The Future Of Biopharma Hinges On The Ability To Manufacture




The biopharmaceutical industry is often credited as being one of the most innovative of all sectors. And while scientists in R&D are charged with finding new treatments and perhaps even cures for a wide variety of diseases with unmet medical needs, it is the engineers and their efforts in manufacturing that often make the difference in biopharma being able to deliver to patients. But the manufacturing process is becoming more challenging as we continue to move toward personalized medicines. For example, in 2017 Novartis received the first-ever FDA approval for a CAR-T cell therapy treatment — Kymriah (pronounced kim-rye-ah) for the treatment of patients up to age 25 with B-cell precursor acute lymphoblastic leukemia (ALL) that is refractory or in second or later relapse. Yet less than one year later, the company announced having some variability in the manufacture of the product.

For this year’s annual outlook article on the future of biopharmaceutical manufacturing, we engaged the help of eight biopharmaceutical manufacturing experts hailing from companies as big (> 55,000 employees) as Merck to ones hoping to get their first drug approved just this year (e.g., Sage Therapeutics, founded in 2010). We even sought the insight from an academic, B. Frank Gupton, Ph.D., professor and chair of the department of chemical engineering at Virginia Commonwealth University, who spent 31 years in biopharmaceutical manufacturing before “retiring” to his current role in 2007. To these experts we posed questions around future biopharma manufacturing trends, challenges, technologies, performance metrics, risk mitigation, people, and outsourcing.

Derek Adams, Ph.D., chief technology and manufacturing officer, bluebird bio: The hot trend our company is “adopting” is personalized medicine (autologous gene and cell therapy). One batch. One patient. The scale of manufacturing (<1L batches) combined with the exceptional importance and link of each batch to the patient forces these early-generation manufacturing processes to be manual and inefficient compared to processes that can be scaled up. The real innovation is in the design and control of the supply chain from and back to the patient. In some cases, there are time limits on intermediate-product stability, and in other cases, patients with life-threatening and progressive diseases need their cells as soon as possible. These circumstances, combined with a focus on the best patient experience in the process, have led us to link clinical and commercial operations very tightly with manufacturing operations. The logistics of patient treatment are completely linked to the logistics of manufacturing.

John Kehoe, VP of manufacturing, Ipsen Bioscience: 3D printing is a technology that Ipsen has implemented at a number of our manufacturing sites for the purpose of creating components and spare parts. This gives our engineering and maintenance teams the autonomy and ability to react and resolve issues faster, with the added benefit of reducing our spare-parts inventory value. We are now also using building information modeling (BIM) [a digital representation of physical and functional characteristics of a facility] as a standard element of our new facility design process (and throughout the construction phase of the projects) to better identify and overcome issues without impacting project schedules. Our 2019 plans include a pilot for augmented reality, firstly in non-GMP usage, and the continued development of an integrated manufacturing execution system (MES).

Tom McCubbins, Ph.D., SVP global pharma operations, Merck: There is a huge opportunity for utilizing 3D printing within the biopharma industry, and thus, we have invested significantly in this technology. Several experiments have been completed to prototype equipment components, produce replacement parts, and create placebo tablets for packaging studies. We are also developing a potential approach for implementing 3D printing in flexible and adaptive clinical trials. Our scientists and engineers in our research laboratories and manufacturing division are collaborating and initially seeking to speed up the R&D process. These teams believe that 3D printing techniques have the potential to accelerate the production of new formulations with different doses and release types and thereby also shorten the time it takes to complete the clinical evaluation process to find the optimal drug product.


Frank Gupton, Ph.D., professor and chair of the department of chemical engineering at Virginia Commonwealth University (VCU): The ability to minimize process waste is one example of an opportunity to improve biopharma processing. Pharma operations for both small and large molecules have suffered from this deficiency for some time. Great progress has been made in process intensification related to small molecule manufacturing. However, biopharma manufacturing clearly lags behind in this area and may benefit from a continuous manufacturing platform. The challenges in applying the principles of process intensification to these systems are more complex and, therefore, have been lagging in innovative solutions. I see this as fertile ground for new and innovative ideas to address the issue of process waste and biopharma manufacturing operations.

Tom McCubbins, Ph.D, SVP global pharmaceutical operations, Merck: The implementation of continuous manufacturing is a big opportunity across the biopharma industry. Our experience is that there are substantial technical challenges involved in fully integrating a continuous manufacturing line that starts with raw material charge and includes integrated at-line testing. Many companies have been working to develop pilot installations for many years. It has been challenging to establish requirements for continuous manufacturing across the various agencies that enable worldwide approvals, as each agency has differences in batch-definition criteria and specifications. We plan to use the conversion of existing batch-manufactured products initially to gain regulatory experience on a specific marketing authorization first, so that we can incorporate these learnings into the way we develop new products to be introduced in a continuous manufacturing format. We believe that continuous manufacturing is going to be very important to enable biopharma companies to manufacture high-value/low-volume and highly potent compounds that constitute a significant share of the industry pipeline.

Christopher Sinko, Ph.D., SVP and head of product development, Bristol-Myers Squibb: Current biopharma industry trends such as continuous processing, “plant in a box” concepts, real-time and automated release, on-floor analytics, and even automated sampling are within our capability and are being implemented today. The benefits to patients can be significant: improved quality, reproducibility, cost and demand responsiveness, along with reduced environmental impact. There are challenges and barriers, however. For example, an unclear global regulatory environment for these operations in GMP manufacturing and uncertain efficiency benefits in today’s operating models both present challenges. The control of material properties in the drug product space and supply chain management are critical and need to be understood as these new platforms are adopted. Technical barriers such as understanding how to better leverage cell physiology for biopharma production need to be addressed in biologics. Improved data generation is a direct outcome of the application of continuous technologies; however, basic issues with data aggregation and use (the ability to gather and mine Big Data) represent an important opportunity, potentially enabling the significant advantages of real-time automation and self-corrective processes to be realized.

The global regulatory acceptance of continuous processing, particularly in drug substance manufacturing, and the translation of this technology into quality and control systems (e.g., batch definition and defining/achieving steady state) are potential challenges to the adoption and embracement of what is a known technology. For products marketed globally, diverse viewpoints from regulators generate additional complexity and implementation risk. Supply chains also face challenges, especially in aseptic processing and the cold-chain management of inventory. A final barrier to adoption can be inertia within organizations due to existing infrastructure “sunk” costs, coupled with a desire to keep existing capacity full. While global supplier networks are starting to deploy more continuous capabilities, for example, existing batch infrastructure will continue to be relied upon initially for a majority of operations, without a significant push from both industry and regulators.



Andrew Knudten, SVP technical operation and CTO, AveXis, a Novartis company: The biopharma industry is moving away from treating symptoms and now focuses largely on addressing the underlying biology of a disease at its root cause. The greatest future challenge will be to translate the amazing research and new discoveries from proof-of-concept into therapies that can be commercialized at a large scale. There are incredible advances happening all the time in scalable, disposable, environmentally sound manufacturing that bench scientists aren’t aware of — and they are making research decisions without the benefit of this information. Gene therapy is an example where there was a stalling period in the field due to this lack of collaboration and true partnership. We weren’t helping scientists think about things in an appropriate way that would help make the transition to the commercial stage more efficient.

Industry will need to better partner with translational and bench scientists in academia to think early about commercial manufacturing. An unselfish and transparent approach across functions is the key to making better decisions. This will ensure scientific breakthroughs can be successfully scaled and transformed into a reality that benefits patients as quickly as possible.

Heinrich Schlieker, Ph.D., SVP technical operations, Sage Therapeutics: New challenges are rapidly emerging, making it important to keep current on trends both directly and indirectly affecting the industry. For example, international trade tensions pose risks such as challenging the ability to secure pharmaceutical supply chains, as most starting materials, intermediates, and increasingly, APIs are manufactured in lower-cost locations like China and India. Comprehensive supply-risk-mitigation approaches tailored to each product are essential and typically include safety stock at multiple stages of the supply chain and tight oversight of operations at contract manufacturing partners. It’s also important to develop backup plans for each supply point and implement dual- supply options in many cases.

Looking forward, new technical and supply chain challenges will arise from new treatment paradigms such as personalized medicines and cell-based therapies. To meet these challenges, we will need novel manufacturing technologies, the development of quality standards, and new approaches to control and oversight. Changes in regulatory and quality requirements require constant adjustments and evolution; a good example of such a change is the introduction of product serialization.



John Kehoe, VP of manufacturing, Ipsen Bioscience: The industry needs to become better educated on Pharma 4.0 to gain a greater understanding of the benefits that are possible in the areas of quality and productivity. Clearly the level of investment required to design, construct, and qualify a Pharma 4.0 biopharma manufacturing site is high — but that is not the only barrier. Finding the highly skilled talent needed to bring a facility and production processes into this new era is also a challenge. Implementing these technology strategies goes beyond upgrading machines and equipment with hardware and smart sensors; we must ensure we are developing technically skilled workers with deep industry knowledge to analyze the massive volume of data these next-generation technologies provide if we are to realize the benefits.

Christopher Sinko, Ph.D., SVP and head of product development, Bristol-Myers Squibb: Biopharma companies must develop their strategies in the context of their external environment, which includes Industry 4.0. There are fewer and fewer chronic disease targets, and that is pressuring us to focus on acute conditions with smaller patient populations. As a result, precision/personalized medicine is becoming a reality. Another factor is the explosion of IT capabilities driven by ubiquitous connectivity, the advancement of data-mining tools and analytics, and a dramatic decrease in storage and computing costs. Our strategies must address cultural and behavioral barriers such as our aversion to new-technology adoption across the value chain, a risk-intolerant mindset influenced by perceptions and interpretations of the regulatory climate, and a limited line of sight from laboratory bench to the plant floor.

Biopharma companies need to eliminate financial hurdles (e.g., overhead absorption) that provide disincentives to small-batch approaches so widely embraced in other, more progressive industries. This would allow companies to more broadly deploy modular, portable designs for manufacturing that enable small batches, improving quality, GMP, and regulatory compliance. Our industry needs to embrace machine learning and automation, which will drive down reliance on human intervention with manufacturing processes as a way to improve quality.

As part of this we need to broadly deploy both process analytical technology (PAT) and attribute science through intensive laboratory automation to generate the large, contextualized data sets that we can begin to interrogate through machine learning. While Quality by Design (QbD) has allowed us to establish rigid parameter-attribute relationships, a deeper understanding of a much wider variable space allows us to focus on the few parameters that really matter. Finally, we need to address our aversion to risk by embracing a more balanced approach. This requires partnering with health authorities and academia to address the regulatory/compliance hurdles that may hinder future improvements in risk management. We have an opportunity to embrace a new mindset of “move quickly/fail fast/fail small” that other industries have adopted as a more balanced path between risk and innovation.


Kehoe: Yes, there are many new manufacturing software packages and applications being released to the market each year, all of which claim to offer improved usability, flexibility, and customization. However, in my experience, the three challenges in this area are the potential underutilization of existing applications, the inefficiency and risk of managing multiple applications with limited interconnectivity between stand-alone applications, and data protection and management.

I would challenge companies to analyze the extent to which they are utilizing existing ERP and software applications to better measure and record process and equipment performance and ask if this is resulting in improved efficiencies and data-driven decision-making. As an industry, we have made substantial investments in these existing systems, of which we need to fully utilize and realize the business benefits.

Nevertheless, the cost of many of these newer sensor- based systems has been reduced substantially in terms of installation and ongoing operation due to the advances in application development and wireless technology. One of the best examples of this is in asset care and equipment monitoring systems.


Derek Adams, Ph.D., chief technology and manufacturing officer, bluebird bio: The real key to successful integration of applications within manufacturing is ensuring that they all serve the same purpose. Each company can phrase or characterize that purpose differently; however, often what happens is that each function within a company (or manufacturing site) defines the purpose on their own. For instance, a manufacturing execution system may be scoped and built through the lens of manufacturing operations and GMP compliance, but perhaps it requires a huge effort to link the data and functionality to enterprisewide systems, and it may cause finance teams to scramble to ensure it is all financially compliant. For true integration, the systems must serve clear and well-defined business processes that are themselves well-integrated. The applications don’t make the business, they serve the business.



Frank Gupton, Ph.D., professor and chair of the department of chemical engineering at Virginia Commonwealth University (VCU): The concept of using in-line analytics to predictively manage manufacturing operations has become popularized as a means to effectively control processes in real time. In reality, the principles of quality by design are equally important in the development of robust pharmaceutical operations. The most effective use of real-time analytics can be best applied during the initial phases of process development. By judiciously identifying the design space using real-time monitoring, process chemists and engineers can home in on the optimal operating conditions for a given process. Once these conditions have been established, particularly in the case of continuous manufacturing, these processes can operate with minimal limits on process control. These principles have been applied recently in commercial operations where the in-line analytics chosen were quite simple (conductivity) in order to identify changes to the system rather than controlling the system. This alternative approach can have a dramatic positive impact on operational complexity as well as capital investments. We are applying these principles to the development of new global health drugs.

Christopher Sinko, Ph.D., SVP and head of product development, Bristol-Myers Squibb: Overall equipment effectiveness (OEE) and other traditional metrics are providing greater insights to measure manufacturing performance, and there are a few key factors enabling this to happen. Factors include advanced models enabled by Big Data, holistic measurement of process performance rather than through individual step/unit operations, and new tools that allow us to look beyond OEE to new improved measurements of performance. The era of Big Data allows for better models to be developed. Models for real-time release testing drive faster batch release, and models for real-time fault detection now enable timely corrective action. The next logical step is to develop and deploy predictive models enabling advanced control. This will require more investment in talent (data scientists), data-rich technologies (e.g., PAT), and data infrastructure/workflows that enable data capture and analysis. To enhance performance beyond an individual process step, we are aggregating data to continuously update performance metrics and moving toward the direct application of data science to better understand overall process variability.

New complex process monitoring systems can spot process deviations as they occur and warn operators, or even self-correct, in a timely fashion. In biologics, traditional monitoring can detect an increase in oxygen consumption or a pH value that moves away from a center point. Neither of these changes may be great enough to trigger an alarm on their own, but new multivariate modeling could detect these trends moving together, recognize a potential fault developing in the reactor, and alert operators or control systems. In the current state, the operators can then react to the alarm or trigger an investigation before the anticipated deviation has time to impact performance. In the future, a control system can automatically adjust the necessary parameters such as temperature or feed schedules to avoid deviations entirely. These methods require previous-run data to learn when a process is trending toward a deviation from a historic norm.

Tools such as Raman spectroscopy are now being employed to monitor and control bioreactors. The capability to monitor glucose, metabolites, and select amino acids in real time provides the basis of enhanced control of the bioreactor along with traditional measurements such as pO2, pH, and temperature. Raman is providing a deeper understanding of our processes, including glucose metabolism, which leads to a more robust process and ease of scale-up and transfer. Similarly, real-time spectroscopy is in use to support robustness in small molecule syntheses. By employing in-line FTIR (fourier-transform infrared spectroscopy) during flow chemistry, we can sustain a steady-state operation by instantly detecting small fluctuations in output concentration and quality and then adjust inputs (flow rates, temperature) accordingly.

A future state where performance can be monitored across an entire network allows us to leverage data to efficiently transfer learnings between sites. We also can consider alternative measurements that permit us to look beyond OEE.



Joanne Beck, Ph.D., EVP global pharmaceutical development and operations, Celgene: The use of robotics and automation is not intended to replace workers. Quite the opposite will be true. What will change is the type of workers the industry will be seeking. Rather than sourcing staffwho are primarily skilled at ensuring the quality implementation of repetitive tasks, the industry will be looking to find individuals who can think differently. Those who can bring new perspectives to existing methodologies and processes, who can leverage predictive analytics to improve process development and manufacturing, and who can improve quality assurance and overall supply chain operations will be the drivers of the future workforce. Automation will free engineers and scientists to better meet the challenges of delivering a robust CMC (chemistry, manufacturing, and controls) package and supply chain in order to accelerate the delivery of biopharma programs. As a result, companies also will see lower COGS, improved consistency, and the portability of manufacturing processes. To reap the most benefit from the expansion of automation, technology experts will need to work closely with their counterparts in human resources on accurate forecasting to ensure that the workforce a company is building not only meets its needs for today, but also meets its anticipated needs for the technologies of tomorrow.

Heinrich Schlieker, Ph.D., SVP technical operations, Sage Therapeutics: We are not yet at the point of a paradigm shift similar to what has been seen in automobile manufacturing, where the industry went from workers assembling cars to highly automated production using robot technology.

I see automation as much further off for biopharma companies, as many therapeutics are produced at relatively small scale. Most biopharma companies partner with CMOs to produce their drugs in batches or campaigns that only utilize equipment for a limited time. For contract manufacturers, these frequent product change-overs make it difficult to fully automate processes. As a result, most contract manufacturing still relies on paper batch records and operators.

Fully automated production may be decades down the line for biopharmas relying on outsourcing. Conversely, facilities dedicated to one or a few products already have a higher degree of automation often driven by quality considerations to reduce human errors.


Beck: Partnerships and external collaborations can help ensure that the right talent and skills are being directed into the biopharma industry. For one, the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) is a public-private collaboration of industry, academia, nonprofits, and government agencies dedicated to advancing innovations in manufacturing, as well as to developing a biopharma workforce through training and education programs. MIT’s Biomanufacturing Program (BioMAN) also focuses the discussion on appropriate skillsets and potential approaches to training and talent development. Depending on specific needs, collaborating with local universities to suggest targeted curricula that is in line with real-world industry needs is another opportunity. Also, for some, having a corporate headquarters with access to leading academic institutions provides an ability to discuss future talent needs and the potential to develop educational materials and help teach courses. Finally, offering internships to students with appropriate backgrounds in science, engineering, and computer science not only immerses them in company operations, but also provides them with an insider’s view of the biopharma industry, the issues companies face, and most of all, the passion those in the industry feel for the patients they serve. When students return to their universities, the stories they tell is perhaps the best advertisement to other students who are deciding on their career paths.

Andrew Knudten, SVP technical operation and CTO, AveXis, a Novartis company: With scientific innovation moving us toward cutting-edge technologies, such as gene editing and gene replacement therapy, it will be critical for industry to evolve our relationships with academic institutions to ensure we are preparing the next wave of students to help drive this change forward. Students must be exposed to practical experience and industry perspectives. There is great opportunity for universities and industry to collaborate in the development of curricula to provide students with detailed training. This could include coursework that covers large-scale and small-batch customized processes or creating a pilot manufacturing facility to aid in hands-on training.

Formalized internship programs specific to manufacturing and data science also provide hands-on experience that help cultivate expertise, skill, and interest and can lead to permanent positions. It is these types of programs that will best prepare the next generation of talent for the practical application of their training.



Joanne Beck, Ph.D., EVP global pharmaceutical development and operations, Celgene: In general, for development and manufacturing operations, a biopharma company should consider a balanced approach to internal versus external manufacturing. While this approach is largely driven by the diversity of a company’s development portfolio (e.g., small molecules, biologics, cell therapies), a company also must consider the technical capabilities and quality systems of the CMOs chosen when compared to its own internal capabilities. For well-established platforms — especially large-scale monoclonal antibodies and small molecule APIs — outsourcing to a network of preferred, trusted CMOs can provide a great deal of resource and capacity gains that allow internal networks to refocus on new platforms and products. Ideally, a trusted CMO will have the capacity for more than one modality, which allows a company to consolidate its internal resources even further by working in a more streamlined, efficient manner. However, technical capabilities and quality standards across CMO sites can vary despite the best due diligence. In some cases, a company may need to deploy more resources than it had anticipated on both the technical side and, more importantly, on the quality side so that potential regulatory risks can be mitigated. While very positive results can be realized from strong relationships within a preferred CMO network, the CMOs still need to do some work to deliver on their promise of consistently efficient, effective, and compliant outsourcing.

Andrew Knudten, SVP technical operation and CTO, AveXis, a Novartis company: Early-stage biopharma companies often outsource manufacturing to a CMO because it’s helpful to have that option while you conduct the early work to determine whether the therapy is effective and meets a market need. The right CMO can be a tremendous partner to bridge to internal capacity or provide surge capacity.

Over-reliance on a third party, however, can have limitations. The company is beholden to the CMO’s time and space constraints, talent pool, information flow, and technology transfer process — and this all comes at a premium cost. Startup biotechnology companies that have value-creation catalysts — companies that are innovating and developing therapies based on emerging science, such as gene replacement therapy — should consider making the investment to take ownership over their process early. Bringing manufacturing in-house provides that much-needed control, allows for nimble decision-making and risk-taking, while also helping to decrease the cost of goods. With a stable set of resources and talent who are focused solely on your product, this combination better positions the company for success.

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