Achieving and Promoting Industry-Wide Automation for Advanced Therapies
This Event Report evaluates the key obstacles and barriers that need to be considered when approaching industry-wide automation opportunities for cell and gene therapies.
Working group 5 met to discuss the first part of the ‘what, when, and how’ of achieving and promoting automation in the industry. Starting with the fact that the understanding of automation varies, breakout groups drilled further into what defines automation, key barriers and operational hurdles, commercialisation, when automation should begin, and more.
We have a paradox: there are scientists doing science with others coming in at the end to attempt automation. Further to this, tools are limited in flexibility and are hard to scale. Automation should be brought into the conversation sooner and guidance could be provided to defend arguments for this to investors and stakeholders.
Before breaking out, analytics and regulatory challenges were also touched on. It was raised that if it were possible to better leverage algorithms, AI, and automation, more could be processed. This is increasingly possible as regulators become more educated, with a subsequent increase in appetite for information and data that can improve how predictive it’s possible to be on patient health.
Breakout Group One
“Automation shouldn’t come last. If we’re taking tools and forcing them on processes; we’re stuck in a dead-end.”
Key hurdles: A major issue we see is a lack of standardisation; we don’t even have this on cell counting yet.
The need for automation is also often sudden, and genuine effort is rarely made until people and technicians hit a wall. If we had better tools to do capacity planning early, could we encourage this realisation to happen earlier?
Early adopters are indicating a growing appetite for automation. Customers are increasingly pre-clinical and are working towards this, partly because changes are easier to make in such an early phase. A common answer to automation, however, is simple: ‘we’re not there yet. We’ll deal with it later.’ To combat this, is there merit in steering discourse away from the time value of clinical data and instead towards the time value of process development?
Supporting decisions towards automation: What tech and processes can aid this? Urgent requirements such as materiel and budget often limit willingness to try new options. Some CMOs also have little interest in automation as their focus is on short-term concerns. This conflict goes hand-in-hand with the subject of education, with a strong marketing team often proving more compelling than efforts towards innovation and automation.
Recommendations to the industry: Practically speaking, if we can lower the bar – financial or otherwise – for testing equipment, it would answer common conflicts around automation and early adoption. The sooner we get tech in people’s hands, the more testing and consideration can occur.
Breakout Group Two
“Thinking in milestones is common, and it’s a poor choice versus considering the whole journey”.
Challenges in the development cycle: Adoption is difficult. Automation takes time and resources, and it’s hard to know if your process is ready for it. Buckets are large and trying to lessen that makes it difficult to get to market.
Price is a key issue, as labour is often cheaper at most scales. Down the line, adopting automation is often too late, too risky, and too expensive.
Most people think a primary driver is reducing cost and increasing speed, but another element is the consistency and stability of assays across sites. This could be promoted more as a driver for automation.
Cost matters, but it’s less of a driver in Phase I. If we can identify more relevant drivers for the earlier stages, we could induce automation more consistently.
Thinking in milestones is common, and it’s a poor choice versus considering the whole journey. Mapping your end goal and producing a roadmap is effective here. Phase I manufacturing, for example, may need to cost more for commercial to then be cheaper. This activity can be powerful when the time comes to defend proposals towards automation to investors. This brings us to the potential of a ‘semi-automated’ approach, where the popular subject of modularity can be functional and effective until you’re ready to go commercial.
Engineering support to integrate and on-board systems is a further challenge. This is often a cost and a money-pit, particularly for smaller companies. Efforts like providing APIs that can speak to and bring together disparate systems could help here.
Time as a factor: The number of factors present make this tough to answer: process, pipeline, funding, clinical trial status, and patient numbers, are just a few examples. Ultimately, laying a foundation of automation early is necessary for it to truly survive, take root, and provide value later.
Automating cell and gene products: Gene products are easier to automate, but what about the driver for it? It’s easy to forget that labour is often cheap. Right now, IPs are also relevant; we’re starting to see some conclude and drop out, leaving opportunities for cheaper alternatives that can drive tech and automation.
Defining the group’s purpose: It’s difficult to focus solely on one part of the overall picture here as it’s all relevant. We can’t just do process automation without data automation and integration, for example. For our output to survive real conditions, we should consider an approach that touches on all stages of the journey.
Tech transfers: Flexibility from an outside party can aid in the creation of a fluid transfer. Ideally, early automation makes the transfer more likely to be smooth.
These transfers are key and are best driven by robust gap assessments. This is difficult, however, when it comes to balancing time constraints – even a one-day process can take months. It’s also easy to sail through transfer and analysis until your first engineering run. At this point, it’s easy to end up trapped in an expensive situation where you are effectively performing process development in a cleanroom.
Flexible automation: Should we focus on process development automation or a later stage? Knowing that labour is often cheap, adopting early so that relevant late-stage benefits can be had is challenging. In addition, many companies are too limited on short-term funding to pursue automation effectively.
Another problem we have is the age of the industry. Predicting and showing comparability is difficult and expensive, and, in the eyes of many, automation must save drastic amounts of money across development and manufacture to have tangible value. Deviating for automation can also pose issues with regulators, particularly from a failure recovery perspective.
The concept of ‘off-process automation’ is interesting. If we can automate data and analytics, we can grasp long-hanging wins without encroaching on a given core process to the point of disrupting it heavily.
Questions to enable early automation: A set of recommendations on this will be ideal as a group output. At this early stage, we identify the following as important to this:
- Know your processes
- Know your TPPs
- Understand what’s available
- Connect academic groups to equipment vendors to lower the hurdle of evaluation
This session was moderated by:
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