The Pistoia Alliance was founded more than 15 years ago as a not-for-profit with the mission to lower barriers to innovation in the life sciences through pre-competitive collaboration. Core to this mission is working with our member organizations to overcome common obstacles that are holding back technological innovation in our industry. We know that adopting new technologies in R&D and proving their value is both a huge undertaking and a great expense. By working together, we are able to break down silos and remove the interoperability problems often created when companies choose to “go it alone,” allowing our members to integrate emerging innovations more seamlessly and continue delivering life-changing therapies to patients.
The “lab of the future” is one such area where we are keen to see more innovation. To see how our members are progressing with integrating new technologies into the lab, we worked with the Lab of the Future Congress to survey experts from top pharma companies, medium enterprises, startups, and beyond. Our survey reveals which technologies are top of the investment agenda, what organizations are struggling with, and how we can help make the tech of tomorrow a reality in the labs of today. Over half the experts we surveyed said their labs are already using robotics; 40 percent said they expect to be using virtual reality, augmented reality, and wearables in the next two years. AI and machine learning also feature highly in the adoption curve, with AI seen as being able to significantly accelerate existing workflows in small molecule discovery and lead optimization for new drug candidates. Given this proven potential, it’s not surprising that AI and ML topped the list as the technology most companies (60 percent) plan on investing in during the next two years.
Underpinning the successful use of any new technology in the lab, however, is the need to establish a foundational data backbone. What does that mean? Well, all the behind-the-scenes, less headline-grabbing systems and data science techniques that are critical to unlocking the benefits of AI and machine learning. For example, cloud technologies that provide storage space and computer capacity are being invested in by more than half of companies, while 60 percent expect to be using laboratory information management systems (LIMS) in the next two years to digitally capture and share methods and results. Such foundational data management technologies can lay the groundwork for more hyped-up technologies, such as generative AI. After all, companies must learn to walk before they can run.
Despite encouraging investment in foundational data technologies, the survey also revealed there are still significant data quality and management challenges that prevent companies from realizing a return on their investments. For example, data silos were cited as barriers by 66 percent of respondents, followed by unstructured data (58 percent), and lack of metadata standardization (42 percent). These insights suggest research environments continue to be what we call “unFAIR” (findable, accessible, interoperable, reusable), preventing data from moving freely through the research environment – and thus leading to longer, more costly workflows.
The other barrier called out by almost half of respondents was cultural resistance – specifically, hesitancy over data sharing. And that’s despite the industry now generally acknowledging that sharing expertise is essential for overcoming regulatory and ethical hurdles, mitigating risk, and preventing duplication of costly R&D work. A third of our experts also pointed to a lack of proven business cases for senior stakeholders, such as time saved by using LIMS, or number of new targets identified by AI. Though labs continue to adopt new technologies at pace, measuring and proving the value of technology with such tangible business case studies is important for fuelling further investment.
The good news is there are some steps that can be taken to overcome common barriers and ensure researchers, investors, and patients can all reap the benefits of more efficient drug discovery brought about by technology. Some of the resources and actions our respondents called for include:
- Best practice use cases that demonstrate the value of AI (55 percent) and FAIR implementation (43 percent).
- Data governance principles/frameworks for AI (32 percent) and FAIR implementation(40 percent).
- AI algorithm skills training (38 percent).
- Management of data standards and ontologies for FAIR Implementation (42 percent).
- Maturity models to benchmark FAIR implementation against other companies (31 percent).
What all our experts’ suggestions have in common is a clear need to collaborate, share knowledge, and share risk. If companies come together, the industry can collectively reap the benefits of the labs of the future – improving the accuracy and reproducibility of research, preventing duplicated efforts, reducing long term costs, and more besides.
Since its inception, the Pistoia Alliance has been making headway on some of the above suggestions through our member-led projects and new training initiatives. We are shaped by the priorities of the life sciences community and our members, and we invite organizations to bring ideas to us today so we can realize the journey to the lab of the future together.
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