As cell therapies become increasingly complex, analytical strategy can no longer be treated simply as a testing requirement at the end of development. According to Stephan O. Krause, Executive Director of Analytical Strategy at Bristol Myers Squibb, the greatest opportunity lies in bringing process understanding, analytical development and quality decision-making together much earlier.
Speaking in an Oxford Global thought leadership interview on advancing quality and analytical strategies in cell therapy development and manufacturing, Krause highlighted quality by design as a practical framework for reducing uncertainty and building greater confidence in both product and process.
“Analytical and manufacturing controls need to evolve together if we want a system that is resilient, scalable and ready for the future.”
Building analytical strategy from the start
Many of the analytical risks encountered during late-stage development originate much earlier, Krause explained. Establishing an analytical target profile, reference materials, assay controls, system suitability and critical reagents early can therefore have a significant impact on future clinical and commercial readiness.
Rather than developing individual analytical solutions for each programme, he also advocated creating platform approaches capable of supporting multiple products and variants.
Reducing manual steps, introducing risk-based decision-making and understanding where analytical variability could influence manufacturing decisions can help create methods that are more transferable and robust as programmes progress.
Comparability should build confidence
Process changes and scale-up remain significant challenges for cell therapy manufacturers, particularly when demonstrating that product quality, potency and consistency have been maintained.
Krause argued that comparability should not become simply a regulatory “box-checking” exercise. Instead, it should bring together process conditions, analytical methods, QC testing and lifecycle controls to create a coherent body of evidence.
Important elements include understanding critical process parameters and quality attributes, using engineering and scale-up batches to demonstrate robustness, establishing comparability protocols before changes occur and applying analytical characterisation to compare products before and after manufacturing changes.
From reactive testing to proactive quality
Emerging technologies could fundamentally change this model.
Automation can reduce manual variability, while real-time analytical approaches can provide earlier insight into characteristics such as phenotype, viability, function and potency during manufacturing.
Artificial intelligence and machine learning could also help identify patterns across increasingly complex analytical datasets, allowing scientists to detect emerging quality signals before they develop into manufacturing problems.
Connecting manufacturing, analytical and release data could ultimately enable faster and more transparent quality decisions.
The future: real-time, connected and scalable
Looking five to ten years ahead, Krause expects cell therapy quality systems to move increasingly from reactive testing towards proactive confidence.
Online and multi-attribute monitoring, automation, closed manufacturing systems and advanced analytics could allow quality signals to be identified earlier in the production process.
At the same time, closer integration between analytical development and QC will be essential to ensure methods are not only scientifically rigorous, but also practical, transferable and suitable throughout the product lifecycle.
The ultimate measure of progress will not simply be better analytical technology. It will be whether these approaches translate into fewer batch failures, faster product release, lower manufacturing costs and broader patient access.
For the next generation of cell therapies, analytical science is therefore becoming more than a quality-control function. It is increasingly a central part of designing therapies and manufacturing systems that can deliver consistent quality at scale.
