Accelerating Biomanufacturing by Modeling of Continuous Bioprocessing—Piloting Case Study of Monoclonal Antibody Manufacturing

Digital Twin for Continuous Production of Virus-like Particles toward Autonomous Operation

Lentiviral vector and virus-like particle (VLP) manufacturing have been published in fed-batch upstream and batch downstream modes before. Batch downstream and continuous upstream in perfusion mode were reported as well. This study exemplifies development and validation steps for a digital twin combining a physical-chemical-based mechanistic model for all unit operations with a process analytical technology strategy in order to show the efforts and benefits of autonomous operation approaches for manufacturing scale. As the general models are available from various other biologic manufacturing studies, the main step is model calibration for the human embryo kidney cell-based VLPs with experimental quantitative validation within the Quality-by-Design (QbD) approach, including risk assessment to define design and control space. For continuous operation in perfusion mode, the main challenge is the efficient separation of large particle manifolds for VLPs and cells, including cell debris, which is of similar size. Here, innovative tangential flow filtration operations are needed to avoid fast blocking with low mechanical stress pumps. A twofold increase of productivity was achieved using simulation case studies. This increase is similar to improvements previously described for other entities like plasmid DNAs, monoclonal antibodies (mAbs), and single-chain fragments of variability (scFv) fragments. The advantages of applying a digital twin for an advanced process control strategy have proven additional productivity gains of 20% at 99.9% reliability.

Process Automation and Control Strategy by Quality-by-Design in Total Continuous mRNA Manufacturing Platforms

Vaccine supply has a bottleneck in manufacturing capacity due to operation personnel and chemicals needed. Assessment of existing mRNA (messenger ribonucleic acid) vaccine processing show needs for continuous manufacturing processes. This is enabled by strict application of the regulatory demanded quality by design process based on digital twins, process analytical technology, and control automation strategies in order to improve process transfer for manufacturing capacity, reduction out-of-specification batch failures, qualified personnel training and number, optimal utilization of buffers and chemicals as well as speed-up of product release. In this work, process control concepts, which are necessary for achieving autonomous, continuous manufacturing, for mRNA manufacturing are explained and proven to be ready for industrialization. The application of the process control strategies developed in this work enable the previously pointed out benefits. By switching from batch-wise to continuous mRNA production as was shown in previous work, which was the base for this study, a potential cost reduction by a factor 5 (i.e., from EUR 0.380 per dose to EUR 0.085 per dose) is achievable. Mainly, based on reduction of personnel (factor 30) and consumable (factor 7.5) per campaign due to the significant share of raw materials in the manufacturing costs (74–97). Future research focus following this work may be on model-based predictive control to gain further optimization potential of potential batch failure and out of specification (OOS) number reduction.

Model-based evaluation and model-free strategy for process development of three-column periodic counter-current chromatography

Multi-column counter-current chromatography is an advanced technology used for continuous capture processes to improve process productivity, resin capacity utilization and product consistency. However, process development is difficult due to process complexity. In this work, some general and convenient guidances for three-column periodic counter-current chromatography (3C-PCC) were developed. Boundaries and distributions of operating windows of 3C-PCC processes were clarified by model-based predictions. Interactive effects of feed concentration (c₀), resin properties (q_max and D_e), recovery and regeneration times (t_RR) were evaluated over a wide range for maximum productivity (P_max). Furthermore, variation of P_max was analyzed considering the constraint factors (capacity utilization target and flow rate limitation). The plateau value of P_max was determined by q_max and t_RR. The operating conditions for P_max were controlled by q_max, t_RR and c₀ interactively, and a critical concentration existed to judge whether the operating conditions of P_max under constraints. Based on the comprehensive understanding on 3C-PCC processes, a model-free strategy was proposed for process development. The optimal operating conditions could be determined based on a set of breakthrough curves, which was used to optimize process performance and screen resins. The approach proposed was validated using monoclonal antibody (mAb) capture with a 3C-PCC system under various mAb and feed concentrations. The results demonstrated that a thorough model-based process understanding on multi-column counter-current chromatography is important and could improve process development and establish a model-free strategy for more convenient applications.

Artificial Neural Network for Fast and Versatile Model Parameter Adjustment Utilizing PAT Signals of Chromatography Processes for Process Control under Production Conditions

Preparative chromatography is a well-established operation in chemical and biotechnology manufacturing. Chromatography achieves high separation performances, but often has to deal with the yield versus purity trade-off as the optimization criterium regarding through-put. The initial trade-off is often disturbed by the well-known phenomenon of chromatogram shifts over process lifetime, and has to be corrected by operators via adjustment of peak fraction cutting. Nevertheless, with regard to autonomous operation and batch to continuous processing modes, an advanced process control strategy is needed to identify and correct shifts from the optimal operation point automatically. Previous studies have already presented solutions for batch-to-batch variance and process control options with the aid of rigorous physico-chemical process modeling. These models can be implemented as distinct digital twins as well as statistical process operation data analyzers. In order to utilize such models for advanced process control (APC), the model parameters have to be updated with the aid of inline Process Analytical Technology (PAT) data to describe the actual operational status. This updating process also includes any operational change phenomena that occur, and its relation to their physico-chemical root cause. Typical phenomena are fluid dynamic changes due to packing breakage, channelling or compression as well as mass transfer and phase equilibrium-related separation performance decrease due to adsorbent aging or feed and buffer composition changes. In order to track these changes, an Artificial Neural Network (ANN) is trained in this work. The ANN training is in this first step, based on the simulation results of a distinct and previously experimentally validated process model. The model is implemented in the open source tool CasADi for Python. This allows the implementation of interfaces to process control systems, among others, with relatively low effort. Therefore, PAT signals can easily be incorporated for sufficient adjustment of the process model for appropriate process control. Further steps would be the implementation of optimization routines based on PAT and ANN predictions to derive optimal operation points with the model.

Fast and Versatile Chromatography Process Design and Operation Optimization with the Aid of Artificial Intelligence

Preparative and process chromatography is a versatile unit operation for the capture, purification, and polishing of a broad variety of molecules, especially very similar and complex compounds such as sugars, isomers, enantiomers, diastereomers, plant extracts, and metal ions such as rare earth elements. Another steadily growing field of application is biochromatography, with a diversity of complex compounds such as peptides, proteins, mAbs, fragments, VLPs, and even mRNA vaccines. Aside from molecular diversity, separation mechanisms range from selective affinity ligands, hydrophobic interaction, ion exchange, and mixed modes. Biochromatography is utilized on a scale of a few kilograms to 100,000 tons annually at about 20 to 250 cm in column diameter. Hence, a versatile and fast tool is needed for process design as well as operation optimization and process control. Existing process modeling approaches have the obstacle of sophisticated laboratory scale experimental setups for model parameter determination and model validation. For a broader application in daily project work, the approach has to be faster and require less effort for non-chromatography experts. Through the extensive advances in the field of artificial intelligence, new methods have emerged to address this need. This paper proposes an artificial neural network-based approach which enables the identification of competitive Langmuir-isotherm parameters of arbitrary three-component mixtures on a previously specified column. This is realized by training an ANN with simulated chromatograms varying in isotherm parameters. In contrast to traditional parameter estimation techniques, the estimation time is reduced to milliseconds, and the need for expert or prior knowledge to obtain feasible estimates is reduced.

Digital Twins for Continuous mRNA Production

The global coronavirus pandemic continues to restrict public life worldwide. An effective means of limiting the pandemic is vaccination. Messenger ribonucleic acid (mRNA) vaccines currently available on the market have proven to be a well-tolerated and effective class of vaccine against coronavirus type 2 (CoV2). Accordingly, demand is presently outstripping mRNA vaccine production. One way to increase productivity is to switch from the currently performed batch to continuous in vitro transcription, which has proven to be a crucial material-consuming step. In this article, a physico-chemical model of in vitro mRNA transcription in a tubular reactor is presented and compared to classical batch and continuous in vitro transcription in a stirred tank. The three models are validated based on a distinct and quantitative validation workflow. Statistically significant parameters are identified as part of the parameter determination concept. Monte Carlo simulations showed that the model is precise, with a deviation of less than 1%. The advantages of continuous production are pointed out compared to batchwise in vitro transcription by optimization of the space–time yield. Improvements of a factor of 56 (0.011 µM/min) in the case of the continuously stirred tank reactor (CSTR) and 68 (0.013 µM/min) in the case of the plug flow reactor (PFR) were found.

Fast and Flexible mRNA Vaccine Manufacturing as a Solution to Pandemic Situations by Adopting Chemical Engineering Good Practice—Continuous Autonomous Operation in Stainless Steel Equipment Concepts

SARS-COVID-19 vaccine supply for the total worldwide population has a bottleneck in manufacturing capacity. Assessment of existing messenger ribonucleic acid (mRNA) vaccine processing shows a need for digital twins enabled by process analytical technology approaches in order to improve process transfer for manufacturing capacity multiplication, a reduction in out-of-specification batch failures, qualified personal training for faster validation and efficient operation, optimal utilization of scarce buffers and chemicals and speed-up of product release by continuous manufacturing. In this work, three manufacturing concepts for mRNA-based vaccines are evaluated: Batch, full-continuous and semi-continuous. Technical transfer from batch single-use to semi-continuous stainless-steel, i.e., plasmid deoxyribonucleic acid (pDNA) in batch and mRNA in continuous operation mode, is recommended, in order to gain: faster plant commissioning and start-up times of about 8–12 months and a rise in dose number by a factor of about 30 per year, with almost identical efforts in capital expenditures (CAPEX) and personnel resources, which are the dominant bottlenecks at the moment, at about 25% lower operating expenses (OPEX). Consumables are also reduceable by a factor of 6 as outcome of this study. Further optimization potential is seen at consequent digital twin and PAT (Process Analytical Technology) concept integration as key-enabling technologies towards autonomous operation including real-time release-testing.

Online monitoring and control of upstream cell culture process using 1D and 2D-LC with SegFlow interface

The biopharmaceutical industry is transitioning from currently deployed batch-mode bioprocessing to a highly efficient and agile next-generation bioprocessing with the adaptation of continuous bioprocessing, which reduces capital investment and operational costs. Continuous bioprocessing, aligned with FDA's quality-by-design platform, is designed to develop robust processes to deliver safe and effective drugs. With the deployment of knowledge-based operations, product quality can be built into the process to achieve desired critical quality attributes (CQAs) with reduced variability. To facilitate next-generation continuous bioprocessing, it is essential to embrace a fundamental shift-in-paradigm from "quality-by-testing" to "quality-by-design," which requires the deployment of process analytical technologies (PAT). With the adaptation of PAT, a systematic approach of process and product understanding and timely process control are feasible. Deployment of PAT tools for real-time monitoring of CQAs and feedback control is critical for continuous bioprocessing. Given the current deficiency in PAT tools to support continuous bioprocessing, we have integrated Infinity 2D-LC with a post-flow-splitter in conjunction with the SegFlow autosampler to the bioreactors. With this integrated system, we have established a platform for online measurements of titer and CQAs of monoclonal antibodies as well as amino acid analysis of bioreactor cell culture.

Process Analytical Technology for Precipitation Process Integration into Biologics Manufacturing towards Autonomous Operation—mAb Case Study

The integration of real time release testing into an advanced process control (APC) concept in combination with digital twins accelerates the process towards autonomous operation. In order to implement this, on the one hand, measurement technology is required that is capable of measuring relevant process data online, and on the other hand, a suitable model must be available to calculate new process parameters from this data, which are then used for process control. Therefore, the feasibility of online measurement techniques including Raman-spectroscopy, attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR), diode array detector (DAD) and fluorescence is demonstrated within the framework of the process analytical technology (PAT) initiative. The best result is achieved by Raman, which reliably detected mAb concentration (R2 of 0.93) and purity (R2 of 0.85) in real time, followed by DAD. Furthermore, the combination of DAD and Raman has been investigated, which provides a promising extension due to the orthogonal measurement methods and higher process robustness. The combination led to a prediction for concentration with a R2 of 0.90 ± 3.9% and for purity of 0.72 ± 4.9%. These data are used to run simulation studies to show the feasibility of process control with a suitable digital twin within the APC concept.

PAT for Continuous Chromatography Integrated into Continuous Manufacturing of Biologics towards Autonomous Operation

This study proposes a reliable inline PAT concept for the simultaneous monitoring of different product components after chromatography. The feed for purification consisted of four main components, IgG monomer, dimer, and two lower molecular weight components of 4.4 kDa and 1 kDa molecular weight. The proposed measurement setup consists of a UV–VIS diode-array detector and a fluorescence detector. Applying this system, a R2 of 0.93 for the target component, a R2 of 0.67 for the dimer, a R2 of 0.91 for the first side component and a R2 of 0.93 for the second side component is achieved. Root mean square error for IgG monomer was 0.027 g/L, for dimer 0.0047 g/L, for side component 1 0.016 g/L and for the side component 2 0.014 g/L. The proposed measurement concept tracked component concentration reliably down to 0.05 g/L. Zero-point fluctuations were kept within a standard deviation of 0.018 g/L for samples with no IgG concentration but with side components present, allowing a reliable detection of the target component. The main reason inline concentration measurements have not been established yet, is the false-positive measurement of target components when side components are present. This problem was eliminated using the combination of fluorescence and UV–VIS data for the test system. The use of this measurement system is simulated for the test system, allowing an automatic fraction cut at 0.05 g/L. In this simulation a consistent yield of >99% was achieved. Process disturbances for processed feed volume, feed purity and feed IgG concentration can be compensated with this setup. Compared to a timed process control, yield can be increased by up to 12.5%, if unexpected process disturbances occur.

Towards Autonomous Operation by Advanced Process Control—Process Analytical Technology for Continuous Biologics Antibody Manufacturing

Continuous manufacturing opens up new operation windows with improved product quality in contrast to documented lot deviations in batch or fed-batch operations. A more sophisticated process control strategy is needed to adjust operation parameters and keep product quality constant during long-term operations. In the present study, the applicability of a combination of spectroscopic methods was evaluated to enable Advanced Process Control (APC) in continuous manufacturing by Process Analytical Technology (PAT). In upstream processing (USP) and aqueous two-phase extraction (ATPE), Raman-, Fourier-transformed infrared (FTIR), fluorescence- and ultraviolet/visible- (UV/Vis) spectroscopy have been successfully applied for titer and purity prediction. Raman spectroscopy was the most versatile and robust method in USP, ATPE, and precipitation and is therefore recommended as primary PAT. In later process stages, the combination of UV/Vis and fluorescence spectroscopy was able to overcome difficulties in titer and purity prediction induced by overlapping side component spectra. Based on the developed spectroscopic predictions, dynamic control of unit operations was demonstrated in sophisticated simulation studies. A PAT development workflow for holistic process development was proposed.

Digital Twins in Biomanufacturing

In recent years process modelling has become an established method which generates digital twins of manufacturing plant operation with the aid of numerically solved process models. This article discusses the benefits of establishing process modelling, in-house or by cooperation, in order to support the workflow from process development, piloting and engineering up to manufacturing. The examples are chosen from the variety of botanicals and biologics manufacturing thus proving the broad applicability from variable feedstock of natural plant extracts of secondary metabolites to fermentation of complex molecules like mAbs, fragments, proteins and peptides.Consistent models and methods to simulate whole processes are available. To determine the physical properties used as model parameters, efficient laboratory-scale experiments are implemented. These parameters are case specific since there is no database for complex molecules of biologics and botanicals in pharmaceutical industry, yet.Moreover, Quality-by-Design approaches, demanded by regulatory authorities, are integrated within those predictive modelling procedures. The models could be proven to be valid and predictive under regulatory aspects. Process modelling does earn its money from the first day of application. Process modelling is a key-enabling tool towards cost-efficient digitalization in chemical-pharmaceutical industries.

Why Is Batch Processing Still Dominating the Biologics Landscape? Towards an Integrated Continuous Bioprocessing Alternative

Continuous manufacturing of biologics (biopharmaceuticals) has been an area of active research and development for many reasons, ranging from the demand for operational streamlining to the requirement of achieving obvious economic benefits. At the same time, biopharma strives to develop systems and concepts that can operate at similar scales for clinical and commercial production—using flexible infrastructures, such as single-use flow paths and small surge vessels. These developments should simplify technology transfer, reduce footprint and capital investment, and will allow to react readily to changing market pressures while maintaining quality attributes. Despite a number of clearly identified benefits compared to traditional batch processes, continuous bioprocessing is still not widely adopted for commercial manufacturing. This paper details how industry-specific technological, organizational, economic, and regulatory barriers that exist in biopharmaceutical manufacturing are hindering the adoption of continuous production processes. Based on this understanding, the roles of process systems engineering (PSE), process analytical technologies, and process modeling and simulation are highlighted as key enabling tools in overcoming these multi-faceted barriers in today’s manufacturing environment. Of course, we do recognize that there is also a need for a clear set of regulations to guide a transition of biologics manufacturing towards continuous processing. Furthermore, the role played by the emerging fields of process integration and automation as well as digitalization is explored, as these are the tools of the future to facilitate this transition from batch to continuous production. Finally, an outlook focusing on technology, management, and regulatory aspects is presented to identify key concerted efforts required to drive the broad adaptation of continuous manufacturing in biopharmaceutical processes.

Technical Potential for Energy and GWP Reduction in Chemical–Pharmaceutical Industry in Germany and EU—Focused on Biologics and Botanicals Manufacturing

European policy demands climate neutrality by the year 2050. Therefore, any manufacturing optimization needs to be achieved in the well-known pareto of global warming potential (GWP) reduction combined with cost of goods (COG) reduction at increasing product amounts, while still being able to compete in the world market. The chemical–pharmaceutical industry is one of the most energy-intensive industries. The pharmaceutical industry operates with low batch sizes, but high margins. This study analyzes, based on the literature and Bundesministerium für Wirtschaft und Energie (BMWi; English: Federal Ministry for Economic Affairs and Energy)-funded project results, the technical potentials for energy and GWP reduction, while focusing on biologics and botanicals, because those are already widely based on natural raw material resources. The potential impact for green technologies is pointed out in relation to climate-neutral manufacturing.

Accelerating Biologics Manufacturing by Modeling: Process Integration of Precipitation in mAb Downstream Processing

The demand on biologics has been constantly rising over the past decades and has become crucial in modern medicine. Promising approaches to cope with widespread diseases like cancer and diabetes are gene therapy, plasmid DNA, virus-like particles, and exosomes. Due to progress that has been made in upstream processing (USP), difficulties arise in downstream processing and demand for innovative solutions. This work focuses on the integration of precipitation using a quality by design (QbD) approach for process development. Selective precipitation is achieved with PEG 4000 resulting in an HCP depletion of ≥80% respectively to IgG. Dissolution was executed with a sodium phosphate buffer (pH = 5/50 mM) reaching an IgG recovery of ≥95%. However, the central challenge in process development is still an optimal process design, which is transferable for a broad molecular variety of new products. This is where rigorous modeling becomes vital in order to generate digital twins to support early-stage process development and reduce the experimental overhead. Therefore, a model development and validation concept for construction of a process model for precipitation is also presented.