Advances and opportunities in process analytical technologies for viral vector manufacturing

Viral vectors are an emerging, exciting class of biologics whose application in vaccines, oncology, and gene therapy has grown exponentially in recent years. Following first regulatory approval, this class of therapeutics has been vigorously pursued to treat monogenic disorders including orphan diseases, entering hundreds of new products into pipelines. Viral vector manufacturing supporting clinical efforts has spurred the introduction of a broad swath of analytical techniques dedicated to assessing the diverse and evolving panel of Critical Quality Attributes (CQAs) of these products. Herein, we provide an overview of the current state of analytics enabling measurement of CQAs such as capsid and vector identities, product titer, transduction efficiency, impurity clearance etc. We highlight orthogonal methods and discuss the advantages and limitations of these techniques while evaluating their adaptation as process analytical technologies. Finally, we identify gaps and propose opportunities in enabling existing technologies for real-time monitoring from hardware, software, and data analysis viewpoints for technology development within viral vector biomanufacturing.

Multivariate data analysis on multisensor measurement for inline process monitoring of adenovirus production in HEK293 cells

In the era of Biopharma 4.0, process digitalization fundamentally requires accurate and timely monitoring of critical process parameters (CPPs) and quality attributes. Bioreactor systems are equipped with a variety of sensors to ensure process robustness and product quality. However, during the biphasic production of viral vectors or replication-competent viruses for gene and cell therapies and vaccination, current monitoring techniques relying on a single working sensor can be affected by the physiological state change of the cells due to infection/transduction/transfection step required to initiate production. To address this limitation, a multisensor (MS) monitoring system, which includes dual-wavelength fluorescence spectroscopy, dielectric signals, and a set of CPPs, such as oxygen uptake rate and pH control outputs, was employed to monitor the upstream process of adenovirus production in HEK293 cells in bioreactor. This system successfully identified characteristic responses to infection by comparing variations in these signals, and the correlation between signals and target critical variables was analyzed mechanistically and statistically. The predictive performance of several target CPPs using different multivariate data analysis (MVDA) methods on data from a single sensor/source or fused from multiple sensors were compared. An MS regression model can accurately predict viable cell density with a relative root mean squared error (rRMSE) as low as 8.3% regardless of the changes occurring over the infection phase. This is a significant improvement over the 12% rRMSE achieved with models based on a single source. The MS models also provide the best predictions for glucose, glutamine, lactate, and ammonium. These results demonstrate the potential of using MVDA on MS systems as a real-time monitoring approach for biphasic bioproduction processes. Yet, models based solely on the multiplicity and timing of infection outperformed both single-sensor and MS models, emphasizing the need for a deeper mechanistic understanding in virus production prediction.

Image processing-based online analysis and feedback control system for droplet dripping process

Droplets find wide application across diverse industries, where maintaining their quality is paramount. Precise control over the substance content within droplets demands non-destructive and online analysis techniques, such as Process Analytical Technology (PAT), often integrated with control strategies. In this context, the present study focuses on the example of controlling droplet quality during the dripping process of pills. Leveraging the dripping and image acquisition systems established in previous research, a novel feedback control system centered on image processing was devised for the quality control of dripping pills. The system was developed and its efficacy was assessed, yielding satisfactory outcomes. The proposed system facilitates real-time monitoring of pill weight through the analysis of droplet images during the dripping process, thereby offering real-time feedback control of pill weight. Importantly, this system holds potential for broader applications beyond the scope of this study.

Impact of dual-baculovirus infection on the Sf9 insect cell transcriptome during rAAV production using single-cell RNA-seq

The insect cell-baculovirus expression vector system (IC-BEVS) has shown to be a powerful platform to produce complex biopharmaceutical products, such as recombinant proteins and virus-like particles. More recently, IC-BEVS has also been used as an alternative to produce recombinant adeno-associated virus (rAAV). However, little is known about the variability of insect cell populations and the potential effect of heterogeneity (e.g., stochastic infection process and differences in infection kinetics) on product titer and/or quality. In this study, transcriptomics analysis of Sf9 insect cells during the production of rAAV of serotype 2 (rAAV2) using a low multiplicity of infection, dual-baculovirus system was performed via single-cell RNA-sequencing (scRNA-seq). Before infection, the principal source of variability in Sf9 insect cells was associated with the cell cycle. Over the course of infection, an increase in transcriptional heterogeneity was detected, which was linked to the expression of baculovirus genes as well as to differences in rAAV transgenes (rep, cap and gfp) expression. Noteworthy, at 24 h post-infection, only 29.4% of cells enclosed all three necessary rAAV transgenes to produce packed rAAV2 particles, indicating limitations of the dual-baculovirus system. In addition, the trajectory analysis herein performed highlighted that biological processes such as protein folding, metabolic processes, translation, and stress response have been significantly altered upon infection. Overall, this work reports the first application of scRNA-seq to the IC-BEVS and highlights significant variations in individual cells within the population, providing insight into the rational cell and process engineering toward improved rAAV2 production in IC-BEVS.

Transcriptome analysis of Sf9 insect cells during production of recombinant Adeno-associated virus

The insect cell-baculovirus expression vector system (IC-BEVS) has emerged as an alternative time- and cost-efficient production platform for recombinant Adeno-associated virus (AAV) for gene therapy. However, a better understanding of the underlying biological mechanisms of IC-BEVS is fundamental to further optimize this expression system toward increased product titer and quality. Here, gene expression of Sf9 insect cells producing recombinant AAV through a dual baculovirus expression system, with low multiplicity of infection (MOI), was profiled by RNA-seq. An 8-fold increase in reads mapping to either baculovirus or AAV transgene sequences was observed between 24 and 48 h post-infection (hpi), confirming a take-over of the host cell transcriptome by the baculovirus. A total of 336 and 4784 genes were identified as differentially expressed at 24 hpi (vs non-infected cells) and at 48 hpi (vs. infected cells at 24 hpi), respectively, including dronc, birc5/iap5, and prp1. Functional annotation found biological processes such as cell cycle, cell growth, protein folding, and cellular amino acid metabolic processes enriched along infection. This work uncovers transcriptional changes in Sf9 in response to baculovirus infection, which provide new insights into cell and/or metabolic engineering targets that can be leveraged for rational bioprocess engineering of IC-BEVS for AAV production.

Machine learning and metabolic modelling assisted implementation of a novel process analytical technology in cell and gene therapy manufacturing

Process analytical technology (PAT) has demonstrated huge potential to enable the development of improved biopharmaceutical manufacturing processes by ensuring the reliable provision of quality products. However, the complexities associated with the manufacture of advanced therapy medicinal products have resulted in a slow adoption of PAT tools into industrial bioprocessing operations, particularly in the manufacture of cell and gene therapy products. Here we describe the applicability of a novel refractometry-based PAT system (Ranger system), which was used to monitor the metabolic activity of HEK293T cell cultures during lentiviral vector (LVV) production processes in real time. The PAT system was able to rapidly identify a relationship between bioreactor pH and culture metabolic activity and this was used to devise a pH operating strategy that resulted in a 1.8-fold increase in metabolic activity compared to an unoptimised bioprocess in a minimal number of bioreactor experiments; this was achieved using both pre-programmed and autonomous pH control strategies. The increased metabolic activity of the cultures, achieved via the implementation of the PAT technology, was not associated with increased LVV production. We employed a metabolic modelling strategy to elucidate the relationship between these bioprocess level events and HEK293T cell metabolism. The modelling showed that culturing of HEK293T cells in a low pH (pH 6.40) environment directly impacted the intracellular maintenance of pH and the intracellular availability of oxygen. We provide evidence that the elevated metabolic activity was a response to cope with the stress associated with low pH to maintain the favourable intracellular conditions, rather than being indicative of a superior active state of the HEK293T cell culture resulting in enhanced LVV production. Forecasting strategies were used to construct data models which identified that the novel PAT system not only had a direct relationship with process pH but also with oxygen availability; the interaction and interdependencies between these two parameters had a direct effect on the responses observed at the bioprocess level. We present data which indicate that process control and intervention using this novel refractometry-based PAT system has the potential to facilitate the fine tuning and rapid optimisation of the production environment and enable adaptive process control for enhanced process performance and robustness.

Insect Cells for High-Yield Production of SARS-CoV-2 Spike Protein: Building a Virosome-Based COVID-19 Vaccine Candidate

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) homotrimeric spike (S) protein is responsible for mediating host cell entry by binding to the angiotensin-converting enzyme 2 (ACE2) receptor, thus being a key viral antigen to target in a coronavirus disease 19 (COVID-19) vaccine. Despite the availability of COVID-19 vaccines, low vaccine coverage as well as unvaccinated and immune compromised subjects are contributing to the emergence of SARS-CoV-2 variants of concern. Therefore, continued development of novel and/or updated vaccines is essential for protecting against such new variants. In this study, we developed a scalable bioprocess using the insect cells-baculovirus expression vector system (IC-BEVS) to produce high-quality S protein, stabilized in its pre-fusion conformation, for inclusion in a virosome-based COVID-19 vaccine candidate. By exploring different bioprocess engineering strategies (i.e., signal peptides, baculovirus transfer vectors, cell lines, infection strategies and formulation buffers), we were able to obtain ~4 mg/L of purified S protein, which, to the best of our knowledge, is the highest value achieved to date using insect cells. In addition, the insect cell-derived S protein exhibited glycan processing similar to mammalian cells and mid-term stability upon storage (up to 90 days at −80 and 4 °C or after 5 freeze-thaw cycles). Noteworthy, antigenicity of S protein, either as single antigen or displayed on the surface of virosomes, was confirmed by ELISA, with binding of ACE2 receptor, pan-SARS antibody CR3022 and neutralizing antibodies to the various epitope clusters on the S protein. Binding capacity was also maintained on virosomes-S stored at 4 °C for 1 month. This work demonstrates the potential of using IC-BEVS to produce the highly glycosylated and complex S protein, without compromising its integrity and antigenicity, to be included in a virosome-based COVID-19 vaccine candidate.

Leveraging rAAV bioprocess understanding and next generation bioanalytics development

Recombinant adeno-associated (rAAV) vector-based gene therapy has been the focus of intense research driven by the safety profile and several recent clinical breakthroughs. As of April 2021, there are two rAAV-based gene therapies approved and more than two-hundred active clinical trials (approximately thirty in Phase III). However, the expected increase in demand for rAAV vectors still poses several challenges. Discussed herein are key aspects related to R&D needs and Chemistry, Manufacturing and Control (CMC) efforts required to attend this growing demand. Authors provide their perspective on strategic topics for rAAV-based therapies success: scalability and productivity; improved safety; increased process understanding combined with development of orthogonal bioanalytics that are able to identify, monitor and control Critical Quality Attributes (CQAs) during bioprocessing.

Analytical methods to characterize recombinant adeno-associated virus vectors and the benefit of standardization and reference materials

Recombinant adeno-associated virus (rAAV) is an increasingly important gene therapy vector, but its properties present unique challenges to critical quality attribute (CQA) identification and analytics development. Advances in, and ongoing hurdles to, characterizing rAAV proteins, nucleic acids, and vector potency are discussed in this review. For nucleic acids and vector potency, current analytical techniques for defined CQAs would benefit from further optimization, while for proteins, more complete characterization and mapping of properties to safety and efficacy is needed to finalize CQAs. The benefits of leveraging reference vectors to validate analytics and CQA ranges are also proposed. Once defined, CQA specifications can be used to establish target parameters for and inform the development of next generation rAAV processes.

Practical monitoring technologies for cells and substrates in biomanufacturing

Precise control over bioreactor operation is desired for optimal productivity and product quality, and there is an increased drive to automation in biomanufacturing. All of these goals require sensors, not only of the basic parameters of temperature, pH, and dissolved oxygen, but of the biomass and substrate concentrations, which directly determine the outcome of the bioprocess. While there are many innovative sensing concepts for biomass and substrate concentrations, this review focuses on sensors that are in-line with the bioreactor, providing data continuously without the removal of sample from the system. The discussion emphasizes the requirements of industry for these sensors, including performance, ease of use, and cost. As the bioeconomy grows, advances in sensing technologies will be needed to achieve the automation of the future for a wider array of bioreactors.

Prospective inference of bioprocess cell viability through chemometric modeling of fluorescence multiway data

In this investigation, the fermentation step of a standard mammalian cell-based industrial bioprocess for the production of a therapeutic protein was studied, with particular emphasis on the evolution of cell viability. This parameter constitutes one of the critical variables for bioprocess monitoring since it can affect downstream operations and the quality of the final product. In addition, when the cells experiment an unpredictable drop in viability, the assessment of this variable through classic off-line methods may not provide information sufficiently in advance to take corrective actions. In this context, Process Analytical Technology (PAT) framework aims to develop novel strategies for more efficient monitoring of critical variables, in order to improve the bioprocess performance. Thus, in this work, a set of chemometric tools were integrated to establish a PAT strategy to monitor cell viability, based on fluorescence multiway data obtained from fermentation samples of a particular bioprocess, in two different scales of operation. The spectral information, together with data regarding process variables, was integrated through chemometric exploratory tools to characterize the bioprocess and stablish novel criteria for the monitoring of cell viability. These findings motivated the development of a multivariate classification model, aiming to obtain predictive tools for the monitoring of future lots of the same bioprocess. The model could be satisfactorily fitted, showing the non-error rate of prediction of 100%.

Monitoring E. coli Cell Integrity by ATR-FTIR Spectroscopy and Chemometrics: Opportunities and Caveats

During recombinant protein production with E. coli, the integrity of the inner and outer membrane changes, which leads to product leakage (loss of outer membrane integrity) or lysis (loss of inner membrane integrity). Motivated by current Quality by Design guidelines, there is a need for monitoring tools to determine leakiness and lysis in real-time. In this work, we assessed a novel approach to monitoring E. coli cell integrity by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Various preprocessing strategies were tested in combination with regression (partial least squares, random forest) or classification models (partial least squares discriminant analysis, linear discriminant analysis, random forest, artificial neural network). Models were validated using standard procedures, and well-performing methods were additionally scrutinized by removing putatively important features and assessing the decrease in performance. Whereas the prediction of target compound concentration via regression was unsuccessful, possibly due to a lack of samples and low sensitivity, random forest classifiers achieved prediction accuracies of over 90% within the datasets tested in this study. However, strong correlations with untargeted spectral regions were revealed by feature selection, thereby demonstrating the need to rigorously validate chemometric models for bioprocesses, including the evaluation of feature importance.

Analytical methods for process and product characterization of recombinant adeno-associated virus-based gene therapies

The optimization of upstream and downstream processes for production of recombinant adeno-associated virus (rAAV) with consistent quality depends on the ability to rapidly characterize critical quality attributes (CQAs). In the context of rAAV production, the virus titer, capsid content, and aggregation are identified as potential CQAs, affecting the potency, purity, and safety of rAAV-mediated gene therapy products. Analytical methods to measure these attributes commonly suffer from long turnaround times or low throughput for process development, although rapid, high-throughput methods are beginning to be developed and commercialized. These methods are not yet well established in academic or industrial practice, and supportive data are scarce. Here, we review both established and upcoming analytical methods for the quantification of rAAV quality attributes. In assessing each method, we highlight the progress toward rapid, at-line characterization of rAAV. Furthermore, we identify that a key challenge for transitioning from traditional to newer methods is the scarcity of academic and industrial experience with the latter. This literature review serves as a guide for the selection of analytical methods targeting quality attributes for rapid, high-throughput process characterization during process development of rAAV-mediated gene therapies.

Dielectric Spectroscopy to Improve the Production of rAAV Used in Gene Therapy

The insect cell-baculovirus expression vector system is an established method for large scale recombinant adeno-associated virus (rAAV) production, largely due to its scalability and high volumetric productivities. During rAAV production it is critical to monitor process parameters such as Spodoptera frugiperda (Sf9) cell concentration, infection timing, and cell harvest viabilities since they can have a significant influence on rAAV productivity and product quality. Herein we developed the use of dielectric spectroscopy as a process analytical technology (PAT) tool used to continuously monitor the production of rAAV in 2 L stirred tank bioreactors, achieving enhanced control over the production process. This study resulted in improved manufacturing robustness through continuous monitoring of cell culture parameters, eliminating sampling needs, increasing the accuracy of infection timing, and reliably estimating the time of harvest. To increase the accuracy of baculovirus infection timing, the cell growth/permittivity model was coupled to a feedback loop with real-time monitoring. This system was able to predict baculovirus infection timing up to 24 h in advance for greatly improved accuracy of infection and ensuring consistent high rAAV productivities. Furthermore, predictive models were developed based on the dielectric measurements of the culture. These multiple linear regression-based models resulted in correlation coefficients (Q2) of 0.89 for viable cell concentration, 0.97 for viability, and 0.92 for cell diameter. Finally, models were developed to predict rAAV titer providing the capability to distinguish in real time between high and low titer production batches.

History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance

Biological systems are typically composed of highly interconnected subunits and possess an inherent complexity that make monitoring, control and optimization of a bioprocess a challenging task. Today a toolset of modeling techniques can provide guidance in understanding complexity and in meeting those challenges. Over the last four decades, computational performance increased exponentially. This increase in hardware capacity allowed ever more detailed and computationally intensive models approaching a “one-to-one” representation of the biological reality. Fueled by governmental guidelines like the PAT initiative of the FDA, novel soft sensors and techniques were developed in the past to ensure product quality and provide data in real time. The estimation of current process state and prediction of future process course eventually enabled dynamic process control. In this review, past, present and envisioned future of models in biotechnology are compared and discussed with regard to application in process monitoring, control and optimization. In addition, hardware requirements and availability to fit the needs of increasingly more complex models are summarized. The major techniques and diverse approaches of modeling in industrial biotechnology are compared, and current as well as future trends and perspectives are outlined.

Holographic Imaging of Insect Cell Cultures: Online Non-Invasive Monitoring of Adeno-Associated Virus Production and Cell Concentration

The insect cell-baculovirus vector system has become one of the favorite platforms for the expression of viral vectors for vaccination and gene therapy purposes. As it is a lytic system, it is essential to balance maximum recombinant product expression with harvest time, minimizing product exposure to detrimental proteases. With this purpose, new bioprocess monitoring solutions are needed to accurately estimate culture progression. Herein, we used online digital holographic microscopy (DHM) to monitor bioreactor cultures of Sf9 insect cells. Batches of baculovirus-infected Sf9 cells producing recombinant adeno-associated virus (AAV) and non-infected cells were used to evaluate DHM prediction capabilities for viable cell concentration, culture viability and AAV titer. Over 30 cell-related optical attributes were quantified using DHM, followed by a forward stepwise regression to select the most significant (p < 0.05) parameters for each variable. We then applied multiple linear regression to obtain models which were able to predict culture variables with root mean squared errors (RMSE) of 7 × 105 cells/mL, 3% for cell viability and 2 × 103 AAV/cell for 3-fold cross-validation. Overall, this work shows that DHM can be implemented for online monitoring of Sf9 concentration and viability, also permitting to monitor product titer, namely AAV, or culture progression in lytic systems, making it a valuable tool to support the time of harvest decision and for the establishment of controlled feeding strategies.

Fiberoptic-Coupled Spectrofluorometer with Array Detection as a Process Analytical Chemistry Tool for Continuous Flow Monitoring of Fluoroquinolone Antibiotics

Nowadays, there is an increasing need for sensitive real-time measurements of various analytes and monitoring of industrial products and environmental processes. Herein, we describe a fluorescence spectrometer in continuous flow mode in which the sample is fed to the flow cell using a peristaltic pump. The excitation beam is introduced to the sample chamber by an optical fiber. The fluorescence emitted upon excitation is collected at the right angle using another optical fiber and then transmitted to the fluorescence spectrometer which utilizes an array detector. The array detection, as a key factor in process analytical chemistry, made the fluorescence spectrometer suited for multiwavelength detection of the fluorescence spectrum of the analytes. After optimization of the experimental parameters, the system has been successfully employed for sensitive determination of four fluoroquinolone antibiotics such as ciprofloxacin, ofloxacin, levofloxacin, and moxifloxacin. The linear dynamic ranges of four fluoroquinolones were between 0.25 and 20 μg·mL-1, and the detection limit of the method for ciprofloxacin, ofloxacin, levofloxacin, and moxifloxacin were 81, 36, 35, and 93 ng·mL-1, respectively. Finally, the proposed system is carried out for determination of fluoroquinolones in some pharmaceutical formulations.