BigHat Biosciences raised $19 million in an oversubscribed Series A funding round to integrate a wet lab with machine-learning technologies to guide the search for better antibodies, and as a result, better drug treatments, particularly in areas where no other treatments are available.
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Andreessen Horowitz led the round with participation from prior investors 8VC, 1 AME Cloud Ventures and Innovation Endeavors. BigHat raised a total of $24.3 million in funding since it was founded in 2019. That includes a $5.3 million seed round in 2019, also led by 8VC, according to Crunchbase data.
Biologics is growing rapidly, BigHat co-founder and CEO Mark DePristo, Ph.D., told Crunchbase News. Many of the medications on the market right now are considered biologics. They are typically made of tiny components like cells, tissues, sugars, proteins or DNA and can originate from living sources such as animals, plants and bacteria.
The global biologics market is expected to be valued at $456 billion by 2027. As of 2019, seven of the top 10 drugs are biologics. More than 200 biotherapeutics are used today, generating more than $100 billion in annual revenue for drug companies developing treatments, for example in oncology to auto-immunity to infectious disease, DePristo said.
The San Carlos, California-based protein therapeutics company is developing an antibody design platform, guided by artificial intelligence, aimed at speeding up the design and discovery of potential antibodies to days rather than weeks or months, as well as enabling simultaneous measurement of many molecules at the same time, said Peyton Greenside, Ph.D., co-founder and chief scientific officer at BigHat.
“The result is that the platform can unlock antibody designs currently not possible,” Greenside said. “There is only so much time, but if you can drive it down to days, you can pursue difficult engineering platforms at the same time.”
One of the company’s successful use cases so far is engineering a potent neutralizing SARS-CoV-2 bispecific antibody in the lab, she said.
BigHat intends to put the new capital to work by expanding its scientific and technical teams, including hiring antibody and protein engineers, AI/ML engineers and computational biologists, DePristo said. In addition, the company will continue developing its platform and internal therapeutic programs toward human clinical trials.
“We want to attract world-class talent that will help grow the company significantly to service the opportunity in front of us,” he said. “There are so many interesting applications and real opportunities for therapeutics, and we want to run all of them to the ground over the next two to three years — we don’t want to have to choose.”
As part of the investment, Andreessen Horowitz General Partner Vineeta Agarwala M.D., Ph.D., joins BigHat’s board of directors.
Agarwala has known DePristo and his work on bioinformatics software for over a decade, and was one of the first founders she reached out to when joining Andreessen Horowitz. When learning about BigHat, she felt the vision for a machine learning-enabled biologics discovery platform was aligned with her team’s investing theses.
In an environment where most antibody companies experimentally screen a large number of protein sequences to nominate lead candidates, it typically represents only a tiny fraction of all possible sequences, Agarwala said via email. BigHat is taking an alternative approach.
“BigHat’s platform brings together advanced computational models and a large, ever-growing experimental dataset that is generated within the company; in so doing, they can create tight learning loops which enable them to efficiently traverse the multi-parameter space of protein optimization,” she said. “This shift from screening to engineering will enable us to access therapeutically powerful, yet manufacturable, sequences that may not even exist in nature. We believe this could unlock a whole new wave of productivity in biologics drug development.”
Illustration: Li-Anne Dias
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