A Look into the Future of Bioprocessing by Dr. Svea Grieb, Kai Touw and Dan Kopec

The internet of things and robotics are already the working standard in fast- adapting industries. These technologies are now also being implemented in the biopharma industry, which is more conservative due to strong regulations and intrinsically complex processes. How is this change going to proceed and how does it change the industry? Does it provide a bigger benefit than its risk? We asked three experts for process analytical technologies, automation and data analytics from Sartorius about their view on the future of bioprocessing. Advantages of Next Generation Manufacturing

We would first like to discuss the main advantages of implementing Process Analytical Technology (PAT) and advanced data analytics, which enable automation of bioprocesses. This allows for the production of higher quality and more consistent biologic drugs and regenerative therapies at reduced costs of goods (CoGS), with higher flexibility and faster time to market1,2. We consider the five main advantages of the implementation of PAT, automation and data analytics to be the following:

Consistent, High Product Fast and Predictive Account for Cell Variation Quality Up- and Down-Scaling from Different Sources

Consistency in product quality A well characterized and The field of personalized and quantity is achieved, as monitored process together medicine benefits greatly from variations of critical process with scalable hardware can automation. In regenerative parameters (CPPs) are reduced significantly reduce the cost medicine applications, the and process robustness is and efforts of process scale autologous nature of the increased. This is summarized up | down, as scale variations treatment demands a process in figure 1. can be accounted for in an that is flexible, and can automated and predictive dynamically adjust to wide fashion. variations in starting material. Reduced Risk of Lost PAT can account for the Batches and Increased variations and peculiarities of Freeing up Operators Process Safety the cells from different patients in an automated fashion. Advanced Automation reduces Reduced risks of operator errors This result in a high process the requirements of operator and of contamination through consistency irrespective of interference, which on the manual sampling. The timely the starting material. identification and correction of one hand reduces the risk of process irregularities reduces operator errors, and on the the risk of lost batches. other hand allows the operators to focus on other tasks.

Traditional Process: Post-Process Quality Testing

Fixed Fixed Fixed process process process Variability in product quality and Post-process testing of quality to ensure product is within quantity specification before the release of the stage

PAT Process: Quality by Design

Variable Variable Variable process process process Consistency in product quality and Continuous online monitoring and control of critical process quantity parameters (CPPs) that affect the critical quality attributes (CQAs) of the product

Figure 1: PAT and automation result in consistent, high product quality

2 Key Technologies of Next Generation Manufacturing

We would like to discuss three key players of next generation manufacturing that are currently driving the change.

Flexible, Automated Skids Spectroscopy Multivariate Data Analytics A technological development, We believe that spectroscopic which is key for use in down- techniques will become more The application of sophisticated stream processes are flexible abundant in both upstream and PAT tools in combination with automated skids, capable of downstream bioprocessing, due multivariate data analytics has handling different type of unit to its capability of label-free, a high impact on commercial operations, all based on S88 online measurements of several processing. Measurements are compliant recipes. These type analytes, cell properties and moved forward in the process of process skids make it product quality attributes. to the point of controllability. possible to run standardized Hence, spectroscopy has the Using process fingerprints, and automated processes potential to replace offline the state of the process can in facilities making use of measurements during the be assessed at any time. a ‘ballroom’ concept. bioprocess. We envision the use Furthermore through real-time of a combination of different univariate and multivariate spectroscopic techniques, such process monitoring, data can as NIR, Raman and UV-Vis to be used for simulation and be required for this. That said, modelling of process design there will be a continuing need and control and ultimately lead to use and further develop to prescriptive analytics of other technologies, such as product quality. bio-capacitance, and dedicated nutrient | metabolite sensors, for application for which spectroscopy does not provide a solution. Furthermore, to propagate the use in GMP, we envision a combination of sensors which crosscheck each other.

3 On the Challenges of Next Generation Manufacturing

When speaking about automation of bioprocesses, we need to evaluate technical feasibility and cost-benefit analysis. Furthermore, there are regulatory, logistics and safety issues that have to be solved before automation can really be adopted widely in the biopharma industry.

Processes Challenging be established. Further, there is technical advances in the field to Automate the challenge of aligning the of automation, PAT and ad- process automation concept vanced analytics, as is shown by While we do not think that of a supplier to the facility the creation of the ‘Emerging there is an application in the automation concept in terms Technology Program’ by the process of biopharmaceutical of environmental monitoring, FDA3,4. drug production that would building monitoring and not benefit from automation a certain level of integration at all, we do not envision that into resource planning systems. IT Concerns | Data Integrity we will see a high degree of A comprehensive automation automation added to already Regulatory Challenges strategy for an entire biopro- existing pipelines, such as cess, and potentially an entire well-established fed-batch Some concepts of modern production site, requires processes. Unless, of course, the automation technologies and connectivity of all components automation adds a significant sensor technologies are not yet and a centralized control unit. improvement to the process, as covered by regulatory guide- However, that would require we have seen it for automated lines. This is especially true for data sharing and access that im- temperature shifts at a certain multivariate data analysis that plies safety risks. We experience viable cell density, for example. takes all available data and inte- reluctance among our custom- Further, there are processes grates them into a fingerprint. ers to adopt new technologies that are possible to automate The adoption of such batch- such as cloud computing and but do not benefit massively fingerprinting concepts must wireless communication of from the automation, as be considered by the regulatory PAT components. the manual interference is bodies. The same questions very limited, such as dead-end arise for multi-analyte sensors We are convinced that the task filtration. Finally, there are that are based on computation- of meeting the requirement of processes that will be quite al models, as it is the case for next generation manufacturing challenging to find an automa- spectroscopy, for example. in terms of hardware, software, tion for. Product quantification How do we validate a model for data analytics and infrastruc- with a background of many the use of GMP? What are the ture is too demanding and other proteins could be one characteristics of a ‘good and complex to be addressed by just such example. robust’ model? These questions one supplier. It requires the have to be addressed. A last collaboration of several indus- Compatibility and case we want to mention is the tries in strong exchange with definition of ‘a batch’ for con- the customers to guide new Infrastructure Challenges tinuous processing. Regulations developments. Sartorius has The seamless integration that were once established realized this need, as reflected of process equipment and for a 2-weeks process have to by the integration of Sartorius be adjusted to processes that Stedim Data Analytics process skids into the automa- ® tion system, especially when can potentially run for months (former Umetrics ) and the considering flexible manufac- without interruption. The Regu- collaboration with Siemens turing facilities, is an issue. latory Agencies are well aware for our newest automation Communication between of the challenges which come platform NewAP. competitor solutions is not with modernizing the industry, always given and there is a lack but are open and cooperative of standards that still need to to new concepts coming from

4 On the Potential of Next Generation Manufacturing

We expect the upstream processes to benefit the most from automation, due to the highly variable nature of the biological process. A higher degree of automation and standardization of the process steps will lead to improved batch-to-batch consistency, and in turn, product quality. There are three application areas that will benefit the most from automation and therefore drive the development of PAT integration and advanced data analytics.

Intensified Processing | Viral Processes Cell and Gene Therapy Continuous Processing When producing viral vectors In personalized medicine Intensified | continuous for novel vaccines or gene applications in the field of bioprocessing is a very hot therapy, the product is no cell therapy, every process is topic in the biopharma industry longer a well characterized unique. As the starting material at the moment, as it increases molecule, such as a monoclonal are the patient’s cells, there the productivity of single-use antibody, but a complex of naturally is a high variation. (SU) facilities, while decreasing various proteins, DNA | RNA and Furthermore, these processes the footprint5. This renders SU in some cases lipid membranes. run at very small scales, with facilities competitive to This complexity makes it hard terrifically high costs per batch 6 conventional stainless steel to identify and understand the and high risk . In these cases, plants for commercial supply factors influencing the product lost batches must be prevented of biopharmaceutical drugs. Critical Quality Attributes in any way possible. Online However, intensified processes (CQAs). Hence, these processes sensors for monitoring and are much more complex than benefit from a stricter control control reduce the contamina- conventional fed-batch strategy, where high levels of tion risk of manual sampling processes and therefore require automation and implementa- and account for process a tighter monitoring and tion of PAT and advanced data variabilities. Because of the control. PAT and automation analytics play a key role. small batch size, these type do not only provide this, but Another crucial aspect to con- of processes will also greatly also reduce the complexity for sider when setting up a viral benefit from parallelization, the operator. Another reason vector production process is the where a refined automation for why we think intensified | operator safety. Using PAT and concept is of vital importance to continuous processing will automation minimizes the lower the costs of goods (CoGs) boost novel solutions, is that need of manual sampling and and enhance patient safety. establishing new manufactur- off-line monitoring, hence Advanced data analytics in ing pipelines with unique reducing the risks of spills or CAR-T processes can improve requirements justifies the costs leakages. process robustness by, on the and efforts of going through one hand, controlling the the approval for commercial quality of viral vectors and, on manufacturing. the other hand, account for the intrinsic variation in raw material attributes and their effect on the patient’s response.

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A look Into the Future

2 Years from Now 5 Years from Now 10 Years from Now

In the near future, we expect In the mid-future, we expect The far-future vision is highly a wider spread adoption of an- that modern facilities will apply influenced by the industry 4.0 alytics in GMP that are already intensified and continuous approach and related concepts available nowadays, such as processing with advanced auto- such as machine learning and spectroscopy for metabolite mation. They will be using state the internet of things. We will control and bio-capacitance of the art automated process see fully automated, continu- for viable biomass. We also batch management and S88 ous bioprocessing pipelines that foresee that multivariate data compliant batch recipe control require no operator interven- analysis (MVDA) and design of functionalities, as well as plant- tions. Processes can be moni- experiments (DOE) are adopted wide visualization and electronic tored and controlled remotely. by more users. Furthermore batch records. Furthermore, Every process will have a digital standardization has been sophisticated analysis tools, such twin that can be used for pro- realized to allow a real plug & as HPLC and mass spectrometry cess simulation and prediction. produce scenario in a (multi- will be automated and integrated More and different data will be product-) facility setup. Also in the bioprocess. Together with gathered and will reside in the the field of hybrid modelling, an increased use of data science, cloud, where data analytics can where statistical and determin- quality by design approaches can be applied easily to improve istic modelling principles are be applied allowing real-time processes, regardless of combined, will advance within release testing of product quality manufacturing location. the biopharmaceutical industry, based on batch finger printing. further improving process Robotics will take over tasks, understanding and simulation. which cannot be automated Within systems biology, for otherwise. E.g. materials are example, these approaches transported to and from the are starting to be applied to production location in-time. enhance the production cell lines commonly used in bio- pharmaceutical processing in a pragmatic way7,8. Industry 4.0 | Internet of Things

Advanced automation for intensified and continuous processing

Wider spread adoption of analytics in GMP

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About the Authors

Dr. Svea Grieb Kai Touw Dan Kopec Dr. Svea Grieb is product Kai Touw works as (Bio)Pharma Dan Kopec is a PAT Technology manager for Process Analytical Market Manager at Sartorius Expert for Sartorius Stedim Technology (PAT) for upstream Stedim Data Analytics. Biotech, covering the North processes at Sartorius Stedim He focusses on the application American region. Dan is based Biotech. In this role, she has of advanced data analytics in out of Denver, CO and has over global responsibility for sensors the biomanufacturing industry 10 years with Sartorius Stedim and analytical systems. Before and how process data can be and 20+ years’ experience in joining Sartorius in 2017, Svea used for statistical modelling process sensors for monitoring, did her PhD at the TU Dresden and simulation for process control, and automation in in single-molecule spectrosco- design and control. Before biopharma, food, and chemical py. Throughout her education taking up this role Kai worked industries. Svea has worked at the MIT on process intensification in Boston, US, the Max Planck as a Process Development Institute in Stuttgart, Germany, Consultant within Sartorius and the Pasteur Institute in Stedim Biotech. He has Paris, France. several years of PD experience, working at Janssen Vaccines & Prevention in Leiden (NL). Kai holds an engineering degree from the technical university of Delft (NL).

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Reference List

1 K. Jhamb, Bioprocess Optimization and Digital Biomanufacturing: Global Markets, BCC Research, JAN. 2019 2 R. Rader, E. Langer, Top Trends in the Biopharmaceutical Industry and bioprocessing for 2019, BioPlan Associates, 2019 3 C. Hill, Continued Process Verification and the Drive to digitize the process validation life cycle, BioPharm International, JUL 2018 4 R. Peters, Bio | Pharma Needs Ideas and Incentives to advance manufacturing, Biopharmaceutical Technology, jan 2019 5 Biomanufacturing Technology Roadmap, August 2017, BioPhorum Operations Group, https://www.biophorum.com/process-technologies/ 6 B. Levine et al. Global Manufacturing of CAR T Cell Therapy, Molecular Therapy: Methods and clinical development, vol. 4, March 2017 7 S. Selvarasu et al., combined in silico modeling and metabolomics analysis to characterize fed-batch CHO cell culture, Biotechnology and Bioengineers, vol. 109, 2012 8 H. Hefzi et al. A consensus genome-scale reconstruction of chines hamster ovary cell metabolism, Cell Systems 3, November 2016

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