Jennifer Hayne on the operational realities behind successful development
In biopharma, programs rarely stall because the science doesn’t work. More often, they slow down because something wasn’t fully understood early enough—something that only becomes visible once timelines tighten, partners are involved, and decisions carry real operational weight. The gap between what a company believes it has built and what it can actually explain, transfer, and reproduce tends to surface later, when the cost of uncertainty is highest.
Jennifer Hayne has spent more than two decades working inside that gap. Now Vice President and Head of Biologics Analytical Services at Catalent, her career spans the full arc of development—from the bench as an analytical chemist to manufacturing environments and into the CDMO model, where programs are no longer theoretical but must hold up under execution. That perspective—watching programs evolve, scale, and sometimes break—has shaped how she understands what really determines progress.
“I started my career on the bench as an analytical chemist… I’ve been in this space for over 20 years,” she said. “Analytics is where it’s fun. Things move quick. And when you get to the right data for a customer, it really moves their programs.”
What Successful Programs Get Right
Over time, her view has sharpened into something more direct: the difference between programs that scale and those that stall is rarely about innovation itself. It’s about when clarity is introduced into the system. “Paying attention to your analytics and doing that early… that’s where it starts,” she said. Too often, analytical strategy is treated as something that can be layered in later—after process development begins, or once a CDMO is engaged. But by that point, key decisions have already been made, often without a complete understanding of variability, risk, or what truly defines the product.
What early analytics provides isn’t just data—it provides orientation. It allows teams to see where variability exists, understand what matters, and make decisions with context before those decisions become constraints. Without that early clarity, programs don’t fail outright. They slow down, and that slowdown compounds over time, showing up in longer tech transfer timelines, misalignment between partners, and increased friction in decision-making.
Where Biopharma Programs Actually Break
Hayne sees that breakdown most clearly during tech transfer, where differences in perspective become difficult to ignore. Teams are often deeply invested in what they’ve built, which is both natural and necessary. But that proximity can create blind spots. “They’re very proud of what they’ve built… understandably so,” she said. That pride, however, can lead to what she describes as a “myopic view of the molecule or the process.” When that happens, assumptions start to fill in the gaps—assumptions about how a process behaves, how an assay performs, or what information is already understood.
“When you’re trying to tech transfer something… if you’re too close to it, you’ll make assumptions.” Those assumptions, especially when paired with incomplete documentation or unclear analytical context, don’t stay small. They surface as delays, rework, and misalignment between teams that are now responsible for executing the process under different conditions. In a development model that increasingly spans internal teams, CDMOs, and analytical partners, that friction doesn’t remain isolated—it spreads across the system.
That shift—from centralized development to a distributed, multi-partner model—is one of the defining changes in modern biopharma. Success no longer depends on how well a single team performs, but on how well multiple groups, often operating independently, can align around a shared understanding of the product. In that environment, analytics becomes more than a technical function. It becomes the connective layer that allows that alignment to happen. It defines how the product is described, how it is interpreted, and how decisions are made across the lifecycle.
When that foundation is strong, programs move with confidence. When it’s not, uncertainty shows up later, at the exact moment when there is the least room for it.
The System Has Changed
The industry has spent years pushing the boundaries of what can be built. Today, the challenge is different. It is no longer just about innovation—it is about making that innovation hold under real-world conditions. Hayne’s perspective doesn’t position analytics as a solution in isolation, but as a discipline that shapes how decisions are made from the beginning.
Because the programs that ultimately scale aren’t just the ones with the most promising science. They are the ones that understand their own systems early enough to move forward without hesitation. And in modern biopharma, that understanding doesn’t emerge at the end of development. It starts at the beginning—and carries everything that follows.
