How CDMOs Can Digitalize Their Cell and Gene Therapy Processes

The demand for novel biologics is increasing on a global scale, making cell and gene therapies (CGT) among the most rapidly growing areas of the biopharma industry. In order to keep up with the rising demand, CGT developers are turning to CDMOs to outsource both development and manufacturing activities. CDMOs that uses data analytics to address key industry challenges is at a competitive advantage when it comes to process optimization and delivery time for these therapies. 

This article is posted on our Science Snippets Blog.

Faster time to market, process improvement and flexible processes are key drivers of change for viral vector manufacturing that can be addressed with integrated data analytics.


With a projected growth from $1 billion in 2017 to $44 billion in 2024, the CGT space will likely continue to be one of the most keenly watched pharma segments for business development.
 

Gene therapy involves the transfer of genetic material into cells, usually via some sort of carrier like a vector, whereas cell therapy is the transfer of those cells directly into a patient.

Current CDMO Market Landscape: Cell and Gene Therapies 

Nearly 100 companies currently compete in the cell and gene global CDMO marketplace. Most operate in both sectors, but some work exclusively in either cell or gene therapy. From a regional perspective, CGT CDMOs are primarily located in Europe and North America, however, we do see a growing demand in Asia. The majority, about 70 percent, of cell and gene therapy CDMO functions lie in lab and clinical scale operations.

The types of cell therapies handled by CDMOs include: Immune Cells (T-cell, NK, Dendritic, Tumor) and Stem Cells (Adult, hESCs, iPSCs).
The types of viral therapies handled by CDMOs include: Viral Vectors (AAV, Lentivirus, Adenovirus, Retrovirus) and Plasmid DNA.

Trend Towards Strategic Collaborations 

Collaborations between cell and gene therapy manufacturers and CDMOs partners are increasing. The number of active collaborations grew ten-fold in five years, from 42 in 2013 to more than 444 in 2018. Collaboration models may be in the form of joint ventures, manufacturing agreements, PD agreements, licensing agreements, or even service alliances.  It is worth noting that, over the years, stakeholders in this industry have demonstrated an increased preference for long-term strategic collaborations in order to better align their capabilities and service offerings to the growing demand in the market. 

As shown in the figure below, cell therapies and antibodies are presently the most popular (in terms of a number of associated deals) types of biologics in this field, representing 25% and 20% of the total number of instances, respectively. In the past few years, several small and mid-sized companies focused on the development of cell-based therapies, such as CAR-Ts, T-cell receptors (TCRs), dendritic cell cancer vaccines, and iPSCs, have been established. 

Most of these companies prefer to outsource various aspects of their business requirements to CDMOs as they lack sufficient capital and resource to establish their own manufacturing capabilities. 

If we look at some of the active players in the CDMO market, especially in the cell and gene therapy space, most of those collaboration players are represented by top cell and gene therapy CDMOs. Lonza is one of the most active in the market.
 

(Source: Roots Analysis, 2019)

Some Predictions for the Future

If we look at where this is heading in the future, we expect that:

  • Accelerated regulatory approvals will lead to a growth in demand for cell and gene therapy products.
  • Phases of development are advancing quickly, so in order to be ready for full-scale manufacturing and commercialization, companies should be considering manufacturing since the beginning of development and assessing the potential to outsource.
  • Portfolio breadth, including the ability to harness data, regulatory compliance, market presence, the ability to execute and implement, and cost will be used as key criteria for CDMO selection.
  • The increase in demand and need for quick scale-up will lead to more cell and gene therapy developers looking to outsource.

By 2025, the US FDA expects to be approving 10 to 20 cell and gene therapy products a year.1

Outsourcing in this area offers a number of benefits, such as increased productivity, efficiency, faster time-to-market, and quality gains, and can make up for shortfalls in development and manufacturing facilities. CDMOs can also deliver cell and gene therapy competencies in development, scale-up, and manufacturing, which the partners may not have in-house.

Addressing Gene Therapy Challenges with Data Analytics

CDMOs can address some of the key challenges of viral vector business development and manufacturing and establish a framework for digitalization by integrated tools and technology that focus on data analytics. Selecting the right tools to move toward digitalization and biopharma 4.0 can make a big difference when it comes to process optimization.

The current technology in use in gene therapy focuses on transient transfection of DNA plasmids. This typically means one of two options: serum supplemented cultures or a chemical defined medium. With respect to platform, we see both adherent and suspension culture systems in use.  The decision about which platform to select is an important one because the downstream may need to be adjusted to fit different serotypes. You don’t want to invest a lot of time and money on a platform and then face scalability challenges down the road.

We also see a lot of challenges with respect to the purification of the viral vector, either from sheer sensitivities or an inefficient transient transfection step. 

Some the common pain points are: 

  • Scaleup issue with the adherent platform 
  • No standard solution for downstream
  • Empty vs full capsid separation
  • Different process needs for different capsids
  • Viral envelope protein toxic to host cells 
  • High COGS

Need for Resilience Drives Biopharma 4.0

The development landscape in the biopharma industry over the last 20 years has seen impressive growth in the area of innovation, both in technology and in addressing manufacturing challenges. But when it comes to the core areas of Biopharma 4.0 – automation and digitization – where does cell and gene therapy stand?

In comparison, the protein market is very mature with respect to automation and digitization innovations, but with cell and gene therapy, there are some holes. One that may come to mind is resilience. The capacity to recover quickly if something goes wrong. Because if you look at the journey of a molecule all the way from the lab to commercialization and even the supply chain logistics, it’s clear we need to build a better understanding of the process. What’s needed is a solution to help you establish better process control and define critical quality attributes.

The ability to support specific needs is apparent. These include:

  • Speed to market
  • Process improvement both to support speed to market, as well as to increase productivity and product robustness
  • Flexible processes that can accommodate multiple pipeline molecules are important in the CDMO space

Addressing Viral Vector Challenges with Data Analytics: Process Development & Scale-up

The key to achieving the ability to support your CDMO partners is the digitalization of your processes. Sartorius has collected a number of case studies that showcase how developing an advanced understanding of your process can improve scale-up and help to establish better process control. The earlier in your process you develop understanding and establish control, the more robust and scalable your process will be.
 

Find out more in this webinar


1. Rapid, High Throughput Process Improvement and Optimization

This example shows how using MODDE® for Design of Experiments (DOE) and evaluation in combination with the high-throughput capabilities of Ambr® 15 allows for rapid process parameter optimization. Ambr® 15 is a micro-scale bioreactor system that mimics the features and process control of larger bioreactors. By establishing clear process controls early, you can develop a more robust and scalable process.
 


Download the Case Study


2. Optimization of HEK293T Culture Parameters with Ambr® 15 and DOE

The case study shows how DOE helps identify optimal and robust HEK293T culture conditions in Ambr® 15. The screening process helped establish the right set point in a controlled manner (and more quickly) than would be possible using a shake flask. pH and stirring speed were identified as critical process parameters.


Watch the Recorded Webinar Here 
 


3. Using Ambr® 15 and DOE for LVV Production Optimization

This case study of viral vector production in a serum-free suspension culture showed how three DOE runs helped optimized 9 parameters. In this use case taking the DOE approach allowed experimenters to reduce what would normally be an 8-week experiment to 3 weeks. These parameters incorporated media composition, supplements, additives, cell density, stirring speed, aeration rate/set-point, pH, and transfection-specific factors (DNA quantity, transfection reagent, and timing). A ~10 fold increase of overall infectious titre was achieved via DOE modeling.


4. HEK293T expansion on Biostat® RM and seeding on Ambr® 250

This case study shows how using the BioPAT® Viamass and Ambr ® 250 modular with a combined MODDE® DOE and MVDA PCA analysis approach provides insights for tech transfer. The high- level view indicated that seed agitation speed may impact cell growth and that gene copy titre seemed to be linked to rpm and seed. This give a much more rapid feedback if something is wrong and offers the potential of a hands-off process operation.

Contour plot with best optimal zones for gene copy titre (left) and cell aggregation (right)
 

Watch the Webinar Here!
 

Addressing Cell Therapy Challenges with Data Analytics

We are entering an immensely exciting period for the field of cell therapy, where CDMOs are expected to play an integral part. There are two approaches to cell therapy: autologous (self-derived i.e. T-Cell and other immune cells) and allogeneic (donor-derived i.e. MSCs or PSCs). For both autologous and allogeneic therapies, two of the major challenges cell therapy companies and cell therapy CDMOs face are finding ways to lower COGs and reduce time to market. 

In a recent publication, Catapult, a major CGT CDMO published that for Denreons’ product PROVENGE®, “manufacturing COGs [make] up to 77% of the $94,000 price tag” 

Data analytics can help to solve these challenges within the cell therapy space by:

  • Incorporating a QbD Approach to drive the development of process knowledge
  • Translating data to insights by combining process and cell characterization data to inform the optimization strategy for obtaining the target phenotype
  • Enabling remote monitoring to deliver reproducible, high-quality cell products batch after batch

Accelerating Cell Therapy Process Development with a Data Analytics Approach

When developing cell therapies, process knowledge builds a connection between process parameters and the resulting cell product phenotype and function (quality attributes). Process knowledge then enables the identification of the critical process parameters, or CPPs, as well as critical quality attributes, or CQAs. The process can then be further optimized based on CPPs to obtain cells with desired CQAs.

The establishment of CPPs and CQAs can be a lengthy process and is one of the bottlenecks in cell therapy development. One way to swiftly develop process knowledge and identify CPPs and CQAs is by taking a QbD approach to cell therapy process development. A key component in the QbD framework is DOE. DOE can be used during the development process of cell therapies to perform:

  • Process Screening
  • Process Optimization & Scaling
  • Product Characterization

Here’s an example of how DOE is used to optimize and scale a T-cell expansion process.  

Optimization of T-Cell Expansion Using Ambr® 15 and MODDE®

In a CAR-T process workflow, the expansion of T-cells must be carefully monitored and optimized to ensure desired phenotype and yield. One challenge is ensuring high simultaneous fold expansion and viability.

In this use case, MODDE® in combination with Ambr773318 15 Cell Culture enabled fast and systematic evaluation of process parameters (stirring, DO, pH, medium type, seeding density, Interleukin-2 (IL-2) to identify CPPs and optimize T-cell expansion. Through DOE, IL-2 concentration was identified as the key driver of fold expansion and viability, while high levels of DO had a negative effect.

“MODDE® provides tools for data interpretation and for identifying optimal process conditions for T-cell expansion.”


Want to know more?

Watch the recorded webinar: “How CDMOs can digitalize their cell therapy processes
 

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References
1. FDA, “Statement from FDA Commissioner Scott Gottlieb, M.D. and Peter Marks, M.D., Ph.D., director of the Center for Biologics Evaluation and Research on new policies to advance the development of safe and effective cell and gene therapies,” press release, January 15, 2019.
2. Source: EvaluatePharma, March 2019
3. Source: Roots Analysis, 2019 
4. https://www.pharmtech.com/view/sourcing-success-cell-and-gene-therapy-development

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