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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.

Multiple obstacles are driving the digital transformation of the biopharmaceutical industry. Novel digital techniques, often marketed as 'Pharma 4.0', are thought to solve some long-existing obstacles in the biopharma life cycle. Pharma 4.0 concepts, such as cyberphysical systems and dark factories, require data science tools as technological core components. Here, we review current data science applications at various stages of the bioprocess life cycle, including their scopes and data sources. We are convinced that the scope and usefulness of these tools are currently limited by technical and nontechnical problems experienced during their development and deployment. We suggest that the establishment of DevOps mind- and toolsets could improve this situation and would be essential cornerstones in the further development of Pharma 4.0 systems.

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.

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.