The UK’s ‘science superpower’ agenda needs more tech: Q&A with Ori Biotech and Benchling

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The Financial Times said it bluntly: “No more visions please.” There has been no shortage of policies to bolster UK biotech, with life science visions, science superpower plans, and life science industrial strategies. But the ‘execution gap,’ as GSK CEO Emma Walmsley calls it, between policy and action, is where the opportunity lies. Industry and government feel the urgency: the UK’s share of global pharma R&D has dropped sharply since 2012, from 7.7% to 4.2% in 2020, with manufacturing and exports falling too.  

Still, the UK has shown that it can punch above its weight in pharma R&D, with countless examples of innovation and firsts. Look at Exscientia, Deepmind/AlphaFold, and HealX all at the forefront of AI and pharma globally. Initiatives like the 100,000 Genomes Project are doing groundbreaking work to accelerate diagnostics through data. Oxford Biomedica is using tech to scale up access to cell and gene therapy. The UK is also winning big investments, such as the recent £1bn Moderna vaccine centre, and AstraZeneca is a national and global hero with its Covid vaccine. 

These recent wins are all strong examples of what’s possible with a tech-forward approach to life sciences. They show a new model of science that’s powered by advancements with data, automation, digital infrastructure, AI, and technical talent; a model that can make the UK life sciences industry competitive for the long haul. 

Now is the time for tech to step up and play a larger role in driving the UK’s global pharma ambitions. The UK has real and unique strengths in emerging areas of technology, particularly with AI in the life sciences. It also has competitive assets with its top universities and private sector. Combine this with the backing of a supportive government: UK Research and Innovation (UKRI) is turning vision into action, recently announcing a record £20bn investment by 2025 to help build necessary biomedical digital infrastructure. There is no better challenge for tech to meet today than modernising biotech and biomanufacturing. 

In this conversation, we connect with Jason C. Foster, CEO of Ori Biotech, a London- and New Jersey-based leader in cell and gene therapy (CGT) manufacturing technology. Along with Bob Burke, Benchling EMEA general manager, the two discuss what they’re seeing on the ground with UK pharma and the opportunities for more tech and digital innovation.   

Q: What is the status quo when it comes to digitally enabled technology and software in labs in the UK today? 

Jason C. Foster: Compared to most other fields, academic research labs and pharma are further behind in adopting systems and processes that are digitally enabled. It’s easy to see why this has happened. Scientists traditionally work in paper lab notebooks to run and track experiments, especially in academic settings where funding is limited. Paperless systems and digital technologies are seen as “nice-to-haves” and not critical investments. Scientists then move to a commercial lab, where they know that speed and getting results are what matters. So they usually invest their resources in science, not in digital-first technologies and software. In the industry’s defence, high quality software that fits seamlessly into the scientific workflow and makes the scientist’s job easier, has only recently become available over the last five to ten years.

I’m particularly passionate about how technologies will impact the manufacturing of cell and gene therapies (CGT). The common trend I see in CGT is that academic researchers and therapy developers are creating incredible breakthroughs that are essentially cures for cancers, but they are not able to scale their manufacturing process to treat the large numbers of patients who could benefit. A key challenge in CGT manufacturing is the reliance on paper-based processes, which are truly the enemy of scale. Unfortunately, these are status quo in CGT manufacturing today. 

Yesterday’s manufacturing processes are a major bottleneck to patient access to these life-saving CGTs — we urgently need to solve this problem. Using a digital-first approach is one of the keys to unlocking scale. Having cures for cancer that patients can’t access is an unacceptable outcome. 

Bob Burke: If you give a lab ten pounds to split evenly across R&D, where will it invest? Five to ten years ago, the response was four in talent, five in equipment and machinery, one in software. When I visit labs throughout the UK and Europe, I see state of the art machinery, impressive new robotics. And then that same lab is still using paper and pen to capture and share the data from its sophisticated new machines, or they’re using a homemade Excel doc tracking system. The maths need to change. With the data-driven and computational requirements of modern science, software, digital infrastructure, and collaboration tools demand a much higher investment.

In general, the biotech industry has been slower to adopt new tech, trailing other industries by 2-3x. The industry is rife with legacy, on-prem tech and homegrown solutions. This is not ‘just a UK problem.’ But the trend we’ve observed is that the UK and Europe are further behind than the US in terms of their use of digital and software in labs. I see this firsthand when I meet with local customers who haven’t switched to the cloud, or are still recording experiments in paper notebooks.  

Q: What are the catalysts for change? How do you see UK life sciences evolving and adopting more tech? 

Foster: Digitisation can’t be ‘tech for tech’s sake’ in the lab. There must be time, cost, and/or quality benefits. 

Take the example of CGT for multiple myeloma. There are several companies that have effective, approved products ready for usage. But they’re treating around 1,000 patients per year, not the 160,000 newly-diagnosed annually. These companies have been very open about the fact that they can’t manufacture and ship products fast enough — preventing them from fully achieving their own business objectives and from opening up patient access. Digitisation has a critical role to play here: leading companies in CGT are starting to incorporate digital-first thinking to improve their competitive advantage in efficiently getting to scale and treating more patients. We see CGT leaders like Resilience, ElevateBio, Center for Breakthrough Medicine, CTMC, AdThera, InceptorBio and many others prioritising digital enablement as part of their service offering and product roadmap. 

Burke: Challenges with biotech infrastructure are coming to a head in the UK. Look at the current dearth in real estate, where labs are literally leaving the country because there’s not enough space or access to state of the art labs. Infrastructure extends to the tools too. When labs run in the cloud and digitally, it's possible to streamline and do more, even when you can’t grow your physical footprint. The government is indeed unlocking more funding to provide world-class lab space and updated infrastructure. These real limitations with physical space are motivating companies to invest more in digital and cloud infrastructure.  

It’s not just about the challenges; it’s also the opportunity. The potential with AI will lead to a digital shape shift in bio. The number one question I get from pharmas large and small is about how to operationalize AI in their R&D. Mentions of AI in pharma filings increased by 190% in Q1 2023. It’s clear, companies can’t afford to miss this opportunity, and they recognise that in order to benefit from AI and ML, they first need to build their data strategy and systems. The opportunity with AI is a massive catalyst for change. 

Q: What big trends are you optimistic about in pharma?

Foster: Post-Covid, global supply chains are very fragile in life sciences, and we see this throughout the UK, Europe, and the US. Covid exposed the weaknesses of a ‘just-in-time’ supply chain/resourcing model causing basic consumables (e.g. PPE, gas permeable bags, pipettes) as well as critical reagents to go out of stock for long periods of time. When patients' lives hang in the balance — and this is certainly the case with cell and gene therapy, Covid vaccines, and with countless other biological products — our priorities must shift from working capital optimisation to business continuity, manufacturing redundancy, and 100% supply.

Biomanufacturing is now seen as a critical area for investment and modernisation. When a company has spent so much time, resources, and money on R&D, but then can’t manufacture the product to deliver the clinical benefits to patients at scale, that is a disaster for all parties involved — developer, investors, providers, and most importantly, patient. This lack of manufacturability has cascading implications for investors and pre-commercial companies. Now, common questions such as — “What’s your strategy on manufacturing? How will you manufacture and distribute this at scale, and do so in an affordable manner?” —  are being asked at even preclinical stages. 

Approvability is extremely important, but so too is accessibility and affordability. Developers who only focus on clinical efficacy and safety endpoints may find themselves in a dead end when they try to launch commercially. In fact, 8 out of the 25 advanced therapies approved in Europe have been withdrawn for commercial not clinical reasons. Bringing digital solutions and automation platforms into the early phases of R&D will massively help with this. Paperless systems, digitalisation, and automation are the only reliable paths forward to get to scale — this means real time insights and visibility, ease of transferring massive amounts of dynamic information and data, the ability to deliver high throughput, high quality, and low cost of goods.

Burke: I’m excited to see how AI starts to move beyond just drug discovery and more downstream. AI and ML can make the entire lifecycle, from research to development, clinical trials, filing, and manufacturing more efficient. Since large language models, or LLMs, can process vast amounts of information quickly and (mostly) accurately, they’ve opened up a wide range of possibilities for researchers: generating hypotheses, extracting information from large datasets, detecting patterns, simplifying literature searches, aiding the learning process, streamlining IND filings, predicting better respondents for clinical trials and much more. In manufacturing, for example, supervised and semi-supervised machine learning models could be used to optimise manufacturing processes: a company inputs all run and raw material data from previous production runs, and the model learns over time which conditions lead to an optimised yield. 

Q: What can the UK do to speed up tech adoption in life sciences? 

Burke: A big part of speeding up tech adoption in life sciences is about attracting more tech talent. The UK has a thriving tech sector, perhaps even more so than its European neighbours. Talent is here and the roots are here. We need to do a better job of connecting the dots between tech innovation and the opportunity in life sciences. Data scientists, engineers, and programmers have been attracted predominantly to the tech industry, where they see excitement, big opportunities and challenges, and frankly the money. But this is changing. The UK has prime examples like AlphaFold, which show the transformative impact that technologists working in science are making today.

The influx of technologists to life science won’t happen overnight. I offer two pieces of advice here. First, biotechs can make engineers or data scientists part of the leadership team or an early hire. This helps ensure that decisions are made with tech valued as critical; tech has a seat at the table. 

Second, technologists want to know they’ll be able to make an impact, be set up for success, and enjoy the working environment. Especially when they’re entering a new industry, like life sciences, they need to understand that you’re investing in tech and data for the long haul, and it’s not just a phase. This means having the right tools in place — does your organisation have a healthy infrastructure for generated data? Data needs to be organised and well-formed for modelling.  Talent gravitates towards businesses with good foundations in place; it’s a signal their work will be valued. And this doesn’t just benefit the engineers and data scientists, modern systems and tools attract scientists as well. 

Foster: Just to add a final thought to that, we need highly skilled people, we need the technology, progrowth policies (like R&D tax credits), and also the capital to develop innovation and keep it in the UK. Companies like Ori and Benchling rely on venture investment (alongside revenue) to continue to grow, employ more people in the UK, and develop innovation that will solve the current and future problems of new industries like CGT.

Path forward

Britain can lead with a new approach to biotech, specifically adapted to 21st-century science. Now is the time for companies to build their scientific infrastructure that’s data-first and tech-enabled, making UK biotech competitive for the long haul, to recapture its lead. 

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