Biopharmaceutical Manufacturing Processes Research Articles

The design and development of a robust and consistent manufacturing process for monoclonal antibodies (mAbs), augmented by advanced process analytics capabilities, is a key current focus area in the pharmaceutical industry. In this work, we describe the development and operationalization of multivariate statistical process monitoring (MSPM), a data-driven modelling approach, to monitor biopharmaceutical manufacturing processes. This approach helps in understanding the correlations between the various variables and is used for the detection of the deviations and anomalies that may indicate abnormalities or changes in the process compared to the historical dataspace. Therefore, MSPM enables early fault detection with a scope for preventative intervention and corrective actions. In this work, we will additionally cover the value of in silico data in the development of MSPM models, principal component analysis (PCA), and batch modelling methods, as well as refining and validating the models in real time.

Proteolytic degradation of recombinant proteins in Chinese hamster ovary (CHO) cells remains a major challenge in biopharmaceutical manufacturing, often reducing product yield and quality. Matriptase-1, a type II transmembrane serine protease, has been identified as a key contributor to unwanted proteolysis. This study investigates hepatocyte growth factor activator inhibitor-1 (HAI-1) overexpression as a strategy to mitigate matriptase-1-mediated degradation in CHO cell culture. Using an IL-12 IgG1 Fc fusion protein and a multi-specific antibody (M molecule) as model proteins, we employed genetic and biochemical approaches to assess the impact of Chinese hamster HAI-1 overexpression on protein quality and yield. Our results demonstrate that HAI-1 overexpression effectively inhibits matriptase-1 activity, achieving up to a 98% reduction in proteolytic clipping while maintaining cell growth, viability, and product quality. Compared to other protease control strategies, HAI-1 overexpression presents a practical and scalable solution that does not disrupt essential cellular functions. These findings establish HAI-1 as a key modulatory tool in CHO-based protein production, with implications for reducing proteolysis of therapeutic proteins and optimizing biopharmaceutical manufacturing processes.

Biopharmaceutical manufacturing processes in which the product of interest is extracellularly expressed typically employ a clarification step following cell culture or fermentation. During clarification, crude cell culture fluid or fermentation broth is processed to remove insoluble solids, cells, debris, and other particulates, with the extracellular product of interest retained in the filtrate. Soluble impurities, such as host cell proteins (HCPs), may also be partially removed. Historically, the clarification process has been considered a limited contributor to Critical Quality Attributes (CQA). As part of upstream harvest, many biopharmaceutical companies have not fully developed quality control strategies from process development to manufacturing, complicating the application of Quality by Design (QbD) principles to this step. However, advancements in upstream and downstream processing (DSP) technologies, alongside increasing cell counts and titers, necessitate reevaluating clarification as a critical process contributing to drug product quality. Conducting controlled studies to define the process and establish parameters using QbD principles can improve control over process impurities and facilitate a logical quality control strategy, integrating quality into the process. This article describes a systematic approach to QbD for a harvest clarification process where the product of interest is extracellular and impurities are removed in the filtrate post-clarification. It highlights methods for optimizing the clarification unit operation using QbD principles, ensuring better process efficiency, and product quality.

Chinese hamster ovary (CHO) cells serve as a cornerstone platform for producing diverse therapeutic recombinant proteins, including monoclonal antibodies, vaccines, and hormones. Apoptosis and autophagy emerge as critical biological factors that directly impair cell proliferation and limit recombinant protein production. These cellular processes diminish CHO cell viability and reduce culture density, ultimately compromising protein yield and product quality. While apoptosis and autophagy exhibit distinct molecular mechanisms, they demonstrate functional interdependence in cellular regulation. This interrelationship highlights the importance of coordinated pathway modulation as an effective approach to improve both cell growth performance and recombinant protein synthesis. This review examines the complex interplay between apoptosis and autophagy pathways, their collective impact on recombinant protein expression, and contemporary strategies for developing stable anti-apoptotic/anti-autophagy cell lines. Through systematic analysis of these critical elements, we present optimized engineering approaches to enhance CHO cell culture systems and biopharmaceutical manufacturing processes, with the goal of facilitating more efficient therapeutic protein production. SUMMARY: Crosstalk mechanisms between apoptosis and autophagy in CHO cell cultures. Recent advances in real-time monitoring of apoptosis and autophagy. Strategic mitigation of apoptosis/autophagy to improve recombinant protein yields.

A robust scale-down model development and process characterization for monoclonal antibody biomanufacturing using multivariate data analysis.

Bioreactors are essential for the production of biopharmaceuticals and bioproducts, requiring continuous monitoring to ensure quality assurance. Manual processes in manufacturing plants often lead to anomalies such as out-of-trend and out-of-spec incidents, necessitating extensive root cause analysis that typically takes 2–8 weeks. This paper introduces an innovative methodology that uses the golden batch profile as a benchmark to identify deviations and root causes in subsequent industrial batches. The methodology involves normalizing the data and calculating the variances of a specified batch from the golden batch profile. By examining the contribution of each critical process parameter to these variances, the study highlights their importance in root cause analysis. The application of this methodology to the IndPenSim dataset demonstrated its effectiveness by significantly reducing false positives and negatives compared to traditional PCA-based methods. Emphasis on the deviations of critical quality attributes and critical process parameters from the specified batch compared to the golden batch profile offers valuable insights into industrial process analysis. This approach not only enhances anomaly detection accuracy but also improves the efficiency and reliability of biopharmaceutical and bioproduct manufacturing processes.

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

Additive manufacturing, particularly Vat photopolymerization,presentsa promising technique for producing complex, tailor-made structures,making it an attractive option for generating single-use componentsused in biopharmaceutical manufacturing equipment or cell culturedevices. However, the potential leaching of cytotoxic compounds fromVat photopolymer resins poses a significant concern, especially regardingcell growth and viability in cell culture applications. This studyexplores the potential of parylene C coating to enhance the inertnessof a polyurethane-based photopolymer resin, aiming to prevent cytotoxicityand improve biocompatibility. The study includes an analysis of extractablesfrom the resin and photoinitiator to evaluate the resin’s compositionand to define selected marker compounds for investigating the coatingefficiency. The time-dependent accumulation of relevant extractablecompounds over a 70-day period are assessed to address the long-termuse of the coated components. The impact of irradiation on the materialand the coating was evaluated, along with an accelerated aging studyto address the long-term performance of the coating. Biocompatibilityin terms of in vitro cell growth studies is evaluatedusing Chinese hamster ovary cells, a standard cell line in biopharmaceuticalmanufacturing. Results demonstrate that parylene C coating significantlyreduces the release of cytotoxic compounds, such as the photoinitiatordiphenyl(2,4,6-trimethylbenzoyl)phosphine oxide (TPO). Although acceleratedaging indicates a reduction in the barrier properties of the coatingover time, the parylene C coating still effectively slows the releaseof extractables and significantly improves cell compatibility of the3D printed parts. The findings suggest that parylene C-coated componentscan be safely integrated into biopharmaceutical manufacturing processes,with recommendations to minimize storage times between coating applicationand use to ensure optimal performance.

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.

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.

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.

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.

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.

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.

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.

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.

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

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