Surface plasmon resonance (SPR) offers researchers a powerful way to observe molecular interactions directly, measuring binding events with exceptional sensitivity and without fluorescent or chemical labels. Despite its strengths, the technique has typically been reserved for downstream validation in drug discovery because conventional SPR systems can process only a limited number of samples at a time.
Tim Germann
Chief Commercial Officer
Carterra
In this Innovation Spotlight, Tim Germann, the chief commercial officer of Carterra, introduces the new Vega platform, which expands the reach of SPR by dramatically increasing throughput. Vega enables researchers to analyze tens of thousands of compounds per day and assess both target and off-target interactions, bringing SPR into primary screening and earlier discovery workflows.
What role has SPR played in drug discovery traditionally, and how is that changing?
SPR is an amazingly sensitive and elegant technique. At its core it’s essentially a sophisticated scale—it measures mass at the molecular level. It allows researchers to observe, in real time and without labels, how molecules interact: how quickly they bind, how tightly they bind, and how they separate.
Historically, SPR has been used downstream in drug discovery. That wasn’t because it was scientifically preferable, it was because the tools that used SPR were slow and low throughput. About 20 years ago at the University of Utah, David Myszka, one of the pioneers of SPR, approached the engineering department where Carterra’s technical founder Josh Eckman was a graduate student. Myszka said, “I’m using this technique called SPR, but it’s slow and low throughput. Can we make it faster?” That conversation became the origin of Carterra.
The goal was to improve SPR so that it could be used earlier in the drug discovery process. How much throughput could we achieve, and how much detail could we provide earlier so that scientists could make better decisions sooner? For the past two decades—and especially the last eight years commercially—our mission has been to move SPR upstream to the earliest stage of discovery. With our newest product, Vega, we’re now bringing it into primary screening, which wasn’t possible before.
How are researchers reacting to the possibility of collecting SPR data earlier in the discovery process?
This question reminds me of a conversation I had recently with a researcher who manages large small-molecule libraries at a major pharmaceutical company. When they learned what Vega can do, they said, “I’ve been sending only small numbers of compounds from early screening to the analytical team for SPR analysis. I know we’re missing things. I know we should be looking at more data earlier in the process.”
With Vega, they realized that they could generate SPR data on entire compound libraries at the beginning of the workflow. Their reaction was essentially, “I’m going to tell everyone in the company we should be doing this.”
So, once scientists understand this capability exists, they immediately want to use it. It’s not about convincing people—it’s about enabling something they’ve wanted to do all along.
What are some of the key features of the new Vega platform, and how might they change traditional drug discovery strategies?
Most SPR devices used for small-molecule discovery today have eight channels. That means they can analyze eight compounds at a time against a single protein target. Vega has 48 channels, which results in six times the throughput. Each channel can also contain both the primary target and a potential off-target protein. That means researchers can evaluate specificity at the same time.
Vega is roughly 12 times faster than the best existing systems in our space while shifting SPR analysis upstream in the drug discovery process. The instrument also integrates a robotic system that manipulates plates of small molecules. The physical throughput limit for Vega is about 40,000 compounds per day, compared with roughly 2,000 to 3,000 per day for current technologies.
So, with 48 channels, target and off-target analysis in the same run, and integrated robotics, Vega dramatically expands what’s possible. There are two dimensions of value here. You can shorten early discovery, potentially saving tens of millions of dollars and weeks or months of time. But just as important, you generate better information earlier, which helps researchers decide which candidates to move forward. The biggest mistake in drug development isn’t spending too long in early discovery; it’s letting something reach the clinic and fail. That’s a billion-dollar mistake. If you can avoid that by making better decisions earlier, that’s enormously valuable.
By generating large-scale binding data from tens of thousands of compounds per day, the Vega platform provides the experimental datasets needed to train and refine AI-driven drug discovery models.
©iStock, phive2015
How could Vega impact AI-based drug discovery?
That’s a very timely question. When we launched our earlier platforms, which are primarily used in biologics discovery, AI drug discovery wasn’t even part of the conversation. But over the past several years, as pharmaceutical companies began designing drugs in silico, we saw a new challenge emerge. Researchers can quickly generate huge numbers of candidate molecules computationally. What they don’t have is enough biological data to train the algorithms. Older SPR technologies simply cannot generate data at the scale required. If you’re analyzing two or eight samples at a time, that’s not going to move the needle for machine learning.
Vega is going to allow scientists to look at 40,000 compounds a day and gain full binding data. Feeding that data back into AI models is what will ultimately make those models better.
What has been the reception to Vega among early adopters?
We worked closely with three large pharmaceutical companies and one major CRO before the launch. They served as key opinion leaders (KOLs) and beta testers for the instrument. Their feedback was, “We’re buying one of those.” One of the KOLs—among the top 10 pharma companies globally—has already placed an order. Right before launch, a group contacted us asking whether we had solutions for large-scale small-molecule screening. We gave them an early look at Vega, and they’ve already bought as well. The amount of interest that we've seen is really quite stunning.
Looking ahead, how do you think tools like Vega could shape the future of drug discovery?
A typical diagram of the drug discovery pipeline shows a series of chevrons: primary screening through early discovery and the clinical phase until you come out with a drug. What is rarely shown is those chevrons shrinking. Vega effectively compresses several of those early steps. It takes primary screening, hit optimization, and specificity testing and puts them into one assay at the very beginning, shrinking early discovery. That cuts out tens of millions of dollars in costs and probably months to a year or more in time. It also compresses the part of the drug survey process, where decisions are made as to what molecule should be taken downstream.
We couldn’t be more excited. We see this being deployed in every company that develops small molecules, allowing them to go faster and make better decisions. Whether researchers are using traditional or AI-driven workflows, they can get the data from Vega.
