Mammalian Biologics Archives - That's Nice

Jeanette Doerr, Ph.D., and Haiou Yang, Ph.D., of Avid Bioservices discuss the need for high-throughput process development and how the right biopharma CDMOs can help, with Pharma’s Almanac Editor in Chief David Alvaro, Ph.D..

David Alvaro (DA): What are the pressures right now on developers and manufacturers to develop robust, reproducible processes more quickly than ever before?

Jeanette Doerr (JD): In the last two years, we saw increased demand globally for therapeutics, and the timelines for their development and commercialization were extremely accelerated. That is going to be the norm now that the industry’s ability to meet those short timeframes has been established.

DA: At the same time, it seems that regulatory agencies are demanding a deeper and deeper level of process understanding. How does this opposing expectation make process development more challenging?

Haiou Yang (HY): Regulatory agencies want to see a more in-depth understanding of the process. Rather than simple linear evaluations, we want to know about complete operational zones, and, rather than individual parameters alone, we want an understanding of how those parameters affect one another. To get all that information requires more experiments using design of experiment (DoE) and quality-by-design (QbD) approaches. High-throughput solutions are absolutely essential to increase the number of experiments that can be completed in shorter and shorter periods of time.

DA: How is Avid Bioservices dealing with these pressures?

HY: Typically, processes have been developed at Avid Bioservices in approximately six months, much shorter if a platform method can be applied. That has now shrunk to three months. The key is to cut the process development timeline in half without negatively impacting quality. That requires a faster turnaround, which can be addressed with high-throughput (HTP) solutions, because they allow the evaluation of many ideas much more quickly. An HTP approach is also beneficial when there is limited starting material to work with, which is typically the case for biologics.

DA: In a general sense, how are you able to increase upstream process development throughput?

JD: Upstream process development is actually really labor-intensive and time-consuming. At Avid, we have 30 benchtop bioreactors, so we can do large sets of experiments, but it is very resource-intensive. Leveraging automation, such as the Ambr^® 250 system from Sartorius, we can run 24 bioreactors with a maximum working volume of 250 mL simultaneously without intense technician involvement and with reduced risk of human error. That helps us achieve our goal of generating more data with less resources in less time, leading to faster process development.

DA: How many different minibioreactor process runs is typically needed before you can advance to the next scale?

JD: It really depends on where we’re starting. If we’re starting pre-IND, then we would normally do several rounds of 10–12 bioreactors in a DOE format to really look at the design space of the process and evaluate and optimize parameter settings.

For clients wanting to enter the clinic quicker, a risk-based approach might be used in which Avid performs one set of runs and then performs a verification run and a pilot run, followed by tech transfer to manufacturing. In this case, a process that is phase appropriate is used to get into the clinic, and then we circle back to do more optimization during phase I trials.

For phase III projects, the work often involves scale-down model qualification and process characterization. That is also very intensive and involves running numerous bioreactors simultaneously to obtain the necessary depth of process knowledge.

DA: How are high-throughput approaches leveraged for downstream process development?

HY: There are several ways. One is to first understand the molecules involved in the process using commercially available modeling software. For instance, before developing a chromatography process, modeling can be used to learn about the physiological and biochemical properties, such as the hydrophobicity/hydrophilicity, of the molecules involved in the process and thus figure out how they will interact with different types of resins. This understanding is generally lacking right now at the process development stage and could really help accelerate column selection.

As another example, the current approach to process development involves a significant amount of trial and error with regard to determining the optimum process equipment, materials, and conditions. When a good result is obtained, we move forward without conducting any further tests. Because there isn’t time to explore other materials and conditions, we don’t fully understand the variability of the process. A high-throughput approach gives you the ability to fully explore the design space of processes.

There is also software that can help accelerate process development by evaluating how processes will perform in existing larger-scale commercial equipment using process-development data. Potential issues with performance can be identified earlier and guide the development of processes optimized for that equipment.

High-throughput methods can also be used to accelerate process scale-up. Using chromatography as an example again, it’s easier to scale up using an HTP method evaluating different column heights and diameters to determine the optimal plate numbers for a given purification.

DA: What can you tell me about the challenges you face with respect to the rapid collection and analysis of larger and larger amounts of data and Avid’s approach to managing that?

JD: We currently use JMP^® data analysis software from the SAS Institute to analyze our DOE data and are evaluating software from another company that would provide additional capabilities and work with both the Sartorius and JMP solutions.

HY: We are definitely expecting that data analysis will become a real bottleneck that must be addressed. The goal with the evaluation of this additional software is to identify a solution that will avoid that bottleneck — ideally within the coming year.

DA: Does high-throughput process development make analytical development more challenging?

JD: Yes. Along with the upstream and downstream investment at Avid in high-throughput technology, we’ve also invested in the analytical development department with the purchase of a Sartorius Octet HTX, some Waters UPLCs, a Maurice Bi-Protein Simple, and a Hamilton Liquid Handler. We have had to increase throughput in the analytics group in order to avoid bottlenecks there. Luckily, we have been able to invest in upstream and downstream process development and analytical development hand-in-hand.

DA: Has Avid invested in any other equipment or technologies for accelerating upstream or downstream process development in addition to the Ambr^® 250 system, data analysis software, and modeling capabilities you mentioned?

JD: Upstream, we are also adopting the Sartorius Univessel^® SU 2L single-use bench-scale bioreactor for transferring from the Ambr^®vessels to bench scale for verification.

HY: Downstream, we’re thinking about implementing a new high-capacity membrane chromatography technology from Cytiva with the potential to speed up this important unit operation — not just for development, but in manufacturing as well. We are also considering the implementation of more inline solutions in the process development lab.

In general, we are always looking for innovative technologies to assess in process development, for use in the lab and/or manufacturing. Avid has been very open-minded in terms of leveraging changes and advances in technology, with the company leadership generally excited for us to pursue new opportunities and transfer them to the plant where appropriate.

DA: How you see the investment in high-throughput process development technologies fitting into or playing a role in this larger growth and expansion of Avid?

JD: Avid is expanding its clinical and commercial manufacturing capabilities. All clients enter Avid through process development before tech transfer to ensure their processes fit our facility and any gaps are addressed. Therefore, to expand commercial manufacturing, we had to invest in process development to ensure that we can keep pace.

DA: What do you see as unique, differentiating, or intrinsic to the culture of Avid that is key to driving your success in process development, particularly increasing throughput?

JD: Two big things that come to my mind. We are very creative and we always make things work. No matter what the gap is or what the issue may be, we always seem to find a solution that works. I’ve been at Avid for 17 years, and that creative culture has been one of the key reasons for me staying at the company. At the end of the day, we get the process and the manufacturing right, no matter what it takes.

HY: I totally agree that creativity is our key asset, and we’re also problem solvers. Whenever we have any problem, we feel like we have a way to get there. In addition, we’re very transparent with the client. We go through every step that we have done and the thought process, how we see the problem, and what we expect the results to be. As a result, clients are informed all the time about the developments, which makes them more comfortable. I think this communication is very important.

JD: Avid has made a sizeable investment into improving our process and analytical development capabilities so that we can be efficient and deliver robust, reproducible processes in a short amount of time and really be competitive with other CDMOs. We have not only added new equipment and instruments but reconstructed the labs to accommodate them and expanded our headcount. In fact, we still have some openings to fill.

DA: Is there any ongoing bottleneck or issue for which a solution would transform your work and further enable high-throughput process development?

JD: For upstream, that would be modeling solutions for scale-up. Going from bench scale in the process development lab to manufacturing scale without having any issues or impacts on product quality is rare. That is the biggest gap, but fortunately it is being filled in by companies such as Sartorius and Thermo Fisher Scientific. Running bioreactors in both the process development lab and manufacturing helps enable modeling to predict performance at larger scale. Avid is working to gather data at the small and large scales to develop and optimize our own models.

HY: I would add to that the concept of continuous processing, which, although it requires a huge investment, represents the future of bioprocessing. The idea is for material to flow from the bioreactor through the different downstream purification steps on a continual basis over an extended period of time.

DA: Are there still opportunities for innovations in the high-throughput space to even further accelerate process development?

JD: For upstream process development, we are still constrained by how fast cells grow — generally a 24-hour doubling time. There is only so much of the timeline that can be accelerated beyond that point. If there is a way to get the data faster, that would be great.

HY: I always feel like there’s room to improve. Process development work today is mostly done in batch mode. We analyze offline samples in order to determine what to do next. That issue is being addressed with inline process analytical technology to enable real-time monitoring in manufacturing. For process development, however, we don’t have those inline analytical capabilities. If such small inline analytical methods were available, we could potentially integrate process development and analytical and thus speed things up even further.

Originally published on PharmasAlmanac.com on November 9, 2021.

Bispecific antibodies (bsAbs) are unique, next-generation antibodies with dual specificity that create numerous opportunities for therapeutic applications, including diseases that had been previously untreatable. Despite their potential, bsAbs pose significant development and manufacturing challenges owing to their complex molecular structure. Samsung Biologics’ S-DUAL™ platform addresses these challenges by combining a knob-in-hole design with an asymmetric antibody structure, achieving excellent pairing and significantly enhancing productivity and quality. This article discusses an upstream process optimization initiative, which led to notably increased productivity and a dramatic improvement in lactate metabolism, thereby enhancing the commercialization readiness of bsAb programs from the development phase. As an integrated drug development partner, Samsung Biologics leverages in-house capabilities to streamline the journey from DNA to regulatory applications, reducing development timelines to provide novel therapeutics to meet urgent patient needs.

Bispecific Antibody Background

Since the approval of the first monoclonal antibody (mAb) therapies in 1986,¹ antibody-based drugs have become the predominant class of biopharmaceuticals. However, in recent years, novel antibody derivatives — multispecific antibodies, antibody-drug conjugates, antibody fragments, and others — have been investigated and offer substantial advantages over traditional mAbs.

Bispecific antibodies (bsAbs) are next-generation antibody therapies that have received significant attention owing to their increased specificity and efficacy relative to mAbs. They possess two distinct “arms” that bind to two distinct antigens (or different epitopes of the same antigen). Their ability to simultaneously bind to two targets and, thus, address two different disease pathways enables them to function as effective treatments for diseases with multiple mechanisms of action. For such diseases, manufacturers only need to produce a single biotherapeutic, and patients only need to take one bsAb rather than two mAbs.²

By the end of 2023, the U.S. Food and Drug Administration had approved nine bsAbs: seven for cancer, one for treatment for hemophilia A, and one for wet age-related macular degeneration.² At the same time, over 100 bsAbs were in clinical development³ for chronic inflammatory, autoimmune, and neurodegenerative diseases; vascular (blood vessel-related), ocular (eye-related), and hematologic (blood-related) disorders; and infections, including COVID-19.² According to Allied Market Research, the global bsAb market was valued at $5.5 billion in 2022 and is expanding at a compound annual growth rate of 34.8%, potentially reaching $109.4 billion by 2032.⁴

Despite this positive outlook, there are challenges associated with developing and manufacturing bsAbs, including issues relating to the quality, stability, and immunogenicity of bsAbs; difficulty maintaining the affinity of the parental mAbs; and complex manufacturability, particularly related to the production of bsAbs with correct heavy chain-light chain (HC–LC) pairing. The third issue arises because manufacturing bsAbs with two distinct chains often yields multiple final forms, only one of which is the desired product, while the other forms are impurities that are difficult to remove owing to their similarity to the preferred form.

Samsung Biologics’ S-DUAL™ BsAb Platform

Samsung Biologics has tackled these challenges head-on by introducing its S-DUAL™ bsAb platform. The commonly used knob-in-hole (KiH) design is combined with a novel asymmetric antibody structure created by introducing a CH3 (the third constant domain of the heavy chain) dimer in the antibody’s fragment antigen-binding region, which delivers a 99% HC–LC pairing success rate (see Figure 1). This design also enables the seamless addition of complementarity-determining regions of interest without additional antibody engineering while maintaining high binding affinity and productivity.

Figure 1. The general structure of a bsAb developed with the Samsung S-DUAL™ platform

S-DUAL™ bsAbs have an immunoglobulin G (IgG)-like structure with an alternative effector function (IgG1/IgG4) and, thus, exhibit low immunogenicity while offering plug-and-play features for the variable domain. The KiH strategy ensures efficient HC–HC pairing, while an additional domain enables highly specific HC–LC pairing. Since the asymmetric structure makes it easy to distinguish, only by size, the desired product among the various impurities that bsAbs can create, it is possible to easily monitor only the desired product through size-exclusion high-performance liquid chromatography in the process development stage, thereby enabling process development that maximizes productivity and quality.

Improving Productivity and Quality

The initial results confirming the effectiveness of the S-DUAL™ bsAb platform used a process that had not been thoroughly optimized. As a result, an upstream process optimization initiative was launched to improve the productivity of the process and the quality of bsAbs, while shortening the development timeline (see Figure 2). An initial feasibility run was implemented to confirm the overall process performance. Different media and addition conditions for additive A were screened, followed by a design-of-experiment (DoE) study for process parameter optimization. Verification runs then demonstrated the robustness of the optimized process.

Figure 2. Upstream process optimization strategy for the S-DUAL™ bsAb platform

To expedite the timeline, pool cell lines producing S-DUAL™ bsAbs were cultured in a 2-L Biostat^® B-DCU (Sartorius) bioreactor to monitor cell growth, metabolite, and productivity profiles. Based on this feasibility run, media and additive screening were conducted in Ambr15^® (Sartorius) to determine media combinations and additives, which led to higher productivity and improved cell metabolism profiles.

Historically, various media combinations have been shown to increase productivity and quality profiles according to the cell line. In this case, the pool exhibited lactate accumulation, and it is challenging to change the lactate metabolic shift from the production to the consumption phase. Additive A was appropriately adjusted to modulate lactate consumption at a late phase of the cell culture (see Figure 3).

Figure 3. Historical data on productivity and lactate control for media and additive screening

A DoE study was then performed to optimize critical process parameters. As one of the DoE approaches, response surface methodology (RSM) was applied in the DoE design platform using JMP^® statistical software, which is specifically used in process optimization. In DoE RSM, a central composite design was chosen to identify the effect of various process parameters, with four factors and two levels that historically showed improvements in productivity and quality. Factors that were considered included cell conditions, pH and gas levels, metabolite profiles, osmolality, titer, and quality/purity.

The determined set of physical experiments was then performed in Ambr15^® bioreactors to increase both the productivity of the process and the quality of the bsAbs using a single clone derived from the pool cell lines and the best media combination established in the previous step. Predicted and actual titer high-performance liquid chromatography (PA–HPLC) results are shown in Figure 4. The correlation was strong.

Figure 4. Statistical analysis of productivity and quality for upstream critical process parameters

Measurable Results

Process verification at the 2-L bioreactor scale was then conducted to confirm that the established upstream development process, including harvest, provided the expected results. Following process development, the operational differences between the feasibility run and the verification run were the cell line (pool to single clone), media combination, the addition of Additive A, and process parameters. It was confirmed that the advanced upstream development efforts for the S-DUAL™ bsAb platform successfully improved lactate metabolism, productivity, and quality (see Figure 5).

The productivity of the verification run was more than 3.4 times higher than that of the feasibility run, with the final average titer approximately 8.0 g/L, as determined by HPLC (Waters Alliance™ 2695 with 2489 UV/Vis detector) using a 2.1 × 30 mm column (Thermo Fisher Scientific POROS™ A20). Lactate metabolism showed more dramatic improvement. In the feasibility run before process development, the final lactate concentration was maintained at 3 g/L or more because the lactate metabolic shift had not occurred; in contrast, the final lactate concentration of the verification run was maintained at 0.1 g/L or less as a result of a complete shift to the lactate consumption phase following the addition of Additive A.

Figure 5. Overall results for S-DUAL™ bsAb upstream platform development

Considering Commercialization from the Outset

The optimization of the upstream S-DUAL™ bsAb platform process is reflective of Samsung Biologics’ commitment to considering commercialization needs from the start of development, particularly the maximization of bsAb titer and quality. Extensive analytics performed during the early stages help identify potential problem areas and mitigate risks. Process optimization efforts, such as the one described here, are routinely performed and often increase titers by between 3 and 5 g/L while achieving purities above 95%, further minimizing risks as projects advance from cell line development to process development and manufacturing. Meanwhile, starting at the 2 L scale and progressing to 100 L before moving to 1,000 L de-risks scale-up. If issues are detected during any run, full testing is conducted to mitigate problems before advancing to the next scale. Equally important, the equipment used for small and large batches is consistent, ensuring that results obtained at smaller scales accurately reflect the results expected during commercial production.

Integrated Drug Development Partner

BsAbs are unique, next-generation antibodies with dual specificity, creating considerable opportunities for therapeutic applications, such as redirecting T cells to tumor cells, blocking two different signaling pathways simultaneously, dual targeting different disease mediators, and delivering payloads to targeted sites. However, as bsAbs are much more complex molecules than traditional mAbs, they present unique manufacturing challenges, including increased incidence of mispairing, the manufacture of undesired fragments, and increased levels of aggregates. The S-DUAL™ bispecific antibody platform by Samsung Biologics addresses these complexities.

Having a development and manufacturing partner with experience and proven expertise in working with and providing strategies for such advanced molecules is fundamental, since creating high-producing and high-yielding processes for this class of products is a complex endeavor. Samsung Biologics embraces its responsibility to overcome these challenges, driving the development of innovative bsAb therapies that offer therapeutic options to patients with currently unmet needs.

By conducting all processes and testing in-house at a central location, Samsung Biologics provides confidence across all concerns associated with each client’s molecule and a plan to effectively address each concern when moving to the next level of production. In addition, as a fully integrated drug development and manufacturing partner providing integrated services from cell line development to commercial manufacturing, Samsung Biologics provides customers with the advantage of reduced timelines from DNA to Investigational New Drug application (IND) and Biologics License Application (BLA). Furthermore, with locations in South Korea and the United States, Samsung Biologics is ideally positioned to support clients worldwide.

References

Lu, Ruei-Min et al. “Development of therapeutic antibodies for the treatment of diseases.” J Biomed Sci. 27:1 (2020).
“Bispecific Antibodies: An Area of Research and Clinical Applications.” U.S. Food and Drug Administration. 2 Aug. 2023.
Brinkmann, Ulrich and Roland E. Kontermann. “Bispecific antibodies.” Science. 372:916–917 (2021).
Bispecific Antibody Market Expected to Reach $109.4 Billion by 2032—Allied Market Research. Allied Market Research. Jul. 2023.

Originally published on PharmasAlmanac.com on March 4, 2024.

Representatives from start-up development company Wheeler Bio and its digitalization partners, DataHow and Synthace, discuss how their collaboration will help Wheeler gain deeper insights into its processes more quickly and accelerate the time to IND for its customers. The digitalization solutions under development will also enable Wheeler to share customer data in real time, helping to build trust and strengthen the partnerships it has with its customers.

David Alvaro (DA): How would you characterize the current state of the industry with regard to the incorporating of automation and artificial intelligence (AI) into process development?

Jesse McCool (Wheeler Bio): The biopharmaceutical manufacturing industry is behind other industries in terms of adopting digitalization tools. However, we have seen in the last several years a wave of “Industry 4.0” or “Pharma 4.0” success stories in large pharma that will drive further adoption of digitalization across multiple industry segments. With the latest high-throughput bioprocess development equipment and PAT (process analytical technology) instrumentation that make up the core of Wheeler Bio’s development capabilities, we felt it was important to incorporate digitalization strategies up front in the conceptualization phase of our services platform. We looked for solutions that would help our scientists generate more data with each experiment while studying more parameters and interactions in an easy to setup DoE (design-of-experiments) format. We looked for powerful predictive modeling tools that can save significant time in process development. We also looked for solutions that improve the linkage between and among different pieces of equipment, allowing our scientist to build automated lab workflows. And most importantly, we wanted to be in a good position to safely share data in real time with our customers and collaborators. We evaluated the options and are excited to move forward in partnership with two leading providers that are offering all these solutions now: Synthace and DataHow. The core of our mission is to increase access to manufacturing technologies by making it easier for customers to tech transfer and reach their clinical milestones. Digitalization of an open-source manufacturing process is how Wheeler is going to achieve this.

Michael Sokolov (DataHow): Although companies would like to be able to perform big data analysis, the biopharma industry is not yet truly data rich. A lot of labor is required to generate each data point, so it is necessary to survive in an environment that is low in data but high in uncertainty. Informed decision-making is crucial, because the ultimate result of the overall processing work is the creation of a pharmaceutical product that is very consistent in terms of its quality. The engineering team thus faces a considerable challenge to deliver consistent quality in what is often a very short time.

Today, the underlying hardware technology has been optimized, which leaves digitalization as the last approach to further improving protocols. Contract development and manufacturing organizations (CDMOs) are ideally positioned to benefit from digitalization, because they work with multiple molecules from different clients. CDMOs also have extensive internal databases that offer the potential to establish process-to-product relationships and enable them to leverage all of the knowledge created in platform processes.

These conditions are effective for data science. It is possible to use these data to create predictive models. In addition, as data science advances toward knowledge science — taking generalized data and converting it into general process understanding — such tools can be used to support decisions on the basis of similarities to previous experiences.

Of course, this is more complex than just relating what has been done before to incoming activities. Human beings are cognitively limited in what we can accomplish, which is why we need tools that enable consideration of multiple perspectives; they allow us to not only think and make decisions but to loop that information back into the system.

Looking forward, we want to advance into a biopharmaceutical environment where humans are supported by many such digital solutions. These technologies will not replace humans, but they should do a lot of the jobs that humans cannot do well, thus allowing the creation of better processes — ideally in less time.

These tools will give a company like Wheeler Bio a very strong competitive advantage, because it will allow them to achieve similar capacity as their larger competitors. For the industry as a whole, there will be a movement away from the current basic linear model to one that supports the natural, nonlinear nature of processes and their interrelationships.

Ultimately, the utilization of these digital tools over the coming years and across many stakeholders in the industry will allow us to raise the standard of how such drugs are produced and to enable the development of completely new drugs. For instance, for personalized medicines, such as cell and gene therapies, there are currently even less data and more complexity, but decisions still need to be made to support the goal of delivering these treatments more quickly to patients. In such an environment, these tools not only improve quality but fundamentally enable such therapies to reach the market.

Markus Gershater (Synthace): The biopharma industry has to make use of 21st century digital tools and automation. We are trying to engineer the most complex things in the known universe, and we’re doing it mostly with whatever springs to mind or is within arm’s reach — essentially e-copies of Excel. That simply isn’t good enough. To really penetrate into the heart of what’s going on with their systems and achieve deeper understanding than is currently possible, researchers need sophisticated modeling, predictive modeling, digital twins, and AI augmenting their abilities.

We all recognize that the only way we can build this is on a foundation of exceptional data — not just the primary data, but the metadata of how those data were produced. Metadata provides critical information about context — the story that led to the result. If we digitize the way that experiments are designed and run and make sure that we’re capturing all the data that result from those experiments, we can fully and completely map exactly what happened in the lab to produce the data.

This means we can provide results to scientists as full data sets with all of the context baked in; they’ll know everything about their data and exactly how those data were produced. This is where we need to get to, because both biology and the experiments we run with it are exceptionally complex. We need to ensure that we really understand those experiments in full and have that digital representation of the experiments we’re running.

We also need to consider how our relationship with the lab will change once everything is digitized and in the cloud. We can design experiments from wherever we are and run them in the lab. Clients of CDMOs like Wheeler Bio who want to know what’s happening with their experiments could log into the same system and see the data. Digitalization tools can benefit many human aspects within the biopharmaceutical industry, particularly around communication and collaboration.

Guy Levy-Yurista (Synthace): I came into biopharma from big data. It was in that space where I first started focusing on machine learning and AI. I believe that the 2020s will be the life sciences decade, including not just biopharma but also agritech, food tech, climate tech, and so on. Leveraging biology and the life sciences sits at the heart of solving problems in these sectors, and the complexity of biology demands that we go beyond the limitations of human ability when working with spreadsheets, or even pencil and paper. We need digitalization to enable the hyper-dimensionalization of data — this is how we will integrate many small, disparate data sets to accelerate the rate of progress within the life sciences.

DA: What was the specific impetus that led to the partnership between Wheeler and Synthace, and what does each organization bring to the collaboration?

Markus Gershater (Synthace): Jesse and I have known each other for ages. We’ve been passionate advocates of DoE and multidimensional experimentation. When we first met, it was pretty rare to come across others who were such passionate advocates of this approach to development. At Synthace, we were constantly attending conferences to try to understand the most current approaches to managing this concept. That is where I met Jesse and other Wheeler folks.

Christa Short (Wheeler Bio): Over the last 10 years, many of the Wheeler team members have been involved in multiple process validations at several companies –– especially around process design (or process characterization), process performance qualifications (PPQ), and continuous process improvement projects. The scopes of work tied to process characterization programs require significant resources and are incredibly important to set the stage for a successful PPQ. These programs typically take advantage of DoE to explore many different input parameters and parameter interactions. However, it takes a lot of resources, skill sets, extensive experimentation, and data analysis to perform and analyze complex DoEs, and as a consequence a lot of process development involves one-factor-at-a-time (OFAT) experiments, especially when there isn’t a lot of time or budget for process development. Today, with the right tools, it is more straightforward for process scientists to use DoE even in early development. This is what we are aiming to do here with this collaboration: to give our scientists access to tools that enable more data points and process understanding.

Yuk Chun Chiu (Wheeler Bio): As one of the founding Wheeler leadership team members with large pharma experience, I am excited by the level of engagement that our process development team has shown in seeking out and taking advantage of digitalization tools. They share the culture of the big pharma R&D groups. These software products allow us to efficiently apply QbD (quality by design) principles to our mAb platform development strategy. However, one challenge that CDMOs face in developing platforms in a fee-for-service model is that the process, products, and data do not typically belong to them. The confidentiality surrounding customer projects is a sacred commitment in the CDMO world. Wheeler is tackling that challenge by using a library of in-house molecules — sequences shared by our partners and from the public domain — to generate the data points and, ultimately, our own bioprocessing model. Christa’s team is currently working with three molecules at five different scales and multiple platforms for upstream and downstream processing. Because they are in-house molecules, we can be infinitely more transparent and collaborative with our platforms once complete and ultimately create more value for our customers with open-source access.

Christa Short (Wheeler Bio): Also, these software products are critical for us to meet our own business goals and have a compelling platform available to customers in a timely manner. Our goal is to build a robust, scalable mAb platform in less than 12 months. We also need to consider the needs of the bispecific molecules that are in the pipeline. All of Wheeler’s clients are moving from late discovery into development for the first time and need to have timely access to manufacturing to scale up material very quickly and as cost effectively as possible. The more insight we can gather about our platform now, the more technical risk we can eliminate for our customers in the future, which makes us a more competitive CDMO.

Our platform features next-generation cell lines and state-of-the-art bioprocessing equipment, PAT, and powerful analytics, including the Ambr^® 250 system from Sartorius, which comprises 24 fully featured single-use 100–250 mL mini-bioreactors. Our scientists are generating a lot of data and require efficient means by which to capture, organize, and analyze. With Synthace and DataHow, we can focus on data and make faster decisions. With this volume of data, and the ability to analyze it efficiently, we will be able to learn and understand our processes more deeply and more quickly, which gets back to reducing risk. We also hope that we might eventually be able to reduce process run times for mammalian cell culture from 14 days down to 10 days or less without sacrificing insights.

David Schmidt (Wheeler Bio): From an analytics standpoint, the new software will also help us drive down errors commonly associated with high volume of samples and increasing number of analysts. More analysts naturally means more operator errors. The digitalization software combined with high-throughput automation allows us to structure the data around the method and drastically increase the number of samples while reducing the number of operators, thus reducing the risk of errors. That means we can create high-quality and better-structured data that feed into some of our predictive modeling and other type of analyses that we’re doing.

DA: How did DataHow become a partner in executing this vision?

Jesse McCool (Wheeler Bio): We met Michael at a conference several years ago in Berlin. We were impressed with him and his team of data scientists and how he was positioning his company to synergize with pharma companies. Early discussions led to multiple meetings, site visits, and pilot programs and finally now they have a unique product that is tuned and ready for Wheeler data. DataHow enables easy access to hardcore bioprocess modelling expertise so we can be more capital efficient without sacrificing the quality of data science that’s needed to develop a scalable, robust biologics platform. DataHow is a leader in bioprocess modelling trying to help the industry grapple with an inherent scarcity of data points –– because running drug substance batches is very expensive. We are thrilled to have them on our tech development roadmap.

Michael Sokolov (DataHow): We have to bridge the fact that we do not truly have a lot of usable data in biopharma. We know the underlying processes, and as engineers we recognize that digitalization can tell us what we have not yet learned — how we can go beyond the cognitive capacities of human beings. The overall goal of data science tools is to help engineers and scientists make better decisions, faster. A solution that augments the current workflow in biopharma by using AI in the background to support decision-making is in high demand.

The key is that data science isn’t just for data scientists. It should be available for any end user, for any scientist, for any stakeholder who is somehow operating along this workflow of bioprocesses. That also means that the solution must be in a framework that is user-friendly for all of the people involved. It must be automated and make suggestions in the background about the best model or tool and then present the final basis for use in decision-making.

To become a standard, a tool must not only be trustworthy but also educational for the community of end users if it is to be considered, similar to how bioreactors and chromatography columns are viewed. Without a high level of buy-in from users, it is very difficult to make data science work in such an easy way. As data and digital solution providers, we have a very important educational commitment to ensuring that data science drives across a broader community.

We listen closely to the feedback of our end users and ultimately create solutions together. For clients like Wheeler Bio, that means developing customized solutions centered around their workflows and the standard decisions they must make.

DA: What real, practical benefits do you expect to realize initially, and how do you see that evolving over time as models continue to optimize?

Markus Gershater (Synthace): Short-term benefits are reduced data management and easier data analysis but with better insight into what the data actually mean — it’s a lot less pain and a lot more insights. That insight then compounds over time. We are not talking about a minor change, either. Once the broader industry succeeds at digitalization, it will be a truly transformative shift from where we are today.

David Schmidt (Wheeler Bio): From the perspective of a service provider, this technology will be transformative for the customer experience and the quality of service. It will allow our early clinical programs to move faster and faster, which is a key value driver influencing customer buying decisions. Thus, especially when competing for the venture-backed biotech business, this will be a key competitive advantage for Wheeler Bio. We anticipate that this collaboration will enable Wheeler Bio to reduce the time to an IND by at least three to six months, and hopefully six to nine months. That will be achieved by a combination of digitalization and a novel business approach that involves integration with contract research organizations (CROs) who are upstream of Wheeler.

We also have an NCI-designated cancer center across the street and look forward to connecting our customers with those resources to see what synergies can develop.

Guy Levy-Yurista (Synthace): From Synthace’s perspective, the benefits include an order-of-magnitude improvement in the time it takes to get to results and the quality of the science produced, as well as a significant reduction in cost. A year’s worth of science can be crunched into one month, during which time drug molecules can be produced and analyzed with science that is 10 times better than what would be possible without digitalization, hyper-dimensionality, and the use of AI and machine learning for modeling and complex data analysis.

Customer satisfaction is also a huge benefit. That is why we were performing initial experiments in our own labs until Wheeler Bio got their equipment installed and operational. We wanted to enable them, once they were ready, to hit the ground running. We are also continuously challenging ourselves to become better, because we know that we are helping others to create a better future for humanity.

Michael Sokolov (DataHow): I think that, when we look into the future, we first need to distinguish what is really possible today in terms of digitization, which is moving away from paper toward digital, and to recognize that this is just the first step. Going forward, digitalization will involve improving current business processes in terms of cost reduction, time savings, and performance gains. Ultimately, digital transformation will lead to completely changed business models — for CDMOs, certainly.

There is tremendous competitive advantage for CDMOs that can move faster in this direction than others, and that advantage can be leveraged in many directions. It will involve the creation of huge databases — knowledge bases to be exact — that provide the unique ability to manufacture completely new drugs that are not off-the-shelf and clearly out-of-the-box. It will also provide the ability to operate in high-throughput mode, smoothly.

Looking further out to what is coming next — I believe that, in five years, we will be deploying quantum computing. I’ve already instructed Markus and his team to start looking at how we can leverage quantum computing into true multi-dimensionality and analysis of complex biological systems. It is still very early days, but we are already looking at bioanalysis from that perspective.

DA: It strikes me that, when you take a bunch of current bottlenecks and optimize them by orders of magnitude, new pain points or bottlenecks that aren’t apparent or relevant right now might be revealed. Have you given any thought to what the next challenges might be?

Markus Gershater (Synthace): I don’t necessarily see us uncovering another big bottleneck like this. There will be others, but nothing so major as one that allows the whole biopharma industry to move so much faster than it has done before. When we were a process development company ourselves, the hardware was available to automate experiments, which made things easier to implement. We found that our software enables people to perform those automated experiments more effectively. Indeed, we shifted the bottlenecks to the data and to the understanding of those data. Overcoming this bottleneck provides access to insights into the biology of biopharmaceutical processes.

Jesse McCool (Wheeler Bio): Subject matter expertise is one of the least scalable resources in any business, but these solutions will change that reality. We think that new bottlenecks will be there for new modalities with much less foundational, commoditized process science; however, we view this data science initiative to be very rinse-and-repeatable. Although we’re laser focused on therapeutic antibodies right now, our CRO partners are continuously innovating around new modalities. Once we’ve applied the solutions from Synthace and DataHow to our laboratory workflows for mAbs, we can shift gears to other modalities.

Markus Gershater (Synthace): I just love the idea that we can use these technologies for ever-more ambitious goals. Yes, faster. Yes, with more certainty. Those are clear business drivers for what we need in this space. But for me, as a scientist, the reason I first got into all of this — and I think the reason that we probably all got into this in the first place — is that the work is about breaking down barriers and solving ever-more challenging problems.

Michael Sokolov (DataHow): I recently finished an MBA, and in my thesis I collaborated with many decision-makers across pharma to identify the key inhibitors to digital transformation. Two clear factors became apparent. One is the need to meet extensive regulatory requirements in order to get new technologies validated. Any new technologies, regardless of the advantage they provide, still need to go through a complex validation process. Hopefully, when regulatory authorities see the tremendous benefits, they will be more incentivized to encourage their use.

The second is that digitalization technologies are available, although some are clearly more mature than others. Most companies have some budget set aside for digitalization. There is a real need for education and training about how all of these digital technologies must be delivered in a very tangible and user-friendly way. Solution providers need to develop digital tools that work smoothly, so that new users will be more willing to accept them and not view them as nice but as necessary for improving major workflows. It is therefore very important that we ensure that digitalization becomes a commodity.

DA: From the points of view of Synthace and DataHow, how unique is this project with Wheeler Bio in comparison to other work you are doing?

Guy Levy-Yurista (Synthace): Jesse and the Wheeler team are really at the forefront, pushing the cutting edge of where things should be and where things are going. That is why there is so much alignment; we are all pursuing the future. In that respect, we are running as fast as we can, right by Wheeler’s side. DataHow also makes perfect sense to us. Again, it’s all part of the same vision: this notion of dropping in all the data, making sure that we make use of it all, and taking the best advantage of it.

Michael Sokolov (DataHow): DataHow started as a spinoff from a university, and we initially would convince scientists of the advantages of our technology and then hope they brought management onboard so we could get a budget for at least feasibility studies. With Wheeler, the CEO was looking for this type of solution, and so we have support from the top. It is a privileged situation for us to have a lot of buy-in based on trust and collaboration to see how we can then develop the overall approach.

There are two types of customers — those with a lot of trust in their data and what can be done with them, and those with little data but the expectation that much can still be done. Wheeler is a good example of the latter case. With a strong belief that digitalization can support decision-making and all of the stakeholders involved, there is a lot of positivity around using the technology and providing feedback, which makes the collaboration very, very fruitful.

Lance Johnson (Wheeler Bio): We think you’ll see the fruits of the labor pretty quickly with customer feedback. CDMOs serve customers, and those customers serve patients. Generally, CDMOs have a bit of a negative reputation for reliance on proprietary platforms and technology-anchored platforms that makes it hard for customers to get data and transfer projects from site to site, which drives up some of the costs of making pharmaceuticals.

The types of tools we are developing through this collaboration will have a big impact on the customer experience. We are building a CDMO service that puts customers at ease with a culture of transparency and partnering. They have access not only to our scientists and their knowledge and expertise but also to their data. When we share information — when we livestream data for clients — they are astounded. That type of service is not something they are typically offered. Sharing data in real time gives both parties the ability to view trends and collaborate to react accordingly to head off potential issues.

That’s our short-term goal. By doing so, we think we will have a real impact on the reputation of CDMOs in this industry. From an IT perspective, it is not overly difficult to deliver this level of service. You must break down the industry’s legacy paradigms and barriers, which is easy to do because the underlying concern is outdated. It takes stepping outside the normal conservative CDMO box and recognizing that there isn’t any greater risk to sharing client data with the client. Doing so elevates the level of trust between the CDMO and the client to the point where the parties become true partners, working toward the success of the process and product. What Wheeler Bio is doing by taking these data and putting them into a format that can be shared with clients and that they can easily understand is unique and rare and is a service very much desired by customers across the biopharmaceutical industry. It’s an exciting time!

Originally published on PharmasAlmanac.com on May 18, 2022

The benefits of rapid drug development platforms were evident during the COVID-19 pandemic when investigational biological drugs were developed for emergency use on unprecedented timelines. Key speed-to-clinic approaches leveraged during the pandemic are being adopted more broadly to both accelerate development milestones and help start-up biotechs satisfy more aggressive investor outlooks. The use of well-controlled SBCs (stable bulk cultures; also referred to as “stable pools”) to accelerate the early development cycle is a scientifically sound and phase-appropriate chemistry, manufacturing, and controls (CMC) strategy that is gaining traction and regulatory acceptance. By deferring traditional single cell cloning (SCC) until a later point in development, a sponsor can utilize SBC-based platforms to generate reliable, reproducible, and representative preclinical and clinical materials months earlier relative to clone-based platforms. Wheeler Bio has developed an SBC-based mAb (monoclonal antibody) platform, Portable CMC™ that is open-source and easy to access. This article summarizes the main rationale behind SBCs and some key points to consider when evaluating a development pathway leveraging the speed of an SBC-based platform.

Next-Gen Biologics Require More Efficient Use of Capital

In 2022, the global market for biologics was valued more than U.S. $460 billion and is expected to grow at a 10.3% compound annual growth rate (CAGR) from 2023 to 2030.¹ The rising burden of cancer, genetic diseases, and autoimmune diseases, coupled with the approval of several disease-modifying therapies of these conditions, is driving the growth. Despite this, however, biological drug access remains constrained by high cost and payer complexities. Unsurprisingly, biologics represent only 2% of all U.S. prescriptions but 37% of net drug spending.² As biologics continue to flood the drug discovery pipeline, translation into widespread clinical impact will require more efficient uses of capital with minimal idle time, lower cost of goods, and improved patient access.

Outsourcing of CMC development continues to be an efficient use of capital for protein therapeutic-centered biotechnology companies, as evidenced by biopharmaceutical CDMO (contract development and manufacturing organization) industry growth.³However, globally, start-ups are facing increased investor hesitancy and a reduction in both funding rate and magnitude, which is driving more conservative pipeline management. Investors are shifting their focus to proven sponsors that have drugs in the clinic and/or have hit clinical milestones for several drug candidates. This has led to poor alignment between the business needs of new start-up biotechs and their CDMO providers in this sector. Drug development sponsors need more cost-effective access to early development capabilities that span the translational space effectively to better bridge Seed and Series A rounds. It is incumbent upon this service sector to continuously improve processes and business models and adopt cutting-edge development strategies being devised and demonstrated by large pharma.

A Real-Time Paradigm Shift

The fastest timeline spanning Seed and Series A projects (i.e., from mAb lead candidate selection to the initiation of first-in-human (FiH) studies) is an important goal for all companies to help the patients they treat, maximize value creation, and advance their pipelines. Many large pharmaceutical companies have worked assiduously over the last 10 years to refine their CMC development strategies for more rapid clinical evaluation of lead candidates. The pandemic, especially in the United States with Operation Warp Speed funding, saw a combinatorial approach using multiple accelerative strategies to delivery extraordinary results.⁴

Among these strategies, application of SBC-based platforms was prevalent with companies developing SARS-CoV-2 neutralizing antibody products. More than six such products were granted emergency use authorization ((EUA): EUA 90, Nov. 9, 2020, EUA 91, EUA 94, EUA 100, EUA 104, EUA 111) within 11 months from the time the Public Health Emergency was declared by the Secretary of the Department of Human and Health Services (March 27, 2020) justifying authorizing the emergency use of certain drugs and biological products during the COVID-19 pandemic.⁴ In one company’s case, it took only 56 days to advance the lead antibody (April 15, 2021) to the initiation of clinical trials (June 10, 2021).⁵ Several more examples are provided below in the Sidebar: Leap-In Transposase^® Platform from ATUM.

When applied more broadly, the use of SBC-based platforms could represent a paradigm shift for start-up biotech companies seeking to disrupt the cost/time cycle of early development. Despite the relative nascency of this timeline reduction strategy, SBCs are well described in scientific literature and are wholly compatible with regulatory guidance. The ICH Q5D specifies a requirement to prepare biological products from cells cultivated from cell banks of cell substrates. The term ‘cell substrates’ refers to cell lines derived from animal sources that possess the full potential for generation of the desired biotechnological/biological products for human in vivo or ex vivo use.⁶ Cell banks can be derived from well-controlled SBC cell banks and still be in keeping with the regulatory guidance.

Well-controlled SBCs (‘well-controlled’ distinction is made here, intentionally) are pools of recombinant cell lines serving as cell substrates (per ICH Q5D) that are derived from animal cells like Chinese hamster ovary (CHO) using specialized recombineering tools (described later). Traditionally, control of the cell substrate is assured through the SCC process. However, well-controlled CHO-based SBCs can be obtained with the proper tools, resulting in homogeneous cell pools characterized by high gene copy numbers and limited phenotypic diversity.

Although SCC is a long-established standard following transfection of parental CHO cell lines to minimize phenotypic diversity (while ensuring process consistency), SCC is not specified as an indication of clonality of transfected cell lines.^6,7 Rather, the determination of cDNA sequences of the predominant transcripts is acceptable as an indication of clonality.⁷ Well-controlled SBCs have limited phenotypic diversity and thus the predominant cDNA sequences are comprised of the target recombinant genes. Due to the relative ease of derivatizing a comparable clone with matching process and product attributes, the homogeneity of well-controlled SBCs de-risks the deferred SCC until after phase I. In other words, when the SBC comprises recombinant cell lines with a limited population diversity, the platform drug substance process doesn’t care whether the cell substrate is based on SBCs or on clones. This assures regulators that well-controlled SBCs possess the full potential for generation of the desired biological products, per ICH Q5D. As such, sponsors can be presented early on with an opportunity to save significant time, without risk to patient safety nor regulatory compliance (provided access to well-controlled SBCs).

SBC-based drug development platforms were hugely impactful during the COVID-19 pandemic response when multiple developers of SARS-CoV-2 neutralizing antibody products received EUA for their products using SBC-based cell substrates. In one example, it took only 56 days to advance the lead antibody (April 15, 2021) to the initiation of clinical trials (June 10, 2021).² Several more examples are provided below (see Sidebar: Leap-In Transposase^® Platform from ATUM).

For non-COVID products, the use of SBC-based cell substrates to generate toxicology and clinical materials would represent a paradigm shift for start-up biotechs seeking to disrupt the typical cost/time structure of early development. A recent (2022) industry survey revealed that 29% of large pharmas already use SBCs to produce toxicology supplies of non-COVID products and that another 39% are considering said SBC use within the next five years.⁸ Thirty percent of large pharmas are considering their use for clinical products in the next five years.⁸ Importantly, the majority of responders to this survey were not small risk-tolerant companies but rather a broad selection of development- and commercial-stage innovators, including AbbVie, Alexion, Bayer, Biogen, Boehringer-Ingelheim, CSL, Eisai, Gilead, GSK, ImmunoGen, Incyte, Janssen, Kyowa Kirin, Leo Pharma, Merck, Novo Nordisk, Pfizer, Regeneron, Roche, Samsung Bioepsis, Seagen, Takeda, UCB, and Vir Biotechnology.⁸The survey results represent the view of medium-to-large companies that have the resources to be more conservative yet have acknowledged the value of their developmental approach. The results are consistent with the view that over half of the medium-to-large companies in our industry will be using SBCs in the next few years to add significant value to development programs.

Better Business Alignment Needed Between Sponsors and CDMOs

Start-up biotechs need better ways to bridge the translation from preclinical to clinical development. Since outsourcing is compulsory for the sake of capital management, the onus is squarely on the pharma services sector to ensure that the most current, capital-efficient CMC development platforms are well designed and accessible to start-ups. As such, the CDMO sector needs to align with a broader, post-pandemic SBC-based time-saving strategy. Start-ups do not only need CDMOs to offer efficient platforms; they also need access to their supporting data sets to facilitate risk assessment and regulatory filings. Vir Biotechnology recently described a scenario of saving 5-6 months off the early development cycle using an accelerated platform featuring SBCs.⁹ At an illustrative “burn rate” of $250,000 per month, this company would save significant capital and help keep its other pipeline molecules advancing. Another example of an accelerated platform featuring SBCs is Wheeler Bio’s Portable CMC™ platform. This platform leverages large data sets garnered from both SBCs and derivative clones using Leap-In Transposase^® (ATUM) technology to enable well-controlled SBCs, high titers, process robustness, scalability, and speed-to-clinic.

Points to Consider for SBCs

It is essential that speed-to-clinic by virtue of SBCs does not compromise the safety, identity, strength, purity, or quality (SISPQ) of investigational biologic drugs. To minimize additional delays and costs during development programs, preclinical materials must be representative of clinical materials. Therefore, to ensure that SBCs provide a reliable cell substrate, the underlying tools and technologies supporting their generation need to be well tested and validated. Several of these have been discussed in the literature and are available “off the shelf” today through vendors and pharma service providers like Wheeler Bio in Oklahoma City, OK.

The CLD Process
The CLD process is a series of time-consuming steps requiring extensive infrastructure, expertise, and recombineering tools. The process enables the developer to interrogate thousands of single cells before identifying a good producer clone with desirable attributes (process and product), from which a clonally derived cell substrate is then established, tested, banked, and stored. Historically, the CLD process can take up to six months or even a full year to complete, depending on the complexity of the target proteins.

The CLD process begins with the transfection of a well-characterized parental cell line with expression plasmids encoding the desired recombinant protein product. Recombinant cells are selected by virtue of plasmid-bearing selectable markers, such as glutamine synthetase (GS) or dihydrofolate reductase (DHFR) genes (to enable methionine sulfoximine (MSX) in a GS knockout background or methotrexate (MTX) selection in thymidine-lacking medium, respectively). Gene delivery to the host cell genome, depending on the recombineering tools being used, is mediated by one of several types of recombination mechanisms, expanded on below.

Following selection to generate a pool of recombinant cells, single recombinant cells (clones) are isolated through a series of dilution, single-cell sorting, imaging, and analysis steps. New technologies, such as the Solentim Ecosystem (VIPS™, Cell Metric^®, ICON™, STUDIUS™), are available to improve the automation, throughput, consistency, repeatability, and success rates of the CLD process. This equipment ecosystem is integrated at ATUM and Wheeler Bio.

Advancements in CLD recombineering tools have been key enablers allowing the derivation of robust cell substrates exhibiting enhanced titers, consistent process performance, and consistent product quality. There are three types of CLD tools that have made it possible to establish SBCs from the lengthier CLD process, although the second two mentioned below are more reliable than the first. SBCs can be banked and used later to resume the full CLD process (i.e., “deferred cloning”) without having to repeat the initial transfection and selection steps of CLD. When integrated with a robust upstream, downstream, and analytical platform for drug substance manufacture designed based on SBCs, the predictability or control of the SBCs aids the robustness and control potential of the entire integrated platform.

Random Integration (RI)
The majority of CLD processes still involve RI for inserting mAb expression plasmids into the genome of CHO cells. RI can be used to generate SBCs; however, there are several recombinational liabilities causing truncations of expression cassettes, random sequence scrambling, and concatamerization that massively limit their robustness and hence utility as a source of well-controlled SBCs enabling use of a deferred cloning strategy.

Targeted Integration (TI)
TI is a precise form of genetic recombination also referred to as site-specific recombination. Several large pharmas have developed cell lines using targeted integration of mAb expression vectors.^10,11 TI can be facilitated by site-specific recombinases and specialized expression vectors containing recombinational sequences, but resulting copy numbers are low. A few relevant tools include CRISPR-associated protein 9 (CRISPR-Cas9), zinc-finger nucleases (ZFNs), and transcription activator-like effector nucleases (TALENs). ZFN- and TALENS-based genome modification methods are costly and time-consuming, extending CLD programs out by several years to allow time to develop and validate genomic target sites. CRISPR-Cas9 is the simplest, most versatile, and most precise method of genetic manipulation; however, there are limitations on commercial licensure.

Semi-Target Integration (STI)
STI is another precise form of genetic recombination and a very robust and accessible modality for SBC generation. STI is mediated by transposase enzymes and specialized transposon expression cassettes, such as the Leap-In Transposase^®.¹² Eleven of the top 20 pharmas, including Janssen, Novartis, AbbVie, Bristol-Myers Squibb, Merck, and Boehringer-Ingelheim, have utilized the Leap-In system, as have several CDMOs including Wheeler Bio, Rentschler, and Just-Evotec. Leap-In is well documented as supporting the generation of consistently high-expression SBCs with homogeneous phenotypes.

Integrated Platform
The intrinsic advantage of well-controlled SBCs is the limited diversity in phenotype. This facilitates the ability to isolate a derivative clone that responds in the same way to bioprocess controls. Because the SBC is homogeneous, the screening effort to identify a comparable clone is significantly reduced. It should be noted that, in some studies, transgene sequence variants can exist in SBCs as determined via next-generation sequencing. Therefore, further data should be gathered on late-stage processes to gain more insight into genetic drift in SBC-based cultures. However, SCBs would not be unique in exhibiting genetic drift. The CHO genome is well known for its plasticity. It has been proposed that there may be less sequence diversity in SBCs than in clones, particularly if generated using TI or STI, which significantly limits the diversity of clones in the population and concomitant risk.¹³ In general, the focus for speed-to-clinic should rest on phenotype integrity (i.e., batch-to-batch product quality and process performance) rather than on genotype integrity, as the tools exist to later ensure clonality and quality.¹⁴ Indeed, STI minimizes cell-to-cell variability, makes the pool and clones more similar, and reduces the effort in finding the ideal clone.

Overall, given the current tools and technologies available and their associated case studies in the public domain, expression of recombinant therapeutic proteins in SBCs for use as toxicology supplies and early clinical development materials is a practical means of dramatically speeding up new drug development without compromising quality, efficacy, or safety.

Well-controlled SBCs can be generated using the early steps of the CLD process and have been well described in scientific literature for nearly a decade. The recombineering tools utilized are important points to consider when contemplating SBCs, since not all tools are useful for enabling well-controlled SBCs.

Old Scientific Precedent for SBCs

SBCs emerged long before the pandemic in the literature, which provided a scientifically valid platform enabling development on unprecedented timelines. Large pharma companies, including Merck,¹⁴ Amgen,¹³ Boehringer Ingelheim,¹⁵ Bristol-Myers Squibb,¹⁶ Pfizer,¹⁷ Genentech,¹⁸ and Biogen,¹⁹ and global CDMOs, including WuXi Biologics²⁰ and Lonza,^21,22 have all examined the utility of SBCs for the generation of toxicology and early-phase clinical materials based on innovative academic research conducted over many years.^12,23,24 This foundational science provided evidence of process consistency, product quality, and phenotype comparability between SBCs and derivatized clones (i.e., recombinant protein expression levels, cell culture properties, generational stability, bioprocess performance, and product quality attributes). Additionally, research directed to the investigation and characterization of genomic instability of CHO clonal populations has resulted in the suggestion that the very notion of a clonal cell substrate, in the context of ICH Q5H, could be considered moot.²⁴

New Clinical Precedent for SBCs

A significant amount of clinical data was collected across the pandemic that effectively correlates the foundational science with clinical drug safety and efficacy. In its Guidance for Industry: Development of Monoclonal Antibody Products Targeting SARS-CoV-2, Including Addressing the Impact of Emerging Variants, During the COVID-19 Public Health Emergency published in February 2021, the FDA supported the use of a stable cell pool in lieu of a clonally derived cell line to generate early clinical batches for early-phase development.²⁵ Dr. Maria-Teresa Gutierrez-Lugo, an FDA product quality review chief in the Office of Biotechnology Products, recently shared similar views on SBCs.⁴ Notably, Gutierrez-Lugo emphasized caution in assessing the appropriateness of certain CMC regulatory strategies in the context of their associated risk and benefit, the intended purpose of the product (treatment vs. prophylaxis), the uncertainties present, the mitigation strategies, and the available knowledge of the disease and technology being applied.

SBCs: Towards Maximum Acceleration

More than ever before, developers are looking for more speed and agility in the face of rising R&D costs and escalating investor concerns over CMC risks. However, such timeline benefits must not risk the quality or safety of investigational biologic drugs.

New tools, technologies, and development strategies are available today to accelerate the translational process between discovery and the initiation of clinical trials. To have broad impact, these approaches need be readily accessible to start-ups, allowing industry-wide adoption and impact on the drug development process.

We argue here that well-controlled SBCs, enabled by STI and TI, allow for significant improvements in speed and agility without compromising product quality nor safety. By incorporating SBCs into biomanufacturing platforms, CDMOs can help democratize access to this important accelerative strategy while fostering a larger impact on the industry.

When contemplating the adoption of this approach for speed-to-clinic, start-ups will now be able to leverage considerable scientific and clinical evidence, as well as assurance of regulatory buy-in. SCC was established by the industry as a standard for INDs in the late 1990s. This standard, however, did not arise in response to any specific safety or quality incident (but rather out of an abundance of caution). SBC-based cell lines still satisfy the original regulatory requirements for cell lines/cell substrates contemplated in PTC 1993, 1997, and ICH Q5D.^4,5

We also noted here that SBC-related efficiencies stem not only from deferring SCC but also from the relative ease of finding the ‘one clone’ later due to the homogeneity of the cellular phenotypes within the SBC.

We also highlighted the common wisdom that CHO genomes naturally vary significantly and often as function of generational age. As a result, from a genotype perspective, there may not be a significant difference between a “clonal CHO cell” derived from RI and a highly homogeneous, well-controlled SBC (i.e., one generated from transposons). The stability of the CHO genome has been challenged in the literature to the extent that the practicality of demonstrating clonality is questionable.³ Nevertheless, the FDA guidance (from the 1990s) has had a lasting impact on risk perception of cell substrates, which has led to a limited exploration of alternative cell substrates – until now. Studies specifically focused on comparing the expression performance and product quality of non-clonal SBCs to those of clonal cell lines provide increasing evidence towards addressing concerns about the heterogeneity of these cultures and have shown that SBCs remain sufficiently stable for the uses described herein. It has further been shown that the SBCs consistently produce high-quality recombinant protein products at small to large scale for many different molecule types while reducing timelines to toxicology studies and early-stage clinical trial materials by a factor of two or more.

As with any approach used for drug development and manufacture, the risks and benefits must be appropriately balanced. The use of STI (such as is achieved using the Leap-In Transposase^® technology) coupled with a standard cell culture process (such as Portable CMC™) helps mitigate potential risks, particularly those related to product comparability and cell bank characterization. This speed-to-clinic option can now be evaluated by start-ups armed with a significant body of scientific literature, clinical safety, and regulatory track record.

Acknowledgements- The authors would like to thank several individuals who provided thoughtful comments and useful discussions; Lorenz Hasler, Jean Bender, Howard Levine, Marc Helouin, Yvonne Lungershausen, Stewart McNaull, Brian Berquist, and David Alvaro.