Scale up, look sharp – why analytics in large-scale cell therapy manufacture is so difficult
CAR-T and other cell and gene therapies represent an exciting new paradigm in how we approach previously untreatable diseases. But as ‘living drugs’ where the cell is the product, QC / analytical testing is more important than ever to ensure safety and efficacy. Analytics can be thought of in terms of:
- Process analytics – collecting data on variables that affect the quality of the cells, like temperature, pH, dissolved oxygen, and metabolite concentration, throughout the process to analyse and refine the process
- Product analytics – collecting data through or at the end of the process to analyse and characterize the product (in this case the cells), including cell count, cell viability, and flow panels
As per GMP guidelines, analytics that are critical to the manufacturing process and not measured automatically, ‘Critical Quality Attributes’ and ‘Critical Process Parameters’, are measured by the QC team. While batched QC analysis is achievable in traditional pharma manufacture, it isn’t optimal for production of complex advanced therapies. In order to scale production of cell and gene therapies and reduce the all important vein-to-vein time developers need to:
- Limit people-intensive manual measurements and recording which are laborious and error-prone
- Minimise manual sample and data exchanges between manufacturing and QC teams
The obvious solution would then be to have online measurements and sample analysis. But how can this be achieved across multiple bioreactors without significant capital outlay? Fitting an array of different analytical technologies all measuring different things on each and every bioreactor is expensive (usually being restricted to just a few bioreactors at a time), and the unfortunate truth is that current technologies can barely automate pH and dissolved oxygen measurement reliably. Automated cell count is often only an estimate, only a handful of early-stage technologies are looking at measuring online cell viability, and no one can do label-free flow cytometry at scale.
“From an engineering perspective, we knew we had to think about things differently to solve the challenge of making online analytics a reality as we designed the Cyto Engine platform. So we came up with the concept of mutualising the analytics we integrated, i.e. one set of analytical tools that can make measurements on multiple bioreactors. We also looked to build in flexibility on what we can integrate – that allows us to add to the system as analytical technology improves as well as allowing customization on what analytics come built in,” says James Davies, VP of Engineering at MFX.
What’s more, the mutualisation contributes negligible CapEx, making manufacturing cost-effective in the long-term.