Investors have poured almost $1 billion into startups using data to upend the expensive and slow process of finding new drugs — but an uphill battle awaits


Clinical trial lab research drug medicine

  • Developing an innovative new medicine is a long and expensive slog. 
  • Upending that process is the goal of a wave of new startups. Investors have put just under $1 billion into the area over the last five years, according to an analysis done by PitchBook for Business Insider. 
  • These new companies are putting data and technology at the center of their approaches, but the sorry state of the two in healthcare could undermine the whole enterprise.
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Making drugs is a notoriously slow and costly proposition.

A wave of new startups is eager to change that. And the space has attracted big money to match its big ambitions.

Startup investors have plowed nearly $1 billion into these new companies over the last five years, according to an analysis done by the private capital-market data provider PitchBook for Business Insider. The size of those private investments has increased over the years, the data show.

The new companies are pitching technological and data-driven approaches to drug development. The startups aren’t actually developing drugs in a lab. Instead, they’re using technology to make the drug-development process more efficient by helping pharma companies do a better job of figuring out which treatments to pursue and how to test them on people in clinical trials.

There are so many of these new companies that drugmakers are being “bombarded,” said Aaron Mitchell, a principal who runs the consulting firm ZS Associates’ research and clinical practice area.

But these disruptive entrants also face challenges, including the limits of their own scale and horsepower.

“One client said, ‘We spent $2 million on one of these programs and we netted one patient,'” Mitchell said. “In the end, you can have all this great thinking; this beautiful, elegant, sophisticated approach…but it fails because the people can’t make it happen in the field.” 

Read more: Pfizer has a new strategy for fighting cancer that could generate $5 billion a year. We got a look inside.

Investors have poured nearly $1 billion into clinical trials startups

Make a new drug — but better

A new drug takes about 10 years to develop, longer than it takes to start a successful company from scratch or walk 25,000 miles around the world.

It’s also expensive. By the pharmaceutical industry’s estimate, the cost is $2.6 billion on average. (That figure has been criticized for being self-serving. Another estimate focused on cancer drugs found it was closer to $650 million.)

The US Food and Drug Administration has called for a more updated clinical trials process, and even criticized drug and research companies broadly for their reluctance to do so.

Finding patients is one common hurdle that extends the drug development timeframe and raises the cost. Drugmakers need to test their treatments on people to make sure that they work, and that they’re safe to use, before they can get approval from the FDA to offer them broadly.

Startups, like Cambridge, Massachusetts-based TriNetX, have focused making the process more efficient. That might mean, for example, bringing in “real-world data” from medical systems to show that there are enough patients with a particular type of disease to do research in the first place. TriNetX is valued at $160 million, according to PitchBook.

Others, like New York-based Antidote, aim to better match patients who are a good fit with a trial. That’s become an increasingly complex task in areas like cancer, where a treatment can be intended for a specific genetic mutation.

Some clinical trials companies do away with bringing patients into a hospital to try out a drug, instead letting them do so from their homes. That’s the model of Los Angeles, California-based Science 37, which licenses out its telemedicine-powered tech platform and has raised $204 million so far to do it.

TrialSpark, also based in New York and one of the biggest private companies in the space, with a $610 million valuation, takes a combination approach. TrialSpark partners with doctors to find the “ideal” patients for trials, using data from across the country, and also offers tech to help manage the studies.

Late last year, it announced a partnership with the Swiss drug giant Novartis that “brings the research study to the patient,” allowing participation from a local doctor’s office, rather than requiring travel to a big academic research center or hospital. 

The top 6 deals in clinical trials startups over the last five years

These new upstarts aren’t just competing with each other — they are also facing down well-established, big-name and often publicly-traded rivals.

Drug companies often work with “contract research organizations” like the health information technology giant IQVIA, which has a nearly $30 billion market value and says it “overcome the root causes of trial inefficiencies that have plagued the industry for many years.”

Another big name in healthcare, the lab testing company LabCorp, bought a clinical research company in 2017 for $1.2 billion.

Challenges ahead 

ZS’s Mitchell jokes that with the buzzwords “real-world data, machine learning and digital health, you combine those three things together and you get a $100 million valuation.”

Read: Novartis is betting that AI is the ‘next great tool’ for finding new, cutting-edge medicines. Here’s how the $220 billion drug giant is using it.

But when it comes to the US and other countries’ health systems, both data and technology are sadly behind.

Technology from the 1990s dominates today, from CD-ROMs to faxes and Microsoft Word documents, said Dr. Sam Volchenboum, a UChicago Medicine physician who specializes in pediatric cancer and often runs clinical trial research.

That’s why Volchenboum co-founded Litmus Health, a platform that collects data from sensors and wearables for use in clinical trials. A research trial that Litmus ran was funded by the drug company Takeda and used data from Fitbit wearables.

Without a standard way of storing or sharing data across institutions and drug companies, advanced approaches using machine learning are “a little bit putting the cart before the horse,” Volchenboum told Business Insider.

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