New immunotherapy approaches to cancer treatment, and the CRISPR technology for editing genetic material in cells, are among the most sophisticated research strategies scientists are using in their quest to defeat disease.
But as researchers develop these techniques based on manipulating biological molecules, they may still be recording their experimental results by a method untouched by technological advances—–in a paper lab notebook.
That’s the anomaly Sajith Wickramasekara was trying to correct in 2012 when he and fellow software engineer Ashu Singhal co-founded the online lab notebook startup Benchling, where he is CEO. Existing data management systems weren’t tailored for work with biologically active molecules such as DNA, enzymes, plasmids, and complex proteins. Since 2012, biological drugs have gained ground rapidly over traditional small-molecule compounds as drug candidates in pharmaceutical company pipelines, and Benchling says it has developed a suite of online tools specialized to capture what researchers are learning about them.
San Francisco-based Benchling announced today it secured $14.5 million in a Series B fundraising round led by new investor Benchmark, joined by another newcomer, F-Prime Capital, and previous investor Thrive Capital. Benchmark general partner Eric Vishria is joining Benchling’s board.
The new money brings the company’s fundraising total to $27.4 million, raised from backers that also include Andreessen Horowitz and Y Combinator, the Mountain View, CA-based accelerator program where Benchling participated in the summer 2012 session.
Benchling offers its research data management system free to all academic laboratories. Researchers at more than 1,000 academic institutions—including Harvard, MIT, U.C. Berkeley, and Stanford—are part of its user base of more than 100,000 scientists from universities as well as businesses, the company says. With its new capital, Benchling plans to increase its team of about 50 employees, speed up product development, and attract more university-based users as well as more paying customers from the ranks of biotech companies and pharmaceutical firms. Startups can access Benchling’s core features for $15,000 a year, while larger businesses can pay more for a fuller set of tools.
Current subscribers to Benchling’s software-as-a-service include Regeneron Pharmaceuticals (NASDAQ: REGN), Incyte (NASDAQ: INCY), Editas Medicine (NASDAQ: EDIT), Agenus (NASDAQ: AGEN), Zymergen, and Obsidian Therapeutics. More than 80 biotechnology and pharmaceutical companies, including top 50 pharma companies, pay to use the software, Wickramasekara says.
Benchling’s core features include a lab notebook for recording experimental results and sharing the data among research team members and supervisors; a Molecular Biology Suite where scientists can design biological molecules such as CRISPR gene-editing elements; and a bioregistry that allows scientists to register and track biological molecules across multiple experiments. Other features help researchers manage their workflows, track their inventories of biological samples, and create models of the structure and properties of large biomolecules.
The design of the software is elastic enough to allow researchers to tuck “free-form annotations,” such as off-the-cuff observations, into their entries as they put experimental data into the structured fields of the software, Wickramasekara says.
Benchling has also tried to address a major pain point observed by its founding team, a bunch of software engineers determined to help friends who were then “beleaguered” PhD candidates in molecular biology, Wickramasekara says. The big pain for medical researchers: At some point, all the details of a research project have to be written up in a dissertation, a grant proposal, a paper submitted to a scientific publication, or an investigational new drug application to the FDA. Scientists with inadequate record-keeping tools have often had to reconstruct the history of experiments that led to a particular drug candidate, Wickramasekara says.
Benchling assembles that history of assays and results from the structured data entered in the software fields over time, making report-writing more efficient, and the evaluation of drug candidates more informed, Wickramasekara says.
“Until now, scientists have had to base decisions on, at best, partial information—and they’ve had to spend ridiculous amounts of time piecing together that information,” Wickramasekara says. “Even at the biggest pharma companies, a lot of pivotal decisions come down to data that’s simply incomplete.”
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