Pharma companies and academic organizations around the world use different tools for managing their research data. Some of the most popular open-source ones are Gen3, iRODS, COLID, CEDAR and Fairspace. As a service provider in the field of (FAIR) research data management, The Hyve has conducted an in-depth analysis and comparison of these tools based on technical and community criteria. We will present the findings and implications in this talk.

The number of efforts with the aim to develop digital twins in biomedicine are continuing to grow at an increasing rate, enabled by dramatic advances in AI, the availability of essential data, and access to computation to create and compose the elements for biomedical digital twins. With multiple efforts underway globally, questions arise on how to both create and sustain a supporting ecosystem the enables open science, fosters fairness for the community, as well as embraces FAIR (findable, accessible, interoperable, and reusable) resources. Drawing from experiences and examples of several ongoing efforts, including efforts in cancer, insights into key elements for just such an ecosystem will be presented and discussed.

Despite COVID-19 vaccine successes there is an urgent need for small-molecule antivirals such as the recent Pfizer M-protease (M-prot) inhibitor nirmatrelvir, PF-07321332 published  in PMID34726479 and WO2021250648. Since 2020 the Guide to Pharmacology and BindingDB have collaborated on curating FAIR database entries for clinical candidate M-prot inhibitors from papers and patents. Patterns of FAIR disclosure are patchy in varying between an open science web portal from the COVID moon-shot consortium, a paper (but no patent) for Shionogi  S-217622 but blinding (i.e.; neither paper nor patent) of SH-879 from Sosei Heptares. Challenges of extracting and mapping chemical structures between papers and patents for M-prot lead inhibitors for FAIR database curation will be outlined.

Collaboration among academia, non-profits, and industry are essential for the creation of disease-specific pre-competitive informational resources for applying AI/ML models in identification of disease biomarkers, patient subpopulations, and creating disease progression and disease staging models. To improve the Findability, Accessibility, Interoperability, and Reuse (FAIR) of digital knowledge, i.e., introduction of precision research, the clinical research community shall agree to operate under similar operational guidelines, from regulatory/legal, to patient identification, to data standards.

The findable, accessible, interoperable, reusable (FAIR) principles for scientific data management and stewardship aim to facilitate data reuse at scale by both humans and machines. Research and development (R&D) in the pharmaceutical industry is becoming increasingly data driven. However, managing its data assets according to FAIR principles remains costly and challenging. To date, little scientific evidence exists about how FAIR is currently implemented in practice, what its associated costs and benefits are, and how decisions are made about the retrospective FAIRification of data sets in pharmaceutical R&D. This talk reports the results of a study with pharmaceutical professionals who participate in various stages of drug R&D in seven pharmaceutical businesses, and the FAIR-Decide decision making tool based on the study outcomes and Cost Benefit Analysis and Multi-Criteria Analysis. Authors: Ebtisam Alharbi, Rigina Skeva, Nick Juty, Caroline Jay, Carole Goble

The notion that data should be Findable, Accessible, Interoperable, Reusable, according to the FAIRPrinciples, has become a global norm for good data stewardship, a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. However, despite such global endorsements, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an online resource of hands-on recipes for “FAIR doers” in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbookcovers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. URL: https://faircookbook.elixir-europe.org/

In recent weeks the National Health Service (NHS) in England has adopted a far-reaching policy of “Secure Data Environments” (SDEs), also referred to as TREs - https://transform.england.nhs.uk/key-tools-and-info/data-saves-lives/accessing-data-for-research-and-analysis/ . In future researchers will be able to “access” health data only through SDEs, rather than having an anonymised copy “shared” with them.This builds on a long history of social science researchers only being able to access (small) sensitive datasets through secure environments and recent policy development work (https://www.gov.uk/government/publications/better-broader-safer-using-health-data-for-research-and-analysis ; https://www.hdruk.ac.uk/access-to-health-data/trusted-research-environments/ ). 

Thanks to computing advances of virtualisation and cloud, the SDE approach has been shown to be practical for researcher access to (large) sensitive datasets that require a substantial compute infrastructure to support analysis. An example is the research environment of Genomics England, providing access to more than 100,000 whole human genomes and associated clinical data (more than 80Pb) - https://www.genomicsengland.co.uk/research/research-environment

The much stronger protection of privacy that the SDE model enables has been well received in engagements with patients and public. It should facilitate research over whole health systems and even across multiple health systems, such as envisaged by the European Health Data Space (EHDS). However, use of SDEs will require new ways of working bring code to data and support FAIR principles.  Parts of the required functionality for FAIR across a future ecosystem of SDEs is being provided in UK by Health Data Research UK (HDRUK) through its Alliance of more than 60 data custodians and its searchable metadata catalogue – the HDR Innovation Gateway - https://www.healthdatagateway.org/

The systematic reuse of health data originating from the clinical care system for research & innovation could become the engine of a truly learning healthcare system, in particular when feeding modern techniques such as machine learning and federated data analysis. Applying these approaches at scale still turns out to be highly challenging due to various practical obstacles: lack of FAIR (Findable, Accessible, Interoperable, Reusable) health data, ethical & legal constraints (real or perceived), unclear incentives for sharing, and  lack of common standards, to name a few. Within The Netherlands, government, research institutes, medical centers, and research infrastructures recently joined forces to address these obstacles. Some practical approaches and use cases will be presented how we try to address these hurdles for data reuse, both on a national scale as well in European projects.

Analytical methods are essential for the progression of a molecule in the pharma research value chain. The data generated is often used only once. The primary purpose of analytical methods is to answer (bio-)chemical structural questions, where. Parallelly, we have data scientists applying AI approaches. This requires consistent and suited data, which are not usually available or accessible. This talk presents our strategy on 1) leveraging analytical data, 2) the analysis and implementation of the workflows for data acquisition, capture, and storage, 3) unlocking the data through conversion, and 4) offering date back to the lab and data scientists through a system built following the FAIR principles.

Making meaning out of data at scale largely depends on how FAIR data is, how good the quality is, and the measures you take to combine it. TICA and the IO resistance strike force (IORSF) are two initiatives at AZ TM where scientists, data, and tech experts are building FAIR multi-omics data products to revolutionize the way we detect prevalence, identify biomarkers, and novel resistance mechanisms in a data-centric way. By doing so we have learned how to set up successful data-centric teams, develop workflows and tools to harmonize and FAIRify millions of data points and spark a new data culture. 

The Real-World Data (RWD) Store is a human- and machine-friendly self-service shop that facilitates Real-World Evidence generation. It is a place to find and share integrated patient related data on specific brands, therapeutic areas, geographies for cross-functional usage and brings together scattered silos and disconnected information. In the use case will be shown how the FAIR data principles and data product thinking were applied to provide a product customers love.