Cell therapy process automation can deliver many benefits for product developers, including reduced risk of contamination, improved consistency of product and reduced cost of production. These benefits are typically delivered by closing the manufacturing process and reducing the direct operator and quality control (QC) technician manipulations.

If full automation is not feasible, some of the benefits of cell therapy manufacturing process automation can also be realized by partial automation[1]. Partial automation is less expensive and presents a lower business and financial risk during clinical phases than full automation.

While there is certainly demand for partial automation, barriers remain. The main barrier to implementation of a partial automation strategy is the lack of availability of commercially available, proven, reliable, configurable devices.

The industry is in urgent need of a range of unit modules for performing unit operations. These modules need to have stable and proven performance that can be adopted in early testing, and then carried through to commercial production.

At present, there are very few examples of modules that provide robust unit operations, particularly when trials transfer from healthy donors to patient material. Standards of interconnectivity are required between these modules for both fluidics as well as for data transfer and control supervision.

In this article, we explore currently available devices and solutions for cell therapy products in preclinical, clinical and commercial phase, and some of their cost and time drawbacks.

As their product transitions from preclinical to clinical production, all cell therapy product developers encounter a dramatic increase in cost and complexity in the laboratory. Aside from the cost of GMP compliance (procedures, sterility, QC, documentation etc.), operators are faced with a major jump in scale from batches of E6 cells to E9 cells.

When scaling up, the impact on manual manipulations and volume of materials is very substantial. At the same time, sterile practice becomes essential, making these manipulations more challenging.

In the clinical phase, two of the main cost drivers are skilled resources and the cost of obtaining and reserving classified space. Other major cost drivers include the cost of critical reagents (vector or growth factors), QC testing, and starting material including logistics, cost and consistency of apheresis donations, or bone marrow.

Automation of process operations

When looking at process operations, automation of the highly manual and dexterous steps – such as Ficoll separation – would be very beneficial in reducing operator effort. Batch to batch consistency could also be greatly improved by automation.

Recent device developments

The industry is currently producing several devices in the primary process operations of separation, transfection/transduction, expansion and formulation/bag filling. However, optimization of these devices is ongoing, and outcomes are often still quite variable and unpredictable. Therefore, the focus for process operation automation currently is the optimization of each process step to minimize cell loss.

Automation of sampling, in-process control and QC

When we look at sampling and in-process controls, the elimination of the need to manually take in-process samples for lab-based analysis would have a substantial impact on reducing operator interactions, closed process and reduced waiting time. An example of an in-process sample is waiting for a cell count to make a formulation decision.

On the regulatory front, there is an urgent need for standardization in what should be measured, and how. A clear and precise definition of cell parameters (phenotyping, potency), improved clarity in terms of terminology and tests used in cell therapy (cell viability, system yield, proliferative capacity) are needed.

This gap in regulatory standardization is one of several barriers to cell therapy system automation, or at least one of the hurdles to developing and implementing tools for inline sampling and real-time monitoring.

Other regulatory issues which need to be addressed include selecting what needs to be monitored. This is something that is difficult to establish as the process is developing; there is a lack of data integration due to variability in methods of assessment as well as a lack of technology and access to tools.

To summarize, the highest priority areas for automation of sampling, IPC and QC in the clinical phases are:

• automation of analytics: having better tools to measure non-invasively in real time with sensors integrated in the bioreactor to support costs and labor reduction while decreasing contamination risk;
• identifying what to measure – less is more; and
• implementation of electronic tracking tools to interconnect product, materials used, test results and decrease tracing errors.

As a product is transferred from clinic to commercial production, there is a heightened focus on costs as well as stable and predictable production. The loss of a batch may have a very severe impact on a patient, but at the same time the impact on the cost of production must not be ignored.

There are many cost drivers during the early commercial phase. Cost drivers include variation of starting material from patient to patient, access to and training of skilled labor, QC and IPC sampling/ analysis/ decision-making/ reporting as well as QC release effort associated with process or batch record deviations. They also include kitting and materials: single source, environmental monitoring and factory scheduling (plant, equipment, operator utilization).

Two significant opportunities for cost reduction in the early commercialization phase are closed processes and supervisory computer systems – Manufacturing Execution Systems (MES) and Electronic Batch Record (eBR):

• Closed processing drives cost improvements in room-classification reduction, leading to reduced environmental monitoring costs, reduced gowning costs, densification, and operational expenditure.
• MES/eBR systems facilitate release by exception and reduce non-process deviations such as transcription errors, dates, missing signatures and other handwriting errors.

Like the clinical phase developments described above, the technology and device gaps for the early commercial phase are mainly in process operation automation, or alternatively in the area of sampling/IPC/QC.

Early commercialization phase automation of process operations

The need for automation of operations in commercial cell therapy production has been recognized and understood for several years. Despite progress by device suppliers and custom engineering, the non-universal approach to interconnectivity between unit modules – both data and fluidic – is keeping processes open in certain areas.

In addition, the connectivity of production equipment to data historian/MES is emerging as increasingly important. Device developers should be aware of this need and plan to accommodate it.

When transitioning from one stage to the next, devices and systems that can work in development, clinical and large-scale commercial will be essential in ensuring smooth process development and regulatory transition.

Automation of sampling, IPC and QC

Connectivity and the development of inline analytics are critical for future commercial cell therapy production.

Several current problems could be addressed if it were possible to perform process and product analytics inline. These problems include timing (delays) associated with turnaround of QC samples, sterile sampling and ensuring no adverse impact on the bulk and the volume impact of samples.

Furthermore, the electronic QP concept is highly anticipated – it is particularly important for autologous products where the full burden of QC cost is carried per dose. The number of batches, and therefore release events, is also a very significant driver.

The current main cost drivers in cell therapy manufacturing are split almost evenly between:

• production direct costs (operators, space, media)
• critical reagents (vector or growth factors)
• QA/QC including batch release testing.

Reducing production direct costs is the first target of automation, and over the next five years we will see it diminish significantly as a proportion. This will have a flow-on effect on reducing the cost of vector and growth factors, as those processes will also transition to being automated as well.

QA/QC costs will increase proportionally, causing the industry to invest more heavily in the development towards adoption of electronic batch records. This should already be occurring; however, it is lagging.

Development of automated in-process and QC testing will require very significant R&D investment. The biopharma sector with large batch sizes has not had sufficient pain from QC testing to drive this R&D. On the other hand, autologous cell therapy products with single dose batches are feeling plenty of financial pressure, and therefore it is more likely that investment will occur.

Looking at process automation, it is likely we will see the evolution of two main streams of advanced therapy medicinal product (ATMP) production.

We are likely to see single patient (autologous) therapies with increasingly personalized features of the therapy, produced at the point of care in highly automated devices. These therapies will be produced and administered in one day.

The other stream will be drug products, including tissue restoration, that are allogeneic and produced in larger batch sizes in biopharmaceutical manufacturing facilities. These therapies will be less expensive than autologous therapies.

Overall, the attention of cell therapy drug product developers and producers is shifting from automated production modules increasingly to inline analytics, IPC and QC, and digital connectivity for electronic batch records.

This doesn’t, however, mean that product developers are satisfied with the production devices and modules that are currently available. More likely we can assume that development is already underway on process modules, whereas development of suitable inline sensors and analytics is still in the early experimental stages.

NOTES

[1] “Partial automation” in this article refers to fairly basic mechanization (operator aids), as well as automation of modules to perform unit operations.

This article was originally published as part of the Phacilitate Cell and Gene Therapy Automation Special Interest Group (SIG). The article has been edited and republished with the permission of Phacilitate. The full report is available from the Phacilitate Automation SIG website.