Influence of Incident Wavelength and Detector Material Selection on Fluorescence in the Application of Raman Spectroscopy to a Fungal Fermentation Process

Raman spectroscopy is a novel tool used in the on-line monitoring and control of bioprocesses, offering both quantitative and qualitative determination of key process variables through spectroscopic analysis. However, the wide-spread application of Raman spectroscopy analysers to industrial fermentation processes has been hindered by problems related to the high background fluorescence signal associated with the analysis of biological samples. To address this issue, we investigated the influence of fluorescence on the spectra collected from two Raman spectroscopic devices with different wavelengths and detectors in the analysis of the critical process parameters (CPPs) and critical quality attributes (CQAs) of a fungal fermentation process. The spectra collected using a Raman analyser with the shorter wavelength (903 nm) and a charged coupled device detector (CCD) was corrupted by high fluorescence and was therefore unusable in the prediction of these CPPs and CQAs. In contrast, the spectra collected using a Raman analyser with the longer wavelength (993 nm) and an indium gallium arsenide (InGaAs) detector was only moderately affected by fluorescence and enabled the generation of accurate estimates of the fermentation's critical variables. This novel work is the first direct comparison of two different Raman spectroscopy probes on the same process highlighting the significant detrimental effect caused by high fluorescence on spectra recorded throughout fermentation runs. Furthermore, this paper demonstrates the importance of correctly selecting both the incident wavelength and detector material type of the Raman spectroscopy devices to ensure corrupting fluorescence is minimised during bioprocess monitoring applications

Enhanced harvest performance predictability through advanced multivariate data analysis of mammalian cell culture particle size distribution

The industry's pursuit for higher antibody production has led to increased cell density cultures that impact the performance of subsequent product recovery steps. This increase in cell concentration has highlighted the critical role of solids concentration in centrifugation yield, while recent product degradation cases have shed light on the impact of cell lysis on product quality. Current methods for measuring solids concentration and cell lysis are not suited for early-stage high-throughput experimentation, which means that these cell culture outputs are not well characterized in early process development. This article describes a novel approach that leveraged the data from a widely-used automated cell counter (Vi-CELL™ XR) to accurately predict solids concentration and a common cell lysis indicator represented as lactate dehydrogenase (LDH) release. For this purpose, partial least squares (PLS) models were derived with k-fold cross-validation from the particle size distribution data generated by the cell counter. The PLS models showed good predictive potential for both LDH release and solids concentration. This novel approach reduced the time required for evaluating the solids concentration and LDH for a typical high-throughput cell culture system (with 48 bioreactors in parallel) from around 7 h down to a few minutes

Machine learning and advanced data analytics automating the exploitation of Raman spectroscopy: From micro-scale to large-scale operation

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Application of multivariate data analysis in the monitoring and control of mammalian cell processes

High throughput (HT) methodologies are increasingly being adopted for bioprocess development activities. However, often the large quantities of data generated from such studies as well as from historical batch records are not fully harnessed for their potential insights. Multivariate data analysis (MVDA) is a well-known technique that can reduce the dimensionality of large data sets and help generate useful correlations of typical process behaviour and determine root causes of process deviations. This work focuses on the application of various MVDA techniques and data mining tools to investigate the impact of process variations on the critical quality attributes (CQAs) of mammalian cell processes. Three separate projects investigating the application of these techniques were explored. The first focuses on understanding the impact of cell culture operation, generation number and initial conditions on final titre and impurity levels. A systematic methodology was applied to analyse HT data generated in a Design of Experiment (DOE) study using the ambr® 48 (advanced micro-bioreactor) system. The project applied multiple MVDA techniques including Principle Component Analysis (PCA) and Partial Least Squares (PLS) to successfully identify the key critical process measurements impacting on the target antibody concentration and host cell protein (HCP) levels at harvest. A similar approach was adopted in the second project that investigated the influence of manufacturing beyond standard operating conditions on the product-related CQAs of a mammalian cell process. The insights from the MVDA allowed the modification of the control limits of key process parameters to be redefined with confidence. The final project focuses on the development of an advanced glucose control strategy for a high titre mammalian cell line. The project aims to predict the on-line glucose concentration through correlations developed between the available on-line process measurements and the off-line metabolic profiles. The predicted glucose concentration is then used to manipulate the substrate feed rate to control the glucose concentration at a desired set-point. Better control of the glucose concentration aims to further increase titre production as well as potentially reducing unwanted post-translation modifications (PTMs) in susceptible cell lines. These techniques can be combined with the application of Process Analytical Technologies (PAT) and Quality by Design (QbD) allowing for the development of more efficient and better controlled processes

A soft sensor of cell concentration in a perfusion bioreactor via a digital twin

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Multivariate data analysis enabling improved clone selection

Selecting a single cell from a heterogeneous transfection pool that will scale-up appropriately from a micro-scale system to a commercial facility is a challenging and hugely important task. This clonal cell line needs to demonstrate the desired product quality attributes and ensure manufacturability throughout the entire drug manufacturing lifecycle. This process typically requires 6 to 12 months and is a time, capital and labour intensive process. High throughput (HT) methodologies are increasingly being adopted to speed up this cell line selection protocol. However, often the large quantities of data generated in combination with the increase in availability of analytics results in a daunting multivariate data analysis problem. Typically, the cell line selection strategy focuses on quality attributes recorded at point of harvest such as final concentrations of process parameters including titre and viable cell density, level of aggregates or addition product quality attributes. Time-series data such as dissolved oxygen, pH or gas flow rates are often overlooked due to challenges with visualization and interpretation of the large number of process variables recorded. This work describes a novel method that implements advanced multivariate tools including principal component analysis (PCA) to better leverage the available data to help guide this challenging decision making process. The inclusion of additional process variables was demonstrated to enhance the selection of a high-yielding mammalian cell line through inclusion of scale-up dependent process parameters related to high oxygen demands and varying nutrient uptake rates. Furthermore, this technique was demonstrated to highlight problematic product heterogeneities of parent clones that were not identified through univariate analysis of the multiple cell lines. The inclusion of this MVDA methodology demonstrated a more efficient and better decision-making protocol compared to conventional cell line selection processes

Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process

This paper outlines real-world control challenges faced by modern-day biopharmaceutical facilities through the extension of a previously developed industrial-scale penicillin fermentation simulation (IndPenSim). The extensions include the addition of a simulated Raman spectroscopy device for the purpose of developing, evaluating and implementation of advanced and innovative control solutions applicable to biotechnology facilities. IndPenSim can be operated in fixed or operator controlled mode and generates all the available on-line, off-line and Raman spectra for each batch. The capabilities of IndPenSim were initially demonstrated through the implementation of a QbD methodology utilising the three stages of the PAT framework. Furthermore, IndPenSim evaluated a fault detection algorithm to detect process faults occurring on different batches recorded throughout a yearly campaign. The simulator and all data presented here are available to download at www.industrialpenicillinsimulation.com and acts as a benchmark for researchers to analyse, improve and optimise the current control strategy implemented on this facility. Additionally, a highly valuable data resource containing 100 batches with all available process and Raman spectroscopy measurements is freely available to download. This data is highly suitable for the development of big data analytics, machine learning (ML) or artificial intelligence (AI) algorithms applicable to the biopharmaceutical industry

Directed evolution for soluble and active periplasmic expression of bovine enterokinase in Escherichia coli

Bovine enterokinase light chain (EKL) is an industrially useful protease for accurate removal of affinity-purification tags from high-value biopharmaceuticals. However, recombinant expression in Escherichia coli produces insoluble inclusion bodies, requiring solubilisation, refolding, and autocatalytic activation to recover functional enzyme. Error-prone PCR and DNA shuffling of the EKL gene, T7 promoter, lac operon, ribosome binding site, and pelB leader sequence, yielded 321 unique variants after screening ~ 6500 colonies. The best variants had > 11,000-fold increased total activity in lysates, producing soluble enzyme that no longer needed refolding. Further characterisation identified the factors that improved total activity from an inactive and insoluble starting point. Stability was a major factor, whereby melting temperatures > 48.4 °C enabled good expression at 37 °C. Variants generally did not alter catalytic efficiency as measured by kcat/Km, which improved for only one variant. Codon optimisation improved the total activity in lysates produced at 37 °C. However, non-optimised codons and expression at 30 °C gave the highest activity through improved protein quality, with increased kcat and Tm values. The 321 variants were statistically analysed and mapped to protein structure. Mutations detrimental to total activity and stability clustered around the active site. By contrast, variants with increased total activity tended to combine stabilising mutations that did not disrupt the active site

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

Recombinant plants provide a new approach to the production of bacterial polysaccharide for vaccines

Bacterial polysaccharides have numerous clinical or industrial uses. Recombinant plants could offer the possibility of producing bacterial polysaccharides on a large scale and free of contaminating bacterial toxins and antigens. We investigated the feasibility of this proposal by cloning and expressing the gene for the type 3 synthase (cps3S) of Streptococcus pneumoniae in Nicotinia tabacum, using the pCambia2301 vector and Agrobacterium tumefaciens-mediated gene transfer. In planta the recombinant synthase polymerised plant-derived UDP-glucose and UDP-glucuronic acid to form type 3 polysaccharide. Expression of the cps3S gene was detected by RT-PCR and production of the pneumococcal polysaccharide was detected in tobacco leaf extracts by double immunodiffusion, Western blotting and high-voltage paper electrophoresis. Because it is used a component of anti-pneumococcal vaccines, the immunogenicity of the plant-derived type 3 polysaccharide was tested. Mice immunised with extracts from recombinant plants were protected from challenge with a lethal dose of pneumococci in a model of pneumonia and the immunised mice had significantly elevated levels of serum anti-pneumococcal polysaccharide antibodies. This study provides the proof of the principle that bacterial polysaccharide can be successfully synthesised in plants and that these recombinant polysaccharides could be used as vaccines to protect against life-threatening infections