How can we get pharma R&D to embrace FAIR data?
FAIR data, which is Findable, Accessible, Interoperable and Reusable, has the power to transform the analyses enabling drug discovery and development—yet, the pharma industry has been slow to adopt it. There are a number of reasons for this unfortunate reality, but there is a path to realizing the potential for FAIR data in pharmaceutical R&D.
In search of a data transformation
Drug developers need to be informed on every aspect of a
problem in order to build the most accurate and detailed picture possible of
patients, diseases and compounds. Multi-dimensional analyses can help researchers
to better understand disease and assess how chemical entities behave in
The ultimate goal is to reduce drug development times and
late-stage failures, which lowers R&D costs while increasing the chances of
bringing successful treatments to patients faster.
A principled approach
In pursuit of these goals, pharmaceutical companies need to
move from disconnected datasets to relevant and complementary data. The FAIR
Data Principles help scientists get the most out of research data. These
- Findable – Data are richly described by metadata and have a unique and persistent identifier
- Accessible – Data and corresponding metadata are understandable to humans and machines, and accessible through defined protocols
- Interoperable – Data and corresponding metadata use formal and accessible knowledge representation to guarantee reuse
- Reusable – Metadata accurately describe the provenance and usage license for the data
Making pharma FAIR
The implementation of the FAIR Principles in pharma R&D
entails a long-term overhaul of how data are created and used within an
organization. It’s an important transition to make, but not easy. Implementation
typically demands that there is a big culture change in an organization,
requiring a shift to a model of sharing and re-using data that can initially
seem counterintuitive for some managers and scientists.
The path to implementation
It’s an evolving process, but there is a growing network of
organizations and tools to help “FAIRify” data. Current FAIR data endeavors are
not the first to attempt to merge data into meaningful information, but this is
the first time when ideas, expertise and technologies align.
Bear in mind that throughout the process, investing in
infrastructure and people will be an absolutely critical component. It’s
crucial to have buy-in from the right people, and to have the structure in
place to unleash the potential of FAIR data.
To learn more about what FAIR data has to offer pharma
R&D, and how it can be made a reality, read the white paper Realizing
the potential of FAIR data for pharmaceutical R&D.