How did Benchling’s story begin, and where are you now in terms of footprint?
Benchling was founded in 2012, to help unlock the power of biotechnology and with the mission to create modern software for modern science. Benchling is a critical source of truth that enables scientists to collaborate and gain access to data-driven insights on a single unified platform.
To this day, our R&D cloud is used by over 1,000 biotech customers across biopharma, agriculture, consumer packaged goods, industrial goods, and diagnostics. Our customers include top companies such as Gilead, Regeneron Pharmaceuticals, Sanofi Biotech, Novozymes, Syngenta and Corteva Agriscience. In fact, in 2021, Benchling customers received nearly one-third of all FDA new biologics approvals. Moreover, the next generation of scientists also uses our products since we make them available for free to a community of over 200,000 researchers across more than 7,500 academic institutions (Harvard, MIT, Stanford, UC Berkeley, and UCSF).
Pharma accounts for the majority of our clientele, which ranges from small startups to large global corporations. We are focusing on large molecule biology-driven drugs, with cell and gene therapies and antibodies being two of our most important subsegments. In fact, we were one of the first software companies to implement CRISPR-specific design tools in 2014.
What effect does your platform have on the lengthy R&D timelines associated with the pharmaceutical industry?
We significantly increase productivity; our customers have reported saving up to 11 hours per week per scientist on simple logistical tasks such as searching for data or sending requests across teams. Furthermore, we provide scientists with a 50% improvement in data quality, which is critical for compliant record keeping and faster regulatory submissions. Finally, we can provide more efficient collaboration, as evidenced by a 64% improvement in the ability to share data between groups across a scientific workflow.
How have you seen the industry’s appetite for modern technologies evolve?
We are seeing an increase in the industry’s appetite for science native software.
Biotech companies are under tremendous pressure to launch cutting-edge medicines in tight timelines, and they are being asked to do more with less in the current resource-constrained economic climate. As a result, the demand for tools that improve efficiency with their data and R&D has increased — there are a few major trends that are hastening this need for new R&D IT capabilities:
1.Science is growing increasingly complex. Legacy software was not built for biological drugs like bispecific antibodies, RNA, or cell and gene therapies. What was once a simple molecule that could be drawn on a whiteboard and digitally represented with a string of text, is now a complex protein whose structure we don’t always know and takes a multi-dimensional data model to represent.
2.There’s an explosion of scientific data from new analytical techniques and lab automation. Labs are faced with exponential increases in the volume and complexity of the data they have to manage. Today that data lives in silos, scattered across multiple disconnected systems in an unstructured way. Labs lack the ability to easily integrate data and context at scale. They struggle to apply AI, ML, and analytics because they don’t have clean, well-organized, structured data or data systems.
3.Science is becoming increasingly specialized, requiring deeper collaboration within and outside organizations. With more collaboration, scientific workflows now require a significant number of hand-offs and better integration across a fragmented software ecosystem.
The reality is that these factors are impacting scientists’ productivity, data quality, and overall R&D timelines and efficiency. Digital transformation has so much potential in biotechnology.
What would be some of the specific challenges that you have encountered across your
To attract pharma companies to these new tech solutions, you must be able to provide not only the software but also the expertise to help them run it. We have found that it is imperative to provide a suite of scientific expert services that can assist our clients in better understanding their own data models. Because R&D evolves at such a rapid pace, it is critical that our customers have access to software that is always up to date.
Were there any particular ways in which the pandemic impacted your business?
During the pandemic, biotechs were forced to reconsider how they manage their data and workflow — they needed to embrace digital transformation, especially since they were often working remote or siloed. Digital transformation for the biotech industry rapidly accelerated during the pandemic. In addition, with vaccines being developed at such a breakneck pace, society now expects all R&D processes to follow suit. Biotechs are embracing tech and software to make their R&D more efficient, to operate with greater speed and agility.
What key objectives does Benchling want to achieve in the near future?
We are still in the early innings of the biotech revolution. From improving lab-grown meats to creating more sustainable consumer packaged goods and materials for the planet, we’re seeing the beginning of a massive global shift towards biotech and the building of a biotech economy. This will accelerate rapidly in the next five years, with today’s niche applications and treatments quickly moving mainstream. The majority of biotech output today is medicine, but I envision in the years to come that food and materials will take a much bigger piece of the pie. Currently, Benchling customers come from pharma, agritech, consumer packaged goods, materials, and more. While we skew more heavily in medicine now, that will change as biotech continues to spread into more areas of our broader economy.
Biotech is going through a data explosion due to next-generation instruments, robotic automation, and new scientific techniques.
The problem with high data throughput is that you then have to make sense of this data. We’ve barely scratched the surface in terms of AI’s application in biotech, and we’ll be at the forefront at Benchling, helping our customers drive R&D impact from this data.
We’ll need to continue to expand our R&D Cloud, adding more powerful tools without making our software too hard to understand or use — a common problem with growing platforms. And we need to do it without losing our deep focus on biotech. It’s been natural to extend our capabilities from research to development and we’re exploring expanding solutions through to manufacturing.
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