Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics.

Cell line development is an essential stage in biopharmaceutical development that often lies on the critical path. Failure to fully characterise the lead clone during initial screening can lead to lengthy project delays during scale-up, which can potentially compromise commercial manufacturing success. In this study, we propose a novel cell line development methodology, referenced as CLD 4, which involves four steps enabling autonomous data-driven selection of the lead clone. The first step involves the digitalisation of the process and storage of all available information within a structured data lake. The second step calculates a new metric referenced as the cell line manufacturability index (MI CL) quantifying the performance of each clone by considering the selection criteria relevant to productivity, growth and product quality. The third step implements machine learning (ML) to identify any potential risks associated with process operation and relevant critical quality attributes (CQAs). The final step of CLD 4 takes into account the available metadata and summaries all relevant statistics generated in steps 1-3 in an automated report utilising a natural language generation (NLG) algorithm. The CLD 4 methodology was implemented to select the lead clone of a recombinant Chinese hamster ovary (CHO) cell line producing high levels of an antibody-peptide fusion with a known product quality issue related to end-point trisulfide bond (TSB) concentration. CLD 4 identified sub-optimal process conditions leading to increased levels of trisulfide bond that would not be identified through conventional cell line development methodologies. CLD 4 embodies the core principles of Industry 4.0 and demonstrates the benefits of increased digitalisation, data lake integration, predictive analytics and autonomous report generation to enable more informed decision making.

Data integrity within the biopharmaceutical sector in the era of Industry 4.0.

Data Integrity (DI) in the highly regulated biopharmaceutical sector is of paramount importance to ensure decisions on meeting product specifications are accurate and hence assure patient safety and product quality. The challenge of ensuring DI within this sector is becoming more complex with the growing amount of data generated given increasing adoption of process analytical technology (PAT), advanced automation, high throughput microscale studies, and managing data models created by machine learning (ML) tools. This paper aims to identify DI risks and mitigation strategies in biopharmaceutical manufacturing facilities as the sector moves towards Industry 4.0. To achieve this, the paper examines common DI violations and links them to the ALCOA+ principles used across the FDA, EMA, and MHRA. The relevant DI guidelines from the ISPE's GAMP5 and ISA-95 standards are also discussed with a focus on the role of validated computerised and automated manufacturing systems to avoid DI risks and generate compliant data. The paper also highlights the importance of DI whilst using data analytics to ensure the developed models meet the required regulatory standards for process monitoring and control. This includes a discussion on possible mitigation strategies and methodologies to ensure data integrity is maintained for smart manufacturing operations such as the use of cloud platforms to facilitate the storage and transfer of manufacturing data, and migrate away from paper-based records.

A decade in review: use of data analytics within the biopharmaceutical sector.

There are large amounts of data generated within the biopharmaceutical sector. Traditionally, data analysis methods labelled as multivariate data analysis have been the standard statistical technique applied to interrogate these complex data sets. However, more recently there has been a surge in the utilisation of a broader set of machine learning algorithms to further exploit these data. In this article, the adoption of data analysis techniques within the biopharmaceutical sector is evaluated through a review of journal articles and patents published within the last ten years. The papers objectives are to identify the most dominant algorithms applied across different applications areas within the biopharmaceutical sector and to explore whether there is a trend between the size of the data set and the algorithm adopted.

Applications of off-gas mass spectrometry in fed-batch mammalian cell culture.

Off-gas analysis using a magnetic sector mass spectrometer was performed in mammalian cell cultures in the fed-batch mode at the 5 L bench and 50 L pilot scales. Factors affecting the MS gas traces were identified during the duration of the fed-batch cultures. Correlation between viable cell concentration (VCC) and oxygen concentration of the inlet gas into the bioreactor (O2-in) resulted in R2 ≈ 0.9; O2-in could be used as a proxy for VCC. Oxygen mass transfer (kLa) was also quantified throughout the culture period with antifoam addition at different time points which is shown to lower the kLa. Real-time specific oxygen consumption rate (qO2) of 2-20 pmol/cell/day throughout the bioreactor runs were within the range of values reported in literature for mammalian cell cultures. We also report, to our knowledge, the first instance of a distinct correlation between respiration quotient (RQ) and the metabolic state of the cell culture with regard to lactate production phase (average RQ > 1) and consumption phase (average RQ < 1).