How To Promote Batch-To-Batch Consistency In CAR-T Therapies

By Life Science Connect Editorial Staff

The use of patient immune cells as starting materials inherently introduces biological variability into the CAR-T therapy manufacturing process. Additional raw materials, including plasmids, viral vectors, lipids and nanoparticles also have batch-to-batch variability. The result—whether the cell therapy is autologous or allogeneic—is a constant balancing act of adjusting processes and technology to create, as much as possible, a standardized manufacturing environment.

In a recent Cell & Gene Live event, Chief Editor Erin Harris welcomed Donna Rill, Chief Technology Officer at Triumvira Immunologics, and Omkar Kawalekar, Ph.D., Senior Manager of Deloitte Consulting, to discuss practical ways to overcome the challenges associated with batch-to-batch variability. The conversation examined factors contributing to such variability, explored the most significant production and analytical challenges, and proposed best practices for reducing variability.

What Factors Impact Batch-to-Batch Variability?

One key contributing factor to batch-to-batch variability is incoming seed material, because of patients’ different treatment backgrounds as well as their varying stages of hematological reconstitution based on those treatments. Divergence between handling manufacturing procedures also contribute to variability, as do release criteria, analytical characterization, and the final drug delivery format.

Understanding Cell Therapy Variability Challenges

To overcome the challenges associated with cell therapy variability, a manufacturer first needs to understand incoming seed material. To this end, Triumvira Immunologics asks that all apheresis collection centers provide a pre-CBC (complete blood count) on subjects as well as CBC results on a loop. Receiving that in advance of leukapheresis product allows the company to understand the material’s hematological composition. It obviously is more difficult to gather pretreatment information on patients, amplifying the importance of being able to characterize incoming seed material.

The same challenge applies when starting material is a tumor biopsy, rather than apheresis product from a patient. The first tumor-infiltrating lymphocyte (TIL) therapy was recently approved,¹ and some types of personalized cancer vaccines rely on a patient's tumor biopsy. However, there exists variability in the way the tumor is collected and processed, plus the inherent variability in dealing with a patient-specific specimen.

The biggest impact of any variability is an out-of-spec/failed batch, leading to doses that cannot be used for a patient. In a commercial setting, it's lost revenue, but more critically, the patient does not receive their dose after having waited for several weeks. So, it is imperative we investigate ways to control or standardize the operational aspects of handling these tissues and advancing them through manufacturing.

Controlling Raw and Apheresis Material Variability for Autologous Products

A company first provides its network of supplier vendors with release testing and analytical testing criteria. Organizations may also wish to contractually obligate the vendor to address root causes of batch-to-batch variability. So, if the manufacturer is not seeing expected results consistently across all batches, the supplier is on the hook to help address the problem.

That communication transparency and a detailed evaluation process is the only way to get both parties fully invested in addressing these variabilities. Pinpointing critical quality attributes (CQAs) that tie to clinical response or final batch yield allows organizations, in a sense, to forward engineer how the production process or material sourcing will change, helping to minimize raw material-driven variability.

As for apheresis materials, harmonizing as much of the apheresis procedure as possible introduces some degree of standardization to the way cells are collected, processed, and shipped to a manufacturer. Consider the devices used for apheresis: these platforms are in a unique position to collect multitudes of data, which can then be analyzed to help identify how to reduce apheresis material variability.

Data collection and analysis from these entities could fuel greater fit-for-purpose manufacturing practices. The leading device-for-apheresis companies have established methods for collecting leukapheresis over generic apheresis products. Medical institutions (i.e., sites hosting study patients) perform these tasks by the established methodologies daily, so asking them to deviate is likely to result in variability.

Additionally, mechanism of action must be considered. Traditionally, most materials are frozen, but some material components of the leukapheresis can interfere. For example, immature granulocytes do not freeze and thaw well; you get sticky, slimy DNA that can interfere with target cells. Room temperature collections are referable in that they maintain the integrity of T-cell functionality, but it is accompanied by its own supply chain logistics challenges. Note that room-temperature shipping will not necessarily work for every product, reinforcing the need to implement a fit-for-purpose approach.  

Controlling Raw and Apheresis Material Variability for Allogenic Products

Theoretically, utilizing the same donor repeatedly controls variability in the source material between batches, but in practice, that is not always the case. For example, a single allogenic donor may be intended to provide hundreds of doses. In reality, that number is significantly lower, perhaps only a few doses per donor, so organizations need to maintain a pool of donors to support a range of potential cell recipients based on human leukocyte antigen (HLA) typing or some other compatibility testing between donor cells and the recipient's body.

Once you have depleted resources from one donor, returning to that same donor may not be straightforward, or even possible. They may move away or develop a health condition that precludes them donating. These factors introduce variability in single donor-based cell therapy. However, because allogenic cell therapies are hyperfocused on reaching the market quickly, developers tend to invest fewer resources and less effort into minimizing variability.

Triumvira works to understand donors for its targeted allogeneic therapy by screening 125+ potential donors initially before narrowing to 10, then down to five, from whom materials will be used to build master peripheral blood mononuclear cell (PBMC) banks. This allows the company to compile incoming donor attributes for future use, since sustainability is critical to an allogeneic program. Additionally, the company can generate a more consistent dataset and product yield to present to regulators. 

Conversely, large, off-the-shelf batches are not realistic for cell and gene therapy. Most of these cells will not tolerate extensive long-term cultures without varying output. Batch manufacturing can produce 20 to 25 doses, depending on a few circumstances.

But traditional bioprocessing manufacturing batches can accumulate into a master lot, and that manufacturing is controlled differently from an autologous product. The manufacturer can use an assembly line of manufacturing devices, so the combined lot of product outcomes can be compared against expected results at the end.

In the migration from, for example, academic R&D to translational and early/late-stage GMP manufacturing, it is important to prioritize an understanding of the target product profile, such as final product expectations, proposed mechanism of action, how to monitor MoA, etc. Even as you work with healthy donors to gather necessary CMC data, you can gain a lot of information to fill details in these project elements.

Start with expectations of product CQAs that can be refined as your dataset increases. Quality by design has a huge impact on cell and gene therapy product manufacturing. It helps control variability not only in the manufacturing process but also within reagents and consumables as you progress from early-stage translational development and scale to GMP manufacturing.

Tips for Data Management and Tracking

Start thinking about how data is collected, the most appropriate repositories for that information, and setting up the right data models/infrastructure as soon as possible. The more data you collect and feed into whatever analytical algorithm you build, the more reliable the outcome. This requires a way to compile data that exists across disparate systems and paper sources, generating that body of evidence over time to create a historical dataset. The larger the dataset, the more you can mine and extract trends and insights. This combined dataset will help inform clinical protocol, manufacturing procedures, starting material collection parameters, out of spec criteria, and more.

How can Automation Reduce Batch-to-Batch Variability?

Automated processes can be incorporated at various stages of manufacturing, so early integration is vital. Generally, the more manual processes involved in production, the more deviations. Automation helps reduce variability and boosts consistent outcomes. It also minimizes expensive and time-consuming comparability studies that may be necessary if automation implementation is delayed.

When dealing with blood products, understanding that composition and what may or may not interfere in your culture process is critical. That can be managed in different ways. Many early-stage companies take a leukapheresis straight into manufacturing or, even if they select for the target seed material, steric hindrance can interfere with that selection process. This speaks to the importance of early implementation of instrumentation that can effectively clean up incoming seed material.

The process noted above, establishing an internal process control cell bank of PBMCs, looks at incoming seed material from healthy donors to establish how those healthy donors react as a group to repeat collections. This initiative is also useful during tech transfer to a CDMO, as it provides an example of the basic process and expected results with healthy donors.

Incorporating in-process analytical assays also can provide guidance. Cell counts, population doublings, and certain metabolites all can yield different growth expectations. You may have to feed a culture a sooner if you've got a rapid grower. Growth curves, particularly in the solid tumor population, differ from those seen with hematological patients. Triumvira responds to this result by adjusting the feed schedule, automating it from Phase 1 with built-in means to adjust based on growth curves and metabolites.

Overall, Kawalekar advises clients to take a holistic view of the end-to end journey, identifying pressure or failure points. Perform a detailed failure modes and effects analysis (FMEA) from this perspective to steer your organization toward the points introducing the greatest degree of variability, from raw materials and media to lentiviral vectors and cost-heavy development processes. Examining unit operations also provides opportunities to identify processes for standardization that potentially serves to reduce batch-to-batch variability.