As biotech and informatics converge, how are VCs adapting?

It’s fair to say we’ve entered a new era in biotech and pharma. Whether it’s diagnostics, drug development or medtech; informatics, mathematics and machine learning are marching their way in. Biology is being digitized. Big Data is taking population-wide information and distilling it down for treatment plans at an individualized scale.

The science within startups is evolving.

So how do venture capitalists feel about the convergence? How do they apply their knowledge and experience in this new world of healthcare? Have the capital requirements and risk equations changed?

As a dual technology and healthcare venture capital firm, Polaris Partners is in a good position to track the industry shift. Via phone, Amir Nashat, a managing partner in the firm’s Boston office, shared his thoughts on a convergence that he believes goes back at least 20 years.

“You start to see it happening in a genomics era,” Nashat said, pointing to a former portfolio company deCODE Genetics, which was founded in 1996. 

Based in Iceland, deCODE amassed a library of medical and hereditary information from the local population. By matching those to early genomics techniques, the company sought to uncover new genetic variants.

That’s a classic informatics problem, Nashat said. They were trying to understand population health from a Big Data perspective. In the past decade, that field has leaped forward alongside the quest for personalized medicine. 

For Polaris, the impact of convergence in diagnostics arrived in the last five years, through companies such as Freenome. Based in San Francisco, Freenome uses machine learning to identify biological signatures that could detect early-stage cancers.

It’s a new world, with vast possibilities and novel challenges. As the firm scopes out these investments, how does the Big Data and modeling component influence the risks and rewards?

Smart software and analytics do have the potential to derisk a lot of the development path, Nashat. With more data and information, some of the guesswork is removed.

On the other the hand, there’s a risk that the data you’re collecting doesn’t have a correlation to biology or disease. The same is true for therapeutics, above and beyond the usual concerns about toxicity and unforeseen effects.

For digital health and health IT companies, there are risks with customer adoption, he explained. Startups in the space need to anticipate the behaviors and patterns of the end-user, to ensure their product integrates into existing workflows and ways of doing business.

What then? Once the seed money is raised, startups at the intersection of biology and informatics have fewer options when it comes to setting up camp.

Nashat said shifts have occurred in both biotech and tech, which have streamlined the early logistics in those fields.

Polaris’ tech portfolio has become much more efficient as technology moved into the cloud. In biotech, the growth of specialized contract research organizations (CROs) has removed the need to build everything from scratch.

“Fifteen years ago, when we were starting biotech companies, you really had to do everything in-house,” Nashat said.

That meant finding the talent, purchasing the equipment and setting up the systems. In 2017, the expertise and services are just a phone call away. In his experience, the CROs deliver high-quality products and they’re also smart when it comes to customer experience. 

“They know how to respond to the needs of young biotechs, in terms of speed and costs,” he said. “So it has been much less of a capital-hungry sport to get these companies off the ground.”

For bioinformatics, the services landscape isn’t there yet. Experts need to be recruited from other companies or academia.

“I’m hopeful that in five or six years that a lot of that capability will be available in third-party settings. Again, it will just be faster.”

It’s not a simple transition from tech, Nashat pointed out. The software experts need to understand the fundamental architecture of biological systems.

“There’s not a lot of companies looking for them, but there’s not a lot of people either,” Nashat said. “So the supply and demand curve is tight.”

There are a few important pockets of talent around schools and centers that educate and train bioinformaticians. They’re critical for recruiting, he said.

And therein lies an interesting geographic trend.

“I think that this could forge a certain democratization,” Nashat said.

Tech was very localized, but as the technology moved to the clouds the intellectual hubs became decentralized. In the life sciences, San Francisco, Boston, San Diego and a handful of other centers dominate the industry. Could that all change with the convergence of informatics?

“You already have this extreme clustering phenomenon. You could imagine that the ability to move from quote-unquote hardware, or wetware I guess, to more informatic software, may actually allow people to build companies much farther afield.”

If that did play out, it would be one more drop in the ocean of change driven by informatics. 

“It will be interesting to see what the impact is for sure,” Nashat said.

Photo: REB Images, Getty Images