Bioprocess development is critical in producing biologics, cell and gene therapies, and vaccines. As the biotechnology industry continues to evolve, so do the strategies and technologies underpinning the successful scaling and commercialization of therapeutic products.
In this article, we explore key insights into bioprocess development from an expert in the field, Neil Templeton, focusing on scale-up, process understanding, and innovative strategies to drive success.
This concept is discussed in greater detail in an episode of the Smart Biotech Scientist Podcast, hosted by David Brühlmann, founder of Brühlmann Consulting.
The Importance of Testing Scale-Up Parameters Early
One of the most debated topics in bioprocess development is when to test the parameters that will ultimately influence a process's scaling. Many in the industry believe that engineering fundamentals, such as power per volume ratios, can be applied directly when transitioning from bench-scale to large-scale reactors.
However, Neil Templeton stresses that relying on these models alone can be risky, especially when working with shear-sensitive cultures or other process-specific variables that may not be evident at smaller scales.
Early Testing to Avoid Late-Stage Surprises
- Templeton advocates for an upfront approach where scale-up parameters are tested early in development.
- By addressing potential issues before reaching large-scale production, such as shear sensitivity or poor yield, teams can avoid the panic that comes with unexpected performance issues during scale-up.
- This proactive approach can be seen as a trade-off between initial optimization and ensuring that scaling will succeed.
Transitioning from R&D to GMP Production: Key Challenges
One of the primary challenges in bioprocess development is the transition from research and development (R&D) to good manufacturing practice (GMP) production. As processes move from smaller-scale lab environments to clinical or commercial production, the timeline becomes increasingly compressed, often creating pressure to optimize quickly.
The Time Constraint Dilemma
The reality of working under tight timelines is one of the most significant hurdles for biotech scientists. While the need to accelerate the development process is universally recognized, cutting corners or sacrificing process understanding can lead to long-term issues.
Templeton points out that balancing the urgency of timelines with the need for thorough optimization remains a challenge across biotech companies, regardless of the modality.
Balancing Speed with Process Understanding
While speed is crucial, it should never come at the cost of neglecting the need for robust process understanding.
Scientists must develop a deep understanding of their processes and critical parameters, which will serve them well during scale-up. However, achieving this comprehensive understanding often requires time—something that is often in short supply when teams are racing against the clock.
Strategies for Streamlining Process Development and Scale-Up
Despite the inherent challenges, several strategies can help biotech scientists more effectively navigate the complexities of process development and scale-up.
The Importance of Design of Experiments (DOE)
Templeton emphasizes the value of using Design of Experiments (DOE) approaches over traditional "one factor at a time" methods. DOE allows scientists to explore multiple variables simultaneously, which is especially helpful in the early stages of development when time is limited.
This approach helps identify critical process parameters more efficiently and accelerates the understanding of how different factors influence the overall process.
The Need for Cross-Functional Collaboration
Effective process development also requires input from across the organization. Templeton advises that scientists engage with colleagues in regulatory, clinical, and other departments to ensure the process aligns with broader project goals.
This holistic view can help prevent the siloing of information, which can lead to critical blind spots when it comes time to lock the process for regulatory filings, such as an Investigational New Drug (IND) application.
It's equally critical to understand what members outside of process development think and wish for this given program. Yeah, that's something I would encourage anyone listening to do because often you can get in a little bit of a silo situation and simply focus on the concerns that really matter to you in process development and forget that when you get to the stage of an ind, the CMC section is just one small part of it.
Innovation and the Future of Bioprocess Development
As bioprocessing advances, new technologies and methodologies pave the way for more efficient and cost-effective manufacturing processes. Templeton points to several key innovations shaping the future of bioprocess development, particularly in the gene therapy space.
Moving Beyond Endpoints: Continuous Process Monitoring
One of the major shifts Templeton sees in the industry is the movement from endpoint-based quality control to a more continuous process monitoring approach.
Integrating real-time data from Process Analytical Technologies (PAT) allows continuous monitoring of critical quality attributes (CQAs) throughout the manufacturing process.
This allows for a more nuanced understanding of process performance, which could improve patient safety and product consistency.
Harnessing the Power of Metabolic Analysis
In addition to continuous monitoring, Templeton also highlights the value of metabolic analysis for understanding process dynamics. Techniques like 13C metabolic flux analysis can provide invaluable insights into the metabolic shifts during bioproduction.
Although these techniques are typically reserved for late-stage development or optimization, they can help identify key correlations between metabolic activity and product yield, offering predictive insights for process improvement.
Leveraging Metabolic Insights for Process Optimization
Metabolic analysis allows for process optimization by revealing trends in nutrient consumption, waste production, and overall cell behavior. By tracking metrics such as glucose consumption and lactate production, scientists can gauge the efficiency of cell culture processes and identify areas where performance can be improved.
Managing Variability: The Role of Lactate and Glucose Ratios
Templeton shares that simple metabolic metrics like lactate and glucose ratios can be incredibly useful for monitoring process consistency. High lactate production often correlates with high growth rates, which are necessary for achieving high productivity.
Furthermore, maintaining a balanced ratio of glucose to lactate during fermentation or cell culture phases is an important indicator of overall process performance.
Robustness and Consistency Across Batches
Understanding metabolic shifts can also inform the robustness of a process. Even slight variations in cell inoculation density or environmental factors such as dissolved oxygen can introduce inconsistencies.
By analyzing metabolic data, scientists can gain early indicators of potential issues, allowing for timely adjustments to maintain consistent performance across batches.
Process Development in Different Biotech Modalities
Regarding process development and scale-up in the biotech industry, the differences between monoclonal antibodies, gene therapy, and vaccines are significant and subtle. While each area has unique challenges, some common themes emerge that guide the development of robust and scalable processes.
Key Consistencies in Biotech Process Development
Despite the variety of therapeutic modalities, one constant is the energy-intensive nature of protein production.
Whether producing monoclonal antibodies or viral vectors for gene therapy, proteins demand considerable energy, often measured in terms of glucose or lactate consumption. Protein is the most energetically expensive macromolecule, far more than lipids, carbohydrates, or nucleotides.
This is a consistent challenge across various biotech modalities, particularly in upstream bioprocessing, where cell culture conditions are critical.
Variability Across Modalities: Tolerance to Shear Stress
One key difference arises in the tolerance of cell lines to shear stress during the scale-up process. Historically, CHO (Chinese Hamster Ovary) cells used in monoclonal antibody production were known for their robust resistance to shear forces.
This is in contrast to HEK293 cells, often used in gene therapy, which exhibit less tolerance to shear. Additionally, when working with certain cell lines that cannot grow in suspension, microcarriers are sometimes used to simulate suspension growth—a challenge less common in traditional monoclonal antibody production.
In gene therapy and vaccine production, choosing impeller size and mixing strategies is crucial to avoid shear damage, which can negatively impact cell viability and product yield. These considerations require a detailed understanding of the specific cell line's behavior and how the process parameters can be optimized for maximum efficiency.
Challenges in Gene Therapy and Cost Reduction Strategies
Gene therapy represents one of the most promising yet expensive areas of biotechnology. Treatments for rare genetic disorders, such as sickle cell anemia, can cost between $2.2 million and $3.1 million per patient. While the potential for curing genetic diseases is immense, the cost of gene therapies remains a significant barrier to widespread adoption.
Addressing the High Cost of Goods
The cost of goods (COGS) in gene therapy manufacturing is a primary area where process engineers can make an impact.
One of the most effective ways to reduce costs is to improve the yield of the upstream process. By increasing yields early in the production process—such as optimizing cell culture conditions—companies can significantly reduce COGS, which has a much more significant impact than improving yields in downstream steps like chromatography.
Another strategy is identifying areas where expenses are greatest in the manufacturing process. This involves conducting a thorough cost analysis early in the development cycle to pinpoint inefficiencies. By improving these critical areas, engineers can help reduce the overall cost of gene therapies, making them more affordable and accessible.
If you find yourself where your cost of goods are 1%, the sale price, well, even if you're the best there ever was and you reduce the cost of goods hundredfold, now you're looking at a situation where instead of 1%, it's 1/100 of a percent.
The Impact of Immature Processes in Gene Therapy
One reason gene therapies are so expensive is the industry's relatively immature state. Many gene therapy processes still face low yields and scalability issues. Additionally, since these therapies are often targeted at rare diseases with limited patient populations, the cost of development and manufacturing is high to compensate for the smaller market.
However, there is significant potential to reduce these costs by focusing on improving process efficiencies. This includes scaling production methods, refining transfection techniques, and exploring alternative cell lines or production systems.
Leveraging Learnings from Monoclonal Antibodies
Given the more established nature of monoclonal antibody (mAb) production, valuable lessons can be learned that could benefit the gene therapy field. mAb production has undergone decades of refinement, and many of the methodologies and approaches used in this space can be applied to gene therapy and other emerging modalities.
Transferring Expertise from Biologics to Gene Therapy
Both monoclonal antibody production and gene therapy rely heavily on mammalian cell culture and share similar challenges, such as dealing with post-cell impurities and managing short development timelines. By leveraging expertise from the mAb space, gene therapy developers can apply proven techniques, such as design of experiments (DOE), multivariate data analysis, and machine learning approaches, to optimize their processes.
Design of experiments and meta-analyses can be particularly helpful in identifying key variables and optimizing cell culture conditions. Machine learning techniques, which have seen increasing use in biologics development, can assist in predicting outcomes and guiding process optimization efforts, ultimately leading to reduced costs and more efficient manufacturing processes.
Reducing Costs and Improving Efficiency: Key Takeaways
While gene therapy and other emerging modalities present unique challenges, there are several strategies that scientists and engineers can employ to reduce costs and improve efficiency:
- Focus on Upstream Yield Optimization: Improving yields during the upstream process has the most significant potential for reducing COGS. Minor improvements in early-stage yields can disproportionately impact the overall cost structure.
- Conduct Thorough Cost Analysis: Conduct a comprehensive cost analysis before scaling up a process to identify areas where efficiencies can be gained. This early evaluation can lead to significant cost reductions in the long run.
- Leverage Cross-Modality Expertise: Knowledge gained from established fields like mAb production can be transferred to emerging modalities like gene therapy and vaccines. Techniques such as DOE, multivariate analysis, and machine learning can also drive efficiencies in these areas.
- Test and Optimize for Shear Sensitivity: Early identification and mitigation of shear-related issues are critical to avoiding process breakdowns. Understanding the shear tolerance of different cell lines and optimizing mixing strategies can prevent costly setbacks during scale-up.
Conclusion
As the biotech industry continues to evolve, process development and scale-up challenges remain ever-present. Whether working with monoclonal antibodies, gene therapies, or vaccines, the fundamental principles of optimizing yield, reducing costs, and leveraging cross-disciplinary knowledge will remain key to driving innovation.
By applying lessons learned from established biological production methods, we can create more efficient, cost-effective processes that make life-saving therapies more accessible to patients worldwide.
The journey from process development to commercialization is long and complex, but we can continue to make significant strides toward more affordable and scalable biomanufacturing solutions with the right strategies.
About Neil Templeton
Neil Templeton is a Director in Upstream Process Development at Solid Biosciences and an engineer deeply committed to process scaling and optimization. Known for his proactive approach, he advocates for early testing of scale-up parameters to identify potential process-breaking features before transitioning to larger reactors. He prioritizes successful scale-up over strict optimization, recognizing the value of balancing both rather than relying solely on engineering fundamentals like power per volume. While perspectives in the field may vary, his methodology has consistently proven effective, establishing him as a careful and forward-thinking leader in his discipline.
Connect with Neil Templeton on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
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