Biopharmaceutical Manufacturing Processes Research Articles

Virus inactivation using an electrically conducting virus filter in biopharmaceutical manufacturing process

Biopharmaceutical manufacturing processes using mammalian cells or plasma carry the risk of viral contamination. To mitigate these risks, it is essential to ensure viral clearance during the downstream process. Virus-retentive filters are used for size-based virus filtration, offering robust viral removal of more than 99.99%. However, virus breakthroughs have also been reported during virus filtration under certain conditions. In addition, these virus-retentive filters are disposable to ensure the safety of bioproducts, leading to significant costs and environmental concerns. In this study, innovative electrically conducting virus filters were fabricated using free-standing carbon veils (CV) and used to achieve additional virus inactivation after filtration. The viruses were captured in a CV-assisted virus filter, which was electrically heated using direct current to inactivate the viruses. This electrically conducting virus filter can inactivate viruses and can be reused up to five times. These results demonstrate that electrical conduction through electrical conducting damaged the phage capsid and eliminated the RNA genome, leading to bacteriophage inactivation. Moreover, it was confirmed that the electrically conducting virus filter could be reused up to five times without any changes to its physical or chemical structure. This study contributes to the reduction of process costs and environmental impacts by enabling the reuse of virus filters and enhancing the safety of the virus filtration process by preventing undesired virus breakthroughs.

Parylene C Coating Efficacy Studies: Enhancing Biocompatibility of 3D Printed Polyurethane Parts for Biopharmaceutical and CGT Applications.

Additive manufacturing, particularly Vat photopolymerization, presents a promising technique for producing complex, tailor-made structures, making it an attractive option for generating single-use components used in biopharmaceutical manufacturing equipment or cell culture devices. However, the potential leaching of cytotoxic compounds from Vat photopolymer resins poses a significant concern, especially regarding cell growth and viability in cell culture applications. This study explores the potential of parylene C coating to enhance the inertness of a polyurethane-based photopolymer resin, aiming to prevent cytotoxicity and improve biocompatibility. The study includes an analysis of extractables from the resin and photoinitiator to evaluate the resin's composition and to define selected marker compounds for investigating the coating efficiency. The time-dependent accumulation of relevant extractable compounds over a 70-day period are assessed to address the long-term use of the coated components. The impact of irradiation on the material and the coating was evaluated, along with an accelerated aging study to address the long-term performance of the coating. Biocompatibility in terms of in vitro cell growth studies is evaluated using Chinese hamster ovary cells, a standard cell line in biopharmaceutical manufacturing. Results demonstrate that parylene C coating significantly reduces the release of cytotoxic compounds, such as the photoinitiator diphenyl(2,4,6-trimethylbenzoyl)phosphine oxide (TPO). Although accelerated aging indicates a reduction in the barrier properties of the coating over time, the parylene C coating still effectively slows the release of extractables and significantly improves cell compatibility of the 3D printed parts. The findings suggest that parylene C-coated components can be safely integrated into biopharmaceutical manufacturing processes, with recommendations to minimize storage times between coating application and use to ensure optimal performance.

Predictive modeling for cell culture in commercial manufacturing of biotherapeutics.

The biopharmaceutical industry continually seeks advancements in the commercial manufacturing of therapeutic proteins, where mammalian cell culture plays a pivotal role. The current work presents a novel data-driven predictive modeling application designed to enhance the efficiency and predictability of cell culture processes in biotherapeutic production. The capability of the cloud-based digital data science application, developed using open-source tools, is demonstrated with respect to predicting bioreactor potency from at-line process parameters over a 5-day horizon. The uncertainty in model's prediction is quantified, providing valuable insights for process control and decision-making. Model validation on unseen data confirms the model's robust generalizability. An interactive dashboard, tailored to process scientist's requirements is also developed to streamline biopharmaceutical manufacturing processes, ultimately leading to enhanced productivity and product quality.

An in-silico approach towards multivariate acceptable ranges in biopharmaceutical manufacturing

Multivariate interactions between process parameters can heavily impact product quality and process performance in biopharmaceutical manufacturing processes. Thus, multivariate interactions should be identified and appropriately controlled. This article describes an in-silico approach to establish multivariate acceptable ranges; these ranges help to illustrate the combined impact of multiple input variables on product quality and process performance. Additionally, this article includes a case study for a monoclonal antibody polishing application.Proven acceptable ranges are set by changing only one input parameter at a time while keeping all others constant to understand the impact of process variability on product quality or process performance, but the impact of synergistic variables are not evaluated. Within multivariate acceptable ranges, any combination of input parameters of a unit operation yields the desired product quality and process performance. The layered approach applied in this article is based on risk assessment and statistical models to leverage prior knowledge and existing data. The risk assessment is specific for a manufacturing facility but is applicable to multiple products manufactured in the same facility. No additional wet-lab experiments are required for building the statistical models when development and process characterization are executed using a design of experiments approach, compared to a univariate evaluation of data. The established multivariate acceptable range justifies revised normal operating ranges to ensure process control. Further, the determination of multivariate acceptable ranges adds to overall process knowledge, ultimately supporting the implementation of a more effective control strategy.

Protein loss during membrane processes in biopharmaceutical manufacturing.

Maximizing product yield in biopharmaceutical manufacturing processes is a critical factor in determining the overall cost of goods, especially given the high value of these biological products. However, there has been relatively limited research on the quantitative analysis of protein losses due to adsorption and fouling during the different membrane filtration processes employed in typical downstream operations. This study aims to provide a comprehensive analysis of protein loss in the range of membrane systems used in downstream processing including clarification, virus removal filtration, ultrafiltration/diafiltration for formulation, and final sterile filtration, all using commercially available membranes with three model proteins (bovine serum albumin, human serum albumin, and immunoglobulin G). The correlation between protein loss and various parameters (i.e., protein type, protein concentration, throughput, membrane morphology, and protein removal mechanism) was also investigated. This study provides important insights into the nature of protein loss during membrane processes as well as a methodology for quantifying protein yield loss in bioprocesses.

Stochastic Economic Lot Scheduling via Self-Attention Based Deep Reinforcement Learning

The Stochastic Economic Lot Scheduling Problem (SELSP) is a difficult dynamic optimization problem with wide industrial applications. Traditional methods such as hyper-heuristics are manually designed based on substantial expert knowledge, which may limit their optimization performance. Recently, Deep Reinforcement Learning (DRL) is shown to be promising in automatically learning scheduling policies for SELSP. However, its performance is still quite far from that of hyper-heuristics, due to the lack of suitable deep models. In this paper, we propose a novel DRL method to learn dynamic scheduling policies for SELSP in an end-to-end fashion. Based on self-attention, our method can effectively extract useful features from raw state information, and is flexible in handling different numbers of products, which is not viable for previous methods. Experiments on a complex biopharmaceutical manufacturing process show that our method outperforms a recent DRL method and state-of-the-art hyper-heuristics. Moreover, the trained policy performs better in environments different from training with demand forecast errors and varying number of products, showing its strong robustness and generalization ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The Stochastic Economic Lot Scheduling Problem (SELSP) is an important problem for manufacturing enterprises, which is to optimally balance the production and inventory so as to minimize the total cost. However, SELSP is very challenging to solve due to the involvement of uncertain factors such as customer demands and machine failures. Traditional methods for solving SELSP, such as heuristic policies and hyper-heuristics, heavily rely on human experiences to design and hence the performance could be limited. This paper proposes a Deep Reinforcement Learning (DRL) based method to automatically learn scheduling policy for solving SELSP, which could alleviate the above limitation through a self-attention based feature extraction mechanism and reward based training. Experimental results on a realistic manufacturing process show that our method can deliver higher revenue than conventional manual policy and an existing DRL based method.

From Fluids to Pharmaceuticals: How Computational Fluid Dynamics (CFD) Transforms Drug Design

Computational Fluid Dynamics (CFD) is a powerful tool that helps scientists and engineers understand how fluids behave and move, whether in the air, liquids, or biopharmaceutical manufacturing processes. This technology, which uses computer simulations to replicate real-world scenarios, plays a critical role in various industries, including medicine and drug development. By creating virtual experiments on computers, CFD allows researchers to gain insights into the interaction of biological components for drug production at different scales, from small laboratory experiments to large-scale manufacturing processes and fast-track process optimisation. The strategy and implementation of CFD for a bioreactor system has been explained in this article.

A single-step high-throughput bioassay for quantifying Fc-containing recombinant proteins based on non-classical calculation of fluorescence polarization.

The titer of recombinant proteins is one of the key parameters in biopharmaceutical manufacturing processes. The fluorescence polarization (FP)-based assay, a homogeneous, high-throughput and real-time analytical method, had emerged as a powerful tool for biochemical analysis and environmental monitoring. In this study, an FP-based bioassay was utilized to quantify antibody fragment crystallizable (Fc)-containing proteins, such as recombinant monoclonal antibodies (mAbs) and mAb derivatives, in the cell culture supernatant, and the impacts of tracer molecular weight and FITC-coupling conditions on fluorescence polarization were methodically examined. Distinct from the fluorescence polarization potency calculated by classical formula, we for the first time proposed a new concept and calculation of fluorescence polarization intensity, based on which an analytical method with broader detection range and analysis window was established for quantifying Fc-containing proteins. This provided new ideas for the practical application of fluorescence polarization theory. The established method could detect 96 samples within 30 minutes, with dynamic titer range of 2.5-400 mg L-1, and a linear fitting R2 between the measured and actual concentration reaching 0.99. The method had great application prospects in determining the titer of recombinant proteins with Fc fragments, especially when applied to large-scale screening of high-yield and stable expression CHO cell lines commonly used in biopharmaceutical industry.

Incorporating random effects in biopharmaceutical control strategies

ObjectiveRandom effects are often neglected when defining the control strategy for a biopharmaceutical process. In this article, we present a case study that highlights the importance of considering the variance introduced by random effects in the calculation of proven acceptable ranges (PAR), which form the basis of the control strategy.MethodsLinear mixed models were used to model relations between process parameters and critical quality attributes in a set of unit operations that comprises a typical biopharmaceutical manufacturing process. Fitting such models yields estimates of fixed and random effect sizes as well as random and residual variance components. To form PARs, tolerance intervals specific to mixed models were applied that incorporate the random effect contribution to variance.ResultsWe compared standardized fixed and random effect sizes for each unit operation and CQA. The results show that the investigated random effect is not only significant but in some unit operations even larger than the average fixed effect. A comparison between ordinary least squares and mixed models tolerance intervals shows that neglecting the contribution of the random effect can result in PARs that are too optimistic.ConclusionsUncontrollable effects such as week-to-week variability play a major role in process variability and can be modelled as a random effect. Following a workflow such as the one suggested in this article, random effects can be incorporated into a statistically sound control strategy, leading to lowered out of specification results and reduced patient risk.

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.

Novel Potency Assay for MSC Secretome-Based Treatment of Idiopathic Male Infertility Employed Leydig Cells and Revealed Vascular Endothelial Growth Factor as a Promising Potency Marker.

Idiopathic male infertility is a highly prevalent diagnosis in developed countries with no specific treatment options. Although empirical medical treatment is widely used to restore male fertility, its efficacy remains limited and inconclusively proven. Therefore, the development of novel therapeutic approaches in this field is a high-priority task. Since the failure of testicular microenvironment components might be involved in the pathogenesis of idiopathic male infertility, application of mesenchymal stromal cells (MSCs) as well as the MSC secretome is worth considering. Previously, we showed that the intratesticular injection of MSCs or the MSC secretome led to the recovery of spermatogenesis at least through replenishing the testicular microenvironment and its maintenance by MSC-secreted paracrine factors. However, the clinical use of such products has been limited to single trials to date. This may be due to the lack of relevant potency tests reflecting mechanisms of action of the MSC secretome in male infertility models. Based on the presumptive MSC secretome mode of action on the testicular microenvironment, we suggest a novel approach to test the potential efficacy of the MSC secretome for idiopathic male infertility treatment. It represents a potency assay based on evaluation of testosterone production by isolated Leydig cells. We demonstrated that the MSC secretome stimulated testosterone secretion by Leydig cells in vitro. We then hypothesized that among the major factors of the MSC secretome, vascular endothelial growth factor (VEGF) could be responsible for the observed effects, which we confirmed by the revealed correlation between the extent of stimulated testosterone production and VEGF concentration in the MSC secretome. The pilot results obtained from the doxorubicin-induced male infertility murine model also indicate the important impact of VEGF in the MSC secretome’s regenerative effects. Utilizing VEGF as a surrogate factor, a novel approach to study the potency of MSC secretome-based products for idiopathic male infertility treatment is suggested. Further validation is required for its implementation into the biopharmaceutical manufacturing process.

Integrated process model for the prediction of biopharmaceutical manufacturing chromatography and adjustment steps

A fundamental process understanding of an entire downstream process is essential for achieving and maintaining the high-quality standards demanded for biopharmaceutical drugs. A holistic process model based on mechanistic insights could support process development by identifying dependencies between process parameters and critical quality attributes across unit operations to design a holistic control strategy. In this study, state-of-the-art mechanistic models were calibrated and validated as digital representations of a biopharmaceutical manufacturing process. The polishing ion exchange chromatography steps (Q Sepharose FF, Poros 50 HS) were described by a transport-dispersive model combined with a colloidal particle adsorption model. The elution behavior of four size variants was analyzed and included in the model. Titration curves of pH adjustments were simulated using a mean-field approach considering interactions between the protein of interest and other ions in solution. By including adjustment steps the important process control inputs ionic strength, dilution, and pH were integrated. The final process model was capable to predict online and offline data at manufacturing scale. Process variations at manufacturing scale of 94 runs were adequately reproduced by the model. Furthermore, the process robustness against a 20% input variation of concentration, size variant and ion composition, volume, and pH could be confirmed with the model. The presented model demonstrates the potential of the integrated approach for predicting manufacturing process performance across scales and operating units.

Xer recombination for the automatic deletion of selectable marker genes from plasmids in enteric bacteria.

Antibiotic resistance genes are widely used to select bacteria transformed with plasmids and to prevent plasmid loss from cultures, yet antibiotics represent contaminants in the biopharmaceutical manufacturing process, and retaining antibiotic resistance genes in vaccines and biological therapies is discouraged by regulatory agencies. To overcome these limitations, we have developed X-mark™, a novel technology that leverages Xer recombination to generate selectable marker gene-free plasmids for downstream therapeutic applications. Using this technique, X-mark plasmids with antibiotic resistance genes flanked by XerC/D target sites are generated in Escherichia coli cytosol aminopeptidase (E. coli pepA) mutants, which are deficient in Xer recombination on plasmids, and subsequently transformed into enteric bacteria with a functional Xer system. This results in rapid deletion of the resistance gene at high resolution (100%) and stable replication of resolved plasmids for more than 40 generations in the absence of antibiotic selective pressure. This technology is effective in both Escherichia coli and Salmonella enterica bacteria due to the high degree of homology between accessory sequences, including strains that have been developed as oral vaccines for clinical use. X-mark effectively eliminates any regulatory and safety concerns around antibiotic resistance carryover in biopharmaceutical products, such as vaccines and therapeutic proteins. Graphical

GEN Interview: Supporting Single-Use Systems all along the Biopharmaceutical Manufacturing Process

GEN Interview: Supporting Single-Use Systems all along the Biopharmaceutical Manufacturing Process

Rational design and construction of multi-copy biomanufacturing islands in mammalian cells.

Cell line development is a critical step in the establishment of a biopharmaceutical manufacturing process. Current protocols rely on random transgene integration and amplification. Due to considerable variability in transgene integration profiles, this workflow results in laborious screening campaigns before stable producers can be identified. Alternative approaches for transgene dosage increase and integration are therefore highly desirable. In this study, we present a novel strategy for the rapid design, construction, and genomic integration of engineered multiple-copy gene constructs consisting of up to 10 gene expression cassettes. Key to this strategy is the diversification, at the sequence level, of the individual gene cassettes without altering their protein products. We show a computational workflow for coding and regulatory sequence diversification and optimization followed by experimental assembly of up to nine gene copies and a sentinel reporter on a contiguous scaffold. Transient transfections in CHO cells indicates that protein expression increases with the gene copy number on the scaffold. Further, we stably integrate these cassettes into a pre-validated genomic locus. Altogether, our findings point to the feasibility of engineering a fully mapped multi-copy recombinant protein ‘production island’ in a mammalian cell line with greatly reduced screening effort, improved stability, and predictable product titers.

In silico process characterization for biopharmaceutical development following the quality by design concept

With the quality by design (QbD) initiative, regulatory authorities demand a consistent drug quality originating from a well-understood manufacturing process. This study demonstrates the application of a previously published mechanistic chromatography model to the in silico process characterization (PCS) of a monoclonal antibody polishing step. The proposed modeling workflow covered the main tasks of traditional PCS studies following the QbD principles, including criticality assessment of 11 process parameters and establishment of their proven acceptable ranges of operation. Analyzing effects of multi-variate sampling of process parameters on the purification outcome allowed identification of the edge-of-failure. Experimental validation of in silico results demanded approximately 75% less experiments compared to a purely wet-lab based PCS study. Stochastic simulation, considering the measured variances of process parameters and loading material composition, was used to estimate the capability of the process to meet the acceptance criteria for critical quality attributes and key performance indicators. The proposed workflow enables the implementation of digital process twins as QbD tool for improved development of biopharmaceutical manufacturing processes.

Integrated design of biopharmaceutical manufacturing processes: Operation modes and process configurations for monoclonal antibody production

Monoclonal antibodies are leading the growing biopharmaceutical markets. To sustain the increasing demand, manufacturing processes should be optimized to increase efficiency and flexibility. This work investigates the impacts of the application of single-use equipment in batch, continuous, and hybrid scenarios. Operating costs and time are compared at different production scales from clinical to commercial manufacturing for individual unit operations and for the final integrated scenarios. A modified kinetic model is presented for the main cultivation unit. The model is validated using experimental data from literature and from a pilot facility using Chinese hamster ovary cells. Integrated scenarios with fed-batch cultivation and continuous capture have the lowest operating costs. The selection of operating modes across production scales is discussed along with further considerations for the design of integrated processes. The presented models could be used for the development of process design, control, and scheduling efforts for achieving sustainable biopharmaceutical manufacturing.

Twenty plus Years of Data Demonstrating Virus Filtration as an Effective and Robust Step for Large Virus Removal.

Virus filtration has been demonstrated to be an effective and robust dedicated viral clearance step that is used in biopharmaceutical manufacturing processes. Here we present virus filtration data from a multicompany collaboration with data compiled from WuXi Advanced Therapies' and Charles River Laboratories' internal viral clearance databases spanning more than 25 years. The data were sorted by virus removal and type and then further subdivided into murine leukemia virus only, pseudorabies virus only, and reovirus type 3 only categories to allow for analyses of viral clearance results. A total of 2311 virus filtrations were analyzed, composed of 1516 murine leukemia virus, 385 pseudorabies virus, and 410 reovirus type 3 virus filtrations. These data provide clear evidence that will help supplement both internal and industry-wide initiatives focused on using prior knowledge for the creation of modular claims for small virus retentive filters and allow better allocations of resources typically spent on potentially unnecessary studies.

A CFD Digital Twin to Understand Miscible Fluid Blending.

The mixing of stratified miscible fluids with widely different material properties is a common step in biopharmaceutical manufacturing processes. Differences between the fluid densities and viscosities, however, can lead to order-of-magnitude increase in blend times relative to the blending of single-fluid systems. Moreover, the mixing performance in two-fluid systems can be strongly dependent on the Richardson number defined as the ratio of fluid buoyancy to fluid inertia. In this work, we combine lattice Boltzmann transport algorithms with graphics card-based computing hardware to build accelerated digital twins of a physical mixing tanks. The digital twins are designed to predict real-time fluid mechanics with a fidelity that rivals experimental characterization at orders-of-magnitude less cost than physical testing. After validating the twins against measured single- and multi-fluid mixing data, we use them to explore the physics governing fluid blending in stratified two-fluid systems. We use output from the twins to provide general guidance on stratified two-fluid mixing processes, as well as guidance for building such models for other types of physical systems.

Modeling of copy number variability in Pichia pastoris.

Development of continuous biopharmaceutical manufacturing processes is an area of active research. This study considers the long-term transgene copy number stability of Pichia pastoris in continuous bioreactors. We propose a model of copy number loss that quantifies population heterogeneity. An analytical solution is derived and compared with existing experimental data. The model is then used to provide guidance for stable operating timescales. The model is extended to consider copy number dependent growth such as in the case of Zeocin supplementation. The model is also extended to analyze a continuous seeding strategy. This study is a critical step towards understanding the impact of continuous processing on the stability of Pichia pastoris and the resultant products.