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Inactivated polio vaccine production using attenuated Sabin strains (sIPV) instead of wild type polio viruses (cIPV) is an initiative encouraged by the World Health Organization. This use of attenuated viruses is preferred as it reduces risks related to potential outbreaks during IPV production. Previously, an sIPV production process was set up based on the cIPV production process. Optimizing this process while using only animal component free (ACF) substances allows reduction of operational costs and mitigates risks of adverse effects related with animal derived compounds. Here, development of a process for production of sIPV using only ACF compounds, is described. The upstream process required a change in cell growth medium from serum-containing medium to ACF medium, while virus production media remained the same as the already used M199 medium was free of animal components. In the downstream process multiple modifications in existing unit operations were made including addition of a diafiltration step prior to inactivation. After optimizing each unit operation, robustness of the whole process was demonstrated using design of experiments (DoE) methodology. By using DoE we were able to vary different process parameters across unit operations to assess the impact on our quality attributes. The developed process was robust as the observed variation for quality attributes due to differences in process parameters remained within specification. The resulting pilot process showed not only to be robust, but also to have a considerable higher product yield when compared to the serum containing sIPV process. Product yields are now comparable to the cIPV process based on using wild type polio viruses. Moreover, the potency of the produced vaccine was comparable that of cIPV vaccine. The developed ACF sIPV process can be transferred to vaccine manufacturers at the end-of pre-clinical development phase, at lab- or pilot scale, before production of clinical trial material., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

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.

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., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.

The occurrence of anaerobic oxidation of methane (AOM) and trace methane oxidation (TMO) was investigated in a freshwater natural gas source. Sediment samples were taken and analyzed for potential electron acceptors coupled to AOM. Long-term incubations with (13)C-labeled CH4 ((13)CH4) and different electron acceptors showed that both AOM and TMO occurred. In most conditions, (13)C-labeled CO2 ((13)CO2) simultaneously increased with methane formation, which is typical for TMO. In the presence of nitrate, neither methane formation nor methane oxidation occurred. Net AOM was measured only with sulfate as electron acceptor. Here, sulfide production occurred simultaneously with (13)CO2 production and no methanogenesis occurred, excluding TMO as a possible source for (13)CO2 production from (13)CH4. Archaeal 16S rRNA gene analysis showed the highest presence of ANME-2a/b (ANaerobic MEthane oxidizing archaea) and AAA (AOM Associated Archaea) sequences in the incubations with methane and sulfate as compared with only methane addition. Higher abundance of ANME-2a/b in incubations with methane and sulfate as compared with only sulfate addition was shown by qPCR analysis. Bacterial 16S rRNA gene analysis showed the presence of sulfate-reducing bacteria belonging to SEEP-SRB1. This is the first report that explicitly shows that AOM is associated with sulfate reduction in an enrichment culture of ANME-2a/b and AAA methanotrophs and SEEP-SRB1 sulfate reducers from a low-saline environment.

The use of methane and acetate as electron donors for biological reduction of thiosulphate in a 5-L laboratory membrane bioreactor was studied and compared to disproportionation of thiosulphate as competing biological reaction. The reactor was operated for 454 days in semi-batch mode; 30 % of its liquid phase was removed and periodically replenished (days 77, 119, 166, 258, 312 and 385). Although the reactor was operated under conditions favourable to promote thiosulphate reduction coupled to methane oxidation, thiosulphate disproportionation was the dominant microbial process. Pyrosequencing analysis showed that the most abundant microorganisms in the bioreactor were phototrophic green sulphur bacteria (GSB) belonging to the family Chlorobiaceae and thiosulphate-disproportionating bacteria belonging to the genus Desulfocapsa. Even though the reactor system was surrounded with opaque plastic capable of filtering most of the light, the GSB used it to oxidize the hydrogen sulphide produced from thiosulphate disproportionation to elemental sulphur. Interrupting methane and acetate supply did not have any effect on the microbial processes taking place. The ultimate goal of our research was to develop a process that could be applied for thiosulphate and sulphate removal and biogenic sulphide formation for metal precipitation. Even though the system achieved in this study did not accomplish the targeted conversion using methane as electron donor, it does perform microbial conversions which allow to directly obtain elemental sulphur from thiosulphate.

Anaerobic methane oxidation coupled to sulphate reduction and the use of ethane and propane as electron donors by sulphate-reducing bacteria represent new opportunities for the treatment of streams contaminated with sulphur oxyanions. However, growth of microbial sulphate-reducing populations with methane, propane or butane is extremely slow, which hampers research and development of bioprocesses based on these conversions. Thermodynamic calculations indicate that the growth rate with possible alternative terminal electron acceptors such as thiosulphate and elemental sulphur may be higher, which would facilitate future research. Here, we investigate the use of these electron acceptors for oxidation of methane, ethane and propane, with marine sediment as inoculum. Mixed marine sediments originating from Aarhus Bay (Denmark) and Eckernförde Bay (Germany) were cultivated anaerobically at a pH between 7.2 and 7.8 and a temperature of 15 °C in the presence of methane, ethane and propane and various sulphur electron acceptors. The sulphide production rates in the conditions with methane, ethane and propane with sulphate were respectively 2.3, 2.2 and 1.8 μmol S L(-1) day(-1). For sulphur, no reduction was demonstrated. For thiosulphate, the sulphide production rates were up to 50 times higher compared to those of sulphate, with 86.2, 90.7 and 108.1 μmol S L(-1) day(-1) for methane, ethane and propane respectively. This sulphide production was partly due to disproportionation, 50 % for ethane but only 7 and 14 % for methane and propane respectively. The oxidation of the alkanes in the presence of thiosulphate was confirmed by carbon dioxide production. This is, to our knowledge, the first report of thiosulphate use as electron acceptor with ethane and propane as electron donors. Additionally, these results indicate that thiosulphate is a promising electron acceptor to increase start-up rates for sulphate-reducing bioprocesses coupled to short-chain alkane oxidation.