help researchers do a new kind of large-scale genetic experiment, where they might, for example, compare 100 genes or more from 100 different patients with diabetes to see how they respond to certain therapies. In the $1 billion market for gene expression instruments, the NanoString technology is made to compete with real-time polymerase chain reaction machines from big players like Carlsbad, CA-based Life Technologies (NASDAQ: [[ticker:LIFE]]), San Diego-based Illumina (NASDAQ: [[ticker:ILMN]]), and Switzerland-based Roche.
NanoString claims it has a competitive edge because it is digital; it doesn’t require complicated sample preparation steps that make other tools difficult to use for all but the most skilled technicians; and it is good at multiplexed experiments, in which a researcher can look at expression of as many as 700 to 900 genes at once, Young says. This kind of capability is becoming increasingly valuable as researchers study complex diseases driven by multiple genetic factors—like cancer, diabetes, or autoimmune diseases. And Young stressed that the NanoString technology is simple enough to be used in hospitals, and possibly even doctors’ offices someday.
NanoString, being privately held, doesn’t disclose details on its financial performance. But the company has only captured “a small percentage” of the potential market among academic researchers who might use a tool like this, Young says. It has even more work to do to grab market share among scientists at Big Pharma companies like Merck or Pfizer, he adds.
“When you’re selling equipment and reagents for researchers, there are always people who will adapt to new technologies quickly and will become your allies, and we have those in the current installed base,” Young says. “The issue is how you get to the next level for the rest of the scientific community? It’s usually one by one, each researcher and institution, working on their specific project. We provide them data to show what the technology can do, to close the sale. I think we’re at that point, and can push through that barrier.”
Winning converts in the scientific community is one of the main goals of 2010, but Young also put a lot of emphasis on the new frontier of clinical diagnostics. This is something close to his core expertise. At Monogram, a company he sold to LabCorp of America last year for $107 million, he oversaw development of a companion diagnostic called Trofile that helped doctors determine whether patients were likely to respond to Pfizer’s maraviroc (Selzentry), for HIV.
Obviously, there are a ton of hard questions to consider if NanoString is serious about getting into the diagnostics business. Should it go it alone, or find a marketing partner? Which particular disease indications should be the top priorities? Who are the right academic collaborators to help gather the proof that this can be valuable, and maybe help evangelize about it among their peers? Who can help navigate the changing regulatory standards? In an era of cost-containment, how will the NanoString diagnostic test prove it isn’t just effective, but that the improvement in patient outcomes justifies the added expense? How to do you find a niche so that you don’t spread a team of 55 employees too thin?
It’s unlikely that NanoString will have a commercially available diagnostic test ready this year, Young says. Instead, tThis year will be about working out the strategy with the new CEO, and the board. But despite all the challenges and risks, Young sounded like the potential reward is worth it.
“The technology base is so profoundly exciting, and frankly, the world is still looking for the ideal technology that can pair drugs and outcomes. That potential is immense,” Young says. “We all know that the world of personalized medicine is the direction we have to go. Just in the area of oncology, for example, we have good targeted drugs now. The problem is, we don’t know how to use them in combinations, and we don’t know which patients we should be treating, and at what stage. In the field of oncology alone, there is unbelievable potential to match patients and drugs much more readily to get better patient outcomes.”
