// Jul 2026
Rapid Release Testing Starts With Faster Contaminant Detection
Autologous cell therapies have transformed what's possible for patients. Now it's time to rethink the testing methods used to release them.
For many cell therapy programs, the manufacturing processes have been engineered to compress timelines where possible. Cells are collected, engineered, expanded, tested, and prepared for reinfusion on timelines where even small delays matter. But contaminant detection remains one of the areas where the field still depends heavily on growth-based methods for sterility, mycoplasma, and adventitious agent testing.
That mismatch creates a practical problem. Advanced therapies are increasingly personalized, time-sensitive, and operationally complex. Release testing needs to evolve with the products it supports.
Growth-Based Testing Was Not Built for Modern Cell Therapy Timelines
Traditional contaminant testing is effective because it is conservative, familiar, and rooted in established regulatory expectations. But it is also slow by design. Many methods depend on giving bacteria, fungi, mycoplasma, or viruses enough time to grow to detectable levels.
That time requirement creates a major bottleneck for autologous cell therapies. When the full vein-to-vein process may take about a month, waiting for safety results consumes a meaningful share of the overall timeline. The issue is not that legacy methods lack value. The issue is that cell therapy manufacturing has outpaced the speed of the testing model.
Sequencing-Based Analytics Could Create a Faster Detection Model
Sequencing-based contaminant detection offers a different model: identify microbial or viral nucleic acids directly rather than waiting for organisms to grow.
At ElevateBio, we are developing NGS-based rapid analytics for cell therapy release testing. The workflow extracts DNA or RNA from a representative cell therapy product, prepares sequencing libraries, uses hybrid capture to enrich relevant targets, and applies custom bioinformatics to identify bacterial, fungal, or viral species. In early feasibility studies, this approach detected two fungal and three bacterial contaminants at approximately 10 CFU per 75,000 cells, as well as five viral contaminants at 1–10 genome copies per 7,500 cells (see the data).
Those results do not eliminate the need for further development. They do show that sequencing-based methods detect a diverse range of potential contaminants in a cell therapy-relevant background.
The Bigger Opportunity Is One Analytical Framework for Multiple Contaminant Classes
The value of sequencing is not only speed. It is breadth.
A sequencing-based approach has the potential to consolidate detection across bacteria, fungi, and adventitious viruses within a common analytical framework. It also produces digital, species-level information that may support more informative investigations when a signal is detected.
That matters because rapid release is not simply about getting to a pass/fail answer faster. It is about creating a more responsive quality model: one that can detect risk earlier, characterize it more precisely, and support better decisions before product release.
Rapid Release Will Require Both Innovation and Validation
Sequencing-based analytics are not a shortcut around safety expectations. They must be characterized, qualified, and validated with the same rigor expected of any release-relevant method.
The next work is clear: further define limits of detection, reduce cost and turnaround time, evaluate additional technologies, and demonstrate performance across more representative product types. The long-term opportunity is equally clear. Faster contaminant detection could help cell therapy programs reduce release bottlenecks while maintaining the safety standards patients depend on.
Rapid release starts with faster, more informative contaminant detection. Sequencing-based analytics are not just a new test. They are a path toward a release model better matched to the timelines and complexity of modern cell therapy.
Gary Sommerville Ph.D.,
Principal Scientist, NGS, Technical Services
Gary Sommerville is a Principal Scientist in ElevateBio's Next-Generation Sequencing core lab, where he leverages over a decade of genomics expertise as the wet lab lead to drive cross-functional collaboration supporting internal R&D initiatives. He also leads assay development and onboards external sequencing clients seeking to utilize ElevateBio's NGS technical capabilities.
Prior to ElevateBio, Gary spent six years as a Senior Scientist at Dana-Farber Cancer Institute's Molecular Biology Core Facilities, where he led sequencing data generation for internal stakeholders and Harvard Medical School collaborators.
Gary earned his Ph.D. in Molecular Medicine from Trinity College Dublin, Ireland, investigating molecular signatures of cancer stem cells across various cancer types with and without BRAF V600E mutations.
In his free time, Gary enjoys traveling, attending sporting events with his children, and pursuing an ever-elusive scratch handicap in golf.
