafety goals.

Saccharomyces cerevisiae carrying a multicopy integrated expression vector containing the gene encoding a Llama antibody fragment, has been cultivated in continuous cultures both under carbon and nitrogen limiting conditions with galactose as the sole carbon source. VHH-R2 expression was under control of the inducible GAL7 promoter. Induction however, was independent of the galactose consumption rate and maximal at all growth rates. VHH-R2 was secreted with 70% efficiency at all growth rates and under both limitations. The specific production rate increased linear with increasing growth rate in a growth-associated manner. However, when grown under nitrogen limitation at growth rates above 0.09 h(-1), the extracellular VHH-R2 was less active or part of the VHH-R2 was in an inactive form. From our results we conclude that to obtain a maximal amount of VHH per kilogram biomass per hour, VHH production should be done in carbon limited continuous cultures at high specific growth rates.