Andrea Moore, AskBio, on why measurement—not discovery—is now the constraint.
In gene therapy, innovation is no longer the constraint.
Analytics is.
As modalities grow more complex, the question is no longer what can be built—but what can be measured, characterized, and defended. The ability to generate insight is now pacing the ability to deliver therapies to patients.
Andrea Moore, Vice President of Analytical Development and Quality Control at AskBio, sits in that gap.
“I think that a lot of people don’t talk about analytics a whole lot,” she said. “It’s definitely starting to pop up more and more… which is good. It’s a good opportunity to put it in the forefront of what we’re doing.”
From Back-End Function to Front-Line Strategy
For years, analytical development lived at the end of the process—confirming whether a product met specifications. That model no longer holds. Gene therapies—particularly viral vector and cell-based platforms—are not static products. They are complex, multi-attribute systems where variability is expected. In that environment, analytics doesn’t just validate quality.
It defines it.
At AskBio, Moore’s work in assay development and product characterization reflects that shift. Analytical strategy now informs how products are designed, how processes are built, and how programs move forward.
The Measurement Problem
The science has advanced faster than the tools used to measure it.
“We have to figure out how to get more creative with the tools that we have,” Moore said. “Simply looking at a protein or a DNA sequence isn’t quite enough.”
The result is a growing gap—between what therapies are and what current analytical frameworks can capture. Gene therapy programs are now forced to answer questions traditional biologics never had to:
- What defines quality when variability is inherent?
- How is potency measured in multi-functional systems?
- What level of characterization is sufficient for consistency and safety?
New platforms and AI-driven tools are starting to close that gap. But they introduce a different dependency: data quality.
“If we have the right data to feed into the tools, then yes,” she said. “But that’s the challenge, making sure you have the right data.”
Where Programs Are Won or Lost
Analytics exerts its greatest influence at the beginning. Decisions made early—what to measure, how to measure it, and how to define quality—propagate through the entire lifecycle of a therapy. They shape process development, manufacturing strategy, regulatory positioning, and ultimately risk.
Programs that treat analytics as a late-stage requirement absorb that cost downstream. Programs that build it early move faster and with fewer surprises.
A System Catching Up to Its Science
The rise of analytics reflects a broader shift across the industry. Biotech is moving out of a discovery-first phase and into an execution-driven one. The constraint is no longer generating innovation, it is operationalizing it. That shift touches every stakeholder:
- Developers building increasingly complex products.
- QC and analytical teams responsible for defining them.
- Regulators evaluating whether they are understood well enough to approve.
- And patients, waiting on systems that can deliver consistency at scale.
The Inflection Point
The next phase of gene therapy will not be defined by what can be invented. It will be defined by what can be understood. Andrea Moore’s perspective makes that clear. Analytics is no longer a supporting function. It is the gate.
