Lab of the Future 2024 Day 1 Review

Day 1 of Lab of the Future meeting, Amsterdam
Weather was just like Britain 🌧 ☂ but there were excellent talks and discussions

With the various hype cycles across the industry, there was a good deal of realism around the importance of data as the primary value asset in R&D.
•Adoption of Lab of Future and what has delayed delivery and unmet expectation for stakeholders Michael Shanler
•Labs are rapidly evolving with new data platforms, requiring connectivity and collaboration Richard Milne Mark Fish Vincent Sanfiorenzo
•Value of FAIR was questioned!! FAIR needs to be better aligned to business value Anthony Rowe
•Examples of ML/AI in use especially GenAI with LLMs – humans in the loop Daniel Taylor
•Innovation models are also evolving. – Innovation hubs. Enabling closer pre-competitive sharing Pernilla Isberg
•From EGOsystem to ECOsystem
•Start up to scale up
•Innovation is a contact sport

Delivering medicines to patients is the ultimate goals whether small molecules, biologics, vaccines or natural products 🥅

Challenges of the Lab of the Future 🚧
•Culture alone cannot solve things
•Start from the scientist
•Data Value Focussed – enabling decisions
•Not be technology driven (Automation, ML/AI, LLMs)
•Correct skills are needed
•Processes are not mature
•Change management is critical – “It takes a village”

Supporting the Digital Transformation 🔑
Establish a R&D digital board Julie Klint
•Leverage digital tech -> broader than AI
•Enhance use of data
•Data is the backbone Sophie Ollivier

Building the pipelines from instruments to data platforms and analysis

Has hashtag#FAIR failed?
•Lack of clarity on the value that FAIR delivers.
•Has it really engaged with scientists?
•AI has driven the hype around data. Highlights the value of findability and accessibility
•The value of FAIR for Drug Discovery needs to be described in outputs

What does the Future hold?
•Moving to being “digital first”
•Invest in flexible buildings and infrastructure
•Augmenting & Assist the scientist
•Research assistants
•Recommenders with Intelligent agents
•Integrated Research Loops – Closed loop DMTA
•Workflow Automation
•Access to existing knowledge and experience (data born FAIR) – Knowledge graphs
•Bridging the gaps between silos: – R&D and Manufacturing

More to come on Day 2!

Parker Moss James Malone Sona Chandra Vanessa Henning David Gering
hashtag#LOTFEU2024 hashtag#FAIR hashtag#data hashtag#lifesciences Pistoia Alliance