How Automation Improves Safety And Efficacy Of Source Material Collection
By Life Science Editorial Connect Staff
Incorporating automation into a cell therapy workflow is a complex endeavor. Determining the right technological approach for a unique therapeutic asset and working to mitigate risk when introducing automation to a workflow are crucial to achieving optimized, standardized manufacturing.
Yet achieving key improvements to a workflow through automation can greatly improve the consistency of production, bolster a product’s safety and quality, and reduce rework and improve costs. Implementing more automated workflows in cell therapy manufacturing can serve to minimize open processes and manual touchpoints, increasing reproducibility and improving traceability. This, in turn, has the potential to reduce manufacturing failures and lower the cost of goods sold, facilitating increased patient access.
In a recent installment of Cell & Gene Live, Craig Beasley, chief technical officer at BlueRock Therapeutics, and Narinder Singh, chief technical officer at Arcellx, explored the potential advancements in cell therapy process automation that improve safety and efficacy during source material collection.
Pursuing Automation ‘Step By Step’ For Best Results
When it comes to determining how to maximize safety and minimize risk while incorporating automation in a process, what operators don’t automate is just as important as what they do automate. “You don’t want to automate a bad process,” Beasley said. “If you don’t actually understand the processing step – and honestly, many programs are preclinical or Phase 1, and you probably don’t have a good understanding of the process and the process variables that matter yet – those steps I shy away from. Focus on the media handling and other, better-understood steps to make it as robust and scalable as possible.” For steps that are at low volumes and possess a tight window of process design space, automation is unlikely to be easily achieved or truly robust until the technologies supporting those steps advance, Beasley said.
The automation potential for allogeneic and autologous processes can diverge greatly. This is especially apparent when considering source material collection, as allogeneic applications are again more likely to achieve a more simplified materials collection paradigm, owing to the comparative ease of donor material collection. For these programs, the greater focus is on clone selection and subsequent expansion, and progress made in mAb applications may be beneficial in standardizing and optimizing cell banking.
In autologous applications, striking a balance between necessity and simplicity can be more difficult – can you achieve fresh selection for patient materials? Are you able to perform cryopreservation before material is transported? Will you introduce automation solutions at the donor collection site? If so, how can you maximize consistency to ensure that any variability in a selection comes from a patient and not a process? The nature of the materials being collected can limit or prohibit many of the safety measures that typify more traditional modalities, such as filtration or sterilization using heat or radiation. “For allogeneic, you can possibly perform automated visual inspections, whereas for autologous products, where you have a few bags per patient, automating visual inspection would make the process much more complex,” Singh explained.
Another key consideration for automated technologies is tied to closing processes. By closing systems where possible and enabling more reproducible materials transfer and handling, operators can greatly reduce contamination risk. “At some points where a process is open, automation may be valuable, even for patient-specific batch manufacturing,” Singh said. Pairing closed processing with more at-line analytics, as well as pursuing improving cryopreservation to enable more immediate freezing, will serve to safeguard cell quality and improve consistency for these fragile therapeutics.
All of these variables must likewise be linked to scalability; for autologous applications, this means focusing on driving down processing time in order to facilitate the production of as many lots as possible within a fixed overhead. To this end, there are two areas positioned for immediate improvement: controlling raw material inputs to limit variability and moving toward achieving allogeneic production for these modalities.
Conclusion
Ultimately, automation is likely to play an increasingly important role in reducing costs and enabling greater standardization for cell therapy manufacturing. As more organizations move toward automating highly manual and repetitive tasks, cell therapy workflows are bound to see greater consistency and reproducibility, particularly as a program scales. This trend exists alongside increased interest in artificial intelligence (AI) and machine learning (ML) techniques aimed at bolstering analytics and enabling more comprehensive, targeted automation. The result will be safer, more replicable processes that can be more easily adapted to unique molecules, improving their affordability and accessibility for patients who need them.