Quality control (QC) in GMP cell therapy manufacturing is different from QC in traditional manufacturing environments.
The product may be patient-specific. Materials and samples move through tightly controlled processes with limited windows for testing and release. Multiple operators, instruments, assays, reagents, and manufacturing steps contribute to the final product record. A single unexpected result can trigger an investigation spanning the entire chain of custody or identity.
At the same time, cell therapy processes are still evolving. Manufacturing methods change, assays are added or refined, release criteria develop, and organizations move from early clinical toward commercial-scale operations.
For QC teams, this creates a difficult balance: how do you maintain rigorous GMP control and complete traceability while remaining flexible enough to support a process that is still changing?
Lab informatics platforms like laboratory information management systems (LIMS) can play a central role in solving this problem. The most effective platforms do more than record test results. They connect execution, compliance, traceability, investigation, and data in a way that supports quality throughout the lifecycle of the product.
Here are five ways a lab informatics platform supports quality control in cell therapy operations.
1. Control: Execute Complex GMP Workflows Without Making Change Expensive
Cell therapy QC workflows involve complex, interconnected steps that must be performed consistently.
A GMP laboratory needs a platform that guides users through approved workflows, enforces required steps and business rules, captures the right data at each stage, and routes work to the appropriate reviews and decisions.
Consider a cell therapy QC lab running a potency assay. The workflow may require specific inputs, calculations, acceptance criteria, review steps, and a decision on whether the result can support product release. A workflow-driven platform orchestrates these steps as work happens, ensuring required actions occur in the right sequence and that the necessary reviews and approvals happen before the result can be used for release.
GMP workflows also need to evolve. The challenge is adapting how work is performed without undermining the controls governing it.
In a rigid LIMS, even straightforward workflow changes can become major IT projects, making labs reluctant to adapt the system even when changes would improve operations.
An informatics platform should separate the validated platform from the configuration of how work is performed. This lets workflows, data capture, and business rules adapt within an established framework, while changes stay subject to review, approval, and validation.
This is the distinction between flexibility and uncontrolled change: controlled flexibility that guides and enforces GMP execution while allowing labs to adapt processes without turning every workflow change into a major development and validation exercise.
2. Prove: Keep Complete Records From Sample to Result
In a GMP environment, quality records need to do more than document the final result. They need to provide evidence that the work was performed as required, by the right person, using the right materials, equipment, and procedures, and that required reviews and approvals occurred along the way.
This can be difficult when information about a single activity is distributed across multiple records or systems. Reconstructing a result’s history may require piecing together records from samples, instruments, reagents, operators, workflows, and approvals. The more manual that reconstruction becomes, the harder it is to demonstrate a complete and consistent record during an audit or inspection.
A connected data model can make that evidence part of the record itself.
When relationships between samples, materials, processes, instruments, operators, methods, results, and approvals are captured as work occurs, the record preserves the context in which the activity took place.
That creates a more complete and defensible record of execution, one that can demonstrate what happened, who did it, what was used, and whether the required controls were followed.
This is especially important during audits, inspections, and batch review, when organizations need to demonstrate not just an outcome, but the integrity of the process that produced it.
3. Prevent: Enforce GMP Compliance at the Point of Execution
Quality shouldn’t depend on finding problems after the work is complete.
In GMP cell therapy operations, catching a problem during review or disposition means the non-conformance has already occurred, and the impact may already have propagated through the process.
In a well-designed laboratory informatics platform, quality controls are embedded directly into execution.
Consider a technician preparing a critical assay. The system checks the selected reagent and identifies that its expiry date has passed. Rather than letting the technician proceed and relying on a later review to catch the error, the system prevents the reagent from being used.
These controls shift quality from detection to prevention.
Instead of asking, “How do we find this error during review?” the system can ask, “Should this action be allowed to happen at all?”
That distinction matters in cell therapy, where deviations can have consequences beyond a single test result, and where material may be limited or irreplaceable.
Want to know what to look for in a LIMS designed for cell and gene therapy? Read our Buyer’s Guide for Choosing a Cell and Gene Therapy LIMS.
4. Investigate: Move From a GMP Event to Its Full Context
When something goes wrong, speed matters.
An out-of-specification result, environmental monitoring excursion, contamination event, or reagent recall can initiate an investigation involving numerous pieces of information. In a GMP environment, the faster a team can understand an event, the sooner it can determine what happened, assess potential impact, and take appropriate action.
Imagine a potency result falls outside the expected range.
The initial question may be straightforward: What happened to this sample?
But the investigation quickly expands.
Was the sample handled correctly? Which operator performed the test? Was the instrument calibrated? Which reagent lot was involved? Were other samples tested using the same lot?
In fragmented environments, answering these questions can involve multiple systems, reports, spreadsheets, and manual data assembly.
A connected informatics platform can fundamentally change investigations. When relationships between an event and surrounding operational data already exist, investigators can trace the result through the relevant sample, process, material, instrument, operator, and event history without first assembling the dataset.
The result is faster root cause analysis, more complete investigations, and more defensible CAPAs, particularly when decisions must be made within the limited time available for a cell therapy product.
5. Improve: Turn QC Data Into a Foundation for Continuous Improvement
When quality data is captured, connected, and contextualized, it becomes more than a historical record. It becomes operational intelligence.
Cell therapy organizations accumulate data across their processes. Over time, that data can reveal patterns that are difficult to see when information is distributed across disconnected systems.
For example, a laboratory may discover that certain assay failures occur disproportionately with a particular material lot, revealing a pattern that can be addressed before it becomes a larger quality problem.
Finding that pattern can require significant data preparation. Teams may need to extract information from multiple systems, transform it into a common structure, and build custom analyses before they can begin looking for patterns.
When relationships between samples, materials, processes, instruments, operators, and results are preserved in the underlying data, organizations can ask these questions without first rebuilding those relationships.
This creates a path from operational data to process intelligence. As cell therapy manufacturing matures and organizations increasingly explore advanced analytics and AI, connected, contextualized data can make it easier to uncover opportunities for improvement.
The Lab Informatics Platform Should Connect the Entire GMP Quality Control Lifecycle
These five capabilities are closely related.
Control ensures work is performed according to the right process.
Prove ensures the organization can demonstrate what happened and why.
Prevent embeds quality controls directly into execution.
Investigate makes it possible to move quickly from an event to its context.
And ultimately, Improve creates the potential to learn from operational data.
Together, they point toward a broader role for laboratory informatics in cell therapy. The LIMS should be part of the GMP operational system that governs how work happens, preserves the relationships that give records meaning, prevents inappropriate actions, and turns operational data into a resource for investigation and improvement, rather than simply serving as a place to enter QC results.
Where Labbit Fits
Labbit is built around this connected approach to laboratory execution and quality.
As a knowledge graph-native, workflow-first platform, Labbit connects execution, compliance, provenance, investigation, and process data in a single system. Relationships between samples, materials, processes, instruments, operators, and results are captured as work happens, creating a connected record that can be traversed from an individual event to its broader operational context.
For cell and gene therapy organizations, this provides a foundation for one platform for execution, compliance, and process improvement, with the flexibility to evolve as workflows change and the connected controls and data needed to support GMP execution, investigation, and improvement.
That is a major reason why one of Labbit’s clinical-stage cell and gene therapy customers preparing for commercial scale selected Labbit, replacing paper-based sample, batch, and equipment records with a single connected system that could evolve with its growing manufacturing complexity and GMP requirements.
Curious to learn more? Join our upcoming webinar, presented in partnership with Astrix, “Why Graph Architecture Is the Ideal Foundation for LIMS in GMP Manufacturing” where we’ll explore this topic in more detail.
