As drug pipelines become more complex, many sponsor companies are finding that the biggest challenge isn’t just the science—it’s managing everything around it.

From analytical testing and development to manufacturing and data, the number of moving parts continues to grow. And for many organizations, keeping all of that aligned is becoming harder than the science itself.

During DCAT Week, held March 23-26 in New York, Contract Pharma sat down with Agilent Technologies’ Whitney Herd, vice president of business development, sales and marketing for advanced manufacturing partnerships, and Andrew Mitchell, associate vice president of business development for Advanced Therapeutics, to talk through where those pressures are showing up—and how the company is trying to simplify an increasingly complicated process.

From Equipment Supplier to Something Broader

Agilent has traditionally been known for its analytical instruments, but that’s only part of the picture now.

With the acquisition of BioVectra and a growing focus on advanced therapeutics, the company is positioning itself differently—less as a point solution, and more as a partner that can support multiple stages of development and manufacturing.

“The goal is simplicity,” Herd said. “Sponsors don’t want to manage a dozen different vendors across development and manufacturing. The more you can bring together under one umbrella, the easier it is to move programs forward.”

That message is landing at a time when many sponsor companies are rethinking how many partners they want to manage—and where integration actually adds value.

Agilent’s global footprint, with operations in more than 110 countries, gives it scale. But as Mitchell pointed out, scale alone isn’t the story.

“You still have the core of Agilent—analytical instruments, lab systems, data platforms,” he said. “But now you combine that with development and manufacturing capabilities through BioVectra. That’s where it starts to connect.”

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Where Demand is Actually Showing Up

One of the more interesting takeaways from the conversation was where demand is coming from right now.

“A lot is happening in early development—preclinical through Phase II,” Mitchell said. “That’s where companies are trying to de-risk programs as early as possible.”

That early-stage push makes sense given how expensive it’s become to advance programs too far without solid data to back them up.

But the need doesn’t taper off—it just changes.

“Once you get into Phase II and beyond, the conversation shifts,” Herd said. “Now it’s about scale, regulatory readiness, and making sure everything holds up as you move toward commercialization.”

That creates a split pressure: move quickly upfront, but be fully prepared for scrutiny later.

Complexity is Catching Up to Companies

As pipelines evolve, especially with advanced modalities, analytical requirements are getting more demanding.

Oligonucleotides were one area both executives pointed to as seeing strong growth, but the broader issue goes beyond any single modality.

“Companies are realizing that what worked for small molecules doesn’t always translate,” Mitchell said. “You need more sophisticated methods and a deeper understanding of the product.”

In many cases, the challenge isn’t just capability—it’s readiness. Companies are getting into programs where analytical demands ramp up quickly, and they’re not always set up to handle them.

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The Ongoing Tradeoff: Speed vs. Data

Speed is still the expectation. That hasn’t changed.

If anything, the pace set during COVID—what Mitchell described as a lingering “Warp Speed mindset”—has stuck around.

But the reality is that speed can only go so far.

“You can’t compromise on data integrity,” Herd said. “That’s what everything is built on—regulatory approval, product quality, patient safety.”

Where things start to break down is in execution.

“Data integrity and data readiness are still major challenges,” Mitchell added. “It’s not just generating data—it’s making sure it’s usable and connected.”

That’s where many of the current bottlenecks are showing up—not necessarily in science, but in how information is captured, managed, and shared.

The Digital Lab Still Has Gaps

There’s no shortage of talk about digital labs and fully connected systems. But in practice, most organizations aren’t there yet.

“There’s still a lot of fragmentation,” Mitchell said. “Data sits in different places, different systems, and it’s not always easy to pull it together.”

Tools like LIMS are part of the solution, but implementation—and adoption—are still works in progress.

“Technology alone doesn’t solve it,” Herd said. “You need the processes and the mindset to go with it.”

That’s a theme that comes up often: the tools exist, but the challenge is getting everything—and everyone—aligned.

AI: Potential, But Not Plug-and-Play

AI continues to come up in conversations around the lab, but both executives were realistic about where things stand.

“AI will play a role, especially in how we use and interpret data,” Mitchell said. “But it’s not something you just drop in, and everything changes overnight.”

The bigger issue is the foundation underneath it.

“You need clean, connected data first,” Herd said. “Otherwise, AI doesn’t have much to work with.”

For now, the focus is less on flashy applications and more on building the infrastructure that will support them.

Investment and Capacity Pressures

Behind all of this is a more basic constraint: capacity.

Agilent’s ongoing $725 million expansion reflects both current demand and the company’s view of the market’s trajectory—particularly in advanced therapeutics.

“There’s still pressure in certain areas,” Mitchell said. “And as pipelines get more complex, that pressure shifts rather than disappears.”

That’s especially true in areas like oligonucleotides, where demand is strong, but capacity and expertise are still catching up.

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Different Expectations, Same Challenges

The conversation also touched on how expectations differ between large pharma companies and smaller biotechs.

“Biotechs are often focused on speed—they’re trying to hit milestones and move programs forward quickly,” Herd said. “Large pharma tends to look more at scalability and long-term reliability.”

Those differences shape how partnerships are structured—and what each side expects from them.

Simplifying What’s Getting More Complicated

If there was one theme that kept coming up, it was simplification.

Not in the sense of making the science easier—but in reducing the friction around it.

As development becomes more complex, the ability to streamline everything around it—partners, data, workflows—is becoming more valuable.

That’s where Agilent is trying to position itself: not just as a provider of tools, but as a way to make the overall process more manageable.

And in today’s environment, that may be just as important as any single capability.