This article originally appeared in Forbes.
These days, biotech R&D is as much a data problem as a science problem.
Here’s why: in the past decade, the exploding field of synthetic biology has done an incredible job solving the scientific challenges of making biology easier to engineer. I have written about how tools like gene editing, synthesis, sequencing, and automation are changing for the better the way we feed, fuel, heal, and build our world with biology.
But these new techniques have created a new challenge that the life science industry wasn’t ready for: how to get massive amounts of super complex data into a single place where it’s all interlinked and can be fully made into real insights.
“Data science and software engineering problems are now front and center for the life sciences industry,” says Sajith Wickramasekara, CEO and co-founder of Benchling, a San Francisco-based start-up focused on this very problem. “My vision is that once the industry is digitized, you’re going to unlock a lot of potential for breakthrough science.”
He’s thinking about new medicines, new kinds of food at the grocery store, new performance clothing from cell-based fabrics — a new generation of chemicals and materials made using high-tech fermentation, the process that gives us beer, bread, and cocoa.
“What I’m really excited about are all the applications we haven’t even thought of yet,” he says.
But first, life science’s big data problem.
The new digital natives of biotech
Nowadays, biotech startups are founded with data science in mind. Data science is a greater part of their business and their research infrastructure is oriented around how to capture and learn from the large amounts of data available to them. They aren’t bound to legacy data systems, so in some sense they have an advantage over established companies because, well, it’s easier to start from scratch.
“If you look at a company like Zymergen, they built their infrastructure from day one to leverage all the great advancements in computer science and machine learning from other industries,” says Wickramasekara.
Obviously, startups have different constraints. “There are fewer resources and more existential questions over the long run,” Wickramasekara says. In general, though, he believes that they are very well poised to take advantage of the tools and technologies of synthetic biology to innovate and produce at unprecedented speed.
And make no mistake: enterprise companies are learning from their younger rivals. Not only are these large companies embracing the same tools, but many of these small innovators will become parts of bigger organizations down the road. “There’s a lot of M&A in life sciences, and so I think you’ll see a lot of larger, more established pharma companies using their balance sheets to acquire some of the new biotechs and bring that expertise in-house.”
One person with a laptop = a biotech startup?
Wickramasekara jokes that anyone with a laptop and a Red Bull can come to the conference room and prototype their biotech idea. But he’s serious about the power of the cloud to give innovators the ability to tap into powerful on-demand tools and infrastructure.
“Our goal should be to democratize and decentralize the creation and running of a biotech so that geography and capital to build out a lab doesn’t limit innovation,” he says.
Gone are the days when you need to raise millions of dollars of venture capital to stand up data centers before you can even get a product to market and begin testing it. Today, small teams of innovators may not even be in the same place, or depend on on-demand specialized expertise from far places. In this environment, researchers depend on a suite of cloud applications to design DNA, collaborate on experiments, manage research workflows, and make critical R&D decisions.
He believes that computational advances in biology — paired with third-party contract research organizations (CROs) and entities like Amazon Web Services and Google Cloud — may enable you or me to conceive of and run a small biotechnology company from our laptop within five years.
“Biotech still has work to do to get to that point where it’s easy to access all the CROs in the world with great tools to collaborate and work together. It’s science and it’s going to cost money. But does every organization need to build a big wet lab? Is it all going to be in the Bay Area, Boston, San Diego? Probably not.”
Scaling an idea
A growing number of large and small companies seem to agree with Benchling’s approach. Benchling announced today that it added 150 new clients to its roster in 2019 — more than doubling its customer base for the second consecutive year — with a significant expansion of enterprise customers. Biopharma was its largest and fastest-growing customer sector, but it also saw strong initial deployments in a range of new industries, including biomaterials, energy, consumer goods, and food and beverage.
Earlier this year, Benchling closed $34.5 million in Series C funding to extend its product lead and expand commercial relationships and opened its Cambridge, MA, office in April.
Since launching in 2012, Benchling says it has grown to become the most widely adopted life science R&D cloud software, used by over 170,000 scientists worldwide. What’s most impressive about its customer base is its diversity: from start-ups to multinationals, in every sector imaginable, including many of synthetic biology’s best-known names — Zymergen, Synlogic, Regeneron, and Intellia, to name a few.
Benchling’s modular toolkit begins with a free, lightweight version that is commonly used by academics to collaborate across university boundaries, for example. It gives researchers a digital notebook and a registry to store their designs. One step further, companies and academics use Benchling’s basic toolkit for DNA and protein design tools.
At the far end of the spectrum, you have very large pharmaceutical organizations with hundreds of scientists collaborating on projects of tremendous scale and complexity. These large companies can take advantage of Benchling’s tools to integrate analytics, workflow design, dashboards, and more. In a nutshell, Benchling architected a solution that scales with customers’ needs.
Wickramasekara explains, “If we’re going to transform the way our industry works, we think it’s important to work with all organizations, whether it’s two scientists working on a single molecule, or a thousand scientists working across dozens of projects.”
From research to development to biomanufacturing
Benchling has been laser-focused on the research aspect of life sciences. But as synthetic biology has drastically shortened the time to proof-of-concept, Wickramasekara and his team are looking more and more at product development.
“If you look at the newer cutting-edge modalities like cell and gene therapies, we have more and more of our customers at the point in their journey where they are starting clinical trials,” he says. “There’s a lot of work involved in analyzing the samples coming back from the clinic — process development work to scale up the production and then efficiently and reliably make these medicines. It turns out to be a logical extension of what Benchling does, so we have more customers leveraging us in that capacity. I suspect in the near future it is going to extend all the way to biomanufacturing as well.”
Wickramasekara acknowledges that the possibilities around therapeutics are “definitely hyped up,” but maintains that the hype is probably well earned.
“It’s miraculous to think that, within the last decade, we’ve gone from just a handful of cell therapy drug trials to thousands of cell therapy trials going on in different combinations.” Even personalized medicine is “no longer a fantasy… you have folks with late-stage terminal cancer where nothing is working, and then all of the sudden you see these miraculous effects. I find that incredibly motivating.”
Even before we get to miracle drug therapies, Wickramasekara thinks the food industry may be the first way consumers experience the benefits of new biomanufacturing. “You can already go to the store and try some of these new biologically made future foods,” he says. (Memphis Meats and Impossible Foods are just two examples.)
2020 and beyond
Wickramasekara says that 2020 will be another exciting year for the life sciences. As companies like his work to make us all more data-driven, you might agree.
Beginning with high-quality, structured data, there are all sorts of things you can do that you could never do before. Importantly, we will be able to leverage advances in artificial intelligence, opening up new ways to streamline work and completely new insights about biology itself.
There’s talk about this being the century of biology, but it’s really about bio and computing — where tech meets bio, and bio meets tech. Once companies have high-quality data all in one place, it’s time to set sail into uncharted waters to see what we can do.