Bob Burke is the new EMEA GM at biotech R&D cloud platform Benchling. He explains Europe’s importance as a biotech powerhouse and why the ‘digital first’ approach is key to future success.
Tell us about Benchling’s recent expansion in Europe, AND why?
Science and technology is firmly in the spotlight in Europe and the UK, especially with the UK government’s recent ‘science superpower’ ambitions. There’s now more urgency across industry and governments to modernise scientific R&D with a tech-enabled approach, in order to gain a competitive edge.
For the team at Benchling, our work feels all the more relevant during this pivotal time. Biotechs across Europe and the UK care about getting cutting-edge medicines to market faster. They’re asked to do more with less, especially in today’s resource-constrained economic climate. Integrating modern software, becoming cloud-first, and adopting a data-driven, digital approach are increasingly viewed as key to accelerating discovery and development.
Since entering Europe in 2020, Benchling has experienced more than 100% growth in local customer count year over year. We now serve more than 200 biotech customers across the UK and Europe, including AstraZeneca, AC Immune, Bit.bio, Novozymes, Ori Biotech, Syngenta,TATAA Biocenter, and many more. These customers are representative of the digital-first approach to R&D that we’ll see moving forward
Biotechs are essentially reshaping the worlds of medicine, agriculture etc. How has digital innovation reshaped biotech overall, and what’s the single biggest advance you have seen in your career?
Scientists are living through a renaissance of techniques to measure and engineer biology that were unthinkable ten years ago. Decades of research have enabled scientists to reengineer the building blocks of life itself, DNA and RNA. CRISPR is ushering in low-cost and precise gene editing. Advances in robotic automation now enable scientists to run thousands of experiments in parallel. The reality for scientists today is an order of magnitude more experiments, each generating complex data, and at enormous volume.
Meanwhile, the legacy tools they’re using aren’t keeping up with the pace, collaboration, complexity, and data explosion taking place in R&D labs. Scientists have gone from building bikes to jet planes, yet their toolkit has stayed the same. The scientists I meet talk about how they lose valuable time when they’re stuck doing logistical tasks with data and how they’re under-utilising the data when it’s kept in silos.
But this is changing, and quickly. To remain competitive and get ahead, organisations realise they need to digitally transform their R&D.
What are the biggest immediate – and long-term – opportunities and challenges for biotech R&D in the region? How has digital been a game changer? Are there still pockets of ‘resistance’?
One common, immediate challenge we observe is that while many biotech companies aspire to adopt artificial intelligence (AI) and machine learning (ML), few have the ML-ready data and digital infrastructure required to extract clean, structured, harmonised datasets for training machine learning models at scale. Those companies that are excelling today with regards to AI and ML in the UK have done just that – led with a digital infrastructure from day one. I think of Exscientia, Genomics England, and obviously AlphaFold.
AI and ML applications depend on massive volumes of experimental data being processed and analysed in real time, which traditional, highly siloed and on-premise legacy software struggles to support. The lack of a unified data foundation for R&D, including systems that can account for complexity of this data, is holding back the promise of AI and ML.
At Benchling, for example, we deliver a system of record for R&D — centralising, standardising, and governing R&D data at scale. The underlying data model is purpose-built for the complexity of modern science, with a focus on biology. On top of that, we’re democratising access to machine learning and AI; scientists can now use AlphaFold seamlessly within Benchling to generate 3D structures of novel proteins at scale and with convenience.
Most labs today do not have the same AI capabilities and talent that are native to tech companies. But that doesn’t mean they shouldn’t be able to access the benefits of AI and ML.
In light of the huge spotlight on cybersecurity, how can clients be assured that the cloud is a safe, secure platform for the verification, protection and sharing of precious data?
Biotech organisations generate revenue based on intellectual property, and if compromised, a great deal of revenue stands to be lost. These organisations are also highly regulated due to the potential human impact of their products, and complying with regulations can make or break the organisation’s ability to compete.
Both of these factors mean that for cloud-based platforms, maintaining industry-leading security, privacy, and compliance standards for biotech customers is paramount. Enterprise SaaS companies have a responsibility to securely develop cloud software and infrastructure. To do this, they use automated vulnerability management, routine penetration testing, asset management, configuration management, threat detection and response engineering, etc. The end result is that many cloud software products undergo more security scrutiny, on a more frequent basis, than on-premise technologies do.
Not all cloud products are the same when it comes to security, but it is becoming increasingly common for enterprise SaaS companies to approach security in this way. When evaluating cloud platforms, customers should evaluate how much an enterprise SaaS company invests in security on an ongoing basis; is there an economy of scale on security that the customer can benefit from?
How do you see digital intelligence evolving over the next five to 10 years?
The democratisation of computational tools, such as cloud, data analytics, and automation, led to massive shifts in speed and innovation across countless other industries, from banking to commerce to healthcare. Biotech is still early in its digital transformation, but the intersection of technology and biotech is going to be the next big thing. This is how we achieve real scalability and efficiency with science, when the two industries work together to build modern infrastructure for modern science.
Bob brings 25 years of experience across both tech and biotech to his new role – having recently supported Okta through an IPO, as well as leading the IT strategy at a vaccine development company.
