Appropriate Automation in Cell Therapy Manufacturing

Automation is often presented as the inevitable destination for cell therapy manufacturing. Manual operations are viewed as undesirable, while fully integrated robotic systems are assumed to represent the highest standard of manufacturing maturity.

The reality may be more nuanced.

A range of studies have been performed comparing manual manufacturing processes with progressively more automated alternatives. By observing and documenting manual operations, process hazards were identified and quantified for comparative purposes. Alternative automation strategies were then developed, ranging from simple operator aids through to highly integrated manufacturing systems.

For each scenario, estimated development cost, capital investment, manufacturing cost of goods, and operational risk were assessed. While the precision of these estimates can always be challenged, the resulting comparisons reveal several non-intuitive insights.

Most notably, the automation strategy that delivers the best manufacturing outcome is not necessarily the one containing the most automation. In many situations, selectively automating critical operations while retaining human flexibility can improve quality, reduce cost, lower capital exposure, and increase operational resilience simultaneously.

This article explores the concept of appropriate automation and examines how manufacturing systems can be designed to maximise overall performance rather than simply maximise automation.

Defining Success

To set a framework for evaluation, we need to agree or at least acknowledge the preferred beneficial outcomes.

Manual processing of patient materials

When reviewing manual processing of patient material it is tempting to focus on the visible activities and ask how each task might be automated. While this approach identifies automation opportunities, it does not necessarily identify the best manufacturing solution.

Before considering automation options it is useful to define the characteristics of an effective manufacturing process.

A successful manufacturing process should:

• Consistently deliver product meeting quality requirements.
• Protect patient material throughout processing.
• Be resilient to normal operational variation.
• Allow operators to identify and recover from foreseeable problems.
• Scale economically as demand increases.
• Minimise capital exposure during development and commercialisation.
• Support progressive process improvement.
• Remain practical to validate, maintain, and operate.

Importantly, these objectives do not explicitly require maximum automation. Automation is only valuable where it contributes to one or more of these outcomes.

This distinction becomes important when comparing alternative manufacturing strategies. A highly automated process may reduce some operational hazards while simultaneously increasing system complexity, capital requirements, validation effort, and operational inflexibility.

The challenge is therefore not to maximise automation, but to determine the level of automation that delivers the greatest overall manufacturing benefit to the patient and the investors.

Automation of procedures

• Making the actions experienced by the cell product consistent - the product is the process.
• Reinforcing secondary operations - mixing, sampling.
• To capture the batch record - to avoid 100% supervision by a secondary batch monitor.
• Integrating QC derived data into the procedure - dilution, inoculation, fill and finish plan.
• Reducing operator burden - Intense manual procedures are not sustainable.

Functional closure of the process

Allowing processing in lower class clean rooms:

• facility capital and operating cost
• Personnel working efficiency less affected by intense gowning requirements.
• Personnel flexibility to complete multiple tasks across the process.
• Potential to service multiple patients in the one working area.
• Shared resources for multiple patients such as incubators.

Modular process design

• A single active operator can complete the entire process.
• More operators allow more patients to be processed.
• More unit processes increase capacity.
• Minimum capital replication for scale-out to new facility in new region

Why automation is attractive

Manually implemented cell therapy manufacture is not a viable endpoint.  Manual operations create:

• variability
• contamination opportunities
• labour intensity
• documentation burden
• operator fatigue
• scalability challenges

Many of the issues can be overcome with an automated approach:

• Automated handling.
• Automated sampling.
• Automated QC interfaces.
• Fully isolated environment.
• Automated batch data capture.
• Minimal operator intervention.

This will deliver many benefits to approach a better manufacturing outcome.

When investigating automation strategies it can be instructive to consider what the benefits are and what actions deliver them.

Relative to manual processing the benefits sought through automation include:

• Contamination control
• Scalability
• Capital efficiency
• Reliability
• Cost of goods
• Resilience

If we take our automated model, what elements of it address each of these objectives?

Contamination Control

A key objective of automation is reducing contamination risk by process isolation.

Open cell manipulations performed by a skilled operator under a Class A cabinet can achieve very low contamination rates. A fully enclosed robotic manufacturing system appears attractive because it removes direct human interaction from the process.

At first glance, this seems like a compelling justification for extensive automation.

However, it is worth examining where the benefit actually originates.

The contamination control benefit is not necessarily delivered by the robot itself. Rather, it is delivered by isolation of the patient material from the surrounding environment.

A fully enclosed robotic isolator is one method of achieving this isolation. Such systems are widely used within pharmaceutical manufacturing and can provide excellent contamination control.

However, they introduce additional requirements. Following completion of a patient process, all product-contact materials must be reconciled and removed. The isolator then requires a validated decontamination cycle before the next patient can be processed. Sterilisation systems, environmental monitoring, material transfer processes, and cycle validation become important elements of the manufacturing system.

An alternative approach is functionally closed manufacture.

In this model, the patient material remains within a pre-sterilised disposable kit. Sterile welding, sealing, and transfer technologies derived from blood banking enable materials to enter and leave the process while maintaining closure. The contamination control benefit is achieved through the disposable process path rather than through robotic isolation.

Neither approach eliminates risk. They simply create different risk profiles.

Scalability

Scaling patient-specific cell therapies is one of the most important challenges facing the industry.

Historically, manufacturing processes have been developed using manual operations performed within Class B cleanroom environments. As demand grows, additional cleanrooms and increasing numbers of highly skilled operators are required. While this approach is technically viable, both facility costs and personnel requirements eventually become significant constraints.

Automation appears to provide a compelling solution.

Fully integrated robotic manufacturing systems can substantially reduce the number of operators directly involved in processing. Capacity can be increased through replication of the robotic cells, while duplicated systems may support technology transfer into new facilities and geographic regions.

However, these benefits are accompanied by an important commitment. The robotic platform must be designed, developed, validated, and integrated around the manufacturing process it will support. In practice, this often requires significant investment before the commercial success of the therapy has been established. Because clinical trials should ideally be conducted using a process representative of the final manufacturing approach, automation decisions may need to be made very early in the product lifecycle.

An alternative strategy is to develop the therapy using existing modular processing technologies. Rather than building a dedicated manufacturing platform, proven unit processes are assembled into a workflow specific to the therapy. As demand grows, capacity is increased through replication of the unit processes, operators, or facilities.

Both approaches can deliver scalable manufacturing. The distinction lies in the timing and magnitude of the investment. Integrated automation typically commits capital early in anticipation of future demand, while modular manufacturing allows capacity to expand progressively as the commercial opportunity becomes clearer.

The question is therefore not whether scalability can be achieved. The question is which path to scalability creates the most robust commercial outcome.

Cost of Goods

Reducing manufacturing cost is often presented as one of the strongest justifications for automation.

Multiple manufacturing studies have demonstrated that a functionally closed process operating in lower-grade cleanroom environments can substantially reduce manufacturing costs relative to traditional manual processing. Much of this benefit arises from reductions in facility, gowning, supervision, and labour requirements.

Where consumables and reagents already represent a significant proportion of the total cost of goods, the impact of automation on overall patient cost can be less dramatic than expected. In these situations, the greatest economic gains are often realised through the first stages of automation and process closure.

This observation is important because it suggests that the relationship between automation and manufacturing cost is not linear.

The introduction of functionally closed processing, guided workflows, automated data capture, and selective process automation can deliver a large proportion of the available economic benefit. Additional layers of automation continue to improve performance, but often with diminishing returns.

Achieving fully integrated robotic manufacture requires many aspects of the process to be refined and controlled to ensure reliable operation. Components, disposables, fixtures, sensors, and process interactions frequently become more specialised. These refinements increase development costs and can increase the cost and complexity of the single-use systems used to manufacture the therapy.

The challenge is therefore not to minimise labour at any cost. The challenge is to identify the level of automation that minimises total manufacturing cost while maintaining product quality, operational flexibility, and commercial viability.

In many cases, the lowest cost of goods is achieved not through uncompromising automation, but through the careful selection of the activities that genuinely benefit from automation.

Reliability

Manufacturing reliability describes the ability of a process to operate consistently and repeatedly with minimal unplanned interruptions.

In cell therapy manufacturing, interruptions may arise from equipment failures, personnel absences, maintenance activities, software faults, or shortages of consumables. Reliable processes minimise the frequency of these events and consistently deliver product meeting quality requirements.

Automation can significantly improve reliability by reducing operator variability, standardising procedures, automating data capture, and controlling process parameters with greater precision than is practical during manual operation.

However, automation also introduces new dependencies. Integrated manufacturing platforms rely on hardware, software, sensors, networks, and specialist support personnel. While such systems may achieve very high reliability during normal operation, failures may become less frequent but potentially more significant.

Modular manufacturing approaches distribute these dependencies differently. Individual unit processes can be supported by duplicate equipment, while workflows may continue despite disruption to a single module. Rather than concentrating risk within a single platform, modular systems distribute risk across multiple components.

Neither approach eliminates operational risk. The challenge is to design manufacturing systems that consistently deliver patient therapies while minimising the frequency and impact of operational interruptions.

One practical measure of manufacturing reliability is the frequency of process variation events. In patient-specific manufacture, each process variation consumes time, resources, and management attention, while potentially placing irreplaceable patient material at risk. A highly reliable process is therefore one that minimises the occurrence of such events.

Hazard analysis provides one indicator of this reliability. By identifying and tallying potential process hazards across alternative manufacturing designs, it becomes possible to estimate the relative frequency of operational deviations. In our studies, the transition from manual processing to functionally closed and selectively automated workflows reduced the total process hazard count by approximately 80%, with further automation providing progressively smaller improvements. This suggests that much of the available reliability benefit may be captured without pursuing fully integrated automation.

Resilience

Patient-specific cell therapies carry an unusual burden of manufacturing failure. Unlike conventional pharmaceuticals, there is often no inventory to replace a failed batch. For some therapies, a manufacturing failure may represent the loss of a patient’s only opportunity for treatment.

In this context, resilience becomes a critical measure of process maturity. Reliability seeks to minimise failures. Resilience determines how effectively the manufacturing system responds when failures inevitably occur.

A resilient manufacturing system anticipates disruption and provides pathways for recovery.

Redundancy is often one element of this strategy. If loss of a critical instrument could result in loss of patient therapy, a validated backup system may require little justification.

However, resilience extends beyond equipment. The entire manufacturing system must be considered. Critical services such as power, environmental control, material supply chains, and personnel availability all contribute to the ability to continue serving patients.

Process design should also anticipate abnormal product conditions. Poor cell yield or viability, often arising from the health of the patient rather than the process itself, may require alternative dosing strategies or modified release criteria. Manufacturing systems that can accommodate such variation are inherently more resilient.

Personnel resilience is frequently overlooked when considering automation. Highly integrated systems may depend upon specialist automation engineers to recover from equipment failures or software faults. However, these specialists may not be qualified to enter controlled manufacturing environments or perform aseptic interventions.

Automation should remove routine burden from operators, not remove operators from the manufacturing system.

A completely hands-off workforce is a liability for resilience.

Quantifying Resilience

How can the relative resilience of different manufacturing approaches be compared?

For patient-specific therapies, resilience may be considered as the probability that a patient’s therapy can still be successfully manufactured and delivered despite a significant process disruption.

One approach is to define a Patient Batch Resilience (PBR) score based on the resources and procedures available to recover from abnormal events.

Potential contributors to resilience include:

• Facility redundancy – backup power, environmental controls, and supporting utilities.
• Supply chain redundancy – availability of alternative suppliers for consumables and critical materials.
• Equipment redundancy – duplicate or alternative systems available to continue manufacture.
• Personnel redundancy – availability of suitably trained operators and support staff.
• Recovery procedures – existence of validated procedures for foreseeable disruptions.
• Recovery capability – availability of personnel qualified to execute recovery procedures.
• Product specification flexibility – ability to accommodate poor starting material or variable product yield.

Each parameter may be scored from 0 to 5, where:

0 = no recovery capability exists; 5 = robust recovery capability with demonstrated procedures and resources.

The resulting score does not provide an absolute measure of resilience. Rather, it enables comparison of alternative manufacturing strategies and helps identify weaknesses in the overall manufacturing system.

Ultimately, resilience may be considered the probability that patient therapy can still be delivered when the unexpected occurs.

So where is the sweet spot?

Appropriate automation sits between manual fragility and excessive technical dependence. The following comparison summarises the trade-off.

Attribute	Too Little Automation	Appropriate Automation	Excessive Automation
Operator burden	High	Moderate	Low
Variability	High	Low	Low
Capital cost	Low	Moderate	Very high
Recovery from failures	High flexibility	High resilience	Low flexibility
Specialist dependency	Low	Moderate	High
Scale-out	Difficult	Straightforward	Expensive
Commercial risk	Moderate	Lowest	High

Conclusion

The objective of automation in cell therapy manufacturing is not to maximise technology, but to maximise patient outcomes. Appropriate automation reduces variability, improves quality, lowers cost, and supports scale while preserving the flexibility and resilience required to recover from inevitable disruptions.

The highest level of manufacturing maturity is not achieved when people are removed from the process. It is achieved when automation and people work together to reliably deliver therapies to patients under both normal and abnormal conditions.

References

1. Tallying Hazards to Compare Automation Options