‘Intersection of innovation’: Why top tech VC firms are betting big on life science startups

Machine learning, data science and other computational methods are wielded by a growing number of biopharma startups to find new drug targets, design new therapeutics, and support their testing.

Venture capitalists — including those that haven’t touched the sciences previously — are paying attention.

As more biotech companies move into realms that once belonged to tech, traditional tech venture capital firms are also turning to life sciences, including Silicon Valley heavyweight Andreessen Horowitz (a16z) and Seattle’s Madrona Venture Group.

New technologies such as artificial intelligence are changing the way drugs are discovered and developed, and more biotech startups have adopted an engineering mindset. Investors who may have seen life sciences as too risky in the past are now sensing opportunity.

Tech connections

Madrona’s first investments into what it calls the “intersection of innovation” began through its tech connections.

“The common threads are people that we had worked with before who were also getting passionate about life sciences and healthcare,” said Matt McIlwain, Madrona managing director.

Sujal Patel, for instance, crossed into life sciences after founding Madrona-backed data storage company Isilon Systems, which sold to EMC for $2.25 billion more than a decade ago. In 2016 he co-founded protein analysis company Nautilus Biotechnology, and Madrona crossed over with him, investing in the startup before it went public last year in a $345 million SPAC merger.

Patel is among a growing cadre of biotech and life sciences execs who made the leap from software startups.

For example: Terry Myerson, a former top Microsoft exec, is now leading Truveta, a Seattle startup that aims to aggregate medical records data.

Insitro founder Daphne Koller was co-founder of Coursera and a computer science professor at Stanford. Now she’s leading Insitro, a giant drug discovery and development startup backed by a16z.

“I’m seeing more and more people from tech moving into these types of companies,” said Pande. “They see the opportunities, and also, frankly, they’re driven by the mission, by the desire to have a huge impact on human health.”

McIlwain also has a personal passion for the life sciences. Both of his parents are cancer survivors, and he is a board member and previous board chair of Fred Hutchinson Cancer Research Center.

Madrona has strengthened its life sciences team and its links to Seattle-area scientists. Madrona-backed companies include A-Alpha Bio, spun out of the University of Washington’s Institute for Protein Design, Fred Hutch spinout Ozette, and Modulus Therapeutics, a cell therapy spinout of the Allen Institute for Artificial Intelligence.

Sensing opportunity

Marc Andreessen, co-founder of Netscape who helped start a16z, was historically not a fan of life sciences investment. Andreessen had said the firm “’would not do bio, would not do life sciences, would not do healthcare,’” recalled Vijay Pande, a former Stanford biophysics professor who oversees a16z’s Bio funds.

“A lot of life sciences was dominated by binary risk, where something would work or not. It’s almost like a sort of scientific lottery. And healthcare was dominated by services — so low margins,” said Pande. “Both of these things are really against the tech ethos.”

But in 2015, the venture firm brought on Pande and launched its first Bio Fund with $200 million. More life sciences startups were embracing a tech and engineering mindset, offering the possibility of faster returns. The emergence of more platform-based startups that contract with larger pharma companies also had appeal, said Pande.

This January, the company raised $1.5 billion for its fourth Bio Fund. The firm has invested in a bevy of life sciences companies such as Insitro and Nautilus, as well as health tech companies, where AI has the potential to reduce the cost of providing services.

Investors are also paying close attention to the Seattle area, with its strong life science institutions and powerhouse tech companies, as a prime region for startups at the confluence. “Seattle checks both boxes in ways that not many other places do,” said Pande.

De-risking biotech

Biopharma companies traditionally face a slow slog to clinical trials and a plod through their end. A lot of treatments simply don’t make it.

“That world seemed kind of scary to us, to be honest,” said McIlwain.

Madrona began to notice the emergence of more platform-oriented companies with new ways to collect biological data and analyze it. “That felt more in our kind of wheelhouse,” said McIlwain.

Drug discovery is a “barbell challenge,” said Pande. One side of the barbell is earlier-stage research and the other side is clinical trials. Both sides are expensive.

“The middle part actually is where traditional pharma has done pretty well. Given a target, they can come up with something [a testable drug] reasonably fast,” said Pande. New technology such as AI can help in the middle. But where it really has the most potential is at the big barbell at the beginning, he said.

By identifying better drug targets up front, AI has the potential to bring better therapeutic candidates to clinic and eliminate the waste of failed clinical trials, said Pande.

Human biology is increasingly understood as a collection of rich datasets of genes, proteins, cells and their interactions.

With deeper understanding, “there can be this shift from science to engineering, from discovery to design,” said Pande. “I think that’s the aspect that’s getting people very excited.”

Pande added: “People make drugs all the time. The question is, are they changing how drugs are made?”

Since 2014, investors have poured $5.1 billion into 103 venture rounds for AI and machine learning biotech startups — and $3.3 billion came last year alone, reported Endpoints.

More than a new tech step

AI is not just the next step in a biotech methods march, akin to milestones such as gene editing and next generation sequencing, said Pande. AI is fostering a larger shift in mindset from discovery to design.

“That is not just having more tools in artisanal process,” said Pande. “That’s actually changing the nature of the process. That’s actually industrializing, making this much more like a factory than an artisanal activity.”

“People make drugs all the time. The question is, are they changing how drugs are made?”

As drug companies emphasize synthetic biology and AI-driven drug design, they are looking increasingly like engineering firms, he said.

“That shift is a huge culture clash,” he added.

Bringing biologists and tech experts together to effectively collaborate is a challenge, Insitro’s Koller previously told GeekWire.

Meanwhile, more biologists are being trained with dual expertise. “I think they’re the ones that will really make the biggest contributions,” said Pande.

Tech-ing up biotech

Life sciences companies operate with more caution than tech companies in part because of their more constrained regulatory environment as well as the need for a stronger ethical focus, said McIlwain. They also need to pay more attention to intellectual property. But there’s room for change.

“There are certain circumstances where you can bring more of the agile, iterative, continuous learning mindset that lives in the software and technology world,” said McIlwain.

Pande sees some of that change starting to happen as scientists better understand biology.

“In tech or any other engineering discipline, if something doesn’t work, you can iterate and improve. And that’s actually what we’re starting to see in engineering applied in drug design as well,” said Pande.

Tech, health tech and life sciences companies are increasingly finding overlap. They are “moving to the center,” said Pande. According to a recent report from CB Insights, digital health startups pulled in a record $57.2 billion in funding last year, up 79% from 2020.

It’s still early days

Companies working at the convergence between life sciences and tech are young and their first treatments have only recently entered clinical trials. Their approaches may seem opaque and secretive to outside scientists. Is the promise of AI-driven discovery overhyped?

“I think it’s still early,” said Pande. “Think about the sort of 20-year arc of the internet from maybe 1995 to 2015 or so. There were early days when there were skeptics that said ‘It’s ridiculous, you’ll never buy dog food on the internet, I can give you like 10 reasons why that’s impossible.’”

Pande estimates that the arc is now about five years in, at a stage where a lot of companies are sprouting up, before the field consolidates and dominant players emerge.

McIlwain has a similar view. While some of Madrona’s early investments in another area, AR/VR, sputtered, VR startup Rec Room reached a $3.5 billion valuation last December.

“It’s so important for us to have a prepared mind, to be thoughtful about emerging categories like this intersection of innovation theme,” said McIlwain. “‘Day one for the long run’ is our strategy, so we’re going to be too early. That is who we are. That’s what we do.” Sometimes that means that a few companies won’t succeed, said McIlwain — but others will.