Bioassays Have An Integration Problem: Collaboration Will Be Key To Making Them FAIR

Whilst life science companies have come to recognize data as their greatest asset, it is also their greatest challenge. The answers to the biggest questions facing the industry today could already be held within the countless proprietary experiment notes, published literature, and patient records produced in previously conducted experiments. The data landscape is continuously growing in complexity and scale as organizations generate more research, but much of it is siloed in different formats and locations. This makes it difficult to discover, query, and share—rendering data essentially unusable.  

Bioassay protocols are one such example where legacy data management systems are holding R&D back, and where adopting the FAIR (Findable, Accessible, Interoperable, Reusable) principles would improve the usability of the data. Bioassay protocols constitute the essential metadata for most of the experimental results collected in the process of drug discovery. While assay protocols are widely accessible—often stored in public data banks—they are universally kept in plain-text formats. This means they are not machine-readable and therefore require manual review, which takes considerable time investment by highly qualified professionals. Scientists must spend significant amounts of time sifting through vast libraries of old records; there are currently more than 1.4 million unformatted bioassays. Pistoia Alliance research found that some researchers may spend up to twelve weeks per assay selecting and planning new experiments.